Generated by All in One SEO Pro v5.0.1.1, this is an llms-full.txt file, used by LLMs to index the site. # Redpoint Global Data-Driven Companies Trust Redpoint ## Pages ### [Home Page v7](https://www.redpointglobal.com/) **Published:** May 21, 2026 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint Identity Studio](https://www.redpointglobal.com/redpoint-identity-studio/) **Published:** May 28, 2026 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [What is Identity Resolution?](https://www.redpointglobal.com/what-is-identity-resolution/) **Published:** May 19, 2026 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [What is a Customer Data Platform? CDPs Explained](https://www.redpointglobal.com/customer-data-platform/) **Published:** November 12, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Identity Resolution](https://www.redpointglobal.com/identity-resolution/) **Published:** July 8, 2026 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Request a Demo](https://www.redpointglobal.com/request-demo/) **Published:** March 4, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Readiness for Data Teams](https://www.redpointglobal.com/data-readiness-for-data-teams/) **Published:** August 14, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint AI](https://www.redpointglobal.com/ai/) **Published:** February 26, 2026 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Quality](https://www.redpointglobal.com/automated-data-quality/) **Published:** April 14, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint CDP](https://www.redpointglobal.com/cdp/) **Published:** May 22, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Travel & Hospitality](https://www.redpointglobal.com/travel-hospitality/) **Published:** August 13, 2026 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Healthcare Payers](https://www.redpointglobal.com/healthcare-payers/) **Published:** March 17, 2026 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Readiness for Marketing](https://www.redpointglobal.com/data-readiness-for-marketing/) **Published:** October 30, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Retail](https://www.redpointglobal.com/retail/) **Published:** June 18, 2026 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Profile Unification](https://www.redpointglobal.com/profile-unification/) **Published:** April 15, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Ingestion](https://www.redpointglobal.com/data-ingestion/) **Published:** April 16, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint and Snowflake](https://www.redpointglobal.com/redpoint-and-snowflake/) **Published:** May 28, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Careers](https://www.redpointglobal.com/careers/) **Published:** February 7, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Ethics Reporting](https://www.redpointglobal.com/ethics-reporting/) **Published:** March 13, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [About Redpoint](https://www.redpointglobal.com/about-redpoint/) **Published:** June 18, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Financial Services](https://www.redpointglobal.com/financial-services-archive/) **Published:** February 6, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint for Payers](https://www.redpointglobal.com/healthcare-payers-archive/) **Published:** February 5, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Readiness Hub](https://www.redpointglobal.com/data-readiness-hub/) **Published:** March 25, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint for Providers](https://www.redpointglobal.com/healthcare-providers/) **Published:** February 28, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Readiness for Providers](https://www.redpointglobal.com/provider-resources/) **Published:** October 14, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Readiness for Payers](https://www.redpointglobal.com/payer-resources/) **Published:** October 14, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Ecosystem Partners](https://www.redpointglobal.com/ecosystem-partners/) **Published:** July 31, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint for Healthcare & Life Sciences](https://www.redpointglobal.com/healthcare-and-life-sciences/) **Published:** May 13, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Request a Demo | Healthcare](https://www.redpointglobal.com/request-demo-healthcare/) **Published:** March 19, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Request a Demo | Retail](https://www.redpointglobal.com/request-demo-retail/) **Published:** March 19, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Configurations](https://www.redpointglobal.com/configurations/) **Published:** October 3, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Video Resources](https://www.redpointglobal.com/cdp-video-resources/) **Published:** March 5, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Latest News](https://www.redpointglobal.com/news/) **Published:** February 7, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [eBooks and White Papers](https://www.redpointglobal.com/ebooks-white-papers/) **Published:** March 6, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Configurations](https://www.redpointglobal.com/self-hosted-solutions/) **Published:** October 9, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Integrations](https://www.redpointglobal.com/integrations/) **Published:** March 11, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Analyst reports](https://www.redpointglobal.com/analyst-reports/) **Published:** March 11, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Real-Time Decisions](https://www.redpointglobal.com/real-time-decisions/) **Published:** October 30, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Published:** January 30, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint Interaction](https://www.redpointglobal.com/redpoint-interaction/) **Published:** April 7, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Readiness for IT](https://www.redpointglobal.com/data-readiness-for-it/) **Published:** August 28, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Orchestration](https://www.redpointglobal.com/data-orchestration/) **Published:** October 30, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Customer Case Studies](https://www.redpointglobal.com/customer-case-studies/) **Published:** June 10, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Style Guide](https://www.redpointglobal.com/style-guide/) **Published:** April 12, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Readiness for Advertising](https://www.redpointglobal.com/data-readiness-for-advertising/) **Published:** November 6, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Dynamic Segmentation](https://www.redpointglobal.com/dynamic-segmentation/) **Published:** September 11, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Management](https://www.redpointglobal.com/data-management/) **Published:** April 10, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [CDP - Back to Basics](https://www.redpointglobal.com/cdp-back-to-basics/) **Published:** June 24, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Media & Entertainment](https://www.redpointglobal.com/media-entertainment/) **Published:** February 6, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Pharmaceutical](https://www.redpointglobal.com/pharmaceutical/) **Published:** February 6, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Product & Solution Briefs](https://www.redpointglobal.com/product-solution-briefs/) **Published:** March 11, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Contact](https://www.redpointglobal.com/contact/) **Published:** February 7, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Privacy Policy](https://www.redpointglobal.com/privacy-policy/) **Published:** March 13, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint Architecture Blueprints](https://www.redpointglobal.com/redpoint-architecture-blueprints/) **Published:** September 9, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Our People](https://www.redpointglobal.com/our-people/) **Published:** February 7, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint Real-Time Interactions](https://www.redpointglobal.com/real-time-interactions/) **Published:** February 5, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgNDAwIDI4NCIgd2lkdGg9IjQwMCIgaGVpZ2h0PSIyODQiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PC9zdmc+)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Redpoint Orchestration](https://www.redpointglobal.com/orchestration/) **Published:** February 5, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgNDAwIDI4NCIgd2lkdGg9IjQwMCIgaGVpZ2h0PSIyODQiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PC9zdmc+)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Segmentation & Activation](https://www.redpointglobal.com/segmentation-activation/) **Published:** February 6, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Observability](https://www.redpointglobal.com/data-observability/) **Published:** April 10, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Data Ingestion & Data Quality](https://www.redpointglobal.com/data-quality-and-data-ingestion/) **Published:** February 6, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Elevate Your Personalization Efforts](https://www.redpointglobal.com/data-driven-personalization-cdp-maturity-model/) **Published:** September 11, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Advertising](https://www.redpointglobal.com/advertising/) **Published:** September 13, 2024 **Author:** Renee Graff **Content:** [![The Data Readiness Series | Data: The Defining Difference](data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgNDAwIDI4NCIgd2lkdGg9IjQwMCIgaGVpZ2h0PSIyODQiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PC9zdmc+)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Trust & Security](https://docs.redpointglobal.com/trust/redpoint-s-security-standards-and-compliance) **Published:** January 7, 2025 **Author:** teresa keegan --- ### [Marketing Professional](https://www.redpointglobal.com/cdp-for-marketing/) **Published:** February 15, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgNDAwIDI4NCIgd2lkdGg9IjQwMCIgaGVpZ2h0PSIyODQiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PC9zdmc+)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Cloud Partners](https://www.redpointglobal.com/cloud-partners/) **Published:** July 31, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Solutions Partners](https://www.redpointglobal.com/solutions-partners/) **Published:** July 31, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Technology Partners](https://www.redpointglobal.com/technology-partners/) **Published:** July 29, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [CDP for Data Analytics Professionals](https://www.redpointglobal.com/cdp-for-data-analytics/) **Published:** March 4, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgNDAwIDI4NCIgd2lkdGg9IjQwMCIgaGVpZ2h0PSIyODQiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PC9zdmc+)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [CDP for IT](https://www.redpointglobal.com/cdp-for-it/) **Published:** March 4, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgNDAwIDI4NCIgd2lkdGg9IjQwMCIgaGVpZ2h0PSIyODQiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PC9zdmc+)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [See It In Action | Data Quality](https://www.redpointglobal.com/see-it-in-action-data-quality/) **Published:** July 30, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [See It In Action | Segmentation & Activation](https://www.redpointglobal.com/see-it-in-action-segmentation-and-activation/) **Published:** June 24, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [CDP Glossary](https://www.redpointglobal.com/cdp-glossary/) **Published:** June 11, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [See it in Action](https://www.redpointglobal.com/see-it-in-action/) **Published:** August 5, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Upcoming Events](https://www.redpointglobal.com/events/) **Published:** March 11, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Do not sell](https://www.redpointglobal.com/do-not-sell/) **Published:** March 14, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Report Resources](https://www.redpointglobal.com/report-resources/) **Published:** March 6, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Telecommunications](https://www.redpointglobal.com/telecommunications/) **Published:** February 6, 2024 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgNDAwIDI4NCIgd2lkdGg9IjQwMCIgaGVpZ2h0PSIyODQiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PC9zdmc+)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ## Blog ### [What is Identity Resolution?](https://www.redpointglobal.com/blog/what-is-identity-resolution/) **Published:** October 18, 2024 **Author:** Steve Zisk **Content:** Identity resolution is the process of finding, cleansing, matching, merging and relating every disparate signal about a customer from MarTech touchpoints, enterprise systems and databases/lakes to produce an accurate, complete, up-to-date view of a customer. Depending on the use case or business purpose, “customer” in the context of identity resolution may refer to any party to an interaction such as a household, a prospect, a consumer of goods or services, a prospect, a patient, a member, an employee, a B2B client, a buyer or even a product. Identity resolution is a key function of a [composable CDP](/customer-data-platform/). ## **Why is Identity Resolution Important**? Identity resolution allows companies to differentiate one customer from another and link signals to a unique customer ID even with conflicting or overlapping signals and identifiers such as multiple device IDs, shared devices, different physical and email addresses, various nicknames, heads of household, shared accounts and other various forms of “messy” customer data. Identity resolution is an important process in the creation of a single customer view, which is also known as a Golden Record or Customer 360. Part of a Golden Record is a customer’s full identity graph, which contains everything there is to know about a customer – devices, names, ID’s, addresses, email, nicknames, etc. – and robust [identity resolution](/products/identity-resolution/) generates a more accurate and trustworthy identity graph. ## **What is a Golden Record?** The Redpoint Golden Record is a single customer view that combines data from any source (website, mobile app, eCommerce platform, POS, social media, CRM, etc.) to form a holistic unified record of a customer and the customer’s engagement with a brand across every touchpoint. The Golden Record constructs everything that is knowable about every customer, from every available source. A full identity graph is just part of a Golden Record. It also includes full contact history, transactional, demographic and preference data, and all attributes and data aggregations – all processed and updated in real-time. With [data quality](/products/automated-data-quality/) processes completed at data ingestion, and using persistent key maintenance and tunable matching and merging as part of advanced identity resolution, a Golden Record is the foundation for powering personalized customer experiences that requiring being able to differentiate one customer from another. ## **What is Deterministic vs. Probabilistic Matching in Identity Resolution**? In resolving an identity to build or enhance a Golden Record, [deterministic and probabilistic matching](https://www.redpointglobal.com/blog/tunable-transparent-identity-resolution-propels-personalized-cx/) are the two main ways to determine whether certain signals or interactions are related to the same customer. These two methods provide different degrees of confidence in the match. Basic identity resolution takes a deterministic, exact match approach that only matches identical phone numbers, physical addresses, names or other exact identifiers. A broader type of deterministic matching may identify the same user across different devices by matching the same user profiles with a common identifier, such as an email address. Probabilistic matching uses advanced analytics to link customer records, such as identifying two disparate customer records that represent the same individual using multiple identifiers and “close enough” matches. The output of both deterministic and probabilistic matching is to discern with a high level of certainty that different data fragments or records belong to the same identity. Both are important when the use case for identity resolution is to build a Golden Record because they both contribute to compiling a full, complete and accurate identity graph that represents a detailed collection of all connected experiences, interactions and facets of a customer, person, household, organization or even product. A combination of deterministic and probabilistic matching is important for accurately reconciling multiple signals. An online browsing session that ends with a shopping cart fulfillment will be considered a single interaction yet contain multiple signals about the customer’s ID that may be at odds with one another. The name provided during the online checkout process might not be associated with the credit card number and shipping address, for instance, even if it is associated with the device IP and web behavior. Advanced [identity resolution](/products/identity-resolution/) using both deterministic and probabilistic matching will determine with a high degree of accuracy which customer ID should be linked with the interaction. ## **What Level of Identity Resolution Do I Need?** There are three identity resolution categories, each with its own level of trust and confidence in the record produced and based on the underlying purpose or use case. Broadly speaking, the categories are broken out into identity resolution in AdTech, in MarTech and in the highly regulated industries of healthcare and financial services. Tunable identity resolution capabilities allow companies to adjust match accuracy levels according to the desired use case. - In the AdTech space, identity resolution is often limited to the use of third-party data in an anonymous capacity and is therefore the least stringent of the three. In this use case, an advertiser might use minimal segmentation rules to target a broad audience, where an exact match is unimportant and any PII is encrypted. - In the MarTech space, where marketing and other business functions are intent on providing a consistently personalized customer experience, identity resolution is an important process in generating an accurate Golden Record. First-party data compiled and stored in a [customer data platform](/customer-data-platform/) (CDP) is used to link, analyze and deduplicate customer records to provide a consistent CX, among many other use cases. - For regulated businesses including healthcare and financial services that are subject to customer identity access management (CIAM) protections and guidelines, identity resolution will be tuned to produce an exact match for many business functions, including a provider engaging with a patient about a treatment plan or any other sharing of PHI. For each identity resolution category, the ultimate function is to produce outcomes that meet a business’s specific needs. A highly accurate and trustworthy match, for example, is required to communicate with a person about a transaction perhaps deeper into the customer journey, where a looser match may be sufficient for earlier stages of a journey, e.g. communicating marketing information or educational content to an individual or household. ## **Is Identity Resolution a Function of Every Customer Data Platform**? It should be. The creation of a unified profile is listed by the CDP Institute as one of the five required functionalities to be considered a customer data platform. For that reason alone, every customer data platform will claim some form of identity resolution. However, it’s important to look under the hood because not every CDP treats identity resolution with the same level of importance. CDP’s with basic capabilities limit matching to a deterministic, exact match approach. An example is considering two records a match with an exact match of phone numbers, physical address or first and last name. An exact match approach is insufficient when the goal is to create a differentiated customer experience. One issue is that customers have multiple identifiers. Matching an exact address fails to account for situations where a customer might be using an alternative address, or phone number, email address, loyalty account number, etc. Another is that an exact match does not allow for human error, such as a data entry clerk mis-typing a street address on a form. A customer might make a similar error, or simply write the same physical address or even their name differently from one form to the next. (Street vs. St., Dave vs. David, etc.). A CDP with advanced identity resolution capabilities, on the other hand, will incorporate both deterministic and probabilistic matching techniques to account for all variances in how customers identify themselves. ## **What is Householding in the Context of Identity Resolution?** Advanced identity resolution capabilities will also accommodate householding, or identifying members of a business. By including all disparate signals about a customer (prospect, household, business, B2B client, etc.), advanced identity resolution provides a contextual customer understanding, and identifies relationships (person to household, person to organization, organization to organization) as well as matches. Another distinction between a CDP that performs advanced identity resolution vs. basic identity resolution is the latter often ignores data cleansing steps or relies on third-party reference files with the assumption that the files are updated and accurate. In reality, reference files might be updated infrequently using non-persistent keys, meaning that every time there is new information about a customer there is no ability to make a longitudinal match based on previous keys. If one of your use cases for implementing a composable CDP is to develop a single view of the customer for segment-of-one marketing, it is important to know how the CDP approaches identity resolution, and whether there is an assumption that the data used to create a unified profile has already been cleansed and made ready for business use. ## **What Are the Components of Identity Resolution?** Here are some important capabilities to consider when selecting an identity resolution solution, with details on several components: Data Quality, Data Governance, Data Ingestion, Data Matching, Data Merging, Persistent Key Management, and Data Stewardship. - *Data Quality* High quality data that is ready for business use is crucial for identity resolution to produce accurate, trustworthy results. Data cleansing, normalization and validation reduce errors in the identity resolution process, build trust in the resulting Golden Record and supports the creation and delivery of a consistently personalized customer experience. **Blog categories:** Identity Resolution **Blog tags:** identity resolution --- ### [CDP Identity Resolution: Why It's Core Infrastructure, Not a Feature Checkbox](https://www.redpointglobal.com/blog/identity-resolution-is-a-core-cdp-functionality/) **Published:** August 5, 2022 **Author:** Redpoint Global **Content:** Identity resolution in a [customer data platform (CDP)](/customer-data-platform/) is the process of finding and matching the fragmented signals a business collects about a person across devices, channels, and systems, determining which ones belong to the same individual. It’s the foundation the CDP builds an accurate, current profile or Customer 360 from. Get that foundation wrong, and the profile it produces is wrong too. Not every CDP does this equally well. Identity resolution is one of the most consequential and most overlooked variables in how a CDP performs, and a weak implementation undermines everything downstream of it, from segmentation and personalization to attribution and AI. That’s true whether you’re evaluating a new CDP or living with one you already bought. ## What Identity Resolution Actually Does Inside a CDP Identity resolution isn’t deduplication with a better name, and it isn’t the same job as a master data management (MDM) system. Deduplication looks for exact or near-exact record matches and collapses them. [Identity resolution](https://www.redpointglobal.com/what-is-identity-resolution/) goes further. It determines which partial, conflicting, and time-delayed signals (an email from a mobile app, a loyalty ID from a point-of-sale system, a cookie from a web session) belong to the same person, and keeps that determination accurate as new signals arrive. Profile unification, the step that actually assembles those linked signals into a usable customer record, depends on identity resolution getting that matching right first. MDM, meanwhile, typically governs reference data across enterprise systems; it’s built for consistency of core records, not for resolving high-volume, high-ambiguity customer signals in something close to real time. Staying accurate over time depends on persistent identity keys. As a person moves from anonymous browsing to a known, authenticated customer, a persistent key preserves that thread instead of starting the relationship over. Without it, every channel switch resets what the business knows about that person. Continuity is the entire point of resolving identity in the first place, and without a persistent key, it breaks. That same matching capability also has to work across relationships, not just within one person’s timeline. Household resolution groups people who share an address into one household view. Account resolution rolls an individual’s activity up to the business account they belong to, which matters in B2B contexts. A CDP that resolves individuals well but can’t group them this way will leave gaps that show up downstream, usually as a customer who gets marketed to like a stranger despite months of prior activity from someone in their own household. ## Why Deterministic and Probabilistic Matching Both Matter Good identity resolution uses two matching approaches together, not one or the other. Deterministic matching links records on exact identifiers (the same email address, the same loyalty number) and it’s reliable when those identifiers are clean and consistent. Probabilistic matching weighs multiple imperfect signals and assigns a confidence score, which is what you need when identifiers conflict or don’t exist yet, like a shared IP address, a similar name with a typo, or a device fingerprint. Real customer data produces both kinds of signals constantly, so a platform that only supports deterministic matching will systematically miss the messier case. How much matching confidence you need depends on the industry. A financial services or healthcare organization has a much lower tolerance for a false match than a retail or travel brand does, because the cost of merging the wrong two people’s records is a compliance problem, not just a bad email. That’s why tunable matching logic matters as much as the matching methods themselves. Identity resolution needs to let you set the confidence bar per use case rather than applying one threshold everywhere. ## Not Every CDP Resolves Identity the Same Way A CDP’s identity resolution capability isn’t a single checkbox you either have or don’t. It’s a spectrum, and where a given platform lands on it determines how much you can trust the profile it produces, and what every downstream use case inherits as a result. Weak identity resolution shows up as segments that underperform for no clear reason, personalization that feels generic despite a large data investment, and attribution numbers nobody fully trusts. If you’re evaluating a new CDP, identity resolution needs to be part of that evaluation from the start. We wrote a full framework for it, including the specific capabilities to check for, in our [CDP Selection Guide](https://www.redpointglobal.com/blog/how-to-choose-a-cdp/). If you already have a CDP and its identity resolution isn’t holding up, replacing the whole platform usually isn’t necessary. A [data readiness layer](/data-readiness-hub/) can sit alongside your existing CDP and take over data quality and identity resolution specifically, feeding a cleaner, better-resolved profile into the CDP you already run. That’s materially smaller lift than a platform migration. Either way, the goal is the same: a profile you can actually trust before anything else gets built from it. **Blog categories:** Identity Resolution **Blog tags:** identity resolution --- ### [Redpoint MCP: Make Your Redpoint Customer Data Conversational](https://www.redpointglobal.com/blog/redpoint-mcp-make-customer-data-conversational/) **Published:** August 28, 2026 **Author:** Redpoint Global **Content:** *Open-source MCP servers make your Data Readiness Hub and Redpoint Interaction data conversational, with any LLM you choose.* ![Mcp Blog Featured Image](https://www.redpointglobal.com/wp-content/uploads/2026/08/MCP-Blog-Featured-Image.png) Redpoint’s open-source MCP servers connect any LLM directly to your Redpoint data, allowing your own custom agents to look things up, reason about your setup, and deliver insights using real, live data. The open-source MCP servers, released under the Apache 2.0 license, connect the Data Readiness Hub (DRH) and Redpoint Interaction (RPI) to any LLM via the Model Context Protocol (MCP). Agent developers and engineering teams now have a direct, open connection between whatever LLM they already use and their Redpoint data. ### What are Redpoint’s MCP servers? Redpoint’s MCP servers let you build, connect, and run AI agents against your RPI and DRH instances using any LLM provider you choose. Your teams can ask your DRH and RPI data questions in plain language and get detailed, trustworthy answers. They wrap a curated set of Redpoint’s Integration APIs as standard MCP tools and route each question to only the tools it needs (keeping token costs down), and ship with a lightweight reference agent so you can see it working out of the box. Agent developers can clone the repo, study the patterns, and build their own agent or UI layer as it applies to their organization’s setup and needs. With authentication, tool schemas and data access already handled, developers get to spend their time on the fun part: shaping how the agent behaves and what it can offer teams across the organization. The MCP servers sit on top of your existing Data Readiness Hub or Redpoint Interaction instance, adding a conversational layer that respects your existing RBAC permissions and leaves your data and configuration untouched. ### How it works 1. **Connect.** Point any MCP-compatible client, such as Claude Desktop, Cursor, a custom agent you’ve built, or the included reference web agent, at the relevant Redpoint MCP server. Evaluators can run the whole stack with a single `docker compose up`; agent developers can drop in a standalone binary; contributors can clone the repo directly. 2. **Choose your model.** Add the API key for whichever LLM provider you already use. Both MCP servers are built on the Vercel AI SDK’s bring-your-own-model approach, so even if you switch providers, it’s a configuration change rather than a full rewrite. 3. **Ask, in plain language.** The skill router figures out which domain (audiences, interactions, selection rules, data quality) your question falls into and hands the agent only the specific tools it needs, rather than the entire toolset. A foundation layer keeps track of your tenant, client, and ID structure in the background, so you don’t have to feed the agent UUIDs to get a useful answer. ### Two MCP servers, one framework Redpoint currently ships two MCP servers, with more planned as we expand coverage across the platform. **DRH MCP** wraps the Data Readiness Hub, giving an agent access to feed status, match data quality trends, and source-level error information. You can get faster triage: instead of digging through the data readiness UI to figure out why a feed failed or why match quality dipped, you can ask the agent directly and get a diagnosis with historical context attached. **RPI MCP** wraps the Redpoint Interaction Integration API (audiences, interactions, selection rules, clients, and folders) so an agent can answer questions about your campaigns in natural language instead of requiring someone to open the UI or write a query. Today it’s built for exploration and reporting, tackling some of the investigative questions that tend to crop up frequently across a team, such as “how many audiences ran last month,” “does this folder have duplicate rules in it,” or “how many people currently match this selection rule.” It also carries built-in campaign design guidance that can guide new or infrequent users through selection rule logic and audience design (down to a fully worked win-back campaign example). ### What makes Redpoint MCPs different - **LLM-agnostic by design.** Both MCPs work with Claude, OpenAI, Azure OpenAI, and more. Swapping providers (or even cloud providers) is made easy with a configuration change. - **Token-efficient by default.** The skill router keeps tool exposure narrow, routing a given question to only the 2-3 relevant tools instead of loading the entire catalog. This delivers a meaningful reduction in token overhead compared to handing an agent every available tool at once. - **One setup and governed access.** IT and engineering do a single one-time `.env` configuration and security review that every team building on top inherits. The agent also authenticates as the requesting user, so it never sees more than that person’s own access already allows. - **Open source, not a black box.** The full repository is available on GitHub under the Apache 2.0 license, so your team can inspect exactly what the agent can and can’t do and extend it if you need capabilities not yet available. - **Compatible with the MCP clients you already use.** Because they speak standard MCP, each Redpoint MCP works with Claude Desktop, Cursor, and any other MCP-compatible client without requiring any proprietary plugins. ### FAQs **What are the Redpoint MCPs?** They are open-source MCP servers that expose Data Readiness Hub and Redpoint Interaction as MCP servers, so AI agents built on any LLM can query and act on your Redpoint data using natural language. **Which LLMs do they work with?** Redpoint MCPs are LLM-agnostic and work with Claude, OpenAI, Azure OpenAI and others, right out of the box. **Do Redpoint’s MCPs change or modify my existing Data Readiness Hub or RPI instance?** No. They sit on top of your instance and call the same Integration APIs your team already uses They don’t alter your data, your configuration, or your existing permissions. **Who are the Redpoint MCPs built for?** Once agent developers and IT/engineering teams have cloned the repo, wired up the connection and built customized agents or a UI on top of these MCP servers, business teams (analytics, marketing, CX, and other stakeholders) can reap the benefits of natural language queries against their data once agents are running. **Are Redpoint’s MCPs secure enough for a regulated environment?** Redpoint MCPs support authentication, per-user RBAC when connected to your RPI or DRH instance, and audit logging, and are designed to keep the LLM grounded in deterministic tool calls rather than open-ended actions. They are read-only by default, with no delete endpoints today. As with any AI deployment, we recommend reviewing configuration against your own compliance requirements before exposing them beyond a local evaluation. **Where do I get my Redpoint MCP?** Both MCPs are available now on GitHub, with setup docs for evaluators, agent developers, and contributors. You can also find implementation details in the [Redpoint documentation](https://docs.redpointglobal.com/). **Blog categories:** Agentic AI **Blog tags:** Agentic AI --- ### [Announcing Redpoint Identity Studio](https://www.redpointglobal.com/blog/announcing-redpoint-identity-studio/) **Published:** June 1, 2026 **Author:** Redpoint Global **Content:** ### Introducing Redpoint Identity Studio A Snowflake Native Application that resolves customer identities inside your own environment – with full control over match logic and full visibility into every decision. ![Redpoint Identity Studio Snowflake Native App](https://www.redpointglobal.com/wp-content/uploads/2026/06/Redpoint-Identity-Studio_Snowflake-Native-App.png) ### Introduction Today, we’re launching Redpoint Identity Studio on Snowflake Marketplace. With just a few clicks, data engineering teams can go from raw customer data to matched, persistent identities, directly inside their Snowflake environment, without PII movement, and with full visibility into exactly how every match was made. For data teams that have long been forced to choose between black-box vendor tools and resource-intensive homegrown pipelines, this is a third option: enterprise-grade identity resolution you actually own and can explain. ### The identity resolution problem Data clouds centralize data. They don’t curate it, and identity resolution is where that gap hurts most. Today, teams try to close it two ways, and both fall short. Most organizations sit somewhere uncomfortable on the identity resolution spectrum. On one end are vendor-managed tools that match your customer records against a proprietary data graph: fast to deploy, but opaque. You can’t see why two records merged, you can’t tune the logic, and your PII leaves the environment you control. On the other end is the custom-built approach: SQL models and bespoke processes that give you full control but consume finite engineering resources on a problem that never fully stays solved. > **The hidden cost of homegrown identity logic** > Pipelines break when schemas change. Logic goes undocumented when people leave. Testing any rule change typically means reprocessing large tables at significant cost and risk. And when downstream models start drifting, diagnosing why often leads back to identity keys that silently shifted between runs. For data teams in regulated industries like financial services and healthcare, the vendor path creates an additional blocker: InfoSec and procurement teams won’t approve solutions that require sharing PII externally. Many evaluations stall before features are ever compared. We built Redpoint Identity Studio to solve all three of these problems at once. ### What is Redpoint Identity Studio? Redpoint Identity Studio is a Snowflake Native App that resolves customer identities (individuals and households) entirely inside your Snowflake environment. It combines deterministic and probabilistic matching, a full UI for configuration and match review, and a structured experimentation framework, in a single governed application available on Snowflake Marketplace. The match logic executes on your own compute, under your existing Snowflake security controls. The result is a set of persistent identity keys that all your downstream processes, from your AI to analytics and more, can build on with confidence. ![Match V.03](https://www.redpointglobal.com/wp-content/uploads/2026/05/match-v.03-scaled.png) ### How does it work? Everything a user needs to configure, run, and refine identity matching is accessible through a clean web UI: no SQL, no custom pipelines, no professional services engagement required. 1. **Set up in minutes** — a guided onboarding wizard uses AI assistance to map your data and walks you through an initial match configuration. No code required. 2. **Run a match with one click** — the Production page shows job progress and results in real time, with a Review tab to inspect and correct individual match groups. 3. **Experiment before you commit** — test ruleset changes against your full dataset and compare them side by side with production results before promoting anything. Teams can be looking at matched results against their own data within the same session they install the application. ### Feature differentiators - **Snowflake-native execution:** All matching logic, PII, and output run inside your Snowflake environment, under the security controls you already govern. - **Configurable matching:** Combined deterministic and probabilistic matching gives data engineers explicit control over precision, from one-to-one exact matches to household and relationship linking. - **Transparent match review:** Inspect any match group through an intuitive UI, see exactly which records were grouped together, and override results on a case-by-case basis. - **Built-in experimentation:** Test ruleset changes against your full dataset before promoting anything to production. Identity tuning becomes a governed process, not an open-ended guessing game. - **Persistent identity keys:** Individual and household identities stay stable across runs, so downstream dashboards, AI models, and operational joins remain stable when new data arrives. ![Snowflake Product Shots V.02 Manual Overrides](https://www.redpointglobal.com/wp-content/uploads/2026/05/Snowflake-Product-Shots-v.02_Manual-Overrides-scaled.png) ### FAQs **Does customer data leave our Snowflake environment?** All identity matching logic, PII, and output run inside your Snowflake environment. The data your team maps from existing Snowflake tables is processed on your own compute, under your existing security controls. **What types of matching does it support?** Redpoint Identity Studio supports both deterministic and probabilistic matching. Deterministic matching links records on exact values. Probabilistic matching finds likely connections across imperfect or partial data and is what enables household-level identity, as it groups individuals who share a relationship even when no single field ties them together exactly. **How long does it take to get first results?** Teams can see first matched results against their own data within the same session they install the application. The onboarding wizard guides you through data mapping and initial configuration, and a single click starts your first match run. **How does experimentation work?** Create a new experiment by adjusting match thresholds or modifying field-level tightness settings. The experiment runs against your full dataset, and results are compared side by side with your current production output. Review the differences, then choose to promote or discard. Nothing touches production until you decide it should. ![Snowflake Product Shots V.02 Configure](https://www.redpointglobal.com/wp-content/uploads/2026/05/Snowflake-Product-Shots-v.02_Configure-scaled.png) **Is it suitable for regulated industries?** Yes. The in-Snowflake architecture means your data processing stays within the security boundary your InfoSec and compliance teams have already established. We’re happy to provide full compliance documentation for those in regulated industries. **How do I get started?** You can find Redpoint Identity Studio now on Snowflake Marketplace. If you’re attending Snowflake Summit 2026, we’d love to meet up in person and show you what it could like against your data – just reach out to set up a quick meeting with our team. [Find Redpoint Identity Studio on Snowflake Marketplace](https://app.snowflake.com/marketplace/listing/GZ1MMZ1PF5E4/redpoint-global-inc-redpoint-identity-studio) [Try the Identity Studio Clickable Demo](https://demo.redpointglobal.com/psl/1y140ava) **Blog categories:** Identity Resolution **Blog tags:** identity resolution --- ### [Consumers Weigh in on the Role of Artificial Intelligence (AI) and Machine Learning (ML) in Customer Experience](https://www.redpointglobal.com/blog/consumers-weigh-in-on-the-role-of-artificial-intelligence-ai-and-machine-learning-ml-in-customer-experience/) **Published:** January 31, 2023 **Author:** Redpoint Global **Content:** How do consumers really feel about the use of artificial intelligence (AI) and machine learning (ML) in customer experience (CX)? For the average consumer, do these advanced technologies begin and end with the chatbot? To uncover answers to these and other questions, Redpoint partnered with Dynata to discover what consumer sentiments are around the role AI and ML play in customer experiences and their brand relationships. [Survey responses](https://www.redpointglobal.com/press-releases/73-of-consumers-believe-ai-can-have-a-positive-impact-on-their-customer-experience/) indicate that consumers are generally aware that AI and ML in some way, shape or form have something to do with their experience as a customer, even though many willingly acknowledge that they aren’t quite sure *how* these advanced technologies are being used. Consider that nearly three-quarters of respondents (73 percent) said that AI and ML have a potential to impact customer experience, while at the same time almost half (45 percent) expressed a lack of understanding how these technologies are being implemented. The ubiquitous “How may I help you?” chatbot exemplifies those findings. Despite it being a familiar AI use case for anyone who has logged onto a retail, travel, healthcare or financial services website in the last few years, to name just a few examples, the chatbot’s familiarity does not necessarily make it well understood. In the Dynata survey, consumers rated the chatbot as the *most* ideal use of AI to improve the customer experience (of several options), yet interestingly 70 percent said that they prefer human interaction over chatbots. Thinking of your own experiences interacting with a chatbot, these seemingly contradictory proof points are less of a paradox than they appear. That is, in general a chatbot does a pretty good job with customer triage, such as answering FAQ’s or providing a customer with the right resource, while at the same time for some customers a chatbot is viewed as little more than a gatekeeper or a queue for speaking with a customer service rep. ## **Even with AI/ML, Consistency is Key** Consumers in the Dynata survey may have been thinking about a chatbot experience when answering questions about the direct impact of AI and ML on their customer experience. For instance, not only do 70 percent say they prefer a human interaction over a chatbot, but 77 percent of respondents said they believe a positive customer experience in general still needs an element of human touch. Furthermore, if organizations do employ AI/ML, consumers expect transparency. In the survey, 58 percent of consumers said that they want companies to be clear about when AI is being used. And, if it is used, it is vital that it does not introduce friction. Per the survey results, 76 percent of respondents said that if they sensed disjointed communication with AI across channels, it would make them less likely to trust and continue engaging with a brand. Not to pick on the chatbot, but a familiar example might be answering questions from a chatbot, only to be transferred to a customer service representative who asks the same questions. What can we make of the survey results? Importantly, consumers are clear that they are all for AI/ML customer experience use cases, as long as certain expectations are met. To wit, that an experience is seamless across all channels of engagement, and that AI/ML contribute to a holistic understanding of a customer across an omnichannel journey. If those conditions are met, then consumers welcome AI/ML as important, albeit complementary components of a digital-first, seamless customer experience. For more survey results and coverage of the Dynata survey, [click here](https://www.redpointglobal.com/press-releases/73-of-consumers-believe-ai-can-have-a-positive-impact-on-their-customer-experience/). ## **Related Redpoint Blogs** [Machine Learning Misconceptions, Dispelled](https://www.redpointglobal.com/blog/machine-learning-misconceptions-dispelled/) --- ### [Simplify Complex Customer Journeys with a Single Point of Control](https://www.redpointglobal.com/blog/simplify-complex-customer-journeys-with-a-single-point-of-control/) **Published:** February 3, 2023 **Author:** Vin DelGuercio **Content:** Imagine for a moment that you decided to escape the mid-winder doldrums with a cycling tour through the south of France. You begin to investigate travel options, make a few visits to the website and even sign up for a promotional newsletter. Six months out, you book your all-inclusive adventure through a travel company. From your perspective, your journey through the countryside is still somewhat far off. For the travel brand, however, your customer journey is at full speed. There are cross-sell and upsell opportunities – are you interested in a room upgrade in Nice? A hot air balloon excursion over the Pyrenees? There are notifications to send – flight changes, itinerary confirmations, reminders, and helpful information such as packing lists and exercise tips. Ideally, a brand helping to guide you on this dynamic, complex customer journey will interact with you at the right cadence and on the right channel, optimizing timeliness and frequency based on not just your departure date, but also factoring in your responses and receptiveness to the various messages. A complex customer journey, as this example shows, is often dynamic, non-linear, non-sequential and multi-channel, consisting of a myriad of inbound and outbound touchpoints taking an individual from unknown to prospect to customer and beyond. In a complex journey, a purchase may happen in the beginning, middle or end – or not at all. The inherent vagaries of a complex customer journey that will likely entail multiple touchpoints over an extended period of time raises several challenges for brands interested in delivering a relevant, personalized omnichannel experience despite all of this unpredictability. ## **Master Complex Journeys, Meet Customer Expectations** To appreciate how customer expectations have evolved in terms of not settling for anything less than a personalized, omnichannel experience, let’s think about the cycling excursion and how you’d expect a brand to engage with you as a customer. Wouldn’t you expect, as a matter of course, a series of digital-first engagements to keep you apprised of the status of the trip? If a travel company were to instead “go dark,” you’d probably think you fell victim to a scam. And instead of a disparate string of random communications, you would likely also expect that every email, SMS, mailer, call and website visit to be coordinated – to have the same information about you, and to treat you as the same person, with the same agenda. The complexity of the journey, in other words, has no bearing on the type of experience customers expect – from any industry. In retail, finance, healthcare – every industry with a customer-brand dynamic – consumers assume that a brand will possess a personal understanding, and use it to deliver a hyper-personalized experience. In a recent [Dynana survey](https://www.redpointglobal.com/press-releases/80-of-consumers-more-likely-to-shop-with-brands-that-show-they-understand-them/), 78 percent of consumers said that it is frustrating when a brand’s communications and marketing messages are inconsistent depending on the channel they visit (in-store, online, social, call center, mobile app, etc.). In addition, 80 percent said they are more likely to purchase from brands that demonstrate a personal understanding by sending relevant, personalized offers. ## **Tame Complex Journeys with Consistency** Orchestrating a personalized experience across a complex, omnichannel journey presents several challenges. In many cases, delivering a relevant customer experience requires cross-channel awareness. A shopping cart abandonment strategy – often cited as a prime example of a complex customer journey – will necessitate an integration between email and a brand’s website, as one example. A seamless curbside pickup experience will likewise require coordination between the purchase channel, notifications (SMS/email), the mobile app and the pickup location’s inventory and fulfillment system. Supporting cross-channel awareness in orchestrating a seamless, omnichannel experience requires integrating data that supports each of the various touchpoints. In addition, executing a complex journey strategy must include a single point of operational control where there is essentially one brain orchestrating engagement across channels – one centralized repository of engagement history and strategy (inbound and outbound). Technology of course plays a major role, where it is first and foremost essential that a robust [customer data platform](https://www.redpointglobal.com/customer-data-platform) (CDP) have the capability to not only build an accurate and up-to-date unified customer profile, but to leverage a unified profile to drive real-time decisions in the precise cadence of an omnichannel journey. But people and processes are just as important for executing a successful complex journey. As one example, it is virtually impossible to orchestrate a complex journey requiring cross-channel awareness if, say, operations are siloed across systems, such as where different teams manage strategy and execution for email and the website. But for a company that organizes marketing campaigns on a channel basis, those kinds of data siloes are common. Any data sharing is likely via batch uploads, which will often lack the timeliness and/or contextual understanding to engage a customer with relevance. A unified customer record, known as the [Golden Record](https://www.redpointglobal.com/single-customer-view/), is the foundation for aligning people and processes with technology to orchestrate real-time decisions with a single point of control. That’s because the Golden Record, in addition to containing a resolved customer ID across all identifiers, devices, etc., also incorporates a complete record of every engagement across those identifiers. That Golden Record provides the foundation for operating with one consistent voice across an omnichannel customer journey. A complex journey may make it more challenging for a brand to demonstrate a personal understanding across channels, but customers have made it clear that they expect nothing less, and they will reward the brands that make the effort. ## **Related Redpoint Blogs** [Use a Golden Record to Enhance Customer Experience (CX)](https://www.redpointglobal.com/blog/use-a-golden-record-to-enhance-customer-experience-cx/) [The Role of a Golden Record in Providing a Consistently Relevant, Personalized CX](https://www.redpointglobal.com/blog/the-role-of-a-golden-record-in-providing-a-consistently-relevant-personalized-cx/) [Multichannel vs. Omnichannel Marketing and Keeping Up with a Customer Journey](https://www.redpointglobal.com/blog/multichannel-vs-omnichannel-marketing-and-keeping-up-with-a-customer-journey/) **Blog categories:** Data Quality, Identity Resolution **Blog tags:** CDP --- ### [What is Persistent Key Management?](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) **Published:** September 26, 2022 **Author:** Steve Zisk **Content:** A common allied WWII cipher was to combine a numerical alphabet code with a key word, known only to the message recipient. To crack the code, the enemy would need to have the key word in addition to deciphering the code itself. The key word, for obvious reasons, would change frequently, hopefully leaving the enemy unable to piece together any understanding of allied plans. > **Persistent keys are vital to understand how a customer is proceeding through a customer journey by linking various signals to a unique, persistent customer ID that contains all the interactions pertinent of that customer.** In this context, customer is understood to mean any party to an interaction a business is trying to understand – an individual, business, a household, patient, member, buyer, employee or even a product. In the world of delivering hyper-personalized and agentic customer experiences that depend on possessing a deep understanding of an individual customer, the WWII cipher captures the importance of persistent database key management. If changing keys (or a key word) precludes a deep understanding, the converse is also true. ## **What Actually Counts at the “Key”?** The logical question with persistent key management, then, is what constitutes the key? Say it’s an email address, and you want to attach signals to a unique email, the obvious problem or set of problems is that customers change email addresses, have multiple email addresses, or even have a dedicated email that may be used by multiple individuals. The same holds true for using a device ID or a physical address as a persistent key. One workaround to these problems is to use a set of identifiers as a persistent key, such as a combination of an email and physical address with a phone number, for example. This creates another stumbling block, however, in that there may be several customers for whom the company doesn’t yet have all this information. To avoid these problems, persistent key management relies on a mechanism for creating a unique ID, rules for attaching the unique ID to a signal and rules for keeping it or replacing it over time – as the customer journey evolves. That is what is meant by persistence; a consistency to ensure that business users are not randomly losing or gaining information about a customer over time because of the vagaries of how the customer identifies themselves, or of how the identity resolution process unfolds. All normal operations of an identity resolution system are designed to provide a consistent and trustworthy identity across time in the face of expected changes. ## **How Do You Build a Key That Survives Change?** The ideal in persistent key management is to assign a unique ID that is not related to any of the data received in the signal. A sequential assignment is one method, such as designating a new ID for each new transaction or other type of signal. Another method is to assign a prefix and numbering system for each signal location, such as WS for web server, POS for point of sale, etc. Even with these methods of assigning a persistent key, database key management must account for consistent change, ensuring a unique ID remains unique when, say, a customer moves from an anonymous to known record. If Web\_X\_Y is a unique ID when a customer is anonymous, but the customer at a later point in time logs in and becomes known, perhaps the unique ID then becomes Loyalty\_X\_Y. A common way to account for change is to have a unique ID that might be part of the signal attached to the signal itself. In this way, when attaching a record (Web\_X\_Y) to an overriding record (Loyalty\_X\_Y) the unique ID remains in the signal, but the centralized unique ID is now associated with the loyalty customer. For example, the original web unique ID has all the signals and information for the customer as they proceed through a journey. When the customer identifies themselves as a loyalty member, all the information associated with the web ID moves to the loyalty ID, and there is now a cross-reference in the main database table that says the loyalty customer is the same person from the web account. No signals are lost. ## **Why Does Consistency Beat a Fresh Start?** Another consideration here is to avoid the temptation to reconstruct a set of persistent keys for every major operation, such as a batch update. Reconstructing persistent keys is never a good idea if the goal is to provide a consistently relevant, personalized experience throughout a customer journey. A nightly batch feed provides a host of new signals, which will require some automated matching process to build an identity graph to show the various connections. Assigning new keys within that construct prevents a simple, verifiable way to recognize that the John Doe from today’s batch is the same John Doe from yesterday’s batch run. Without an independent mechanism, constructing an identity graph from 50 million new records will lose the important understanding of incremental change from one batch to the next. A new key assignment might be acceptable if the goal is to simply send a generic blast email to every record that visited a website overnight, but that “key” will lack any contextual understanding of the customer beyond activity in that one channel. Redpoint’s [identity resolution](/what-is-idenitity-resolution/) accounts for this issue with predictive, deterministic and probabilistic matching algorithms that are independent of operational updates. Maintaining a separate accounting of which records should be matched or separated prevents the need to assign new keys, while also providing a longitudinal, contextual view of a customer’s movement through a customer journey. Persistent key management is an important capability for ensuring a consistent, relevant customer experience. We know with certainty that customers will change. They move, they acquire new devices, phone numbers and email addresses. They get married. They are anonymous or known, depending on how and when they engage with a brand. As signals and information are continually added or changing, persistent keys ensure a CDP does not lose any understanding of who the customer really is through change. **Blog categories:** Data Quality, Identity Resolution --- ### [Data Readiness in Banking: How a Golden Record Powers Smarter Cross-Sell and Upsell](https://www.redpointglobal.com/blog/the-power-of-a-golden-record-for-cross-selling-and-upselling-banking-products/) **Published:** August 19, 2025 **Author:** John Nash **Content:** Household debt, mortgage balances and auto loans are all rising to all-time highs, according to data from the [Federal Reserve Bank of New York](https://www.newyorkfed.org/newsevents/news/research/2024/20240514#:~:text=NEW%20YORK%20%E2%80%94%20The%20Federal%20Reserve,of%202024%2C%20to%20%2417.69%20trillion.). For banks and financial services institutions, rising consumer debt makes it more important than ever to know all there is to know about an existing customer, especially when the goal is to maximize cross-sell and upsell opportunities. To mitigate risk, it is advantageous to know which financial product is the best fit for each customer or household. For whom should a bank extend an offer to a premium credit card, for instance, that might allow customers to carry higher balances? A challenge for many banks to mitigate risk and make the right offer to the right customer is that data and processes tend to be siloed. Various lines of business – mortgage, insurance, auto, retirement – typically have no insight into a customer beyond the product portfolio. If the goal is to bundle homeowner’s insurance with a mortgage, as one example, it benefits the company to know everything there is to know about a customer beyond what’s on the mortgage documents. Is the credit score updated? What is the customer’s life stage? Previous policy history? ## **The Power of a Golden Record** Maximizing cross-sell and upsell opportunities in financial services is one use case for data readiness. An enterprise data readiness hub aggregates and unifies customer data from all potential sources, builds a Golden Record and segments and activates audiences to various end channels for the purpose of delivering personalized experiences. For banks and financial institutions, data readiness is important for analyzing customer data and interactions, as well as increasing conversion rates and customer lifetime value (CLV) by making the right offer to the right customer at the right time and on the right channel. A primary objective of a data readiness hub is to build a Golden Record, a real time, unified customer profile that combines a complete identity graph with a customer’s contact graph as well as transactional history, data aggregates and data attributes. When a Golden Record is made available to users across an organization, everyone has the same updated view of a customer. With the use of persistent key, a Golden Record also provides a contextual view of a customer over time. What that means is that everyone who has access to the Golden Record knows everything there is to know about a customer, including all interactions across the organization. And because a Golden Record is built using advanced identity resolution capabilities, including both deterministic and probabilistic matching, the contextual customer understanding extends to household relationships, a vital consideration for a bank. Using a Golden Record, a financial institution can make knowledgeable, informed cross-sell and upsell offers because it knows the customer’s complete history with the company – the product portfolio, usage, and interactions across every line of business. It also knows how to package the offer, i.e., what channel or sequence of channels to use to make the offer, the cadence of the outreach, etc. A customer-facing application using GenAI capabilities might even leverage the Golden Record to alter the tone of the outreach, such as changing the text or speech in a chatbot to align with a customer’s dialect. ## **Dynamic Segmentation for the Win** Dynamic segmentation refers to the ability to divide audiences in real time in the cadence of an ongoing customer journey. This means that if a customer is eligible for a certain upsell offer based on a recent behavior, but then becomes ineligible based on the next behavior, the customer is automatically moved out of the audience segment for that particular offer. Conversely, what is more common is for financial institutions to build segments off lists of customers, with a list beginning to decay the moment it’s created. Lists become less and less effective as customers have more options for engaging with a financial institution, such as 24/7 online banking. Even a routine browsing session provides a wealth of new data – the products a customer researches, the page visits, etc. Just a single browsing session might materially impact what cross-sell or upsell offer a bank makes – and that the customer is eligible for. When segments are built using rules and based off a real time Golden Record, a segment will always reflect the precise context and cadence of a customer’s banking journey. And when a segment is activated at the last possible moment, an organization can trust that the ensuing interaction is relevant for the customer. A hyper-relevant, personalized banking experience increases cross-sell and upsell conversions because the offers are made with confidence. The brand knows the customer is interested in the product. It knows the customer is low risk. It knows the customer wants to retire at 65. It has, in short, every data point it needs to make an educated, informed decision about what type of offer will maximize revenue, increase lifetime value, or achieve whatever metric the institution is after. The institution also builds customer loyalty because it treats the customer as a unique individual, interested not only in a transaction but in helping to guide the customer through a personalized journey. According to a recent study from [Dynamic Yield](https://thefinancialbrand.com/news/bank-marketing/personalization/the-personalization-paradox-can-banks-delight-customers-without-creeping-them-out-178138/), 72 percent of banking consumers said that products that are tailored to their individual needs are more valuable. In the same study, more than 85 percent of financial institutions say that having a personalization strategy is a priority, and 92 percent plan to invest further resources in executing a personalization strategy. For information on how the Redpoint Data Readiness Hub helps financial institutions unlock the power of their customer data, click [here](https://www.redpointglobal.com/financial-services/). **Blog categories:** Financial Services **Blog tags:** CDP, customer data platform, Golden Record --- ### [Data Readiness vs. Data Quality: Understanding the Difference](https://www.redpointglobal.com/blog/data-readiness-vs-data-quality-understanding-the-difference/) **Published:** March 6, 2026 **Author:** John Nash **Content:** Data quality has always played an essential role for enterprise companies seeking to maximize the value of their customer data. Whether for marketing or non-marketing purposes – including emails, onboarding, [patient engagement](https://www.redpointglobal.com/blog/personalization-in-practice-driving-engagement-and-revenue-with-commercially-insured-patients/), customer service, forecasting, and market research – the demand for [high quality data](https://www.redpointglobal.com/data-the-defining-difference/) remains paramount. However, the emergence of [agentic AI](https://www.redpointglobal.com/blog/how-data-readiness-powers-agentic-action/) raises the stakes, highlighting that data readiness – not just data quality – is now crucial for driving successful AI initiatives. Many companies discover, often too late, that data quality and [data readiness](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/) are not interchangeable. According to Gartner, 60 percent of GenAI projects will be abandoned after the proof-of-concept phase through 2026. \[*Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” (Feb 2026)*\]*.* The stated reason is that those projects lack [AI-ready data](https://www.redpointglobal.com/blog/drive-ai-success-with-ai-ready-data/), mainly because companies still rely on standard data quality processes that likely occur downstream, or are otherwise removed from the agents that require high-quality data. Rising consumer expectations coupled with rapid advances in technology have created conditions where data quality is necessary, but insufficient without data readiness. Data quality alone falls short of powering multi-channel [real-time consumer experiences](https://www.redpointglobal.com/blog/real-time-vs-right-time-the-difference-comes-down-to-context/) and/or autonomous agents tasked with completing complex objectives. For example, without continuous access to high-fidelity context, agents may hallucinate or fail. Data readiness fills the gap that was created when the speed of business began to outpace the speed of conventional data cleanup. ### **What is Data Readiness?** Data readiness is a fairly new way of thinking about storing, managing and using enterprise data. If a database is like a pantry full of ingredients, data readiness is having a chef-prepared meal ready to serve. It’s the state when enterprise data is not just stored, but cleansed, unified, and governed so that it can be used instantly – by marketing, by IT, by AI. Data readiness includes data quality as a core component, but data readiness extends the concept of cleansed, accurately matched data to include making data not just right, but fit-for-purpose for its intended use. Data quality might be used to determine whether an email address is correct, whereas data readiness provides the confidence that it’s the *right* customer profile, that it’s updated in real time, and that the enterprise has the legal consent to use it for a specific AI campaign. Similar examples include: #### **Identity Accuracy vs. Identity Confidence**: - *Data Quality*: Customer records are de-duplicated, and fields conform to standards, i.e., correct names, consistent formatting. - *Data Readiness*: Knows [*how* identities were resolved](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/) and can tune the matching rules, confidence thresholds, etc. based on the intended business use. #### **Clean Data vs. Actionable Timing**: - *Data Quality*: Customer attributes are accurate and complete as of the last batch refresh. - *Data Readiness*: Data is updated in real time and aligned to the decision window, i.e., triggers, next-best action models and personalization engines respond to the existing customer journey #### **Compliant Data vs. Permitted Use** - *Data Quality*: Consent and preference fields exist and are populated. - *Data Readiness*: Consent is [enforced dynamically by channel, purpose, and use case,](https://www.redpointglobal.com/blog/ready-data-is-compliant-data-does-your-data-hold-up-to-scrutiny/) so that data is automatically eligible or excluded for specific AI-driven decisions or specific marketing campaigns. #### **Accurate Data Vs. Operational Availability** - *Data Quality*: Customer data is accurate in a central system. - *Data Readiness*: Data is [packaged, accessible, and performance-tested](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/) for downstream systems (journey orchestration, AI agents, etc.) without manual transformation or latency risk. In short, data readiness is a foundational enterprise principle encompassing the holistic process of fully preparing customer data for *any* possible CX or business use case. These may include personalized marketing – the forte of a customer data platform – but also to provide AI agents with the needed context, for retail media networks, or for any other business or CX use case. ### **Passive Storage vs. Active Velocity** Returning to the pantry analogy, data quality is akin to sorting the ingredients; it’s a step up from raw data, but still a passive state. Data readiness emphasizes data velocity. It sorts the ingredients and prepares the meal from the moment the groceries are unpacked. While you cannot have data readiness without high-quality data, data quality alone is not enough to drive modern, hyper-personalized experiences or successful AI initiatives. Data quality determines if data is accurate, where data readiness goes further to provide confidence that data can be trusted, explained, governed, delivered on time and be appropriately used for a specific CX or AI decision right now. For information about how Redpoint can help you close the context gap in your customer data to fuel real results across your AI and CX initiatives, click [here](https://www.redpointglobal.com/data-readiness-hub/). **Blog categories:** Data Governance & Security, Data Quality, Data Readiness **Blog tags:** Data readiness, Golden Record --- ### [First-Party Data Needs Continual Identity Resolution, Not a One-Time Fix](https://www.redpointglobal.com/blog/continual-identity-resolution-first-party-data/) **Published:** August 21, 2026 **Author:** Renee Graff **Content:** Third-party cookies are disappearing across most of the web, and with them, the easy shortcuts brands used to reach people based on someone else’s data. What’s left is first-party data: the information customers hand you directly, across every channel you own. It’s more valuable, but it’s also scattered: a purchase in-store, an account created online, an email opt-in, a support call. Each interaction creates its own version of the same person. [Identity resolution](https://www.redpointglobal.com/what-is-identity-resolution/) is what turns scattered first-party data (a purchase here, an email opt-in there) into one accurate customer. Without it, first-party data stays fragmented, and fragmented data doesn’t deliver the single, trustworthy view of each customer that first-party data is supposed to provide. With it, every new signal, deterministic or probabilistic, sharpens the record you already have instead of creating another disconnected copy. That’s what turns scattered first-party data into a single, usable source of truth. But identity resolution isn’t a one-time fix. Even matching rules calibrated exactly right today face a moving target because a customer profile is never “finished.” Every new interaction can change it. People don’t stand still. They get married and change their name, they move, they get a new phone or start using a new email. They don’t notice they’ve hit a wrong letter when typing in their information on a handheld device. Incomplete, incorrect, and duplicate records get created regularly. And that doesn’t include the influence of new web activity, fresh transactions, and in-person interactions. Harder to spot is a subtler misconception that still trips up identity resolution programs – once the matching rules are configured and the first pass runs clean, teams treat it as finished. In practice, the rules that worked when you had three data sources rarely get revisited once a fourth, fifth, and sixth come online. Thresholds tuned for a smaller, simpler customer base quietly drift out of calibration: too tight, and real matches get missed as fragmentation creeps back in; too loose, and different people start merging into the same profile. Nobody checks until the golden record itself is wrong. Identity resolution ties all these signals back to the right person, as they arrive. Done continually, the profile gets sharper and stays sharp over time. Left on autopilot, rules untouched, new signals unmatched, it falls behind the customers it’s supposed to describe. ### Treating identity resolution as a one-time project is a real liability And customers can tell the difference. People overwhelmingly expect the companies they deal with to provide a personalized experience and exhibit understanding. In a [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236), 82 percent of consumers surveyed said that they are loyal to brands that demonstrate a “thorough understanding” of them as a unique customer. And 39 percent said they will not do business with any company that fails to provide a personalized experience. > **82% of surveyed consumers are loyal to brands that demonstrate a “thorough understanding” of them.** > > **39% will not do business with any company that fails to provide a personalized experience.** > > *-Harris Poll / Redpoint Global consumer survey* ### What continual identity resolution does for your data Modern tooling links every piece of first-party data to a customer record as it arrives. Anonymous records become known, and known records gain detail. Each person’s unified profile keeps evolving with new behavioral, transactional, demographic, and preference data. [Redpoint applies identity resolution](https://www.redpointglobal.com/identity-resolution/) the moment new data lands, not on a nightly or weekly batch. Deterministic and probabilistic matching resolve each new signal to the right individual or household, and the profile updates in real time. That is what lets you treat each customer as an individual, in the moment. When done right, identity resolution answers the question: *Is the person who just browsed our website the same person who transacted in person last week and opened an email this morning?* The answer to that question is the foundation of everything you do downstream: personalization, customer service, suppression, and compliance. > **The difference between identity resolution done once versus done continuously is the difference between a snapshot and a live feed.** ### Clean identities are revenue opportunities A clean profile is only useful if it reflects what the customer just did. A retail marketing team running an end-of-summer swimsuit sale segments an audience and prepares to email the offer to 10,000 customers. But between building the list and hitting send, 5 percent of them buy a swimsuit at full price. If the data updates in nightly batches, those buyers still get the discount offer, which means lost margin and a worse experience. With identity resolution running in real time, each purchase resolves to the customer’s record before the email goes out, so the segment adjusts and those 5 percent get a more relevant message instead of a discount they no longer need. In the hospitality industry, consider a hotel guest that checks in for three nights. Over that stay they could book a spa treatment, reserve a table at the restaurant, buy something in the boutique, book a tee time, or sign up for a guided excursion. Each of those transactions sits in a separate system, and often each knows the guest under a different identifier. No single one sees that they are all the same person, mid-stay. That window is the whole opportunity. An offer for a sunset excursion or a spa afternoon only works while the guest is still on property. Send it the morning after checkout and it’s worthless. Identity resolution running in real time means every touch resolves to the guest’s unified profile as it happens. The hotel sees who’s on site, what they’ve already done, and where there’s still time to influence the folio by sending a spa promo, suggesting dinner reservations or the excursion booking while there are still nights left to fill. That’s the point. The guest is one person moving through spa, dining, retail, and excursions, and the value is in reaching them during the stay, not after it. Continual identity resolution makes that possible. ### How continual identity resolution supports GDPR and CCPA compliance First-party data is regulated data. Laws like GDPR and CCPA govern how it’s collected, stored, and used, and consumers expect privacy. Continual identity resolution supports compliance rather than working against it. A unified profile makes it easier to apply consent and preferences consistently and to act on access or deletion requests, because a fragmented identity means the same person’s data could hide under multiple identifiers where it’s easy to miss. The brands that treat identity resolution as a continuous process are always working with the most current picture of their first-party data. The ones that treat it as a one-time project are perpetually behind. ### Quick answers **Is identity resolution a one-time process?** No. Customer data changes constantly, with new devices, new addresses, and new transactions arriving all the time, so identity resolution has to run continually to stay accurate. **How does identity resolution support a first-party data strategy?** It links every piece of first-party data, from web activity to transactions to in-person visits, back to one accurate profile, which is what makes first-party data usable as third-party cookies phase out. **Blog categories:** Identity Resolution **Blog tags:** identity resolution --- ### [How to Choose and Succeed with a Customer Data Platform](https://www.redpointglobal.com/blog/how-to-choose-a-cdp/) **Published:** August 14, 2026 **Author:** Redpoint Global **Content:** ### Why Most CDP Evaluations Miss the Point [Customer Data Platforms (CDPs)](https://www.redpointglobal.com/customer-data-platform/) rarely fail because they lack functionality. They fall short because organizations underestimate the complexity of turning raw, fragmented data into something that’s consistently usable, trustworthy, and ready for change. Too often, CDP selection starts with a feature checklist instead of business outcomes. But use cases should drive the requirements, and data readiness determines whether those use cases actually succeed. #### What’s the biggest reason CDP initiatives fail? Not a lack of technology. It’s unclear success criteria. CDP initiatives typically stall for the same handful of reasons: - Data readiness deferred to “phase two” - Identity logic treated as fixed - Real-time claims assumed, not validated end to end - Business users blocked by data teams - Architecture optimized for today’s stack only This guide reframes CDP evaluation around a single question: can this platform make your data ready for the use cases you care about, today and tomorrow? ### Step 1: Define Success Before You Evaluate Platforms Before you look at a single vendor, get specific about what you’re trying to achieve. Most CDP initiatives don’t fail because of a technology gap; they fail because nobody defined success up front. #### What should you evaluate first when choosing a CDP? Your use cases. Modern CDP use cases generally fall into three categories, and each places different demands on the platform: - **Foundational activation**: Customer 360, segmentation, campaign execution, and omnichannel activation. These use cases focus on making trusted customer data accessible and actionable across core marketing and engagement workflows. - **Adaptive experiences**: real-time personalization, journey orchestration, and next-best action. These scenarios require responsive data pipelines and identity resolution that can operate at speed and scale. - **Intelligence and automation**: AI-driven insights, agentic workflows, and predictive decisioning. These use cases place the highest demands on data readiness, consistency, and governance, since automation amplifies both value and risk. Each category places different requirements on: - Identity resolution, including accuracy, persistence, and flexibility - Data quality and governance, especially as data volumes and sources grow - Latency and freshness, based on how quickly decisions must be made - Architectural flexibility, to support new use cases without reimplementation Defining which outcomes matter most gives you the lens for evaluating platforms. Without that clarity, you risk selecting a CDP that’s technically capable but strategically misaligned. #### How do you know a CDP is delivering value? Four signs tell you fast: - Time to launch new use cases is shrinking. - Trust in customer data is increasing, not eroding. - Identity remains consistent as data grows. - Business teams move faster without increasing risk. ### Step 2: Determine Whether the CDP Can Make Data Ready — and Keep It Ready Once your use cases are clear, the next question is whether the CDP can make data right (complete, accurate, and timely) and fit for purpose (actionable, trusted, and compliant). #### Does a CDP make data ready as soon as it’s ingested? A modern CDP should treat [data readiness](https://www.redpointglobal.com/data-the-defining-difference/) as a core platform function, not a downstream task. Data readiness can’t be an afterthought or deferred to “phase two,” because every downstream use case depends on it. To support reliable activation, analytics, and AI-driven use cases, data has to be right and fit for purpose as it enters the platform. That means the CDP should: - Cleanse, standardize, and normalize data as it arrives - Resolve identities in-line and continuously - Eliminate the need for downstream fixes, reprocessing, and manual intervention When data has to be “fixed later,” value is delayed, customer data debt compounds, and operational risk increases. In practice, that shows up as missed ROI targets, productivity losses, higher costs from inefficient downstream activation platforms, wasted advertising spend, and even damaged customer trust. A [CDP that makes data ready at ingestion](https://www.redpointglobal.com/data-ingestion/) creates a trusted foundation instead, one that teams can use immediately and keep relying on as use cases evolve. ![How To Choose A Cdp Blog Graphic 1](https://www.redpointglobal.com/wp-content/uploads/2026/08/How-to-Choose-a-CDP-blog-graphic-1.png) #### What should you look for in a CDP’s identity resolution? [Identity resolution](https://www.redpointglobal.com/what-is-identity-resolution/) underpins everything a CDP enables, from segmentation and personalization to measurement, orchestration, and AI-driven decisioning. If identity resolution is opaque or inflexible, confidence in the unified customer profile erodes quickly, and downstream use cases suffer. Look for identity resolution capabilities that align to your specific use cases, rather than assuming a single, fixed approach will work for everything. In particular: - Visible and explainable matching logic, so you can understand why records were matched, merged, or kept separate, rather than relying on a black box you can’t inspect or trust. - Tunable identity resolution by use case, since different scenarios place different demands on identity strictness. What works for marketing activation may not be appropriate for analytics, compliance, or AI, and a one-size-fits-all model will eventually become a constraint. - Support for contextual identity, such as householding or account relationships, which provides critical insight for coordinated engagement, frequency management, and delivering consistent experiences across related individuals. - Persistent identity keys that let you follow a customer’s journey over time, including the transition from anonymous to known, maintaining continuity as new data arrives and identifiers change across channels, devices, and moments. - [Probabilistic identity resolution](https://www.redpointglobal.com/blog/deterministic-probabilistic-matching-identity-resolution/), not just deterministic matching. Weighted signals and confidence scores better reflect the real-world ambiguity of customer data and enable more flexible, accurate decisioning. Trust in the customer profile ultimately depends on visibility, control, and persistence. Platforms that excel at identity resolution let you support multiple use cases without reprocessing data, preserve continuity across the customer lifecycle, and build a durable foundation for real-time engagement and AI-driven experiences. ![How To Choose A Cdp Blog Graphic 2](https://www.redpointglobal.com/wp-content/uploads/2026/08/How-to-Choose-a-CDP-blog-graphic-2.png) #### How does a CDP keep data ready over time? Customer data is never static. Sources change, schemas drift, volumes fluctuate, and quality degrades, often without obvious warning. As use cases evolve from basic activation to real-time engagement and AI-driven decisioning, data that was once “real-time” can quickly become unreliable. Look for built-in [data observability](https://www.redpointglobal.com/blog/what-is-data-observability/) that lets teams continuously assess whether data stays fit for its intended purpose, across use cases and over time. In particular: - Monitor data health metrics such as match rates, profile completeness, and freshness, so both marketing and data teams can see whether the unified profile reflects reality, and whether it’s improving or degrading. - Identify issues at the source, rather than discovering problems only after campaigns underperform or models produce unexpected results. - Maintain confidence as use cases evolve, so changes in data sources, identity logic, or activation requirements don’t silently undermine trust in the unified customer profile. ### Step 3: Assess Architectural Readiness for What Comes Next Choosing a CDP is a long-term decision. The architecture has to support change, growth, and interaction models that don’t exist yet. #### Can a CDP work with your data without moving it? Moving large volumes of customer data introduces unnecessary risk, cost, and latency. As data estates grow more distributed across cloud warehouses, operational systems, and real-time environments, success increasingly depends on bringing processing to the data instead of forcing data into a single centralized store. Look for support for a true [data-in-place model](https://www.redpointglobal.com/blog/a-composable-cdp-is-not-just-data-in-place/) that fits how your data is already organized and governed. In particular, the platform should: - Assemble unified customer profiles where data already resides, avoiding large-scale data migration projects that slow time to value and increase operational risk. - Operate consistently across cloud data warehouses, NoSQL stores, and hybrid environments, rather than assuming a single underlying data architecture. - Support distributed and agentic systems that need shared, trusted context, so multiple applications, services, or AI agents can work from the same accurate understanding of the customer without duplicating data. A CDP built for data-in-place architectures minimizes disruption to existing systems while improving performance, security, and flexibility. More importantly, it lets you evolve your use cases, from foundational activation to real-time and AI-driven experiences, without rearchitecting your data foundation. #### When does a data-in-place model matter most? - Customer data is distributed across warehouses, operational systems, or real-time sources. - Security or regulatory constraints limit data movement. - Low-latency decisions are required for real-time use cases. - Multiple teams or systems rely on shared customer context. - Use cases are expected to evolve without rearchitecture. #### How do you avoid vendor lock-in with a CDP? As customer data ecosystems get more complex, the architecture of a CDP matters as much as its features. Monolithic platforms often promise simplicity, but they can limit flexibility over time, especially as new tools, channels, and AI capabilities emerge. Look for a composable platform that lets you evolve without being locked into a single vendor’s ecosystem. In particular, the platform should: - Integrate cleanly with your existing investments, rather than requiring a rip-and-replace of your current data, analytics, or activation stack. - Avoid vendor lock-in, so you can adopt new tools, models, or channels without being constrained by proprietary architectures or closed ecosystems. - Support incremental adoption and evolution, so your team can start with high-value use cases and expand capabilities over time without re-implementation. [Composable architectures](https://www.redpointglobal.com/blog/driving-composable-cdp-success-with-data-readiness/) are increasingly critical as organizations adopt AI agents, new interaction channels, and specialized best-of-breed tools. A CDP that’s flexible by design enables innovation while protecting your long-term architectural freedom, so today’s platform choice doesn’t become tomorrow’s constraint. #### Does a CDP need to work in real time? Not every use case needs real-time execution, but every use case has a required cadence. As customer interactions get more compressed, the real question isn’t whether a CDP supports real-time in theory, but whether it can operate at the right speed for your specific business needs, consistently and reliably. Look beyond claims of “real-time access” and check whether the platform can support the cadence your use cases demand. In particular, the CDP should: - Update the underlying customer profile at the appropriate cadence, including real-time where the use case requires it, rather than relying solely on scheduled or batch updates. - Support real-time calculations, scores, and decisions when needed, so attributes, eligibility, and next-best actions reflect the most current understanding of the customer. True [real-time engagement](https://www.redpointglobal.com/blog/data-readiness-real-time-relevance-delivering-context-in-every-moment/) is end to end. If any part of the process, such as data ingestion, identity resolution, profile updates, or decisioning, falls back to batch, the experience isn’t truly real-time anymore, no matter how quickly data can be accessed. Real-time engagement requires real-time data readiness, not just real-time access. ### Step 4: Ensure the Platform Can Be Used, Not Just Implemented Even the most sophisticated CDP fails if it can’t be operationalized. Lasting value depends on how easily teams can adopt the platform and turn ready data into action as part of their everyday work. #### Who should own a CDP: Marketing, IT, or both? Many CDP initiatives stall not because of technology limitations, but because [ownership is unclear](https://www.redpointglobal.com/blog/the-composable-cpd-faq-for-marketers/). When responsibilities between marketing, data, and IT are poorly defined, organizations either move too slowly or sacrifice trust and governance in the name of speed. A successful CDP operating model balances self-service for business teams with control and confidence for data teams, so teams can move quickly without reintroducing the very data problems the CDP was meant to solve. In a modern CDP model: - **Data and IT teams own the foundation.** They’re responsible for data ingestion, identity resolution logic, quality standards, governance, privacy controls, and overall data readiness. Their job is to keep customer data accurate, consistent, compliant, and continuously fit for use. - **Business teams own activation and outcomes.** Marketing and CX teams own segmentation, audience strategy, activation, testing, and optimization. They should be able to explore and act on trusted data without needing to understand how it was stitched together or governed. - **The platform enforces guardrails, not gatekeeping.** Governance, identity rules, and quality controls should be embedded in the platform itself, so teams are guided toward correct usage instead of blocked by manual processes or approvals. When ownership is clear, the CDP becomes a shared capability instead of a bottleneck. Data teams keep confidence in the customer profile, while business teams get the speed and autonomy they need to deliver consistent, personalized experiences at scale. #### Can marketers use a CDP without IT or coding help? Adoption drives ROI. Even the most powerful CDP fails if data access is gated by IT, complex workflows, or brittle pipelines. To deliver value, business teams need fast, intuitive access to trusted, ready-to-use data, without creating new risks or bottlenecks. A modern CDP should empower marketers and CX teams to: - Build, refine, and test segments visually, using identity-resolved, governed data. - Explore audiences and surface insights without relying on SQL or data engineering support. - Activate data across any channel, including email, paid media, mobile, web, and emerging destinations, without custom code or one-off integrations. At the same time, this self-service access has to sit on top of a strong data readiness foundation. Business users should be able to move quickly, while data teams keep confidence that the data stays accurate, compliant, and consistent across every activation. #### How does a CDP support AI? AI success depends less on algorithms and more on the quality, consistency, and timeliness of the data feeding them. As organizations move from experimentation to production AI, the CDP’s job is to make sure customer data is not just accessible, but clean, unified, governed, and ready for AI use cases. Look beyond surface-level AI features and check whether the platform gives you the [right foundation to support AI](https://www.redpointglobal.com/blog/the-cdp-reckoning-why-customer-data-platforms-are-at-a-crossroads/) today and adapt as approaches evolve. In particular, the CDP should: - Make customer data AI-ready by design, with in-line data quality, identity resolution, and governance that reduce noise and bias before data is used for modeling or inference. - Support AI execution both inside and outside the platform, integrating with external AI tools and services rather than forcing all intelligence to live inside the CDP. - Enable real-time inference and attribute calculation when required, so AI-driven scores and decisions reflect the customer’s current state. - Allow customer-owned models and analytics alongside native capabilities, preserving flexibility and avoiding lock-in to a single vendor’s AI approach. The stakes are real. Forty-two percent of enterprises say more than half of their AI projects have been delayed, underperformed, or failed due to data readiness issues, according to a Fivetran survey. ### From CDP Selection to Sustainable Value Choosing a CDP was never about finding the platform with the longest feature list. It’s about choosing a foundation that gets your data ready for any AI or CX use case you take on next. The role of the CDP is changing. It’s no longer just a destination system or a marketing tool. It’s the connective layer that keeps customer data trustworthy, usable, and ready as channels change, expectations rise, and AI becomes part of everyday decisions. Organizations succeed with a CDP when: - Use cases are clearly defined and continually refined, grounded in real business outcomes. - Data readiness is a core capability, not an afterthought, ensuring data is fit for activation, analytics, and AI from the start. - Architecture is built to adapt, supporting new technologies and strategies without rework. - Teams move with confidence and speed, acting on data they trust across every touchpoint. This is what separates CDPs that get implemented from the ones that become indispensable. The real value of a CDP isn’t measured at launch. It’s measured in how well it lets the business evolve, experiment, and grow over time. *Learn more about CDPs: [redpointglobal.com/cdp](https://www.redpointglobal.com/cdp)* **Blog categories:** Customer Data Platform **Blog tags:** CDP --- ### [Customer Identity Resolution in Snowflake: Build or Buy?](https://www.redpointglobal.com/blog/customer-identity-resolution-build-or-buy/) **Published:** June 26, 2026 **Author:** Kris Tomes **Content:** *Enterprise financial services companies face a build-or-buy decision on customer identity resolution in Snowflake. Building it in-house honors the right architectural instinct but carries significant maintenance debt. A native capability running inside Snowflake compresses procurement and removes the build burden. The right approach depends on engineering depth, regulatory exposure, and what kind of long-term work the bank wants to own.* Walk into any bank’s data team in 2026 and you’ll find a patchwork of deterministic matching scripts, custom fuzzy-logic services, ML notebooks testing entity resolution, a couple of open-source point solutions, and maybe an AI proof of concept. The data team owns it. The real question is whether to keep building this in the warehouse, or to run a capability that’s already native to the same place. ### Where customer identity resolution lives today Customer identity resolution is the work of matching and unifying customer records across siloed or disparate product systems, channels, and time into a single, persistent identity. Its architectural location has shifted three times in the last fifteen years. It used to live in the Customer MDM: Reltio, Informatica MDM, IBM InfoSphere, or Profisee. The mental model was straightforward. The MDM was where customer identity got resolved, and everything else was supposed to consume from it. That frame held until banks discovered three things about commercial MDM. It was built for stable master attributes (name, address, household hierarchy, ownership relationships), not for behavioral data or real-time customer activity. Extending it to cover modern customer identity needs is slow and expensive because the data model and the licensing both push against the extension, making it hard, costly, or impractical to expand it for newer needs. And even when extended, the MDM still doesn’t sit in the data cloud where downstream tools and AI consumers now read from. The architectural conversation has moved past the MDM as the sole answer. It then lived in custom data engineering: hand-coded SQL, fuzzy matching libraries, internal Python services maintained by whichever engineer drew the short straw, often unmaintained as that engineer moved on. The data leader knew the code existed and tried not to think about it more than necessary. In practice, neither location was ever the sole answer. Even when the MDM was supposed to be master, the CDP built its own customer profile, and the Anti-Money Laundering (AML) monitor, the Know Your Customer (KYC) platform, and the fraud engine each did their own matching. Customer identity has been silently scattered across systems all along. The data cloud era is the first chance to consolidate. Snowflake is now the dominant location for enterprise financial services customer data. The architectural instinct is right. The question is whether to keep building the resolution logic in the warehouse yourself, or to run a native capability. ### What building customer identity resolution in Snowflake takes The technical scope is broader than most build projects budget for at the outset. - **Deterministic and probabilistic matching, tuned to the bank’s data realities.** Deterministic matching catches the easier cases. The harder ones (M&A overlap, channel drift, behavioral joining, name and address variations) require probabilistic models that need ongoing retraining as data quality, customer behavior, and the bank’s data sources evolve (especially after M&A). A dbt model with deterministic rules is achievable in a sprint. A probabilistic matcher that holds up over time is multi-quarter engineering with a permanent maintenance footprint. - **Householding across joint accounts, beneficiaries, business owners, family wealth, and beneficial ownership.** Joint accounts are straightforward, but the rest is genuinely complex and gets harder as the bank grows or acquires. In-warehouse builds typically ship the easy cases first and then accumulate the long tail as backlog that never quite gets cleared. - **Persistent customer keys through M&A and core system replacements.** Every acquisition inherits a different customer schema, and every core replacement breaks the keys. Each event triggers a multi-month reconciliation project. The bank’s customer identity work is never finished, just paused between integrations. - **Lineage propagation to every downstream consumer.** Regulation B adverse-action notices, Consumer Financial Protection Bureau (CFPB) complaint resolution, fair-lending audits, and the customer-data side of KYC and AML obligations all require defensible lineage. Custom matching code rarely builds lineage with any depth, because the engineer writing the matcher isn’t also writing the audit trail. - **Real-time and batch in a single code path.** Most in-warehouse builds end up with two implementations that drift apart over time, and customers get different resolved identities depending on which path is hit. - **Governance and audit-grade traceability on every attribute.** Usually the last capability built into a custom matcher, and usually the first one the regulator asks about. The accumulating cost is what defines most in-warehouse projects over time. Within the first two years of launch, the team is typically spending more time keeping the system working than improving it, and the institutional knowledge tends to leave with the engineers who built it. The bank ends up with a critical piece of customer infrastructure that fewer and fewer people understand. ### How native customer identity resolution in Snowflake works A customer identity capability that runs inside the bank’s Snowflake account inherits the security envelope that’s already approved, places the resolved customer record where every downstream tool already reads from, and adds no data movement, no new InfoSec re-evaluation, and no new residency exposure. Evaluation runs in days rather than quarters because the procurement work is largely already done. Architecturally, that matters because the resolved customer record becomes a data layer capability rather than a separate system. The Customer 360, the AI and agent platforms, and the BI stack all consume from the same resolved record, so every customer-facing system in the bank is acting on the same person. There’s no new integration layer to maintain and no vendor sandbox to keep in sync with production. The Redpoint Identity Studio is the product instance of this pattern. The detailed capability work (matching, householding, persistent keys, real-time and batch, governance and lineage) is the same scope as the build described above. The difference is that the team that would have been building it is freed to work on the parts of the bank’s customer data. ### Build or buy: what to take away Customer identity resolution belongs in the data architecture, in your Snowflake account, at the layer between sources and consumers. The data leader’s instinct on this is correct. The honest question is whether to build it there or run something native in the same place, and both are defensible decisions in the right context. Organizations must decide based on engineering depth, regulatory exposure, and tolerance for maintenance debt. Either way, run it where the data already lives. ### FAQs **Q: What is customer identity resolution?** Customer identity resolution is the work of matching customer records across product systems, channels, and time into a single, persistent identity. The output is a resolved customer record that downstream systems (Customer 360, CDPs, AI and agent platforms, KYC engines, fraud and AML monitors, and BI) can read from consistently. **Q: Why isn’t my MDM enough for customer identity resolution?** Commercial MDM platforms like Reltio, Informatica MDM, IBM InfoSphere, and Profisee are built for stable master attributes (name, address, household hierarchy, ownership relationships) rather than for behavioral data or real-time customer activity. Extending an MDM to cover modern customer identity needs is slow and expensive because the data model and the licensing both push against the extension. Even when extended, the MDM still doesn’t sit in the data cloud where downstream tools and AI consumers now read from. For most enterprise financial services teams, the more productive question is no longer “how do we extend the MDM” but “where does customer identity work belong now.” **Q: Where should customer identity resolution live in a modern bank’s data architecture?** In the data layer, between source systems and downstream consumers. For enterprise financial services, that increasingly means in or near the data cloud where customer data already lives. Snowflake has become the dominant location for customer data in Tier-1 banks. **Q: Should we build customer identity resolution in Snowflake or buy a native capability?** Both are defensible. Build makes sense when the bank has serious in-house data engineering depth, narrow customer-identity scope, and tolerance for multi-year maintenance debt. Buying a native capability makes sense when the bank needs faster time-to-value, has regulatory complexity, or wants to free its data team to work on proprietary capabilities rather than rebuilding common customer-data infrastructure. **Q: How long does customer identity resolution take to deploy in Snowflake?** A native capability running inside the bank’s existing Snowflake account can typically be evaluated in days rather than quarters, because the security envelope and procurement work are already done. In-warehouse builds typically take multiple quarters to ship the easy cases and extend over years to handle the long tail of householding, M&A reconciliation, and governance requirements. **Blog categories:** Financial Services, Identity Resolution **Blog tags:** identity resolution --- ### [The Composable CDP FAQ For Marketers](https://www.redpointglobal.com/blog/the-composable-cpd-faq-for-marketers/) **Published:** January 17, 2025 **Author:** Renee Graff **Content:** At its core, a composable CDP allows businesses to build a customer data solution that integrates best-of-breed specialized technologies. Instead of relying on a single monolithic platform, a company creates a modular system using various tools tailored to the organization’s needs. For IT, this is a dream: flexibility, scalability, and adaptability. But how does this benefit marketers? Here’s what marketers need to know about a [composable CDP](https://www.redpointglobal.com/blog/composable-cdp/), and why it might be the perfect solution for both marketing and IT. ## **What is the Difference Between a Composable CDP and a Traditional CDP?** A traditional CDP bundles everything, storage, identity resolution, segmentation, and activation, into one closed system, and it usually does that by copying your customer data into its own proprietary store. A [composable CDP](https://www.redpointglobal.com/blog/composability-is-not-just-reverse-etl/) swaps that for modular, best-of-breed components that work with data in place, integrating with the infrastructure you already have instead of duplicating it. That difference matters for both Marketing and Data teams. With a composable CDP, the Data team keeps ownership and governance over where the data actually lives, rather than managing a second copy of it. Marketers still get the self-service segmentation and activation they’d expect from either type of platform, but on a foundation IT didn’t have to hand off control to build. Composable means more upfront decisions about which components to use and how they connect. But it also means you’re not locked into one vendor’s roadmap, or one vendor’s copy of your data. ## **Will Marketers Have Control Over Customer Data?** Yes! One of the main concerns for marketers is retaining control over customer data for [segmentation](https://www.redpointglobal.com/segmentation-activation/) and campaign purposes. They want ready access to an updated, accurate customer profile without having to query IT. A composable CDP ensures that marketers maintain this control without needing to submit tickets, request data, or rely entirely on IT. A composable CDP’s no-code environment allows marketers to access, segment and activate customer data without needing to learn SQL or wait for IT to process each request. With direct access to the data, marketers can independently build audience segments and launch personalized campaigns. So, while IT handles the infrastructure, marketers can more easily focus on strategy, execution and improving customer experience (CX). No more waiting for IT to generate a report. A composable CDP delivers: - **Real-time access to customer data**: Batch processes and manual queries are a thing of the past. Access unified customer profiles and segmentation tools directly within the platform. - **Autonomy in data handling**: Whether you’re executing a new campaign or adjusting segments to retarget customers, you can do so without IT intervention. - **No-code tools**: Drag-and-drop interfaces, intuitive dashboards, and automated workflows make it easy for marketing teams to navigate customer data without technical bottlenecks. ## **Does a Composable CDP Handle Data Quality?** A complete, composable CDP prioritizes [data quality](https://www.redpointglobal.com/data-quality-and-data-ingestion/) from the moment data enters the system. In many traditional environments, customer data is scattered across different systems, making it difficult to ensure a single source of truth. This can lead to duplicate records, fragmented profiles, and inconsistent customer experiences. Prioritizing data cleansing and enrichment ensures that every customer data point is accurate, deduplicated, and enriched with additional insights from the moment it enters the system. The prioritization of data quality includes advanced [identity resolution](https://www.redpointglobal.com/identity-resolution/) capabilities that merge fragmented data from multiple sources, creating a unified and reliable customer profile, also known as the Golden Record. This means marketers can trust the data they’re using to build segments and execute personalized campaigns, without having to wonder if IT is returning the latest and greatest customer record. This attention to data quality ensures that marketing campaigns are more effective, segmentation is more accurate, and messaging is hyper-relevant. Duplicate emails or incorrect offers become a thing of the past as data will always be clean and ready to use. Not every composable CDP prioritizes data quality, however, so it’s something to be aware of when looking at which composable CDP might be right for your business. ## **Will a Composable CDP Make Marketers’ Jobs Easier?** The short answer is yes. The goal of a composable CDP is not just to meet IT’s needs for flexibility and scalability, but to also make marketers’ jobs easier by simplifying access, management and activation of customer data. With a no-code environment, marketers can build dynamic segments, activate real-time campaigns, and personalize interactions across channels – all without navigating complex systems or processes. Here’s how it makes marketers’ lives easier: - **Faster campaign execution**: With real-time access to data and pre-built integrations into marketing tools, launching and optimizing campaigns is faster than ever vs. a system that delays the syncing of data and often requires scheduled updates or IT intervention to run processes. This can lead to slower campaign execution, where an abandoned cart campaign (to use one example) might be delayed by hours or even a day, reducing its effectiveness. - **Better segmentation**: Thanks to accurate and unified customer profiles, marketers can create more specific audience segments, improving the relevance and effectiveness of campaigns. There is no limit to how granular or dynamic segments can be. And real-time processing helps ensure that customer segments are always updated, thus relevant in the context of a real-time customer journey. - **Personalization at scale**: A composable CDP allows marketers to personalize customer experiences across multiple channels in real time, whether sending targeted emails, displaying personalized ads, or optimizing in-app messaging. A traditional CDP that relies on periodic data updates rather than instantaneous processing means that personalization efforts might be delayed, static or inconsistent across channels, diminishing the customer experience. - **Agility and flexibility**: The modular nature of a composable CDP means marketers can easily add new tools and capabilities as their needs evolve and the business grows. This lessens the need for customization, vendor support, or even a complete system upgrade to add new functionality or to adapt to new needs of the business, which may slow down marketing initiatives and make it difficult to stay competitive in a fast-moving market. IT departments also benefit from the scalability, flexibility and efficiency that a composable CDP offers. A composable architecture can grow as data needs expand, and it also allows IT to integrate multiple data sources and technologies into the CDP, allowing them to manage data governance and security while providing marketing with the tools it needs. From an efficiency standpoint, because data cleansing and enrichment happen at the point of entry, IT can ensure data accuracy across the organization without creating downstream work. In short, a composable CDP meets IT’s technical requirements for a scalable and flexible data solution while empowering marketing with real-time data access, accurate customer profiles, and no-code tools for segmentation and activation. A composable CDP isn’t just a win for IT; it’s a game-changer for marketing. With the composable Redpoint CDP, marketers get a no-code environment for managing data, while IT retains the flexibility and scalability needed to manage the infrastructure. By prioritizing data quality and giving direct control over customer segmentation and campaign execution, a composable CDP ensures marketers can deliver personalized, data-driven experiences that drive real business results. **Blog tags:** Composable --- ### [You Can’t Have a Data Clean Room without Data Quality](https://www.redpointglobal.com/blog/you-cant-have-a-data-clean-room-without-data-quality/) **Published:** December 2, 2022 **Author:** Steve Zisk **Content:** It is well understood that customers recognize the enormous value of their personal data. The value exchange holds that customers will provide data to a brand in return for a personalized experience. In a 2022 Dynata customer experience (CX) survey, [59 percent of customers](https://www.redpointglobal.com/blog/new-survey-findings-reveal-customers-grasp-the-value-of-their-personal-data/) said that receiving personalized offers, discounts or perks was one of the top ways brands facilitate the sharing of more data. The willing exchange, however, comes to a screeching halt when brands share data with a third-party without consent. In the survey, nearly half (48 percent) of consumers said they will stop doing business with a brand that shared data without consent. Brands, too, particularly retailers, media companies and walled garden behemoths (Amazon, Google) increasingly recognize both sides of the equation. That is, they recognize the value of their own first-party data and they see the untapped potential in monetizing that value, yet they insist on protecting consumer trust and honoring expectations surrounding data privacy. ## **Safeguard Privacy with a Data Clean Room** Enter data clean rooms, which are gaining traction as a way for companies to merge and match two or more first-party data sets without exposing any personally identifiable information (PII) that one company possesses about its customers to another party. Using a specific type of data encryption, data clean rooms allow a company to analyze, match and build models using anonymized data without ever accessing or decrypting PII. [Walmart Connect](https://walmartconnect.com/content/wmg/home/contact.html?utm_source=paidmedia&utm_medium=sem&utm_campaign=wmc_sem_adgroup_walmartconnect&adid=22222222220448680419&wmlspartner=wmtlabs&wl0=e&wl1=g&wl2=c&wl3=592848971414&wl4=kwd-1156095712489&wl5=9001971&wl6=&wl7=&wl8=&veh=sem&gclid=Cj0KCQiA4OybBhCzARIsAIcfn9lSg4ZbKEL6JgK7Hg5ZeSezquYKKpPYPlwUjGlz26lRoesCZYQuF6YaAuD5EALw_wcB&gclsrc=aw.ds) and [Roku](https://newsroom.roku.com/news/2022/04/roku-s-clean-room-debuts-for-advertisers-ahead-of-tv/tefx2kfp-1650310700) are prime examples of a retailer and media company monetizing brand data by using walled garden data clean rooms to sell digital advertising on branded sites – one of many data clean room use cases intended to enhance customer experience (CX). In this case, Walmart and Roku clients provide encrypted customer data and merge it in a data clean room with defined aggregates and cohorts for the customers it wants to target. Advertisers may then query matched data and run their own analyses to understand potential campaign reach. ## **Independent Data Clean Rooms** Separate from walled garden data clean rooms are independent data clean rooms. Independent third parties are not advertisers, but rather perform data matching across different data sets. [Disney Select](https://dmedmedia.disney.com/fact-sheet-disney-select-drives-massive-marketplace-adoption-and-results-) is a notable example of an independent data clean room, where a data manager is a conduit between the parties that want to share data. An independent data clean room is where smaller companies lacking the customer data of a Walmart or an Amazon will band together to approximate larger data sets, sharing encrypted data to better understand how to interact with and advertise to a wider audience. Amazon itself recently validated this idea with the announcement it is extending its walled garden offering to allow independent groups to create and collaborate in clean rooms on AWS. ## **Data Quality Vigilance** Walled garden and independent data clean rooms share one important characteristic, which is that the records they produce by combining data sets are only as good as the data that is shared before it is encrypted. That is, to trust the output, data quality processes and advanced identity resolution steps must be completed as part of the entry criteria. As the data clean rooms have evolved from a concept to reality, there is still a lingering misconception that identity resolution is the responsibility of yet another party in addition to the parties that are putting encrypted data into the data clean room. While this may be true of the walled gardens, which in general do not count identity resolution as a core capability, any company using an independent data clean room should confirm which party is responsible for identity resolution. In addition, if identity resolution is outsourced to a third party, inquiries should also be made about what the process involves. ## **A CDP’s Place in a Data Clean Room** A data clean room that handles data quality at the time data is ingested will then ensure that the resulting matched records are accurate, reflecting the latest, most relevant information about a customer. In essence, a data clean room should not divorce itself from the responsibility it shares with any robust customer data platform (CDP), which is to take a pool of data, perform data quality steps that include data cleansing, normalization, enrichment when appropriate, match/merge tasks and attaching a persistent ID to produce a pristine Golden Record. But for a clean room, instead of storing the matched and identified customer records, once persistent IDs are attached, the data are anonymized, cohort and aggregate information is calculated, and results are put into the data clean room. For a brand-to-brand sharing use case, data quality steps are completed for data sets from each party sharing data, again with privacy-persevering transformations happening before the data is inserted into the data clean room. Essentially, the process is the same as an ordinary CDP process, except with two (or more) sets of data. As a still-evolving technology, data clean room use cases are still being sorted out, in a sort of trial phase to determine how to enhance customer experience. What is clear, however, is that their popularity is due in part because they help monetize audiences, users’ CX or brand data while honoring the data value exchange and preserving privacy. Factoring in the benefits to both the brand and the end customer, the sharing of anonymized data in a data clean room has potential to be a win-win for all interested parties. ## **Related Redpoint Orchard Blogs** [Ease Distrust in Advertising with Data Clean Rooms & PII Vaults](https://www.redpointglobal.com/blog/ease-distrust-in-advertising-with-data-clean-rooms-pii-vaults/) [Crocodile Tears: Do Not Lament the “Extinction” of the Third-Party Cookie](https://www.redpointglobal.com/blog/crocodile-tears-do-not-lament-the-extinction-of-the-third-party-cookie/) [There are no Third-Party Shortcuts to Understanding Customers](https://www.redpointglobal.com/blog/there-are-no-third-party-shortcuts-to-understanding-customers/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Identity Resolution --- ### [AI’s Context Problem Is Actually an Identity Problem](https://www.redpointglobal.com/blog/identity-resolution-ai-context/) **Published:** July 17, 2026 **Author:** Beth Scagnoli **Content:** Every AI roadmap has a line for context. Retrieval pipelines, vector stores, longer context windows, memory layers, agent orchestration. Data leaders are spending real budget making sure models and agents have what they need to reason well. Almost none of those roadmaps have a line for identity resolution. Context isn’t something you bolt onto a model. It’s something you build underneath one, starting with a basic question most AI initiatives skip: does the system actually know which records belong to the same person, account, or organization? If the answer is no, everything built on top of that data, personalization, recommendations, agent decisions, is reasoning from a fragmented picture and calling it context. ### Context is resolved identity, extended over time Ask ten data leaders to define context and you’ll get ten answers: a customer’s complete history, real-time signals blended with static profile attributes, the same customer recognized consistently no matter which channel they show up in. All of those definitions share a hidden dependency. None of them work unless the system can first establish that the website visit logged on Tuesday, the support call placed on Wednesday, and the account opened three years ago all belong to the same customer. That’s identity resolution: matching and linking records across sources, systems, and time into a single, persistent representation of a person, account, household, or organization. It isn’t a new discipline. Marketing teams have used it for deduplication and household mailings for decades. What’s new is the audience. AI models and agents now consume that resolved identity directly, in real time, to make decisions a person used to make with judgment and hindsight. Without identity resolution, there’s no continuity. Every new interaction looks like a stranger to the model, or worse, it looks like the wrong person entirely. ### Fragmented identity doesn’t look like an identity problem It shows up as something else first. An agent gives two different answers to the same customer because it pulled from two different account records. A recommendation engine suggests a product the customer already returned, because the return was logged under a slightly different name and address. A feature store blends behavioral signals from two people who share a household, a login, or a common name, and the model learns a pattern that doesn’t exist in either individual. None of that gets logged as an identity resolution failure. It gets logged as a hallucination, a bad recommendation, or a model that needs retraining. The retraining doesn’t fix it, because the model was never the problem. The data feeding it couldn’t tell two customers apart, and no amount of prompt engineering resolves that upstream. ### Clean data isn’t ready data Data readiness gets used as a synonym for clean data: standardized fields, validated formats, deduplicated tables. Those things matter, but they don’t answer the question that determines whether an AI system can trust what it’s looking at: is this the same customer every time it shows up? A dataset can be pristine and still not be ready. Addresses can be perfectly standardized and still describe three different households under one name. Transaction records can be complete and still be split across two customer IDs that were never merged. Readiness isn’t only about the quality of each record. It’s about whether every record referring to the same customer has been resolved into one persistent thread, with a key that holds as that customer moves across systems, channels, and time. That’s the piece of the data foundation identity resolution provides. It’s also the piece most data readiness conversations skip past on the way to talking about quality and governance. ### What the context layer will actually require The industry doesn’t have a settled definition yet of what a context layer for AI needs to include. Here’s an early view, built from what’s already breaking in production AI systems today. - **Persistent keys** that survive system migrations, mergers, and re-platforming, so a customer’s history doesn’t reset every time the underlying infrastructure changes. - **Resolution that runs close to real time**, because a context layer that updates overnight is already stale by the time an agent uses it mid-conversation. - **Static attributes and current behavioral state in the same resolved profile**, so a model reasoning about a customer sees who they are and what they just did, not one or the other. - **Lineage that travels with the data**, so when an AI system makes a decision, someone can trace the inputs back to source and explain why. None of that is settled science yet. But it’s the direction the requirements are pointing, for any data leader building toward AI that has to be trusted, not just fast. ### Context starts with identity, not with the model The context conversation in AI will keep growing, and most of it will stay focused on the model side: bigger windows, better retrieval, smarter agents. That’s necessary work, but it isn’t sufficient. A model with a longer context window and an unresolved identity underneath it is just reasoning over fragments faster. Identity resolution is the piece of the data foundation that makes context possible in the first place. Get it right, and every AI initiative built on top of it inherits a coherent picture of who and what it’s reasoning about. Skip it, and no amount of context engineering fixes what’s broken underneath. ### FAQs **What is a context layer for AI?** A context layer is the part of an AI architecture that gives a model or agent a persistent, resolved view of who and what it’s reasoning about, not just whatever records a query happens to return. It isn’t a separate layer sitting on top of the data foundation. It’s what the data foundation becomes once identity resolution has done its job: resolved, governed, and served in a form a model or agent can consume directly. **What types of context are there in an AI architecture?** Most AI systems stack three kinds of context: instructional context (voice, tone, guardrails, system prompts), retrieval context (documents and knowledge base content), and identity context (a resolved, persistent view of who or what the model is reasoning about). A context layer, as this piece defines it, means identity context specifically. Identity resolution is what makes that layer trustworthy. It doesn’t touch the other two. **What is context infrastructure for AI?** Context infrastructure is the set of systems that keep a context layer current: identity resolution, persistent keys, real-time updates, and lineage that travels with the data. It’s infrastructure in the same sense a data pipeline or a feature store is infrastructure. It runs continuously in the background so every AI system downstream can rely on it instead of rebuilding its own view of the customer. **How is a context layer different from a data pipeline or a CDP?** A data pipeline moves and transforms data. A Customer Data Platform (CDP) activates audiences for marketing. A context layer does neither. It’s the resolved, governed identity underneath both, the reason a pipeline and a CDP land on the same answer for “who is this customer” as the AI system sitting next to them. Pipelines and CDPs still matter. They’re just not the same layer. **What is a contextual CDP?** A contextual CDP is the market’s newer framing of the CDP: not a static profile store, but a layer that updates in real time, on intent, tone, and behavior, so AI agents can act on a customer’s current state instead of a fixed segment. That real-time layer is only as reliable as the identity underneath it. Blend real-time signals into a fragmented customer record, and what comes out isn’t context. It’s a live feed sitting on top of a stale foundation. **Why isn’t clean data enough for AI to reason well?** Clean data means each record is well-formatted and accurate on its own. It doesn’t mean the system knows that five well-formatted records all describe the same customer. AI models and agents need that second thing, a resolved, persistent identity, to build real context. Without it, clean data still produces fragmented reasoning. **Blog categories:** Identity Resolution **Blog tags:** identity resolution --- ### [Smarter Communication, Better Care: A Data-Driven Approach to Engaging Commercially Insured Patients](https://www.redpointglobal.com/blog/smarter-communication-better-care-a-data-driven-approach-to-engaging-commercially-insured-patients/) **Published:** November 10, 2025 **Author:** Redpoint Global **Content:** As patient expectations rise and [health policies](https://www.beckershospitalreview.com/hospital-management-administration/one-big-beautiful-bill-act-fallout-health-system-ceos-brace-for-change/) related to Medicaid funding shift, it’s imperative for systems to balance more exacting reimbursement procedures against lower federal reimbursements so that they can maintain margins and continue to care for the people in the community who need it, regardless of their coverage. Commercially insured patients are vital to the financial and clinical wellbeing of health systems. But often, they fall through the cracks, not because they lack care access, but because they lack a trusted connection with their system, which limits motivation to close care gaps, receive vital health screenings or attend annual wellness visits. For many health systems, disjointed patient data and fragmented communication channels prevent providers and staff from delivering personalized outreach that keeps patients informed, engaged, and loyal. ## **Poor Communication Comes with Risks** When commercially insured patients don’t hear from their provider they miss more than a message, they often miss care. Gaps in communication can lead to gaps in care, including uncompleted follow-ups, missed screenings, delayed procedures, and unfilled prescriptions. Often, it’s not a lack of outreach that leads to missed care encounters. Providers are sending messages to patients, but those messages are generic and impersonal. Without accurate, real-time patient data and the ability to send communications via a patient’s preferred channel (text, phone call, email, etc.), messages arrive at the wrong time, through the wrong medium, or with outdated information. ## **Why Commercially Insured Patients Deserve Special Attention** Over [65% of Americans](https://www.ncsl.org/health/value-based-care-in-the-commercial-sector-and-with-multi-payer-arrangements) receive their healthcare coverage through commercial health insurance. These patients often have the benefit of provider choice and therefore have higher expectations for convenience, personalization, and transparency. Historically, patients often sought care from the family medicine physician in their town as their default provider. But as time and technology have changed the healthcare landscape, patients are more empowered than ever to seek care from providers outside of their network that better fit their needs. Today, patients follow consumer trends and have the ability to research health systems and read reviews from patients online to inform their care decisions. In fact, nearly [75% of patients](https://www.beckershospitalreview.com/quality/hospital-physician-relationships/online-rating-platforms-direct-patients-to-higher-quality-physicians-study/) use online reviews as the first step in the process of finding a new provider. It is not a default decision for a commercially insured patient, it is one that takes time, effort and careful consideration. Losing touch with commercially insured patients can result in increased care fragmentation, missed revenue opportunities, and erosion of patient trust and satisfaction for health systems. Given the importance of online credibility for this population, it is important to maintain trust and provide comprehensive healthcare to avoid downstream losses. In order to do so, health systems must communicate consistently with reliable, tailored messages to not only acquire and retain patients, but help guide them to the care they need, when they need it and help them understand the care options available to them within the health system. For example, with the right context, providers can predict which patients may need future joint replacement care, and then proactively introduce them to a new joint replacement program, which they may not otherwise have been aware of. For patients scheduled to receive surgery, the right data also means the provider can provide the right pre-operative planning and support and post-operative rehabilitation options. ## **Data-Driven Communication to Keep Patients Engaged** To close care gaps and maintain loyalty among commercially insured patients, providers must move beyond one-size-fits-all outreach to enable truly customized communication. Effective communication depends on the ability to access and act on accurate, up-to-date context about each patient including where they are in their care journey, what benefits they’re eligible for, and how they prefer to engage with their providers. It requires more than clinical and claims data; providers must access consumer data, such as social, behavioral and engagement signals, from a single source of truth to truly understand a patient’s unique health drivers to motivate meaningful action. When data is outdated or fragmented across systems, the best intentions can still lead to missed opportunities or confusing interactions. Data that is fit and ready within a single comprehensive patient profile enables organizations to shift from reactive to proactive communication. It ensures that the right message reaches the right patient at the right moment, whether a preventative screening reminder, a prompt to schedule a specialist visit, or guidance on a specific benefit. Achieving this level of precision and impact begins with a strong foundation of data readiness, ensuring that patient information is accurate, current, and unified across systems to support timely, relevant, and effective engagement. This work begins with a [data readiness platform](https://www.redpointglobal.com/data-readiness-hub/). ## **Better Patient Experience Starts with Better Information** Patients expect more than access to care, they expect providers to truly know them, to provide clear guidance in their care journey, and to communicate with them like they know them well. Every interaction is an opportunity to [reinforce trust and loyalty](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/) in your health system, and by relying on optimized, up-to-date patient data, you are more likely to [acquire and retain patients](https://www.redpointglobal.com/blog/data-readiness-for-providers-is-your-patient-data-healthy-and-ready-for-whats-next/), effectively close their care gaps, and drive revenue for your organization. By investing in data readiness for your health system, you can: - Strengthen relationships and retain commercially insured patients - Guide patients to the right care at the right time - Improve clinical outcomes and close care gaps - Reduce leakage by creating a cohesive, connected experience across the continuum of care - Reduce downstream hospital admissions by addressing health concerns before they escalate - Create a patient acquisition action plan by introducing community members to service line offerings and covert them to care Reliable communication takes more than a call from the front desk. Having the right data can improve patient health and operating margins by keeping patients engaged with their health and your system. Discover how Redpoint helps leading health systems orchestrate more connected, patient-centered experiences. -> [https://www.redpointglobal.com/healthcare-providers/](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.redpointglobal.com%2Fhealthcare-providers%2F&data=05%7C02%7CKenneth.Murphy%40redpointglobal.com%7Cb979e64750af4dcb0f6508dd87567427%7C16a3d2644987408aa6aa69dd136253fc%7C0%7C0%7C638815527574940595%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=rcTg1lpPPiXGiTdhitj9MfzMiT5Zs5qrE2GWEICBNHs%3D&reserved=0) **Blog categories:** 1:1 Personalization, Data Quality, Data Readiness, Healthcare **Blog tags:** Data readiness --- ### [Just Because You Have A CDP Doesn’t Mean Your Data is Ready](https://www.redpointglobal.com/blog/a-cdp-doesnt-mean-your-data-is-ready/) **Published:** July 10, 2025 **Author:** Renee Graff **Content:** Customer Data Platforms (CDPs) are often seen as the silver bullet for achieving unified customer profiles, but that assumption can be dangerously misleading. Simply having a CDP in place does not mean your data is ready for business use. In fact, quite the opposite is true; this misconception is a leading cause of failed CDP implementations and unmet expectations. Many organizations discover too late that their CDP data lacks the completeness, accuracy, and trustworthiness needed to drive meaningful outcomes. This blog explores why CDP data readiness is essential, and how it ensures your data is not just collected, but truly business-ready. ## **What is CDP Data Readiness?** When we consider the [original intent of a CDP](https://www.redpointglobal.com/blog/the-meaning-of-a-cdp-is-your-data-ready-for-business-use/) – to make customer data right and fit-for-purpose – any activity that entails moving data around ([reverse ETL](https://www.redpointglobal.com/blog/the-meaning-of-a-cdp-is-your-data-ready-for-business-use/), etc.) should be secondary to the CDP data itself. That is, CDP data readiness is about meticulously [preparing data for use](https://www.redpointglobal.com/blog/data-quality-and-role-of-cdp/) in the CDP. It elevates the processes central to the building of the unified profile that enables marketers and business users to trust that CDP data accurately reflects the customer, household or other entity the brand is trying to understand. ## **The Six Pillars of Data Readiness** CDP data readiness consists of six distinct criteria vital for ensuring that a CDP’s data is ready for any use case – whether it’s for CX, AI, or another business purpose. The first three pillars are vital to make sure that CDP data builds a unified profile that reflects the right customer or household: - [Complete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/) - [Accurate](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/) - [Timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/) Once CDP data is validated through those three steps, CDP data readiness also entails making sure that the CDP data is fit-for-purpose, which covers the next three pillars of data readiness: - [Actionable](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/) - [Trusted](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/) - [Compliant](https://www.redpointglobal.com/blog/ready-data-is-compliant-data-does-your-data-hold-up-to-scrutiny/) ## **Understand How Your CDP Data is Prepared** Often, CDP data fails to meet the exacting threshold required by the six data readiness pillars because data quality is not given its due. Many CDPs ignore data quality, operating instead on the presumption that CDP data quality occurs either upstream (before it enters the CDP) or downstream (the responsibility of end systems that eventually use CDP data). ![Data Readiness Steps Ingest Cleanse Standardize Identitfy Activate](https://www.redpointglobal.com/wp-content/uploads/2025/08/Data-readiness-steps-ingest-cleanse-standardize-identitfy-activate-scaled.png) For instance, many CDPs often unify flawed inputs without resolving root-level issues, leading to sub-optimal outcomes and high data consumption costs. Ideally, standardization and error correction should occur the moment data enters the system, so downstream processes stay efficient, accurate and on track. Identity resolution and profile unification processes only work when the input data is clean. ## **CDP Data and Advanced Identity Resolution** Furthermore, some CDPs consider performing a simple deterministic match in time to be sufficient in terms of the steps a CDP must take to ensure that CDP data is ready for business use. This is in stark contrast with a CDP that prioritizes all facets of data quality, part of which includes [advanced identity resolution](https://www.redpointglobal.com/identity-resolution/) processes that not only cleanses and standardizes all CDP data, but that also uses the right combination of [deterministic and probabilistic matching](https://www.redpointglobal.com/blog/the-match-game-why-both-probabilistic-and-deterministic-identity-resolution-matter/) and [persistent key management](https://www.redpointglobal.com/blog/the-match-game-why-both-probabilistic-and-deterministic-identity-resolution-matter/) to provide users with a high degree of certainty that the customer or household a brand wants to engage with is the right customer or household. Applying [advanced identity resolution](https://www.redpointglobal.com/identity-resolution/) techniques as a key part of CDP data readiness not only lets the brand know a customer’s identity, but when rolled into an updated unified customer profile it also provides a contextual understanding of a customer across an ongoing customer journey. That’s because unlike a basic match in time, [persistent key management](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) provides a longitudinal view of a customer that is important for understanding a customer’s preferences and behaviors over time – knowledge that helps a brand better understand customer intent. ## **The Impact of Poor CDP Data Quality** The truth is, very few CDPs are designed to address all of the underlying data activities that need to occur to **get data right** and **ready for business use**. The offshoot is a unified profile that fails to accurately capture a customer or household in the moment of engagement, which leads to downstream issues and, ultimately, a poor CX, AI results that can’t fully be trusted, or otherwise poor business results. What also happens is a deprecation of the single customer view – a problem that the CDP was originally intended to solve for. Because when data quality process are either ignored or assumed to be performed elsewhere, different systems will then apply different data quality standards – leading to different interpretations or understandings of a customer. When a system is not purpose-built to handle CDP data readiness, issues having to do with a lack of data quality, governance and a lack of repeatable processes both become harder to identity and harder to resolve. The way forward is not with a run of the mill CDP, but rather CDP data readiness; a [data readiness platform](https://www.redpointglobal.com/data-readiness-hub/) that takes CDP data and makes it both right and fit-for-purpose for whatever your intended use case. For more on how Redpoint can help you get your data right and fit for business purpose, or to talk to a data readiness expert, click [here](https://www.redpointglobal.com/request-demo/). **Blog tags:** CDP, customer data platform, Data readiness --- ### [The Data Whisperer: How Data Readiness Adds Value to Your Existing Technology](https://www.redpointglobal.com/blog/the-data-whisperer-how-data-readiness-adds-value-to-your-existing-technology/) **Published:** July 2, 2026 **Author:** Renee Graff **Content:** Enterprise organizations invest in data clouds, customer data platforms (CDPs), master data management (MDM) solutions and marketing clouds to unify, manage, and activate customer data. Each platform delivers real benefits on its own, but without a comprehensive data readiness strategy, much of their potential remains untapped. Data readiness ensures that data is accurate, complete, timely, and fit-for-purpose, enabling better decision-making, precise activation, and trusted insights across the enterprise. To help clarify the impact of [data readiness](https://www.redpointglobal.com/data-readiness-hub/), we explore several common engagement technologies to show what each solution does well on its own and how a data readiness strategy optimizes its value – making the data more accurate, actionable and fit for driving personalized experiences and AI-powered insights. ## **Customer Data Platforms** CDPs are designed to unify customer data across systems, creating a foundation for better engagement. On their own, CDPs deliver several important advantages: - **Unified customer profiles –** By aggregating data from multiple sources, a CDP helps create a single view of the customer that can be used across marketing, sales, and service. - **Centralized data access –** Business users gain easier access to customer information without relying on IT teams or siloed systems. - **Improved customer engagement –** Unified profiles enable more personalized marketing, consistent CX, and better targeting across channels. - **Faster/Easier campaign execution –** Marketers can activate data more quickly, reducing time-to-market for new campaigns and customer programs. - **Integration across systems –** CDPs connect data across channels, touchpoints, and platforms, creating a shared foundation for analytics and activation. ## **Importance of a Data Readiness Strategy with a CDP** While a CDP provides the structure for unified profiles, it does not guarantee that the data itself is accurate, complete, or business-ready. Without a data readiness strategy, CDPs risk unifying flawed inputs, producing unreliable profiles and disappointing outcomes. CDP data readiness optimizes the platform in several key ways: - **Complete, accurate, timely data –** Data readiness ensures that data feeding the CDP reflects the right customer or household, so [unified profiles](https://www.redpointglobal.com/profile-unification/) are truly reliable. - **Actionable, trusted, compliant data –** Data readiness enforces standards for governance, compliance, and usability, making CDP data safe and effective for business use. - **Advanced identity resolution –** Combining deterministic and [probabilistic matching](https://www.redpointglobal.com/identity-resolution/) with persistent key management builds longitudinal profiles that evolve over time, enabling deeper insights into customer intent and behavior. - **Optimized data processing –** By cleansing and standardizing data at the point of ingestion, readiness prevents redundant processing and reduces costs while keeping downstream operations efficient. - **Consistent single customer view –** With readiness built in, the CDP maintains a dependable and persistent customer profile across all systems, eliminating conflicting interpretations of the same customer. ## **CDP Data Readiness: The Key to a Reliable Single Customer View** A CDP provides the framework, but CDP data readiness makes it work. Without readiness, the “single customer view” risks being incomplete or misleading; with data readiness, it becomes a trusted foundation for CX, AI, and business growth. ### **Data Clouds** Enterprise companies are increasingly turning to data cloud architectures (including data lakes and lakehouses) as the backbone of their data strategy. A data cloud offers a modern, scalable foundation that helps businesses unify, secure, and activate their data across systems and applications. On its own, a data cloud delivers several important advantages: - **Improved security** – Cloud platforms invest heavily in enterprise-grade security, governance, and compliance controls, reducing exposure to threats and minimizing risk. - **Better business performance in a multi-cloud environment** – Enterprises can integrate data across hybrid or multi-cloud ecosystems without having to build or manage additional on-prem infrastructure. - **Greater flexibility and scalability** – A data cloud can expand seamlessly as data volumes, users, and use cases grow, supporting evolving needs such as AI, advanced analytics, and real-time engagement. - **Reduced cost and complexity** – By consolidating data in the cloud, companies lower infrastructure expenses, streamline management, and simplify access to business-critical data. - **Faster time to value** – With cloud-native services and marketplace applications, enterprises can process and analyze customer and business data more quickly, accelerating insight generation and decision-making. A data cloud provides the foundation for storing, scaling, and securing enterprise data, but a data readiness strategy unlocks considerable value and untapped potential. Data readiness ensures that information is [accurate, complete, timely, and actionable](https://www.redpointglobal.com/data-the-defining-difference/) – so organizations aren’t just moving data into the cloud, but actively transforming it into business value. Data readiness provides advantages that include: - **Bridging infrastructure and skills gaps –** A data readiness strategy fills critical voids across infrastructure, applications, data, and talent, ensuring cloud investments fully support business goals. - **Fit-for-purpose data –** By making data both accurate and actionable, readiness lays the foundation for effective cloud management, smooth migrations, and strong governance. - **Medallion architecture improvement** – Medallion architecture makes lakehouses more reliable and productive, and Data Readiness functions as a bridge from the Bronze layer to Silver (with data cleansing and normalization) and from Silver to Gold (with advanced IDR, aggregate calculations, and data observability). - **Unified customer view –** A readiness platform creates a consistent, real-time customer profile while minimizing data movement and replication, driving better governance at lower cost. - **Optimized cost efficiency –** Clean, unified data is prepared before activation, preventing redundant processing and unnecessary movement, which keeps cloud operations efficient and cost-effective. Together, the data cloud provides the scale and flexibility, while data readiness ensures quality and usability – a combination that transforms raw storage into real business outcomes. ### **Marketing Clouds** Marketing clouds are purpose-built to help organizations scale their customer engagement efforts. On their own, they bring several clear advantages: - **Campaign execution and automation –** Marketing clouds excel at running, tracking, and optimizing campaigns while advancing marketing automation capabilities. - **Integrated marketing workflows –** By centralizing campaign management, they streamline processes and improve collaboration across marketing teams. - **Basic data quality functions –** Many marketing clouds offer limited data hygiene, normalization, and deduplication processes to prepare data for campaign activation. - **Personalized engagement –** With built-in segmentation and targeting tools, marketing clouds enable brands to deliver more relevant experiences within their ecosystem. - **Measurable marketing outcomes –** Campaign performance can be tracked directly in the platform, giving marketers insights to refine future strategies. While marketing clouds are powerful activation platforms, their data quality capabilities are typically limited to simple data prep for campaigns within a closed ecosystem. This “activation-first” approach often results in incomplete or inaccurate profiles and restricts value beyond marketing use cases. A data readiness strategy optimizes a marketing cloud by providing: ![Data Readiness For The Win Where Other Solutions Fail](https://www.redpointglobal.com/wp-content/uploads/2025/10/Data-readiness-for-the-win-where-other-solutions-fail.jpg) - **Enterprise-wide data quality –** Data is cleansed, standardized, and unified once, upstream, ensuring consistency across all systems—not just within the marketing cloud. - **Advanced identity resolution –** Beyond simple deterministic matches, data readiness applies tunable rules, probabilistic matching, and [persistent key management](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) to create a Golden Record that evolves with the customer lifecycle. - **A trusted single customer view –** Marketing teams and the wider enterprise gain access to a consistent, contextual understanding of the customer across channels, reducing CX gaps between marketing and service. - **Stronger data governance –** Full auditability, lineage tracking, and policy-driven compliance ensure customer preferences and regulatory requirements are honored across the organization. - **AI- and analytics-ready data –** By preparing complete, accurate, and timely data before it enters downstream systems or AI pipelines, data readiness enables predictive models and decisioning engines to deliver better business outcomes. Put simply, a marketing cloud delivers execution, but data readiness delivers trust. Together, they ensure campaigns are precise, consistent across channels, and powered by enterprise-wide customer data that is truly fit-for-purpose. ### **MDM Solutions** MDM solutions create an accurate “master” record for customers, products, or other entities, ensuring that enterprise systems – including CRM, finance, supply chain, and customer service – operate with a consistent understanding of core data. On their own, MDMs provide several important advantages: - **Accurate master records –** MDM ensures all systems work from the same core view of each customer or entity. - **Consistency across systems –** Core attributes like name, address, or date of birth remain standardized, supporting governance, compliance, and operational integrity. - **Standards ownership –** MDM defines rules for currencies, measurements, and other master data standards. - **Slow-moving data focus –** Ideal for attributes that change infrequently, such as demographic or foundational entity data. However, MDMs are not designed for “fit-for-purpose” data needed for customer-facing applications, fast-changing behaviors, or AI use cases. Without a data readiness strategy, MDM data is insufficient for personalized engagement or real-time decisioning. Data readiness optimizes MDM in several ways: - **Fast-changing, contextual data –** Adds behavioral, transactional, and temporal attributes to provide a richer, more current view of the customer. - **Fit-for-purpose insights –** Ensures data is actionable, observable, and includes the right calculations, aggregates, and models for downstream use. - **Advanced identity resolution –** Maps individuals to households or organizations, integrates deterministic and probabilistic matching, and maintains persistent keys. - **Enhanced governance and metadata –** Tracks trust scores, last usage, engagement history, and classification for PII/PHI compliance. - **Real-time, actionable profiles –** Continuously updates unified customer profiles to support [AI-driven recommendations](https://www.redpointglobal.com/blog/all-successful-ai-projects-start-with-ai-ready-data/) and personalized CX initiatives. An MDM provides the authoritative core; data readiness makes it usable, contextual, and actionable. Together, MDM plus data readiness forms a complementary system: MDM ensures correctness and consistency, while data readiness delivers the timeliness, depth, and behavioral insights needed for modern customer engagement. ### **Do-It-Yourself (DIY) Data Management** Some organizations take a DIY approach to preparing customer data, building custom pipelines, workflows, and rules to meet their internal requirements. This method offers certain advantages: - **Customizable to organizational needs –** Tailored processes can be designed around existing business priorities. - **Control over parameters –** Teams set their own data quality rules, identity resolution logic, and governance policies. - **Budget flexibility –** Investment levels are determined internally, with the ability to allocate resources where desired. - **Alignment with existing practices –** DIY solutions can extend or reinforce the organization’s current data management approach. While DIY may work in the short term, it often becomes brittle, costly, and difficult to scale. A data readiness platform enhances or replaces DIY approaches with clear advantages: - **Lower cost –** Purpose-built readiness prevents duplicated effort and reduces operational overhead. - **Faster implementation –** Prebuilt services accelerate the process of making data right and fit-for-purpose. - **Greater resilience –** Automated, composable capabilities minimize breakage from changing data sources or business rules. - **Scalability and Consistency –** Readiness platforms grow easily with new data volumes, channels, and advanced use cases like AI and real-time engagement, and are easily extended across functions and geographies to provide consistent CX across the enterprise.. DIY solutions may provide control and customization, but data readiness platforms provide sustainability, making customer data management less expensive, less time-consuming, and more adaptable to evolving enterprise needs. ## **Data Readiness: No Data Left Behind** In essence, a data readiness strategy is designed to ensure data is continuously made right and fit-for-purpose across the entire enterprise and throughout the customer lifecycle, unifying data quality, identity resolution, and governance *before* data is activated. While other solutions may manage data effectively for specific purposes, they often fall short in providing the holistic, contextual, and continuously updated customer view necessary for advanced CX and AI use cases. To see how the Redpoint Data Readiness Hub can help you optimize your data management and customer engagement technologies with seamless integration into your existing stack, click [here](https://www.redpointglobal.com/data-readiness-hub/). **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [Deterministic Matching vs. Probabilistic Matching: Why Identity Resolution Needs Both](https://www.redpointglobal.com/blog/deterministic-probabilistic-matching-identity-resolution/) **Published:** June 12, 2026 **Author:** Kris Tomes **Content:** Knowing who your customer is sounds simple. It isn’t. Across devices, channels, and data systems, a single customer leaves dozens of fragmented signals. Connecting them accurately is what separates real personalization from expensive guesswork. That’s where deterministic matching and probabilistic matching come in. They’re the two core techniques behind identity resolution, and understanding both, including when to use each, is essential for any organization that wants a reliable, complete view of its customers. ### What Is Deterministic Matching? **Deterministic matching** links customer records using exact, verified identifiers. When two records share the same email address, phone number, loyalty ID, or customer ID, they’re matched with near-certainty. No inference required. The result is a high-confidence, auditable connection. If a customer logs in on a mobile app and later completes a purchase on a desktop browser using the same email, deterministic matching ties those sessions to a single identity. That match is provable and, critically, defensible. It matters in regulated industries like healthcare and financial services, where data mistakes have real consequences. #### Deterministic matching is the right choice when: - You have a shared, reliable identifier (authenticated login, loyalty number, email) - The use case requires high confidence: sending medical test results, financial notices, or personalized service communications - Auditability and compliance matter The trade-off is coverage. Deterministic matching only works when a known identifier is present. Anonymous or partially known records, which represent the majority of digital interactions, fall outside its reach. ### What Is Probabilistic Matching? **Probabilistic matching** connects records that don’t share an exact identifier by using statistical models to assess the likelihood that two records represent the same person. Rather than requiring a clean, consistent field to match on, it weighs combinations of PII signals (name, address, email, phone, data of birth) to produce a match confidence score. > Where deterministic matching asks “Are these records definitely the same person?”, probabilistic matching asks “How likely is it that these records belong to the same person?” This distinction matters most when your data isn’t clean. A customer record might carry a maiden name in one system and a married name in another. An address might be abbreviated differently across sources. A phone number might be missing an area code. No single field produces a clean match but when you weigh those signals together, a strong confidence score emerges. Probabilistic matching is what makes identity resolution work at scale, across the messy, inconsistent data that real enterprises actually have. #### Probabilistic matching is the right choice when: - No exact identifier is available (anonymous browsing, offline-to-online linkage) or there is ambiguity in the data (e.g., misspellings, character transposition, missing address elements, etc.) - You need broader reach, connecting a larger share of your data to known identities - The use case tolerates some ambiguity (marketing communication, audience segmentation) The trade-off is precision. Probabilistic models introduce the possibility of false positives (incorrectly linking two different people) and false negatives (missing a correct match). Tuning that balance is an ongoing, context-specific process. ### When One Matching Method Isn’t Enough Neither technique alone is sufficient. Deterministic matching without probabilistic reach leaves the majority of your customer data unlinked. Probabilistic matching without deterministic anchors produces matches that can’t withstand scrutiny where it counts. The most effective identity resolution combines them. Deterministic matching establishes high-confidence connections; probabilistic logic then extends coverage to records where exact identifiers are absent or unreliable. Consider a practical example. A healthcare system maintains patient records in an EHR, a billing platform, and a patient portal — three systems that were never designed to share a common identifier. “Robert J. Smith” in billing is “Bob Smith” in the portal, with a slightly different date of birth due to a data entry error, and an address that’s two moves out of date. No single field matches cleanly across all three. A rigid deterministic rule would either fail to link these records or, worse, link them incorrectly to a different Robert Smith entirely. A probabilistic model can weigh the combination of name similarity, shared phone number, overlapping address history, and date of birth proximity to produce a high-confidence match, correctly unifying three fragmented records into a single, accurate customer profile. The same challenge shows up whenever data crosses organizational boundaries: a loyalty program merged after an acquisition, a third-party data append that uses different formatting conventions, or a CRM that predates the current master data standard. Probabilistic matching is what bridges those gaps by systematically evaluating the weight of evidence across every available signal. ### Balancing False Positives and False Negatives One of the most important, and least discussed, aspects of identity resolution is the false positive / false negative trade-off. A **false positive** occurs when two records from different people are incorrectly merged. A **false negative** occurs when two records from the same person are left unlinked. The right balance depends entirely on the use case. A healthcare organization sending general wellness communications can tolerate a wider probabilistic net; the cost of a false positive is low. That same organization sending individual test results or treatment information needs near-certainty. A false positive there is a serious compliance and trust failure. This is why identity resolution can’t rely on a single, static rule set. The matching logic needs to reflect the purpose of the match, the sensitivity of the data, and the regulatory environment the organization operates in. ### Identity Resolution and the Customer 360 The goal of combining deterministic and probabilistic matching is to build a **Customer 360** — an accurate, unified customer profile that connects all known signals: devices, email addresses, phone numbers, physical addresses, transactions, interactions, and behavioral data. A Customer 360 isn’t just a merged profile. It’s the data product that every downstream system depends on: AI models, segmentation engines, activation platforms, and compliance workflows. If the identity resolution layer is wrong, every system built on top of it inherits that error. Redpoint’s [Identity Resolution](https://www.redpointglobal.com/identity-resolution/) capability uses tunable tightness controls, configurable rules, and grouping parameters, that teams can tailor by use case, channel, and data type, so the Customer 360 reflects the real complexity of customer data, not a simplified approximation of it. ### What This Means in Practice If your organization is evaluating identity resolution, here’s a practical frame: 1. **Start with deterministic matching** to anchor known identities. Authenticated logins, loyalty data, and CRM records are your foundation. 2. **Layer probabilistic matching** to extend coverage to anonymous, partial, or conflicting signals, increasing the share of your data connected to a known profile. 3. **Tune the thresholds** by use case. The match confidence required for a marketing email is different from what’s required for a billing notice. 4. **Monitor for drift.** Data changes. Customers move, change emails, share devices. Identity resolution isn’t a one-time project; it’s an ongoing capability. Getting it right doesn’t require perfection at every match. It requires a system that’s honest about confidence levels, configurable by context, and built to improve over time. > Redpoint’s Data Readiness Hub includes identity resolution capabilities designed for exactly this kind of complexity, across industries, deployment models, and regulatory environments. Also available natively in the Snowflake Marketplace. [See how Redpoint resolves identity across complex customer journeys.](https://www.redpointglobal.com/identity-resolution/) ### FAQs **Q: What is deterministic matching?** Deterministic matching is an identity resolution technique that links customer records using exact, verified identifiers, such as email address, phone number, loyalty ID, or customer ID. When two records share the same confirmed identifier, they are matched with near-certainty. Deterministic matching produces high-confidence, auditable connections but is limited to records that contain a known shared identifier. **Q: What is probabilistic matching?** Probabilistic matching is an identity resolution technique that uses statistical models to assess the likelihood that two records represent the same person, even when no exact shared identifier exists. Rather than requiring a clean, consistent field to match on, it weighs combinations of PII signals (name, address, email, phone, and date of birth) to produce a match confidence score. **Q: What is the difference between deterministic and probabilistic matching?** Deterministic matching requires an exact shared identifier (like an email or customer ID) and produces a near-certain match. Probabilistic matching uses statistical inference across multiple signals to estimate whether two records belong to the same person, without requiring a confirmed shared identifier. Deterministic matching offers higher precision; probabilistic matching offers broader reach. A robust enterprise identity resolution system should use both in combination. **Q: When should you use deterministic vs. probabilistic matching?** Use deterministic matching when a verified shared identifier is present and the use case requires high confidence, such as sending medical test results, financial notices, or personalized service communications, or when auditability and compliance matter. Use probabilistic matching when no exact identifier is available, such as anonymous browsing or offline-to-online linkage, or when data inconsistencies prevent a clean match (misspellings, character transpositions, missing address elements, name changes across systems). It’s the right choice when you need broader reach across messy, real-world data and the use case can tolerate some ambiguity, such as marketing communication or audience segmentation. Most organizations use a hybrid approach: deterministic matching anchors known identities, probabilistic matching extends coverage to the partial or inconsistent records that deterministic rules would miss or misidentify. **Q: What is a Customer 360 in identity resolution?** A Customer 360 is a unified, accurate customer profile that consolidates all known data about a single person or household, including devices, email addresses, phone numbers, physical addresses, transactions, and behavioral history. It is built by combining deterministic and probabilistic matching to link fragmented records across systems. The Customer 360 serves as the data foundation for AI models, segmentation, activation platforms, and compliant data workflows. **Blog categories:** Identity Resolution **Blog tags:** identity resolution --- ### [Retail is Evolving at an Accelerated Pace – So Should Your Marketing Strategy](https://www.redpointglobal.com/blog/retail-is-evolving-at-an-accelerated-pace-so-should-your-marketing-strategy/) **Published:** March 19, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/03/Shoping-pace_1172308546-e1552920751992.jpg)Amazon, long a pioneer in the personalization of the consumer digital experience, is cementing a new reputation as a serious player in the physical retail space. Why is the company that revolutionized e-commerce investing billions in physical retail, including Amazon Books, the purchase of Whole Foods, and the recent news that it is [launching its own line](https://www.washingtonpost.com/business/2019/03/01/how-amazon-could-change-countrys-grocery-game/?utm_term=.3626f7a64505) of physical grocery stores? The recent [Shoptalk](https://www.forbes.com/sites/neilstern/2019/03/08/4-takeaways-from-shoptalk-2019/#3dee643244e5) conference in Las Vegas shed some answers. The intertwining of the physical and digital shopping experience was top of mind for many of the more than 8,000 Shoptalk attendees, which included nearly 300 speakers and more than 100 sessions devoted to the future of retail. During the “What’s Next for Voice Retail?” session, David Isbitsky, Chief Evangelist for Amazon Alexa, drove home the importance of speed as a facilitator of personalization. Devices and screen time, he said, slow consumers down as they seamlessly move between physical and digital locations, whereas voice technology that recognizes a consumer’s history, preferences, and intent delivers greater value by more quickly personalizing the experience. New store innovations and brand realignments – including [Gap splitting off the Old Navy business](https://www.businessinsider.com/old-navy-splits-off-from-gap-2019-2) and Walmart.com – were other big Shoptalk topics, but not necessarily because they foretell the decline of traditional brick-and-mortar retail. Rather, according to Brendan Witcher, analyst for Forrester Research, they’re reflective of a need for retailers to be more data-driven in providing a relevant, personalized experience across channels. Amazon may be successful where others have failed because it is better at connecting with customers on a personal level. “When a store closes it is not a sign that there’s a store problem. It’s a sign that there’s a brand problem,” Witcher said in an [interview with Total Retail](https://www.mytotalretail.com/video/single/forresters-brendan-witcher-on-shoptalk-retail-tech-and-an-industry-forecast/). Focusing on a store closing, he said, is to focus on the wrong problem. Becoming more data-driven to create a personal customer experience can turn the physical store from a liability into an asset. In a session titled “Customer Data and Brand-Building”, Witcher moderated a session with executives from Lilly Pulitzer, GUESS? Inc., and Modell’s Sporting Goods who provided examples of how they are using insights collected from customer data to create new and engaging experiences for shoppers, in both the physical and digital realms. **Meet Customers Anywhere in Their Journey** This blending of physical and digital channels into a seamless, “always-on” experience is the evolution of retail, and the prioritization of customer experience throughout an omnichannel journey renders many traditional marketing strategies obsolete. Retail marketers without the deep pockets of an Amazon face several key challenges in competing on customer experience, especially with the bar continuing to rise at a breakneck pace. One challenge is siloed customer data across the enterprise, which makes it exceedingly difficult for marketing to track and manage an omnichannel customer journey. Resource constraints compound this challenge, limiting marketer’s ability to access data, rapidly change customer journeys, and tune programs to hit revenue goals. To keep pace with retail’s evolution and differentiate on a personalized experience, marketers must overcome data limitations and move toward zero segment marketing, which treats each consumer differently according to their buying patterns, preferences, and intent across multiple devices and channels. A one-size-fits-all approach with limited demographic segmentation on a channel-by-channel basis is not tenable when customers expect a personalized experience that spans the digital and physical worlds. Zero segment marketing provides a personalized experience across all channels and devices, and goes further to recognize each interaction as contributing to a customer’s lifetime value, which in turns deepens each future engagement. It adds relevancy to each touchpoint. Reaching this breakthrough capability to optimize engagement across channels at scale requires automation and intelligence. Getting started isn’t as difficult as many retail marketers are led to believe. **Accelerate to Personalized Engagements** Delivering personalization at scale across multiple channels requires, first and foremost, a [single view of the customer](https://www.redpointglobal.com/challenges/single-customer-view/). A single view that combines all customer data from first-party, second-party, and third-party sources is the first step for a retail marketer to recognize a consumer across channels and gain insights into a specific customer’s buying patterns and preferences. When advanced analytics are applied to an accurate and accessible unified view of the customer, marketers can easily determine a next-best action and orchestrate dynamic customer journeys across all touchpoints. With algorithms to guide recommendations based on metrics such as social profiles, lifetime value, and contact history, retail marketers ensure continually optimized engagements. While many large retailers implement custom solutions with tools like Redpoint Customer Engagement Hub™, there is also an option that has helped some retailers jump into the personalization space with a solution that more immediately fits their needs. [Redpoint SaaS Delivery Option for Retail](https://www.redpointglobal.com/solutions/redpoint-accelerator/redpoint-accelerator-for-retail/) is a packaged solution that provides growth-oriented retail marketers with these capabilities with a reduced time to value, with implementation measured in weeks rather than months or years. Built on the Microsoft Azure cloud platform, Redpoint SaaS Delivery Option for Retail helps retailers achieve value by offering turnkey preconfigured marketing templates, reports, and channels, including email, mobile, social, websites, and direct mail. Robust offerings allow marketers to easily capture customer preferences and determine how to react in real time based on anonymous and known customer profiles. An underlying retail data model includes every attribute needed for targeted campaign selections and segmentation for personalized engagement. **An On-Ramp to Personalization at Scale** To keep pace with today’s always-on consumer, retail marketers must recognize a customer at every stage of potentially multiple customer journeys. To provide a personalized experience across engagements and channels at scale is virtually impossible to do manually, especially for a retail marketing organization faced with limited resources and siloed customer data. A packaged solution that eliminates those barriers and automates the delivery of contextually relevant offers and messages provides a quick on-ramp for retail marketers to optimize engagements and compete on customer experience. **RELATED ARTICLE(S)** [Clienteling and the Personalized In-Store Experience](https://www.redpointglobal.com/blog/clienteling-and-the-personalized-in-store-experience/) ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Data Quality, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Retail, Segmentation & Activation --- ### [A Consistently Relevant CX Begins with Anonymous Personalization](https://www.redpointglobal.com/blog/a-consistently-relevant-cx-begins-with-anonymous-personalization/) **Published:** December 7, 2022 **Author:** Will Stuart-Jones **Content:** At first glance, anonymous personalization seems like a perfect oxymoron. After all, if one were to make an anonymous donation to a charity, finding a personalized thank you card in the post might be a tad upsetting. Upon further examination, however, anonymous personalization in the context of customer experience is not only quite real, it is a worthy pursuit that enhances customer experience and drives revenue. First, why do it? The simple answer is that customers expect the brands they engage with to know who they are at an individual level. Consider a [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/) commissioned by Redpoint where 39 percent of all customers – up from 37 percent in 2019 – said they will not do business with a company that fails to deliver a personalized experience, which customers define as knowing who they are across channels – website, in-store, mobile app, call center, etc. While that expectation solidifies the more a customer does business with a brand, it is present from the first interaction. ## **Two Sides of Anonymous Personalization** Before exploring how a brand can personalize an interaction with a first-time website visitor, or perhaps the user of an in-store digital kiosk, let’s get to the bottom of what we mean by personalization and the purpose of anonymous personalization besides satisfying customer expectations. In the context of customer experience, Gartner defines personalization as the process of creating an individualized experience – a tailored interaction between two parties – with a view of enhancing the experience of the participant. Anonymous personalization adds a degree of difficulty with the potential of obviously not knowing much of anything about the audience member, and certainly not any personally identifiable information (PII). The two parties involved in the equation are the unknown visitor and the brand. Done well, anonymous personalization should ensure a positive outcome for both sides. For the brand, that outcome may be measured by metrics tied to revenue, such as driving retention, reducing churn or signing up more loyalty members. For the customer, a positive outcome related to the enhanced customer experience might be a more user-friendly website navigation, fewer steps, or more tailored content. In a dual value exchange, there are many ways to measure success depending on the desired outcome. Whether it’s brand awareness where success is measured by click-through rate, or a goal of capturing first-party data where success is measured by a formerly anonymous customer completing an online form, it is important that a solution that offers anonymous personalization be able to capture and track the success metrics. One reason for doing so is that it helps convert anonymous customers into known customers, while also justifying anonymous personalization by demonstrating the tangible benefits. ## **Self-Optimize Anonymous Personalization** As an example of anonymous personalization, consider a landing page hero image for a sporting goods company. The first time a visitor arrives at the website, anonymous personalization begins with the creation of a digital footprint. Without any PII, the brand has a first-party cookie ID and device information, which may then be filed away for future use along with any behavior from the online session – pages viewed, time on a page, clicks, etc. One method of first-touch anonymous personalization is to perform a/b testing on a hero image. The sporting goods company, for example, could randomly select one of four hero images – a bicyclist, a hiker, a skier and a kayaker, and let that cycle of images run for an hour, day, week or another set timeframe. Through continual monitoring, the brand can determine which image was most successful in reaching the desired outcome – clicks, signs-ups, purchases, etc. After selecting a winner, the brand can then use that image for every new first-time visitor, minus a hold-out group. But knowing that audiences and preferences change, perhaps 20 percent of first-time visitors constitute the hold-out group, and that audience is again divided into four groups with each group receiving a different image (bicyclist, hiker, etc.). A/b split testing continues, self-optimizing the outcome with each iteration to determine whether to switch out the hero image. ## **Expand a Digital Footprint with Real Time** With a [real-time decisioning engine](https://www.redpointglobal.com/orchestration/real-time-decisions), anonymous personalization can also be accomplished during an online session based on a first-time visitor’s behavior during the session. If an anonymous device ID zeros in on cycling products, for example, and that information is captured as part of a new digital footprint, the brand begins to build an affinity score which can then be used to personalize content while the session is ongoing – including updating the hero image and a product recommendation carousel. Using insight to adjust personalization rules might be extended to how a first-time visitor comes to the website. The visitor might come from a link, for example, in which case a brand can identify the traffic source through a URL identifier. This information will also be included in a digital footprint and used to deliver personalization during an online session. The concept of anonymous personalization applies to multiple channels. An in-store kiosk is one example where personalization is similar to the first-time website visitor. On a mobile app, a first-time visitor may have opted into location sharing, in which case a personalized experience might entail notification of a new store opening or another type of personalization based on the anonymous ID breaking a geofence. Even the time of day of an anonymous visit to a website or the breaking of a geofence might be important for a digital footprint, perhaps triggering an urgent call to action before the expiration of an offer. As a digital footprint expands, a brand might deliver anonymous personalization based on a number of combinations of a/b testing combined with a traffic source, website behavior, location, time of day, etc. ## **Get Predictive with Machine Learning** The above examples of anonymous personalization fall into what’s described as rules-based personalization where the marketer sets rules for the content an anonymous visitor will see based on certain behavior. Another type of anonymous personalization is based on predictive analytics. In this form of anonymous personalization, a [machine learning model](https://www.redpointglobal.com/machine-learning/modeling-algorithms/) will segment an audience based on the desired metric or outcome, and however the audience divides according to the algorithm will determine the content an anonymous visitor receives. As an example, a model might analyze what landing pages a group of anonymous visitors looked at, how long they interacted with a page or what specific products they looked at, among other variables. A first-time visitor may then be placed into a certain segment based on commonalities between that visitor’s expanding digital footprint and those of previous first-time visitors. With machine learning embedded into a platform with a decisioning engine, self-learning models can be built on the fly. As with the a/b testing, machine learning segmentation self-optimizes for the desired outcome. If, for example, a model predicts that a visitor has a high propensity of becoming a VIP member, the brand promotes material it knows is most relevant to other VIP members. The brass ring, of course, is when an anonymous visitor becomes known, either through providing a name, completing a form, signing up for a loyalty program, making a purchase, etc. At this point, the digital footprint combines with everything else that is known about a customer to build a [Golden Record](https://www.redpointglobal.com/single-customer-view/) that provides a brand with a single customer view. Because the now-known customer has been receiving a personalized experience from the start, future personalized interactions do not seem out of place, as if the brand is just now figuring out who the customer is. Rather, the consistency of interactions as a customer proceeds along an unknown to known journey breeds familiarity; this brand knows me, and this brand cares enough about me to provide me with a consistently relevant, personalized experience. For a video presentation from Will Stuart-Jones on the value of anonymous personalization, click [here](https://event.on24.com/wcc/r/4050506/CE308AA835D4E061388EB5B3EAFD418F?partnerref=rpgwebresources). **Blog categories:** Anonymous to Known --- ### [A Bridge to the Future: the Redpoint CDP is Architected to Adapt to Change](https://www.redpointglobal.com/blog/the-redpoint-cdp-is-architected-to-adapt-to-change/) **Published:** April 27, 2023 **Author:** John Nash **Content:** The story of the Choluteca Bridge in Honduras offers a lesson on the importance of adaptability. Completed in 1998, the engineering marvel was one of the few structures spared when Hurricane Mitch blew through later that year, causing catastrophic damage to the area. Although the new bridge was spared, the hurricane completely destroyed the connecting road on either side, and the Choluteca River below carved a new path through the canyon. No longer connected to a road, and no longer spanning a body of water, the Choluteca Bridge became a “Bridge to Nowhere.” A monolithic structure may perform as intended and produce expected results, but it is shortsighted to think that anything is impervious to change. The same holds true for a marketing technology stack. ## **Existing Barriers to Managing an Omnichannel CX** In previous articles, we’ve explored how ambitious marketers break through siloed customer data to [transform customer experience](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/), and how aspirational marketers bent on innovation [refuse to be held back](https://www.redpointglobal.com/blog/balancing-goals-and-capabilities-for-the-aspirational-marketer/) by organizational or architectural limitations. This article will explore some of those architectural limitations, and discuss how the Redpoint CDP solves for them. First, consider how a typical MarTech stack prevents marketers from achieving the goal of differentiating on customer experience. In the latest [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/resources/harris-poll/), marketers were asked for the reasons why their MarTech stack prevents them from managing an omnichannel CX. A lack of data integration between systems (41 percent) and the use of a closed integrated marketing cloud (41 percent) were cited as the top reasons. In addition, 77 percent said the number of systems they have make it hard to provide a seamless customer experience, and 70 percent claimed it is becoming increasingly difficult to manage the number of customer touchpoints they have. ## **Quickly Adapt to Emerging Trends** A lack of data integration and difficulty managing the number of existing customer touchpoints make it extremely difficult to keep up with the evolving expectations of the customer. Buy online, pick-up in-store presents a good example, where a real-time omnichannel experience requires a cross-channel awareness between the website, email, SMS, a physical location and inventory management. Traditionally, reacting quickly to consumer behavior changes would likely entail deploying one or more new applications, sunsetting others, as well as process and change management. But with many vendors locking you into a data model and limiting connectors, a rapid change is typically measured in months. One strength of the[ Redpoint CDP](https://www.redpointglobal.com/cdp/) is that its robust native connectors and orchestration capabilities make it a valuable hub for an organization’s MarTech stack, offering a single point of operational control for any customer touchpoint. In addition, unlike a pure-SaaS offering, Redpoint can run on a data cloud as well as in an organization’s private cloud, offering added privacy and security particularly surrounding personally identifiable information (PII). Agility extends to a composable architecture framework that makes it easy to quickly bring in additional data sources and enterprise technology, such as a generative AI application for instance. Or to quickly and easily switch out a database as a cost-effective way to more easily achieve performance benchmarks. AI is ideally accessible across a CDP, journey orchestration and real-time decisions – such as for pointing out data anomalies and missing data, to improve matching and to deliver a next-best action in the precise cadence of a customer journey. The integration of AI exemplifies the importance of having a flexible CDP that can quickly adapt to emerging trends without falling behind the customer. In contrast, many CDP vendors tend to view AI as a monolithic entity rather than as a component of nearly every aspect of an operational marketers’ job function. The Redpoint approach facilitates embedding AI in various workflows, either building your own AI models (BYOA, BYOM) or using those included in Redpoint. As new AI solutions come into the market, Redpoint makes it easy to seamlessly plug them in and immediately connect to existing customer touchpoints and end channels. ## **Agility Through a Single Point of Control** As one example, consider the use of a tag management system for website personalization where an organization develops a new AI-driven use case. Whether bringing in a new model, or spinning one up in Redpoint, the platform offers the single point of operational control to deliver personalized messages in real time. It adapts to your infrastructure rather than vice versa, where you become limited by the constraints of existing technology. As a foundational requirement of a hyper-personalized customer experience, real-time engagement illustrates the need for agility not just in a MarTech stack, but also in terms of omnichannel journey orchestration. Real-time website personalization becomes even more powerful as part of a seamless, unified experience across all customer touchpoints. And that circles back to the single point of operational control that aligns not only technology, but people and processes in control over real-time personalization as well as segmentation, journey design and interactions. Your business requirements, your customers and their expectations, and the way your business uses real time, AI and other emerging technologies to deliver a personalized CX all share one thing in common – constant change. The Redpoint CDP was architected to handle that change, to future-proof your technology investments, to manage the complete customer data integration lifecycle and, just as importantly, to keep up with the cadence of the customer across an omnichannel journey. **Blog categories:** Customer Data Platform **Blog tags:** CDP --- ### [Redpoint Global Included in Forrester Research’s Now Tech: Real-Time Interaction Management (RTIM) Report 2020](https://www.redpointglobal.com/blog/redpoint-global-included-in-forrester-researchs-now-tech-real-time-interaction-management-rtim-report-2020/) **Published:** May 5, 2020 **Author:** Steve Zisk **Content:** Forrester Research’s Now Tech: Real-Time Interaction Management (RTIM) report published in April 2020 recognized Redpoint Global in three key areas: next-best experience with enterprise decision engines, personalization optimization, and cross-channel marketing for inbound and outbound experiences. Redpoint was one of only four providers to be listed for all three of these capabilities (Redpoint Global, Pegasystems, SAS and Engage Hub). The report, written by Forrester’s Rusty Warner, ranked 42 vendors on solutions based on the ability to help customers orchestrate relevant customer experiences, address customer expectations for mutual value exchange and the alignment of marketing with customer-facing functions. Forrester defines RTIM as “Enterprise marketing technology that delivers contextually relevant experiences, value, and utility at the appropriate moment in the customer lifecycle via preferred customer touchpoints.” RTIM that extends beyond marketing to customer-facing functions like sales and service relies on sophisticated decision engines to orchestrate two-way, interactive CX is known as cross-channel campaign management (CCCM). In terms of delivering marketing campaigns, CCCM is evolving to address contextually relevant [customer](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/) interactions. As CCCM and RTIM functionality converge, vendors that excel at outbound digital marketing are enhancing their capabilities for inbound digital channels and building integrations with offline channels. Personalization engines integrate with back- and front-office systems to recommend more relevant offers based on a deeper understanding of customer context. In the report, Forrester writes, “Firms do not achieve overnight success with RTIM; B2C marketers typically describe [real time interaction management](https://www.redpointglobal.com/blog/redpoint-global-included-in-forrester-researchs-now-tech-real-time-interaction-management-rtim-report-2020/) as an evolutionary journey that spans multiple years. It’s critical to define a strategy that adds capabilities in step with the organization’s readiness to exploit them.” Forrester also writes, “B2C marketers, when considering RTIM investments: Refine your martech ecosystem based on CX requirements. Review high-priority CX use cases to determine whether your martech ecosystem is up to the task of RTIM. Prioritizing integration should be a key RTIM function. Every [real time interaction](https://www.redpointglobal.com/real-time-interactions/) solution encompasses disparate capabilities, and your needs might dictate tools from several different vendors. B2C marketers must further ensure that RTIM investments complement their holistic martech ecosystem as well as solutions for enterprise customer data management and CX.” As one of four vendors to be included in all three categories across the board, Redpoint is uniquely positioned to meet the needs of today’s enterprises. With a native customer data platform, Redpoint offers users the real-time tools necessary to meet the needs of today’s customer. Post COVID-19, [digital transformation](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) has only been accelerated and the ability to incorporate real time into all aspects of the customer experience becomes more important than ever. Innovative companies use the Redpoint platform to transform their customer experiences across the enterprise and drive higher revenue. Redpoint solutions provide a unified single point of control where all customer data is connected and every customer touchpoint intelligently orchestrated. Delivering more engaging customer experiences, highly personalized moments, relevant next-best actions, and tangible ROI – this is how leading marketers lead markets. ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Real-Time Personalization --- ### [Redpoint Global Included in Forrester Research’s Now Tech: Real-Time Interaction Management (RTIM) Report Q1 2019](https://www.redpointglobal.com/blog/redpoint-global-included-in-forrester-researchs-now-tech-real-time-interaction-management-rtim-report-q1-2019/) **Published:** January 31, 2019 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/01/shutterstock_448523395-1-e1548944627308.jpg)Forrester Research’s [Now Tech: Real-Time Interaction Management (RTIM), Q1 2019](https://www.forrester.com/report/Now+Tech+RealTime+Interaction+Management+Q1+2019/-/E-RES142812) report published earlier this month recognized Redpoint Global in three key areas: next best experience, personalization, and cross-channel marketing. Redpoint was one of only four providers to be listed for all three of these capabilities (Redpoint Global, IBM, Oracle, and Salesforce). The report, written by Forrester’s Rusty Warner, ranked 42 vendors on solutions based on the ability to help customers orchestrate relevant customer experiences, address customer expectations and the alignment of marketing with customer-facing functions. Forrester defines [real time interaction](https://www.redpointglobal.com/real-time-interactions/) as “enterprise marketing technology that delivers contextually relevant experiences, value and utility at the appropriate moment in the customer life cycle via preferred customer touchpoints.” Forrester writes, “[Firms do not achieve overnight success with RTIM](https://www.forrester.com/report/Create+An+Effective+Enterprise+Marketing+Technology+Blueprint/-/E-RES61277?objectid=RES61277); you must define a strategy that adds capabilities in step with the organization’s readiness to exploit them. You need to prioritize customer-obsessed CX initiatives that will drive competitive differentiation and business value.” “Real-time customer engagement is critical for organizations who wish to provide consumers with personalized, contextually relevant interactions that match expectations at each point in the buying journey,” said Patrick Tripp, VP Product Strategy at Redpoint Global. “Redpoint addresses the three critical capabilities needed for real-time customer engagement: speed and agility, data-driven personalization, and cross-channel optimization. Redpoint’s clients are leading the way with innovative use cases to leverage data, analytics and machine learning, and intelligent orchestration to increase revenue while lowering the costs of interaction.” The next generation of the Redpoint Customer Engagement Hub™ revolutionizes how marketers leverage data and analytics to drive real-time personalized engagement. Redpoint’s recent launch included enhancements to the Redpoint Customer Data Platform™ to connect all data in real-time, Redpoint Automated Machine Learning™ for machine learning and analytics, and Redpoint Intelligent Orchestration™ to personalize every moment of engagement. As one of four vendors to be in all three categories across the board, Redpoint is uniquely positioned to meet the needs of today’s enterprises. With the native customer data platform, Redpoint Customer Engagement Hub offers users the real-time tools necessary to know all that is knowable about the customer and to meet the customer where and when they are in the customer journey. ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![real-time customer engagement paper cover](https://www.redpointglobal.com/wp-content/uploads/2019/04/real-time-customer-engagement-wp.png)](https://www2.redpointglobal.com/white-paper-real-time-customer-engagement-in-practice) **Blog categories:** Customer Data Platform, Omnichannel Marketing, Real-Time Personalization --- ### [Operate at the Pace of Business Change with the Redpoint CDP](https://www.redpointglobal.com/blog/operate-at-the-pace-of-business-change-with-the-redpoint-cdp/) **Published:** August 17, 2023 **Author:** John Nash **Content:** Trying to satisfy the expectations of a modern, digital-first consumer for seamless experiences across an omnichannel customer journey resembles a dog chasing its tail. Brands implement new customer engagement technology, only to have the customer pivot in a new, unexpected direction. Every organization recognizes the need for steady innovation and the flexibility to adapt to the changing needs of the business, but rapid change is difficult when too much time and energy is spent building and maintaining a legacy architecture. In a [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/), 70 percent of marketers said that the number of systems they have make it difficult to provide a seamless customer experience (CX). Marketers cited a lack of data integration between systems (41 percent), a closed integrated marketing cloud (41 percent) and irreplaceable highly specialized applications (40 percent) as the top three ways their MarTech stack prevented them from managing an omnichannel CX. ## **The Redpoint CDP** The Redpoint customer data platform (CDP) gives clients the freedom to adjust to market conditions and new trends and technologies as needed, with the ability to power an omnichannel customer experience personalized for a segment of one. Redpoint is a fully integrated, composable CDP with data ingestion/ETL, automated data quality and identity resolution for every source of customer data, segmented and activated, with options for journey orchestration and real-time interaction capabilities. The ability to quickly adapt to the changing needs of the business while delivering seamless omnichannel experiences are among the key benefits of the Redpoint CDP, a SaaS solution that offers clients managed services in a composable architecture. Developed, designed and deployed as cloud-native containerized services, the Redpoint CDP is available as an end-to-end CDP and marketing hub, or as specific managed services for data ingestion/ETL, data quality, identity resolution, segmentation and activation. As a composable architecture, Redpoint offers a fast route to assembling the required customer data and activating it through to personalized CX. This includes capabilities that deliver fast performance and enhanced agility along with best-in-breed integrations with other vendors. Redpoint operates on standard cloud-based databases or data clouds such as Snowflake or Google BigQuery as part of an organization’s broader data operations architecture. Organizations are empowered to continuously innovate by taking advantage of new features and capabilities as they appear, thus future-proofing their tech stack in a streamlined configuration using standard data pipelines. ## **Cost Reduction, Revenue Growth and Speed-to-Value** How do cloud-native containerized services and composability directly translate to business benefits? From an operations standpoint, consumption pricing is perhaps the most obvious example. Any business with peak seasonal or other demand are all-too familiar with having to pay for unused processing power in a fixed infrastructure, unable to quickly (and inexpensively) scale up or down to account for processing variances. Automatic scaling, real-time controls and Redpoint’s open garden infrastructure account for workload variance issues. Beyond the clear cost benefit, the ability to account for variance improves CX by enabling the shift from being product-centric to customer-centric. Brands can say goodbye to blasting campaigns designed solely to shed excess inventory, for example. Accounting for a talent shortage and/or budget constraints is another benefit of deploying managed services in Redpoint, while also delivering speed-to-value. According to [Gartner](https://www.gartner.com/en/newsroom/press-releases/2021-09-13-gartner-survey-reveals-talent-shortages-as-biggest-barrier-to-emerging-technologies-adoption), a talent shortage is the biggest barrier to the adoption of emerging technologies. IT executives cited talent availability (or the lack thereof) as the main adoption risk factor for the majority of automation technologies (75 percent). Redpoint solves for the resource constraint problem by automating many of the processes required to drive personalized engagements and deliver omnichannel CX, including in-line data quality, identity resolution, and the creation of a Golden Record A Golden Record is a single customer view that is the foundation for delivering a personalized, omnichannel CX. Combining data from any source (website, mobile app, eCommerce platform, POS, social media, CRM, etc.) the Golden Record is a holistic unified record of a customer and the customer’s engagement with a brand across every touchpoint. Including behavioral, transactional, demographic and preference data, and containing all attributes, aggregations, a full identity graph and a full contact history, the Golden Record allows marketers and other business users to profitably differentiate one customer from another. For an organization lacking data scientists, Redpoint automates data quality. Lacking marketing operations? Redpoint ties response data against campaign data and runs marketing operations. Redpoint customers essentially only have to feed as much of their transactional, behavioral and demographic data into the data model as they can, and Redpoint creates and manages the Golden Record, providing the visibility and observability that ensures all customer data is fit for its intended business purpose. ## **Private Cloud** Recognizing that organizations have different business objectives, hosting and data regulations, digital maturity and tech stack makeup, Redpoint can also be implemented in a client’s private cloud. The cloud-native platform is designed for clients who prefer to control their own configurations, customizations and optimizations in a private cloud – maintaining absolute control over their data and technical environment. Organizations that run Redpoint in a private cloud maintain complete oversight, management and maintenance of customer data, with the option to install and upgrade software as needed for the flexibility to configure the software needed for any business use case. Redpoint is designed first and foremost to operate at the pace of business change. With any deployment option, Redpoint meets organizations where they are on the path toward highly personalized customer experiences that drive revenue. **Blog categories:** Customer Data Platform --- ### [Redpoint and Snowflake: No Data Replication and Complete CDP Functionality](https://www.redpointglobal.com/blog/redpoint-and-snowflake-no-data-replication-and-complete-cdp-functionality/) **Published:** December 13, 2023 **Author:** Steve Zisk **Content:** According to a report from [Sheer Analytics and Insights](https://www.sheeranalyticsandinsights.com/market-report-research/cloud-data-warehousing-market-21), the cloud data warehouse market is expected to reach more than $45 billion by 2032, up from $4.6 billion in 2021, representing a nearly 23 percent compound annual growth rate. There are many reasons for the rise of data clouds such as Snowflake, Google BigQuery, Amazon Redshift and SAP Business Warehouse, among them improved security and an increased focus on business performance in a multi-cloud environment vs. taking on more IT infrastructure. Companies also recognize the need to quickly activate an ever-increasing volume of data while reducing cost and complexity. With roughly 20 percent of the market share, Snowflake is the market leader, touted by its approximately 15,000 customers for running in a multi-cloud environment on all major cloud platforms (Azure, AWS, GCP), with a usage-based business model that offers unlimited concurrency and a unique architecture that separates storage from computing capability. What does the prolific rise of Snowflake and other data clouds portend for customer data platforms, the Redpoint CDP among them? Using a CDP with a data cloud is a natural fit for its many benefits in delivering a single source of truth for customer data, but there are considerations to take into account knowing that not every CDP works in Snowflake the same way. ## **No Data Replication, Better Performance** One question is whether a CDP needs to be composable in a data cloud, or what level of composability is needed. Another is how a CDP in a Snowflake environment handles data pipelines from ingestion to activation. To answer these questions, it helps to examine the ultimate purpose of a CDP, and how a data cloud helps further those goals. If the core value of a CDP is to bring together all customer data and eliminate data siloes across marketing, customer success and the enterprise, a data cloud becomes a key asset in a few ways. The Redpoint CDP natively operates with Snowflake as a primary customer database, and existing customers choose this approach for several reasons. First and foremost, doing so achieves the core value of bringing customer data together in a central location and using Redpoint to build a unified customer profile without data replication; no data movement and replication equates to lower cost, better performance and better governance for security and compliance. Redpoint also offers a unique capability in Snowflake: the Redpoint CDP can connect to an additional database, in a shared Snowflake instance, that provides read-only views to any kind of master data – additional customer attributes, product / supply chain information, locations or other situational details – to complement the core operational, campaign, and customer information in the primary customer database. By running the Redpoint CDP instance directly in Snowflake, clients control their data unlike in a pure SaaS application, but – as with all data clouds – do not have to control the software. By maintaining data-in-place, clients execute all queries directly within Snowflake, avoiding having to persist data outside of Snowflake. ## **A Robust, Complete CDP – in Snowflake** In selecting or assembling a CDP in a composable architecture such as Snowflake, marketing and IT need to understand how the different components will work together: What manual processes are involved, how do user interfaces work together, and how are business users expected to perform their day-to-day work? In some “composable CDPs”, core CDP functionality such as data quality, identity resolution, and audience / segment selection may be outsourced to another composable application or service, or may require the marketer to learn coding in SQL, Python, Spark, or other developer-oriented tools. The CDP, in other words, is composable at too low a level, which makes it more difficult and more expensive for marketers to use the CDP for its main responsibility of building and sharing a deep understanding of the customer with consistent, fit-for-purpose customer data. When the Redpoint CDP is used for data ingestion, identity resolution, segmentation and activation, customers avoid the hidden costs of a composable CDP. Because Redpoint offers composable services and APIs for identity resolution, customer profiles, segmentation, data orchestration and real-time interactions, the CDP brings together the various pieces needed to perform a complete business process or function in one environment. And the Redpoint CDP user interface allows the marketer to monitor and visualize customer data, create and activate audience segments. Redpoint lets clients [configure the Redpoint CDP to run on an existing Snowflake database](https://www.redpointglobal.com/redpoint-and-snowflake/) or by creating a new Snowflake instance, and is the only enterprise CDP that offers the full range of core CDP capabilities that can be run in Snowflake: - Automated Data Ingestion and Data Quality: Designed to handle all your enterprise data at the cadence of your customers. - Identity Resolution – Tunable for all your use cases while delivering complex and accurate customer profiles using probabilistic, deterministic and machine learning techniques. - Segmentation and activation – Using selection rules and models to dynamically and precisely segment your audiences and power superior CX at every touchpoint. Companies are increasingly adopting Snowflake to optimize use of their marketing technology stacks, lowering costs while increasing efficiency with regard to making the best use of enterprise data. Redpoint is the only enterprise CDP that performs the full breadth of capabilities directly in Snowflake. With no data replication, Redpoint provides an industry-leading unified view of the customer for activation to any end point and for any business or CX use case. For more on Redpoint and Snowflake, visit [“Redpoint and Snowflake: A Winning Combination.”](https://www.redpointglobal.com/resources/redpoint-and-snowflake-a-winning-combination/) And to view the recording of “Redpoint CDP x Snowflake: The Framework to Support an Evolving MarTech Stack,” click [here](https://event.on24.com/wcc/r/4414778/B97632EF49033C94E63EF573FD11F360?partnerref=rpgblog). **Blog categories:** Composability, Customer Data Platform --- ### [Redpoint Global and Lucerna Health Join Forces to Advance Consumerism in Healthcare for Payers and Providers](https://www.redpointglobal.com/blog/redpoint-global-and-lucerna-health-join-forces-to-advance-consumerism-in-healthcare-for-payers-and-providers/) **Published:** June 16, 2020 **Author:** John Nash **Content:** The digital transformation of the [healthcare](https://www.redpointglobal.com/blog/a-dynamic-healthcare-journey-deserves-a-coordinated-personalized-approach/) industry has been underway for years, but we are now seeing a rapid acceleration of new engagement models as a repercussion of the global pandemic. Healthcare consumers now expect on-demand services, personalization and seamless customer journeys, much as they have come to appreciate in retail and other sectors. Today, [Redpoint Global](https://www.redpointglobal.com/) and [Lucerna Health](https://www.lucernahealth.com/) announced an expansion of their strategic alliance: offering a solution that combines Redpoint software with Lucerna’s Healthcare Data Platform (HDP), the partnership addresses these consumer driven market needs. The acceleration of telehealth combined with new value-based care reimbursement models are creating a seismic shift toward healthcare consumerism, calling for an innovative patient engagement solution that fundamentally changes the healthcare experience for all stakeholders. The Redpoint and Lucerna platform enables better consumer-centric experiences while also achieving higher revenue and lower costs for payers and providers. **Experience Better Healthcare** Redpoint has experience helping healthcare brands across the entire [healthcare](https://www.redpointglobal.com/blog/the-data-driven-acceleration-of-the-consumer-healthcare-journey/) lifecycle, with more than 150 million U.S. consumers being served by the Redpoint platform. The uses include new member acquisition, onboarding, care logistics, chronic care coordination, prescription adherence, ongoing wellness and healthy patient engagement across a wide range of health insurance payers, hospital systems, primary care providers, health services companies and some of the world’s largest retail pharmacies. Flexible and built for scale, Redpoint offers enterprise-class software to integrate consumer data, automate personalized interactions and optimize orchestration of patient engagement across all touchpoints. Lucerna’s HDP integrates payer, provider and EMR data to operate value-based care models and create the most effective patient engagement journeys. The combined solution includes a secured data platform and the expertise to help payers and providers effectively engage today’s healthcare consumer. **Transforming the Healthcare Journey** Deloitte Consulting [surveyed 1,159 health care consumers](https://www2.deloitte.com/us/en/blog/health-care-blog/2020/will-consumers-trust-health-care-organizations-after-covid19.html) to understand the emotional toll of the pandemic and how their attitudes and behaviors in managing their health are changing. The results show that now, more than ever before, consumers want personalized recommendations that enable members and patients to make informed decisions and feel ownership in directing their health. To enable this, consumers now are much more willing to share data with healthcare organizations. The survey found that 69 percent of consumers are now open to sharing personal [health](https://www.redpointglobal.com/blog/reduce-gaps-in-care-and-drive-better-health-outcomes-with-personalized-engagement/) information with their health plan, while 71 percent are willing to sharing with their preferred local health care system. Helping manage the healthcare consumer’s journey in a dynamic way can have major impacts. Working together over the past several years, Redpoint and [Lucerna Health](https://www.redpointglobal.com/blog/redpoint-global-and-lucerna-health-join-forces-to-advance-consumerism-in-healthcare-for-payers-and-providers/) have collaborated to manage patient engagement for a number of healthcare entities. For example, Sanitas USA has already realized dramatic results for patients and medical center operations, including *4x improvement* in closing care gaps in high-risk patients, *3x improvement* in new patient wellness visits and a *320 percent increase* in program enrollment for on-demand telehealth services. These positive outcomes are among the dozens of healthcare engagement use cases that the platform supports across the healthcare ecosystem. Healthcare organizations can achieve these results while leaving their existing systems in place, leading to high speed-to-value. With the expanded partnership the companies will continue to serve the needs of this market for years to come. Craig Thomas, CEO of Lucerna Health says, “The integrated offering from Redpoint and Lucerna will help transform the customer engagement solutions available in a fragmented healthcare system, particularly for payers and providers who are focused on working together to win in a value-based care environment.” **Blog categories:** Customer Data Platform, Data Management, Data Quality, Healthcare, Omnichannel Marketing, Real-Time Personalization --- ### [Real-Time Engagement at Scale Provides the Consistent CX that Customers Crave](https://www.redpointglobal.com/blog/real-time-engagement-at-scale-provides-the-consistent-cx-that-customers-crave/) **Published:** July 8, 2022 **Author:** Vin DelGuercio **Content:** Scale can mean many different things. In the context of the delivery of an omnichannel customer experience (CX), scale is primarily thought of as personalizing an experience for as many individuals as an organization’s people, processes, and technology will support. Scale encompasses the volume of data a decision is based on, the number of branches in a decision tree, the available variations of messages or offers and how quickly a decision is made. When real-time is added to the mix, the ability to do all those things becomes exponentially more difficult. Nevertheless, a real-time capability is a vital component for scaling CX. While it is possible to scale without real-time – meaning you engage with every customer, make decisions using all available data and on hundreds of decision tree branches – the problem is that without a real-time capability a personalized experience will – for many customers – fall short of expectations. The reason is that a real-time component ensures that decisions are made on timely, accurate, and complete data, which is required to keep up with the cadence of a modern, omnichannel customer journey that consists of a combination of physical or digital channels. Consider the results of a [2021 Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/), where consumers ranked a consistent experience across all channels as the most important element of CX. ## **Birds of the Same Feather: Consistency and Real-time** In many cases, consistency depends on real-time. Consider a customer visiting a retail brand’s website. The brand personalizes an offer on the homepage using yesterday’s data. But if it fails to account for every action the consumer may have made in the interim, such as an in-store purchase, an engagement with a call center rep, a social media post, the pages that they’ve just viewed, or a host of other interactions that might change the personalized offer, it runs the risk of alienating the customer with an irrelevant offer. Real-time analysis of an online session will factor in pages visited, time on page, links clicked, etc., with content generated perhaps also dependent on other sources of data compiled in real-time such as purchase history, previous website visits, or in-store activity. That real-time analysis and real-time decisioning could affect dozens of elements of the website experience – images, text, links, and any combination thereof. And what the customer sees and experiences might itself be culled from thousands of options. A major retailer might have 10,000 or more available products in stock – all of which will be available to drive personalized recommendations. It’s important to recognize the distinction between real-time and speed. While the speed with which a brand interacts or engages with a customer across various channels is certainly one element of real-time, it is also possible to make the wrong decision quickly. Mitigating this possibility brings us back to the need to make decisions based on all pertinent data. If data with which a decision is made comes from 10 different sources, the data must from all those sources must be ingested, cleansed, matched, and readied for decisioning in real-time. Unless the original data sources themselves are consistently updated, a brand can quickly compile a [unified customer record](https://www.redpointglobal.com/single-customer-view/), but it may be making an inaccurate decision quickly if it’s based on stale data. Delivering a consistent experience for an individual customer requires determining with a high degree of accuracy that a brand is showing the right thing to the right person at the right time, even with an enormous amount of variables. Real-time at scale has a lot of moving parts, in other words, that can grow in complexity depending on the level of personalization a brand wants to reach. ## **Ease Up on the Brakes: Real-time for Every Channel, Every ID + Key** The operational overhead needed to deliver real-time capabilities at scale can be overwhelming. It’s an enormous change management issue. For starters, businesses must adapt to a world where [machine learning algorithms](https://www.redpointglobal.com/machine-learning) instead of people are making decisions. In the website personalization example, if there’s a universe of say 10,000 products available to recommend in a real-time personalization engine, everything must be tagged in a way to make intelligent decisions about what a piece of content is relevant for (and who it’s relevant for) and what it’s not relevant for. The website, of course, is only one channel. That same calculation must be made for every channel, with a decision returned within milliseconds on not just a web page but for the mobile app, call center, chatbot, an in-store associate, a hotel clerk or any synchronous engagement channel. A consistent experience depends on real-time, yes, but also real-time in the precise cadence of the customer’s journey across all touchpoints. A real-time decision, in other words, cannot be made in isolation. One issue is the risk of presenting a fragmented experience, the other is a brand may limit its options for personalization. For example, many solutions may promise a real-time capability if they’re doing real-time based on a website log-in key, or perhaps on a cookie and a web ID. Here, the brand will limit personalization to the observed behavior on the website, limiting both what’s available to personalize as well as how to identify a customer. If a customer visits a website from multiple devices that a brand does not recognize or tie to a unique customer profile, the brand narrows its options. It’s important to be aware of all of the factors that go into delivering a hyper-personalized CX at scale. Real-time, as we’ve seen, has many different components including speed of decisioning, latency of data driving decisions and availability of a breadth of content to deliver personalized experiences. Scaling real-time is vital to ensure the consistency that customers demand in an omnichannel customer experience. For more on how Redpoint provides customer with relevant interactions informed by real-time data at every touchpoint, [click here](https://www.redpointglobal.com/orchestration/real-time-personalization/). **Blog categories:** Real-Time Personalization --- ### [The Real-Time Difference: Moving at the Speed of the Customer](https://www.redpointglobal.com/blog/the-real-time-difference-moving-at-the-speed-of-the-customer/) **Published:** March 11, 2020 **Author:** Vin DelGuercio **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/03/shutterstock_705249400-300x200.jpg)From a marketing standpoint, the answer to the “what is real time?” question is, in many ways, not complicated: it’s moving at the speed of your customer. But there, as they say, is the rub. Because if we accept this premise, we accept that real time can mean milliseconds, seconds, hours, minutes, or days – depending on the business use case for engaging with the customer. An examination of real time from the perspective of both the customer and the business reveals how real time has evolved in just a few years to become the most important factor for marketers in terms of staying in cadence with a customer’s unique omnichannel journey and delivering a personalized CX that drives revenue. **Real Time for the Connected Consumer** Moving at the speed of the customer can manifest itself in any number of ways. It’s a retailer being ready with a promo code at checkout that accounts for and is relevant to the purchase. It’s sending a special offer rather than a nurture email based on recent website activity. Or it’s making a personalized recommendation on the website based not only on products the brand is trying to move, or that are popular, but instead based on that individuals’ preferences, behaviors, and activity up to that specific website visit. In each of the above examples, the real-time element powers a personalized customer experience that is vital for driving revenue. Compare that with what most marketers thought of as advanced real-time personalization just a few years ago, which may have meant switching out a banner ad on a webpage based on what a customer may have purchased a week ago. The real time decision – switch the banner ad – is just one component of real time. The recency of the data the decision is based on is another, no less important component. The always-on, always-addressable consumer demands more. Consider a recent [SmarterHQ report](https://smarterhq.com/privacy-report), where 72 percent of consumers said that they will only engage with personalized messaging. The [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) produced similar findings, with 37 percent of consumers saying they will flat-out not do business with a company that fails to offer personalization. **Consistent Relevance** From the customer’s perspective, real-time personalization removes friction from the overall customer journey by eliminating the possibility of irrelevance. Ingesting customer data from every source in real-time to create a [golden record](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/) – a single customer view that informs a brand everything there is to know about the customer – empowers a brand to deliver a timely, personalized offer, action, or recommendation across channels that is always in synch with the customer’s unique journey. A complete lack of lag time between when data is sourced and when it is made available to personalize the customer experience is the key for a brand to move at the speed of the customer – at whatever pace the customer chooses. Consider, for example, a customer who abandons an online shopping cart, only to purchase one of the items in-store later the same day. A brand armed with real-time personalization capabilities will know not to include that product on a reminder email – and will instead have the insight to offer the perfect accessory item to incentivize fulfillment. Delivering a seamless, real-time personalized customer experience is not without complexity, which is why consumers are critical of marketers’ ability to meet their expectations. In the Harris Poll, for instance, 73 percent of consumers believe that brands struggle to meet the expectation for a personalized customer experience across all touchpoints. Much of the struggle stems from more of a tactical perspective on real time, which is the processing behind what ultimately becomes the customer-facing decision. Is the information the marketer bases a decision on also in real time? If it’s even seconds or minutes old, the marketer risks introducing friction into the customer journey – with the risk increasing the older the data is. If a customer visits a brand’s homepage, for instance, and clicks to go to another page – true real-time personalization will factor in the customer’s experience and activities on the homepage. If instead a marketer is serving up personalized offers based on customer activity from two days ago, there’s a chance it’s no longer relevant to the customer journey. **Real Time: Not Just for Show** Some brands are willing to take the risk, making the calculation that there will be a small percentage of offended customers. But it doesn’t make sense to apply any part of your valuable marketing resources to risk upsetting even one customer, when the alternative is a seamless, consistently positive customer experience that drives revenue. Real-time data ingestion, real-time model scoring, and real-time rules-based decisioning with SLAs less than one-half second is what sets Redpoint Global apart in delivering an exceptional customer experience that drives revenue. With Redpoint, leading marketers lead markets by always providing relevant personalization at every stage of an omnichannel customer journey. One Redpoint customer, an [internet company](https://www.redpointglobal.com/wp-content/uploads/2019/05/web-services-case-study.pdf), puts the half-second response time to good use. When an end customer has an issue, the golden record gives service reps access to all customer activity to less than a half second of the customer interaction, ensuring that the responding rep confronts the issue with the most relevant information. Another Redpoint customer, a travel and hospitality firm, increased revenue over 150 percent over several properties and reduced marketing costs per interaction and number of touches by 40 percent – all by integrating 100 data sources of data into a comprehensive enterprise-wide golden record. Real time means something different for the two customers mentioned above, but the common denominator is that they both provide a friction-free experience for the customer – in the customer’s preferred channel and in the customer’s preferred cadence. That is the essence of real time – business at the speed of your customer. **RELATED CONTENT** [Real-Time Data Aggregates for Today’s Dynamic Customer Journeys](https://www.redpointglobal.com/blog/real-time-data-aggregates-for-todays-dynamic-customer-journeys/) [The Impact of Real Time on CDP](https://www.redpointglobal.com/blog/the-impact-of-real-time-on-cdp/) [What is Real-Time Customer Engagement](https://www.redpointglobal.com/blog/real-time-customer-engagement/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Customer Data Platform, Real-Time Personalization, Single Customer View --- ### [Why Real Time is Important for Delivering Magical Moments in Travel & Hospitality](https://www.redpointglobal.com/blog/had-a-real-good-time-why-real-time-is-important-for-delivering-magical-moments-in-travel-hospitality/) **Published:** July 7, 2023 **Author:** Redpoint Global **Content:** You check in at the airline kiosk, and as your boarding pass prints out you receive an SMS from the airline notifying you that because of a long delay, you’re eligible for a seat upgrade on your next trip. You’ve booked an all-inclusive vacation through a travel company, and as the trip approaches you are continually apprised of itinerary changes, weather forecasts and updated recommendations, all impeccably timed and optimized according to your departure date. Welcome to real-time interaction management (RTIM) in travel and hospitality, an imperative for travel brands to demonstrate the type of personal understanding that drives retention, lifetime value and revenue. Thinking of your own travel experiences, what stands out as truly memorable apart from the “bucket list” sights and sounds? Do you have fond memories of being treated like everyone else, or are the truly superlative experiences those in which a brand provides a consistent experience across every engagement touchpoint, inbound or outbound, that is completely relevant to your individual customer journey? If a positive customer experience resonates, you’re not alone. In a [PwC survey](https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/future-of-customer-experience.html), 73 percent of travelers said that customer experience is the most important factor in purchasing decisions – and 17 percent said they will leave a travel brand after one bad experience. A [2022 Dynata survey](https://www.redpointglobal.com/press-releases/up-up-away-77-percent-of-americans-are-planning-getaways-this-year/), commissioned by Redpoint, shows plenty of room for improvement, with just 27 percent of consumers stating that travel companies are effective at delivering personalized, consistent and seamless experiences pre-trip, during travel and post-trip. ## **Real Time Interactions and the Golden Record** Crafting a consistent, omnichannel customer experience using real time requires both real-time data as well as the ability to orchestrate real-time decisions in the moment of engagement based on the real-time data. A real-time decision optimized for an omnichannel journey will optimize the experience not just for the channel of engagement, but within the context of a holistic journey with the brand. A customer who visits the website, as an example, might see a different image, offer or content based on the customer’s behavior that preceded the visit. There may be entirely different reasons for a call center interaction or a log-in on the loyalty app preceding the website visit, and a real-time decisioning engine that renders a decision knowing the latest context will provide a relevant experience. A real-time decision in the context and cadence of a customer journey avoids introducing common CX frustrations. A car rental agency might confuse/annoy a customer if it emails drop-off instructions to a customer who has just spoken with an agent to extend a rental, as one example. Real-time decisions that create magical moments for a customer rely on having real-time data from every conceivable source and type of customer data – batch, streaming, first-party, third-party, behavioral, transactional, demographic, preference data, etc. A real-time, single view of a customer, or Golden Record, combines all customer data to form a holistic, unified record of a customer and the customer’s engagement with a brand across every touchpoint. With all attributes, all aggregations, a full identity graph and full contact history all processed and updated in real-time, a Golden Record allows marketers and business users to profitably differentiate one customer from another. A persistently updated Golden Record provides a real-time, single view of a customer across the entire organization. Available and accessible to marketers and business users, it breaks down the data and process siloes that introduce customer experience frictions. An email marketing team, for example, can segment a campaign audience based on everything there is to know about a customer up to the moment a campaign is executed. The campaign is not centered around channel metrics, but rather around the customer, i.e. a customer is in the segment only because it’s what the data dictates according to the real-time individual journey, not because the customer is on a static list. ## **Transform Travel Experiences with Redpoint** Speed and agility, data-driven personalization through a Golden Record and cross-channel optimization are the cornerstones of orchestrating and delivering real-time interactions. The Redpoint Data Readiness Hub and Redpoint Smart Engagement Hub provide customers with operational control over data, decisions and real-time interactions to execute the personalized experiences that customers expect. Redpoint connects all disparate sources of customer data and continuously applies data quality processes the moment data is ingested. Marketers and business users can trust that the resulting Golden Record is an accurate and precise representation of a customer (or household) that provides a real-time, contextual understanding of the customer’s journey progression with your brand. Data-driven travel and hospitality brands are using Redpoint to transform customer experience, increasing engagement, retention and lifetime value by delivering contextually relevant experiences in real time across an omnichannel journey. For more on how Redpoint is helping travel and hospitality brands compete on CX and turn relevance into revenue, click [here](https://www.redpointglobal.com/travel-hospitality/). **Blog categories:** Real-Time Personalization, Travel & Hospitality --- ### [Real-Time Data Aggregates for Today’s Dynamic Customer Journeys](https://www.redpointglobal.com/blog/real-time-data-aggregates-for-todays-dynamic-customer-journeys/) **Published:** November 5, 2019 **Author:** Redpoint Global **Content:** A customer who drives away from a home improvement retailer with a new refrigerator has little awareness of the domino effect the transaction generates, happy enough to have a working freezer. Unbeknownst to the customer, however, the purchase introduces a flurry of changes to the customer’s data aggregates, which have a material impact on the continuing customer journey. Aggregates that require updates will include categories such as date of last in-store transaction, last retail purchase amount, last purchase store number, last purchase date, last purchase amount, and year-to-date purchase amount, to name a few. Other potential changes could include data points such as a new physical address or email, or a customer’s status as head of household. Within each of these broad categories there may be dozens of calculation rules that update a customer’s status, such as customer lifetime value (CLV), and others that determine the retailer’s next-best action based on the aggregations. Does the retailer send a thank you note? An offer for storage organizers and ice trays? Does the updated CLV warrant an invitation to a gold member club? Or does the purchase trigger free delivery and installation? The permutations that kick into high gear with a single transaction underscore the importance of using automated, rules-based decisions that are updated and pushed out in real time to keep pace with a customer journey. Consider a scenario where the customer with the new refrigerator arrives home and sees an email offer for a discount on a water connection kit, with free hook-up. The offer is personalized, relevant, and in the context and cadence of the customer journey. Now consider the alternative where aggregates are updated using lists and batch data. At best, the same offer is made three days after the purchase – after the customer has already bought and installed the water connection kit. ## **Always Keep Pace with the Connected Customer** What differentiates the [Redpoint Customer Data Platform ](https://www.redpointglobal.com/cdp/)from a list-based approach is that aggregates are not only updated in real time, they are also pushed out to subscribers in milliseconds. The [master data management](https://www.redpointglobal.com/solutions/redpoint-data-management/) component eliminates lag time between data ingestion, processing, and calculation updates – ensuring a frictionless customer journey. This means that every engagement channel and external system that subscribes to the real-time updates is in synchronization with a customer at any stage of the customer journey. To be considered an enterprise-grade [CDP](https://www.redpointglobal.com/blog/a-cdp-implementation-reference-guide-what-you-need-to-know/), a solution must ingest data from all sources, retain full detail of the data, store it, convert it into a unified customer profile, and make the profiles available to all external systems. Because these requirements do not specifically include real-time aggregation calculations, there are CDPs in the market today that lay claim to providing a single view of the customer through the aforementioned capabilities. In truth, however, the lack of rules-based aggregations means that they’re not equipped to manage an omnichannel customer journey. Typically, a list-based CDP will instead ingest data, calculate some aggregates for a nightly batch upload to an FTP site, and a subscriber or external system will pick it up via web services or API integrations. ## **A Personalized Experience Requires a Rules-Based Approach** A traditional cadence sufficed when customer journeys were linear, static and rarely involved multiple channels. Marketers could safely rely on lists to generate outbound campaigns where there was little expectation from the customer for a personalized experience. That’s not true today. According to the [Harris Poll survey](https://www2.redpointglobal.com/webinar-harris-poll-cx-2019-ondemand?_ga=2.240850243.1999408358.1571256513-1570104466.1540307570) commissioned by Redpoint, 63 percent of consumers say that personalization is now part of the standard service they expect, with 37 percent of consumers going so far as to say they will flat out stop doing business with a company that fails to offer a personalized experience. Consumers also expect a brand to know who they are – preferences, behaviors, permissions – across every channel. Satisfying the always-on, connected consumer requires two-way, real-time communication across a host of new and emerging channels. To achieve this, brands must have the capability to apply rules dynamically at any stage of the customer journey, producing a next-best action in the context and cadence of an individual journey. The modern customer journey renders a list-based approach and list-based decision-making obsolete. To learn more about the benefits of real-time aggregates and what sets the Redpoint CDP apart from others in the market, I encourage you to read a recent [BOSS Magazine article](https://thebossmagazine.com/redpoint-global-2/) that goes into extensive detail on the shortcomings of what it called “out-of-the-box” CDPs with a “one size fits all approach” that mostly fail to match brands’ aspirations to target customers with highly personalized engagements. The article explains how real-time aggregates provide a fast path between data and revenue, and why many Redpoint clients have declared that the Redpoint platform is the primary revenue generation platform for their organizations. **Blog categories:** Real-Time Personalization --- ### [Real Time vs. Right Time: The Difference Comes Down to Context](https://www.redpointglobal.com/blog/real-time-vs-right-time-the-difference-comes-down-to-context/) **Published:** January 16, 2026 **Author:** Beth Pfefferle **Content:** In the 10th Annual State of Engagement Survey from [Engagys](https://www.engagys.com/insights/press-release-tenth-annual-state-of-engagement-survey-results), a healthcare consulting and advisory firm, health plan leaders identified multichannel orchestration (“right message, right channel, right time”), AI, and next-best action capabilities as their top three strategic priorities. These findings demonstrate the importance of timely, relevant customer engagements in the design and execution of health plan member journeys. Multichannel orchestration, data-driven AI, and next-best actions all require that a healthcare payer has the capability to engage with a plan member in a timely fashion, up to and including real time. When a member logs into the website or portal, interacts with a chatbot, or schedules an appointment, a payer should be ready to engage with the member at that moment – if the time is right. The “right time” distinction is key; just because a payer has the capability to deliver a [real-time customer experience (CX)](https://www.redpointglobal.com/real-time-decisions/), does not necessarily mean that it’s always the optimal way to guide the customer journey, or to achieve the desired outcome. Engaging in real time vs. the right time is an important distinction not just for health plans, but for healthcare providers, financial services, retail – any brand that engages with members, patients, or customers. Right time vs. real time also plays a factor in how to best achieve the desired use case, be it driving retention, acquisition, churn avoidance, increasing lifetime value, or any other result that solidifies the customer-brand relationship. ### **Real Time vs. Right Time: The Core Distinction** Real time is an immediate response to a customer signal (or a collection of customer signals) the moment a signal occurs, delivered the moment a customer appears in a certain channel. Right time is about responding when that interaction will have the highest relevance and create the best outcome, even if that moment is not immediate. In other words, real time prioritizes speed; right time prioritizes impact. The two can overlap, but they are not interchangeable. But one common denominator for any type of engagement, right time or real time, is that a **contextual understanding** of a customer is a must. Context, in fact, is the most important component for determining whether an engagement should happen in real time or whether the right time might be minutes, hours, or days later. ### **Keep Context Top of Mind** Having a [contextual customer understanding](https://www.redpointglobal.com/blog/does-your-customer-data-have-enough-context-heres-how-to-tell/) means that the brand understands the contours of a customer journey. It involves understanding [customer intent signals](https://www.redpointglobal.com/blog/financial-impacts-of-reacting-to-customer-intent-in-real-time/) and being able to predict with a high degree of certainty what a customer will do next. For a health plan marketing team, this might be an analysis of a member’s website behavior that predicts a member is going to schedule a flu shot, and the team then delivering the appropriate next-best action based on the member preferences and opt-ins (an SMS with scheduling options, an email reminder, etc.) Building a contextual understanding entails both situational context as well as a data context, i.e., metadata. Situational context is derived from gathering signals about a customer and how the customer interacts with a brand, with other people, and with products, along with insights into how a customer changes over time. Data context complements this by providing structure and meaning to those interactions. Metadata reveals the status of a data feed, changes in accuracy or timeliness, and being able to observe – and trust – how a data quality score was achieved. Context is critical because knowing the contour of a customer journey is essential for knowing the right time to engage with a customer. If metadata tells you a feed of website activity is late or incomplete, for instance, perhaps the appointment reminder email is pulled. Or if the health plan member’s activity indicates a high likelihood of churn, the marketing team offers targeted advice to help a member optimize their benefits. Building an in-depth, contextual understanding of a customer is a [key component of data readiness](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/), which entails making data right (complete, accurate, timely) and fit-for-purpose for use across the enterprise (actionable, compliant, trusted). ### **Real Time, Right Time: Balancing CX and Outcomes** The determination between right time vs. real time depends on a combination of factors: a brand’s ultimate objective, the active customer journey, the customer’s preferences (channel, frequency, etc.), and the brand’s capabilities for executing real-time decisions all play a role. Real time is about collapsing the time between data, decisions, and interactions while considering the intended outcome. Consider a patient with a newly diagnosed chronic condition. The provider’s goals are twofold; provide education and help the patient build a care plan. When the patient visits the provider’s website, a real-time experience might be to show educational content on the homepage. A personalized care plan, however, with a lot more moving parts, might instead start with an SMS to schedule an initial consultation. The contextual understanding of the patient is what allows a provider to optimize the patient journey with hyper-personalized, relevant experiences. For any engagement, these are the types of questions to consider. How much data is needed? How accurate and timely does the data need to be? Will waiting for more data improve the experience enough to justify the wait? Marketers and data teams need to measure outcomes and bake those into future decisions from a time, cost, and resource perspective about what real time means, factoring in business goals as well as the impact on the overall customer experience. ### **Advance Your CX Goals with Timely Customer Engagement** A timely customer engagement can make or break CX. Relevance and timeliness are closely intertwined; marketing teams are well aware that being too early or too late can both introduce friction into a customer journey. But just because a brand may have the capability to deliver a real time next-best action doesn’t mean it always should. Having a deep situational awareness (context) is what separates a superior CX from the routine, where being timely – right time *or* real time – becomes a matter of course. For more on how the Redpoint Data Readiness Hub helps enterprise companies leverage timeliness as a key advantage in delivering a superior CX that is always in sync with the customer, click [here](https://www.redpointglobal.com/data-readiness-hub/). --- ### [Ready Data is Compliant Data: Does Your Data Hold Up to Scrutiny?](https://www.redpointglobal.com/blog/ready-data-is-compliant-data-does-your-data-hold-up-to-scrutiny/) **Published:** July 9, 2025 **Author:** John Nash **Content:** More so than other components of data readiness, the requirement that data be compliant is an ongoing exercise. Because compliance not only must account for continual changes to how customer data is safeguarded and used – based on changing preferences and updated regulations, it also differs from industry to industry, region to region, and from one company to another. What is compliant data? In essence, it is data accountable to any and all regulations and to the subject of the data, e.g., a customer. Data compliance must satisfy many masters. Company mandates that may account for reputational control, internal security or certification requirements. Customer requirements having to do with preferences and permissions – with permissions broadly including how data is shared and how it’s controlled for any number of different use cases. And then there are regulatory requirements including HIPAA, GDPR and CCPA that are continually updated in terms of what data may be collected, with changing rules for how data is used and protected. ## **Compliant Data, a Checklist** To satisfy the requirement for compliant data as the sixth pillar of data readiness, companies need to ensure that they have the right metadata, have sufficient controls in place to ensure that access is granted to the right people, and have data set up in whatever the security and control mechanisms (i.e., data governance policies) that are approved by the IT organization. Parameters for data governance controls may include whether data must be kept at rest when on-premise, whether calculations must be performed exclusively in the cloud or may be combined with cloud and on-premise data, procedures governing authentication and authorization, for auditing, lineage and history. All fall under the compliance umbrella. ## **Compliant Data Complexity** For a sense of the complexity involved with ensuring data is compliant, satisfying GDPR requirements is a good place to start, particularly with the “right to be forgotten” policy. Compliance requires that companies document and confirm each request for erasure by a consumer, inform a consumer whether the organization will honor the request and explain any reason for noncompliance, decide what information needs to be erased and how to erase it (archived, deletion, etc.), remove the information, notify internal systems and external partners, and record each of the erasure steps taken. The steps are complicated in part because the right to be forgotten under GDPR is not an absolute right, but rather spells out when an organization’s right to possess someone’s data (and for how long it may keep it) overrides the right to erasure – such as when it is being used to comply with a legal ruling. ## **Compliant Data and Methodology** Ensuring data compliance is as much methodology as it is technology. A data readiness platform that gets data right and gets data fit-for-purpose might check all the boxes in terms of making sure that data is [complete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/), [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/), [timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/), [actionable](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/), trusted and compliant, but methodology accounts for the nuances of what being fit-for-purpose means for various industries, or for different use cases. The key is for your data readiness platform to properly support and adapt to an individual organization’s approach. ![Data Readiness Compliant Graphic](https://www.redpointglobal.com/wp-content/uploads/2025/07/Data-Readiness-Compliant-graphic-800x363.jpeg)The Six Pillars of Data Readiness: Compliant Methodology in the context of compliant data, for example, might include the knowledge for how to match a customer record when a regulation prohibits you from saving credit card information in a point of sale (POS) system. Instead, you retain and match the zip code with another unique identifier. Or, lacking the zip code of the customer who made the purchase, you use the zip code of the store to set a geographic boundary and compile a list of possible matches within the boundary using other data points. While the zip code workaround is effective in retail, the point is that there is a different methodology for every industry that stores and uses customer data. A data readiness platform should have the flexibility to account for industry type, intended use cases, and other important variables. ## **Compliant Data and Trusted Data** While trusted and compliant data are both pillars of data readiness, they are distinct and serve different purposes. *Compliant* data adheres to external requirements — regulations, legal mandates, and contractual obligations. It’s about proving that you are managing data according to the law and to standards defined outside the organization. *Trusted* data, on the other hand, is earned. It reflects the organization’s ability to consistently use data in a way that is accurate, transparent, and respectful of customer expectations. It’s customer-facing and experience-driven, and it helps foster long-term relationships through responsible data use. The two are interconnected – you can’t build trust without compliance, but compliance alone doesn’t guarantee trust. A data readiness platform must support both: ensuring regulatory alignment and the ethical, transparent handling of data that inspires confidence. ## **Compliant Data and Data Governance** Just as data quality is part of a holistic approach to data readiness, data quality is incomplete without data governance. A strong data governance program goes beyond privacy and security to include master data management and metadata management. Data governance helps connect all the dots for what it means to have compliant data, but a complete program will not just have policies and procedures in place, it will also establish ownership and define roles and responsibilities – which gets to the heart of having a methodology in place. Coming next, we will turn our attention to data readiness specifically as it applies to AI. **Blog tags:** Data quality, Data readiness --- ### [Financial Impacts of Reacting to Customer Intent in Real Time](https://www.redpointglobal.com/blog/financial-impacts-of-reacting-to-customer-intent-in-real-time/) **Published:** September 9, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/09/shutterstock_1307690896-300x200.jpg) According to McKinsey research, a high-impact product recommendation – such as from a trusted friend or brand – is up to [50 times more likely to trigger](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-new-way-to-measure-word-of-mouth-marketing) a purchase than a low-impact recommendation. An effective way to ensure a recommendation reaches the high-impact threshold is to align it with a consumer’s intent to purchase. Through their actions and behaviors across channels, customers and prospects leave purchase intent signals at every interaction. With an understanding of [consumer intent](https://www.redpointglobal.com/blog/financial-impacts-of-reacting-to-customer-intent-in-real-time/), brands are positioned to optimize customer engagement across every interaction touchpoint and increase customer lifetime value, keys to driving revenue. In a Harris Poll survey commissioned by Redpoint, 53 percent of consumers said that they expect a brand to know their buying habits and preferences and to anticipate their needs, and to demonstrate this understanding through personalized recommendations, unique offers, and recognition across channels. Brands that do this right are rewarded with loyalty; 37 percent of consumers said they will be more likely to purchase from the brand in the future. Another 37 percent said that they will stop doing business with a company that fails to offer this type of experience. Recent IDC research produced similar findings, estimating that by 2021 businesses that invest in providing a frictionless user experience will see a 20 percent decrease in customer attrition. **Form a Consumer Intent Picture Using All Data Sources** A previous [blog on customer intent in this space](https://www.redpointglobal.com/blog/capturing-intent-in-an-omnichannel-customer-journey-is-next-level-marketing/) focused on omnichannel intent listening and detailed how brands use all sources of customer data to mine intent signals and form a real-time response. Here, we’ll shift the focus to tangible, real-world examples of how brands turn intent signals into revenue. Consider an advanced intent marketing use case deployed by one Redpoint customer, a specialty retailer. The retailer, like many others, struggled to curtail shopping cart abandonment. One study estimates that [68 percent of online shopping carts](https://www.martechadvisor.com/articles/ecommerce/reverse-shopping-cart-abandonment/?utm_source=want_to_read) are abandoned before a purchase is made. By recognizing abandonment as a strong intent signal, the retailer integrated the behavior into an innovative buy online, pay in-store (BOPIS) model. If a customer buys paint online and chooses the pay in-store option, a [single customer view](https://www.redpointglobal.com/challenges/single-customer-view/) lets the retailer see if the customer also abandoned accessories such as brushes and rollers. When the customer is approaching or at the store, the retailer leverages a real-time decisioning engine to make a next-best action that is perfectly timed in the context of the customer journey. If the partial abandonment as a purchase intent signal is strengthened by similar behaviors – such as a click-through on an ad for the product – the next-best action could be a discount offer on an accessories package. With perfectly timed next-best recommendations, the retailer delivers highly relevant, high-impact messages and offers to customers through any engagement touchpoint, including mobile, and has significantly increased upsell revenue. **Predicting Intent Beyond a Customer’s Action** With a single customer view that includes all sources and types of customer data, a brand can listen for intent signals beyond even a direct action taken by the [customer](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/). Another Redpoint client, a consumer packaged goods (CPG) company, links external data such as weather forecasts to customer preferences and transactional history to anticipate intent signals. This external data becomes part of the customer’s golden record. A single point of control provides marketers with a capability to mine the golden record for data insights, to gather meaning from a variety of intent signals and decide what action to take to turn intent into purchase. With in-line analytics and a real-time decisioning engine, a single point of control provides the CPG company with the ability to return a next-best action optimized for the intent shown by the customer at that precise time. For example, weather metadata can be used as a data source to predict shifting intent, but that signal might only be activated during a short window. Brands with an advanced intent marketing strategy can strike during the precise window of opportunity wherever the customer appears in a journey. With hyper-personalized messages and relevant offers based in part on the full range of [consumer intent](https://www.redpointglobal.com/blog/financial-impacts-of-reacting-to-customer-intent-in-real-time/) signals, the company enjoyed a 144 percent sales increase in just six weeks after implementing the Redpoint platform compared with the previous baseline. **Intent Marketing Outside Retail** Capturing consumer intent extends to other industries. One Redpoint customer in the healthcare space is using a single point of control to capitalize on the trend toward healthcare consumerism, which recognizes that the healthcare consumer now drives his or her own journey through the healthcare space to include relationships with payers and providers. The consumer’s intent to stay healthy plays a significant role in healthcare consumerism and the shift toward value-based care in which providers are paid according to their role and success in the overall health and well-being of the end consumer rather than by services rendered. With a single point of control over data, analytics and interactions, Redpoint enabled this healthcare company to analyze each consumer’s behaviors and actions (eating right, exercise, keeping appointments, etc.) and proactively manage these intent signals as part of an overall healthcare journey. The next-best actions could be communicating appointment reminders or tips for healthy eating, for example, whichever action best aligns with a patient’s intent and with a preventive treatment plan as prescribed by the provider. In part through capturing and responding to intent signals, the Redpoint customer is helping its customers – payers and providers – advance toward what’s known as the “triple aim” of value-based care – improved quality of care, lowered total cost of care, and an improved member/patient experience. The above examples show that intent marketing reaps financial rewards for brands and businesses that have the platform in place to aggregate every piece of customer data from online and offline channels, apply in-line analytics, and activate intent signals with a real-time decisioning engine that intelligently orchestrates a next-best action or recommendation at the moment of interaction. **Blog categories:** 1:1 Personalization, Anonymous to Known, Customer Data Platform, Real-Time Personalization, Single Customer View --- ### [Reach for the Stars: Yes, a Single Customer View is Attainable](https://www.redpointglobal.com/blog/reach-for-the-stars-yes-a-single-customer-view-is-attainable/) **Published:** March 16, 2022 **Author:** John Nash **Content:** With St. Patrick’s Day coming up, little kids are busy making leprechaun traps with the hope that they can barter with a trapped leprechaun for the famous pot of gold at the end of the rainbow. Their parents tend not to let on that it’s a quixotic pursuit (more on that in a moment), happy the kids are busy and impressed by the strength of their convictions. I bring this up in the context of the pursuit of a 360-degree customer view. A Gartner Maverick\* Research report from last year claims that achieving a “mythical 360” (its words) may be as fruitless as catching a leprechaun. The report acknowledges that it somewhat contradicts the prevailing wisdom that pursuing a 360-degree view of the customer is a mandate for most organizations and that, as such, its findings should be “treated with caution.” The prediction that caused a bit of a stir is that “By 2026, 80 percent of organizations pursuing a 360-degree view of the customer will abandon it because it doesn’t adhere to data privacy regulations, relies on obsolete data collection methods and obliterates customer trust.” To address confusion, Gartner published a [follow-up blog](https://blogs.gartner.com/lizzy-foo-kune/2021/07/29/a-360-degree-view-of-the-customer-is-a-destination-not-a-journey/?_ga=2.109978560.490481172.1643658615-2052642838.1603919098) stating that its research “isn’t an indictment against customer data. It’s an indictment against the *quixotic pursuit* of an omniscient customer view.” ## **A Golden Record and a Personalized Experience** The view here is that a 360-degree view is neither idealistic nor impractical. The prevailing wisdom exists for a reason; achieving a single customer view is a noble pursuit that is a key condition for providing an omnichannel customer experience that drives revenue. In a recent [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/resources/harris-poll/), 39 percent of customers surveyed said they will not do business with any company that fails to offer a personalized experience. Recent [McKinsey research](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying#:~:text=Research%20shows%20that%20personalization%20most,intimacy%2C%20the%20greater%20the%20returns.) found that personalization drives up to a 25 percent revenue lift depending on an organization’s ability to execute. The more skillful a company becomes in applying data to grow customer knowledge and intimacy, the research found, the greater the returns. Before directly addressing the Maverick\* claim, we should level set to make it clear what Redpoint means when we talk about a single customer view. A 360-degree of the customer is also sometimes known as a [Golden Record](https://www.redpointglobal.com/single-customer-view/), a single source which provides a brand everything there is to know about a customer. The Golden Record combines data from every conceivable data source, in real time. It links together all proxy identities for each possible customer – including unknown customers – and provides a robust long-tail of transactional information that includes everything from granular behavior to KPIs to transformations summaries. Because data is ingested and processed in milliseconds, a Golden Record provides business users with a complete, valid contact graph that is current up to the millisecond. With it, users are empowered to deliver a hyper-personalized, omnichannel customer experience that is always relevant to an individual customer journey, persisting the experience across a complete customer journey. With this definition of a single customer view in mind, we can now address the three reasons why the Maverick\* Research report predicts the majority of companies will abandon the pursuit of a 360-degree customer view: 1) it doesn’t adhere to data privacy regulations, 2) relies on obsolete data collection methods, and 3) obliterates customer trust. ## **Safeguard Data Privacy** Claiming that marketers find it increasingly difficult to obtain and secure data needed to build a 360-degree customer view without running afoul of complex data privacy regulations, the report essentially claims the risk is not worth the reward. The counter argument is that a business assumes *more* risk for violating data privacy regulations without a 360-degree view. Yes, managing privacy preferences at an individual customer level is a complex undertaking, but if the alternative is to avoid collecting customer data out of fear that more data translates to more risk, the former option is the better choice. Meeting [privacy and security](https://www.redpointglobal.com/privacy-compliance) needs are a central component of the [Redpoint CDP](https://www.redpointglobal.com/cdp/). Advanced identity resolution capabilities with real-time updates are part of the Golden Record, laying the foundation for privacy compliance at the individual consumer level. The platform specifically addresses [GDPR consent and data governance](https://www.redpointglobal.com/resources/gdpr-consent-and-data-governance/) issues by collecting and managing consent at a granular level, and maintaining a historical archive of consent approval or revocation, among many other security-related tasks. ## **First-Party Data will Never Become Obsolete** As to the prediction that a majority of organizations will abandon pursuit of a 360-degree customer view because of obsolete data collection methodologies, we tend to agree. Data collection methodologies such as the widespread use of third-party cookies *are* obsolete. As too are data collection approaches that rely on “homegrown” customer data platforms that tie together multiple customer engagement applications that are typically organized around process or channel siloes, clouding a single source of truth for customer data. Limiting marketers’ ability to track users across the digital landscape might indeed curtail some organizations’ pursuit of a customer 360, but the Redpoint approach to [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) recognizes that first-party data is the lifeblood of a single customer view. Using persistent keys and built-in probabilistic algorithms using first-party data only, the platform resolves individual identities from multiple sources. First-party data processing at the point of ingestion, applied immediately to a customer record, provides business users with supreme confidence that every customer record consists of the freshest, most up-to-date data – data that customers themselves are providing, rather than relying on a third-party organization’s customer data. ## **Reinforce Long-Lasting Customer Trust** It’s more accurate to say that people don’t want you to *show* that you know everything about them. A hyper-personalized customer experience is not a blank check for a brand to use every piece of information that constitutes a customer profile. The appeal of using a single view of the customer to deliver a [next-best action](https://www.redpointglobal.com/next-best-action/) is that data is used only in so far as it delivers a relevant experience at a precise moment of a customer journey. Actions are delivered (or not delivered) in the context of a dynamic, omnichannel journey. One important component of the rg1 platform is that next-best actions are rule-based, not list-based. On-line, self-training machine learning models ensure that rules are dynamically updated so that they are always in the context of a journey and in the precise cadence of the customer. Every email, message, image, offer, etc. are optimized for each interaction with a customer. A high degree of relevance eliminates any concern about the so-called creep factor. The [Harris Poll survey ](https://www.redpointglobal.com/resources/harris-poll/)commissioned by Redpoint addresses this concept, and finds that consumers are aware that a personalized customer experience depends on the consumer providing data. In the survey, 66 percent of respondents said that they are willing to provide brands with more information about them if it is used to create a more valuable experience. Rather than withdraw from or abandon a brand, consumers presented with a hyper-relevant customer experience will move in the other direction, rewarding the brand with loyalty. In a recent [Dynata survey](https://www.redpointglobal.com/press-releases/74-percent-of-consumers-believe-brand-loyalty-is-about-feeling-understood-and-valued-not-discounts-and-loyalty-perks/) commissioned by Redpoint, 74 percent of consumers said that feeling understood and truly valued by a brand is more important to them than discounts and loyalty points. And 64 percent said they would rather purchase a product from a brand that knows them. ## **Create Value with a Single Customer View** Our argument is that a 360-degree customer view creates value for customers by virtue of creating a personalized experience that demonstrates a personal understanding, and is thus worth the investment. While we would not attach the word “mythical” to the pursuit of a 360-degree customer view, we can certainly stipulate that it is not an overnight sensation. It takes the right technology as well as a shift in [mindset, culture and talent](https://www.redpointglobal.com/blog/what-we-mean-when-we-talk-about-data-quality/) to shift into the top-quartile performance in personalization that [McKinsey estimates](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) will generate over $1 trillion in value across US industries. Is that chasing leprechauns? Technically speaking, the pot of gold at the end of the rainbow may only exist in our imaginations. But there’s something to be said for an aspirational pursuit and a childlike exuberance for the art of the possible that does translate to newfound riches. **Blog categories:** Anonymous to Known, Data Quality, Identity Resolution, Real-Time Personalization, Segmentation & Activation --- ### [Put the Healthcare Consumer at the Center of Both Care and Education, Even Outside the Doctor's Office](https://www.redpointglobal.com/blog/put-the-healthcare-consumer-at-the-center-of-both-care-and-education-even-outside-the-doctors-office/) **Published:** April 20, 2022 **Author:** Sarah Lull **Content:** In a survey on patient loyalty from health technology vendor Welltok, [84 percent of patients](https://www.medicaleconomics.com/view/patients-want-more-well-being-support-doctors) said they would feel a greater sense of loyalty if their overall health and wellbeing was supported outside of a clinical setting, such as help with nutrition, stress management, and weight loss. Additional findings support the idea that, for most patients, a healthcare journey extends outside of the provider setting: - 75 percent of patients said they want “continuous interaction” with a provider between appointments - More than 80 percent said they are more likely to follow a treatment plan if it is personalized The need for ongoing patient education outside of a clinical setting is gaining traction for many reasons. More people are being diagnosed with – and living longer with – a chronic condition that needs to be carefully monitored and treated. For example, about 1 percent of Americans lived with Type 2 diabetes about 60 years ago. According to the [Centers for Disease Control](https://www.cdc.gov/diabetes/pdfs/library/diabetesreportcard2017-508.pdf), the number is now closer to 10 percent, with the rate more than tripling between 1990 and 2010. The CDC estimated the annual medical cost of Type 2 diabetes in 2017 at about $327 billion – up 33 percent over a five-year period. ## **Healthcare Consumerism & Personal Understanding** Another reason is the merging of two key trends. One is the rise in healthcare consumerism, in which healthcare consumers are more invested in monitoring and tracking their health. Smart watches, IoT data, and healthcare apps that track sleep, diet, and exercise all give consumers more control and insight into health behaviors. The other is a solidifying consumer expectation for a personal understanding from the brands they engage with. This expectation is true in healthcare just at is in for retail, banking, travel and other industries with a brand-consumer dynamic. A [2021 Dynata survey](https://www.redpointglobal.com/press-releases/80-of-patients-prefer-to-use-digital-communication-to-interact-with-healthcare-providers-and-brands/), commissioned by Redpoint, aligns with the Welltok findings and highlights the confluence of healthcare consumerism and the desire for a personal understanding. In the survey, 60 percent of respondents said it is important that a provider show how well they understand a consumer beyond basic patient data, and 40 percent defined a deeper understanding as greater personalization – such as consistently updated information accessible across all channels. In short, healthcare consumers expect more control over their experiences across an omnichannel healthcare journey, and they expect providers and other stakeholders to demonstrate an understanding of them as an individual healthcare consumer – far beyond the data that’s shared and compiled during a visit to their doctor. With access to a high volume of real-time personal health data, healthcare consumers expect their providers to use the same data to improve health outcomes. As those expectations relate to ongoing education around a chronic condition such as Type 2 diabetes, a highly personalized care plan might include help with prescription adherence, diet and nutrition advice, blood sugar control and home testing, blood pressure and cholesterol checks, etc. Other chronic conditions have similar long-term treatment plans that require education and assistance outside of a provider setting, and will often include education for family members in addition to the patient. Being diagnosed with a chronic condition is a life event. Highly personalized care plans recognize that a patient’s healthcare journey encompasses not only many stages, but a multitude of physical and digital channels that may be accessed. Consistent patient engagement across every channel throughout a journey helps drives improved outcomes because, as the Welltok survey shows, healthcare consumers react positively to personalization. (80 percent will follow a personalized treatment plan). ## **Close the Personalization Gap with a Single View** To close the gap between the healthcare experience that consumers expect and the one being delivered, providers need to have a [single view of the healthcare consumer](https://www.redpointglobal.com/single-customer-view/). Many struggle to close the gap because their view of the consumer is limited to patient data collected at the point of care. Data that could help a provider personalize care – claims data, behavior data, and social determinants of health (income, geolocation, access to transportation, etc.) is either siloed or inaccessible, belonging to and/or managed by various third parties. By eliminating data siloes through a [single operational platform](https://www.redpointglobal.com/rgone/) that puts the healthcare consumer at the center of a holistic journey, providers are empowered to deliver a holistic experience that is always in the cadence of an individual patient. Treatment plans can be coordinated across an extensive network of caregivers (labs, specialists, clinicians, testing facilities, etc.), and education tailored to a patient’s engagement preferences. As a simple example, a patient might be asked to drop off glucose test strips to a facility. A provider with a single view of the healthcare consumer that includes all data sources, preferences, behaviors, appointments, insurance information, etc., might send an SMS message notifying the patient of the closest facility and its hours of operation. A worrisome test result might automatically schedule a telehealth consultation as well as trigger an email with literature on how to maintain low blood sugar. Or, to help the caregiver of an aging parent with Alzheimer’s experiencing medication side effects, a geriatrician might implement an online portal where the caregiver, a clinical pharmacy specialist, and the provider can enter data, compare notes, and regularly update a care plan. In a way, these examples are simply the tip of the iceberg both in terms of the level of personalization and the degree of potential impact possible from leveraging a single view of the healthcare consumer. For more information about how Redpoint helps healthcare organizations deliver personalized omnichannel experiences that drive better outcomes, visit [Redpoint for Healthcare](https://www.linkedin.com/showcase/redpoint-for-healthcare/) on LinkedIn, or request a demo at the [Healthcare homepage ](https://www.redpointglobal.com/healthcare/)on redpointglobal.com. ## **Related Content** [Align the Healthcare Experience Around the Consumer: How an Omnichannel CX Delivers Improved Health Outcomes](https://www.redpointglobal.com/blog/align-the-healthcare-experience-around-the-consumer-how-an-omnichannel-cx-delivers-improved-health-outcomes/) [It is Time to “Act as One” for the Benefit of the Healthcare Consumer](https://www.redpointglobal.com/blog/it-is-time-to-act-as-one-for-the-benefit-of-the-healthcare-consumer/) [The Data-Driven Acceleration of the Consumer Healthcare Journey](https://www.redpointglobal.com/blog/the-data-driven-acceleration-of-the-consumer-healthcare-journey/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Healthcare, Real-Time Personalization --- ### [Public Media Uses Customer Data to Transform Fundraising in Times of Crisis](https://www.redpointglobal.com/blog/public-media-uses-customer-data-to-transform-fundraising-in-times-of-crisis/) **Published:** November 5, 2020 **Author:** Redpoint Global **Content:** Founded in 2011 as an initiative within WGBH, the [Contributor Development Partnership (CDP)](https://www.cdpcommunity.org/) was spun out in 2018 as an independent public benefit corporation with one mission: to help sustain a thriving public media system and create a “fundraising alliance” for public media. The organization delivers new and innovative fundraising solutions, marketing strategies, technology innovations, data and analytics services, and scaled best practices on behalf of more than 230 public radio and television stations nationwide. The Boston-based CDP grew out of an initiative by the public media’s Major Market Group (MMG), which, with support from the Corporation for Public Broadcasting (CPB) and WGBH, built a solution to help stations identify fundraising areas of opportunity. CDP works with local public media stations to grow their active donor files and increase net revenues through the development of best practices in fundraising, scaled vendor management, innovative fundraising and operational services, and best-of-breed technology systems. ### **Single Customer View in Omnichannel Marketing** One of the best-of-breed solutions was the implementation of [Redpoint’s rg1](https://www.redpointglobal.com/one-platform/) software for data-driven, personalized engagement. Legacy marketing and database systems lacked the ability to create a comprehensive [single customer view](https://www.redpointglobal.com/single-customer-view/) to rely on for donor engagement systems. First implemented at WGBH and carried over to the CDP, the Redpoint rg1 solution offered a simple way to bring together all information about donors, create personalized one-to-one interactions across all marketing channels and have insight into real customer data to make informed decisions. Using Redpoint’s solution, CDP’s Member Service Bureau (MSB) – a large-scale fundraising collaborative that enables participating stations to focus more on local donor relationships and less on back-office processing – is able to perform complex granular segmentations for omnichannel marketing. CDP leverages those segmentations to drive successful direct mail campaigns, gift processing activities and, in pre-pandemic days, door-to-door canvassing using neighborhood and real estate data to target potential donors. Stations that participate in CDP’s MSB benefit from significant scale that is derived from rich collaboration and powerful solutions like RedPoint rg1. ### **Stop, Breathe Deep and Assess What is Next** In early 2020, COVID-19 presented CDP with its biggest challenge yet. Could the organization help find a way to help sustain partner stations in a pandemic? When the pandemic hit, CDP immediately conducted an “economic environmental scan” to determine the potential impact on its client partners. While still looking ahead to upcoming fundraising campaigns, the company also mobilized to protect existing fundraising commitments, counseling its client partners on how to respond to increases in cancellation requests from monthly sustainer donors. CDP used historical data to determine the potential impact of the pandemic on client partners and advised them to prepare to reforecast existing fundraising budgets. Seeing the possibility for long lasting impact from the pandemic, CDP also recommended its client partners take a conservative approach to FY21 planning, going to so far as to suggest provisional budgets that could be revised regularly. There was a consensus that if CDP focused on using the existing public media community cohesion there was potential for sustained donor levels during the worst of times. Donors who believe strongly that public media provides critical information, playing a role in providing medical or educational resources, would be driven to support these stations as their “part to play” in helping people. ### **Don’t Assume, Check the Data** There were factors that indicated this community approach strategy faced a high degree of uncertainty, but working with Redpoint, CDP took the guesswork out of it. They were able to immediately test assumptions, messages and adjust accordingly. CDP president and co-founder [Michal Heiplik](https://www.cdpcommunity.org/michal-bio) said, “Redpoint’s rg1 solution enabled CDP and its partners to react quickly to a changing market and customize for the needs of our member stations and their donors.” CDP tested its initial theories, collecting data from 112 stations representing 2.5 million donors and nearly $100 million in donations from January to March 2020. In mid-May, it produced its first analysis of the effects of the early stages of the pandemic on stations’ individual giving programs. That data, combined with the understanding that public media was providing a vital community service at a time of great uncertainty led CDP to the decision to continue to fundraise at a time when many others scaled back or paused their fundraising completely. What they found was: - Membership revenue was holding its own. - Stations that continued with their March on-air drives were able to build momentum that led to year-over-year growth. - Stations with strong “sustainer” membership programs were also showing resilience. ### **Don’t Stop Requesting Help** As the magnitude of the pandemic became apparent, the CDP Community pivoted an entire cooperative fundraising program consisting of more than 30 client partners representing 800,000+ donors to incorporate an effective omnichannel COVID strategy. While many other non-profits paused, CDP and its client partners ultimately leaned in, updating messaging, creating timely custom one-off direct marketing pieces and literally stopping the presses on an in-progress direct mail campaign that was deemed inappropriate for the moment, shelving it for a later time and replacing it with a fast-tracked COVID-specific campaign. On the digital front, CDP adjusted messaging and doubled the cadence of email communications to break through the clutter and remind donors of their critical role in public media’s public service mission. CDP recognized early on that donors, as people, craved connection, and a sense of control at a time when so much in their lives was out of their control. People wanted to feel that they could do something, anything, to make a difference in the face of this unprecedented moment in time. It worked. By reminding listeners of their “critical role in public media’s public service mission,” stations successfully engaged first-time and recurring donors, and even outperformed 2019’s revenues. Looking back on the initiative, Heiplik’s takeaway was a simple one: “In times of crisis, people want to help. Determine why your organization matters during these times and work to draw your donors and prospects closer to you.” ### **Leaning in When Instincts Say to Pull Back** CDP partner clients rolled out their targeted COVID campaign against the backdrop of these results. CDP then tracked the campaign’s results and found that despite an initial four-week dip in year-over-year performance from mid-March through mid-April, participating stations’ cumulative year-over-year revenue (for gifts < $1000) rebounded in mid-April and began outperforming 2019 by double digits, and continues to do so. The organization bet (correctly) that existing donors, both one time and sustainers, would respond enthusiastically and were also pleasantly surprised at the large number of unsolicited new donors added to the files as well. Heiplik provided the following advice for fundraisers navigating a world transformed by COVID-19: “Don’t let personal circumstances and upheaval cloud your professional judgment. Relentlessly track the events impacting your donors, as well as your fundraising performance, and adjust as needed, letting data drive your decision-making.” ### **Related Content** [Customer Data Insights and the Single Customer View](https://www.redpointglobal.com/blog/customer-data-insights-and-the-single-customer-view/) [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) [A Personalized Customer Experience (CX) Delivers Loyalty](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Management, Omnichannel Marketing, Single Customer View --- ### [Personalization is Essential for Profitable Customer Lifecycle Marketing](https://www.redpointglobal.com/blog/personalization-is-essential-for-profitable-customer-lifecycle-marketing/) **Published:** January 5, 2021 **Author:** Redpoint Global **Content:** Customers today are more unpredictable and demanding than ever before. They want to own their own customer experience, not be led by brands’ directives. So, it may seem impossible to move customers through a pre-planned journey or manage the [customer lifecycle](https://www.redpointglobal.com/blog/personalization-is-essential-for-profitable-customer-lifecycle-marketing/). Actually, doing so is essential—because customers also expect personalized experiences. Guiding them through engaging and relevant journeys not only delivers the value they want from brands today, but it also wins their ongoing business and loyalty. Customer lifecycle management and its partner, customer lifecycle marketing, enable marketers to deliver those relevant customer experiences, interactions and journeys. The first step is to determine what exactly is your organization’s customer lifecycle—the process customers go through as they build a relationship with your brand. Different companies use different terms and stages for the element of the customer lifecycle based on the steps prospects and customers take as they learn about and interact with them. For many companies the customer lifecycle begins with awareness; for others, it begins with some type of engagement, such as a prospect subscribing to an email newsletter. Generally, the customer lifecycle then continues with acquisition (i.e., a purchase), development (e.g., increased value/wallet share), retention and loyalty. Ideally, it’s a continuous cycle, which is why reengagement is a step in some companies’ customer lifecycles. ![](https://www.redpointglobal.com/wp-content/uploads/2021/01/Customer-Lifecycle-Model-300x169.png) Understanding your company’s specific customer lifecycle can help you deliver more value to customers, build a competitive advantage, and maximize revenue. Here are some examples of a customer lifecycle: ![](https://www.redpointglobal.com/wp-content/uploads/2021/01/Examples-of-Customer-Lifecycles-300x154.jpg) Offering personalized experiences throughout the customer lifecycle—even to prospects first discovering your brand—will set your company apart and deliver outsized results. But accomplishing this takes planning; that is, customer lifecycle management. ## **Customer Lifecycle *Management* vs. Customer Lifecycle *Marketing*** Customer lifecycle management (CLM) segments the customer journey into several overarching stages; for example, acquisition, growth and retention. Further, it divides those stages into more detailed steps, such as onboarding, adoption and repurchase. CLM helps to ensure that there are no gaps in the customer journey. Additionally, it helps to build collaboration across different departments to create a more cohesive customer experience. CLM enables you to track progress toward your business and marketing goals by linking each stage to supporting metrics. It also allows you to better measure customer engagement and value. Customer lifecycle *marketing* helps to deliver that engagement and value. It’s the process of marketing to prospects and customers based on where they are in their lifecycle, as well as where they are along a specific customer journey within it. Rather than solely targeting customer segments, marketers who use customer lifecycle marketing can speak relevantly to individual customers at scale. Marketers using customer lifecycle marketing are better able to determine what actions to take based on an individual customer’s lifecycle stage, as well as their current and predicted actions and value—so, it’s most effective when an automated [personalization](https://www.redpointglobal.com/omnichannel-personalization/) engine underlies and supports it. > Rather than solely targeting customer segments, marketers who use customer lifecycle marketing can speak relevantly to individual customers at scale. Some examples of customer lifecycle marketing include ad placements on dynamic webpages followed by retargeting, email onboarding campaign sequences, and next-best actions and offers. But, to maximize the results of customer lifecycle marketing, it’s best to take an integrated approach across all channels and touchpoints. Rather than focusing on one specific campaign, use automated real-time interactions based on where a customer is in their lifecycle and what next action is best for them. Many companies use CRM or CLM software to help manage the customer lifecycle. These tools are helpful but are only one element of the supporting technology needed for customer lifecycle marketing. Successful—i.e., highly personalized— customer lifecycle marketing requires a technology platform that will allow you to pull together data in real time from internal and external sources, channels, and devices; these sources might include behavioral, demographic, financial, geographic, operational, psychographic, and transactional data. This comprehensive view allows you to “see” your customer’s entire journey, so you can optimize your actions and messaging based on all that is knowable about that customer in that moment. The ideal platform for supporting customer lifecycle marketing also bridges organizational and data silos to allow different teams to cohesively address and personalize their part of the customer lifecycle—including in real time. ## **How to Manage Customer Lifecycle Marketing** Real-time [personalization](https://www.redpointglobal.com/omnichannel-personalization/) recognizes customers as individuals wherever they are in their lifecycle—in any channel or at any touchpoint, and at the moment it matters most. Marketers can tailor their messaging with even more accuracy, as a result. This relevance can be so helpful in simplifying the customer journey and so engaging in its ability to deliver a personal touch that it can increase satisfaction (no more irrelevant interactions!), drive sales, bolster loyalty and create advocates. Taking the following steps can help you launch a highly effective customer lifecycle marketing strategy: 1. **Set the parameters:** Define the stages in your customer lifecycle and hone the elements that comprise each (often through journey mapping). Then, set goals and KPIs for each. Along with having goals for stages such as acquisition (e.g. 500 new customers per month), it’s important to have customer-specific goals (e.g. growing a regular customer into a high-value customer by adding X to their annual spending). This will help you to determine your campaign strategies and goals. 2. **Prepare for the unexpected:** As you map out the stages of your company’s ideal customer lifecycle and how you’ll manage it, remember that a customer’s journey is rarely linear. Some customers will skip steps, others might pause for an extended time in between stages, and some might even repeat them (e.g., churning and then repurchasing at a later date without a reactivation campaign). 3. **Get the data:** Implement a platform that will allow you to bridge data silos and create a real-time holistic view of each customer (i.e., a Golden Record) using all available internal and external data sources. You can use this platform to automate mining the data, anticipating and addressing a customer’s unique interests and needs wherever they are in their lifecycle, and taking the next-best action based on what they’ve just done. This will significantly increase the relevance of your customer interactions. 4. **Track progress:** Use the goals and KPIs you’ve set to measure your success and determine what you might want to do differently. By looking at each stage of the customer journey, you can more clearly see what needs to be optimized. For example, if you recommended a next-best action to a customer, did they take that action? If not, what might you need to do differently? 5. **Engage more customers:** Generally speaking, businesses are meritocracies: higher-value customers get more perks and better experiences. Using personalized lifecycle marketing every customer can get a relevant, high-touch experience along their entire journey. 6. **Connect the enterprise:** Each stage of the customer lifecycle touches different parts of the organization, which means it’s essential to share data to facilitate collaboration and coordination efforts—linking marketing to sales and service, for example. You’ll be better able to anticipate customer needs across the enterprise by connecting and automating data sharing. 7. **Automate campaigns:** Personalized customer lifecycle marketing still requires you to create modular campaigns that an automated system can present in real time to the right customer based on the phase of the lifecycle they’re in, what they’ve just done, and what the system predicts they’ll do next. The right platform will automate the delivery of those campaigns, enabling you to personalize your customer interactions at scale. The best customer lifecycle marketing programs are, by definition, personalized. They meaningfully address a customer’s interests and needs based on their specific lifecycle stage. These efforts are also the most effective because they’re the most engaging. Marketers can automate and personalize campaigns like never before, which means they can deliver more value to customers and increase performance as a result. But real-time personalization across the entire customer lifecycle is just gaining traction, so businesses that use this approach can quickly gain a competitive advantage. Start now, and that advantage could be yours. ## **Related Content** [Customer Data Insights and the Single Customer View](https://www.redpointglobal.com/blog/customer-data-insights-and-the-single-customer-view/) [Augment Customer Segmentation with a Personalized CX](https://www.redpointglobal.com/blog/augment-customer-segmentation-with-a-personalized-cx/) [Clear the Hurdles for a Personalized CX](https://www.redpointglobal.com/blog/clear-the-hurdles-for-a-personalized-customer-experience/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Journey Orchestration, Real-Time Personalization --- ### [Don’t Let Architecture Terminology Get in the Way of How CDPs Can Power Your Business Outcomes](https://www.redpointglobal.com/blog/dont-let-architecture-terminology-get-in-the-way-of-how-cdps-can-power-your-business-outcomes/) **Published:** April 9, 2024 **Author:** Ian Clayton **Content:** In the ongoing discourse over how CDPs fit within a Martech ecosystem, there is a divide over perceived advantages and disadvantages of a “zero-copy data” ecosystem. While we can all agree that “zero-copy data” is too vague to be taken at face value (I much prefer [“data-in-place”](https://www.redpointglobal.com/blog/redpoint-and-snowflake-no-data-replication-and-complete-cdp-functionality/)) the argument is less about terminology than it is about which camp you fall into: [CDPs working directly on a data cloud (“zero copy”)](https://www.redpointglobal.com/redpoint-and-snowflake/), or CDPs that work indirectly with a data cloud (traditional). In the former, data-in-place or zero-copy simply refers to the CDP compiling, cleansing and making data ready for business use without having to move or copy the data, which is needed in the traditional construct. Because the [Redpoint CDP](https://www.redpointglobal.com/cdp/) was designed from the ground up to be database agnostic, we sort of straddle both sides of the fence, advocating first and foremost for adaptability without tethering to a specific approach. We do not champion “zero-copy data” as materially better or worse than the alternatives, but we instead offer a flexible framework that aligns with diverse customer needs, offering a SaaS option, an option to run in a data cloud ecosystem, and an option for private cloud/on-premise. With a multitude of available configurations, customers have the freedom to tailor their CDP deployment according to their preferences and business requirements – both now and in the future. Instead of worrying about where the data resides as a primary consideration, an adaptable platform allows the focus to be on cost reduction and performance. If you don’t believe a data cloud can meet your needs today, this approach allows for the adoption of a data cloud approach once requirements change in the future – or once data cloud technology “catches up” with the technical requirements that drive business outcomes. With that said, let’s refute some of the arguments against running your CDP in a data cloud, looking at it from the perspectives of cost, security and agility. ## **A Careful Cost Calculation** On the cost front, the primary argument against a data cloud ecosystem is that it is more expensive because you’re running the CDP 24/7 instead of just for analytical jobs. Because data clouds charge when in use, the savings over a managed database might soon become costs. Those costs, however, are not a 1:1 comparison with a managed database, which can presumably better distribute compute cost (and hope for continued economy of scale). Because unlike a managed database, data clouds will absorb costs associated with supporting the database. This includes the need for skilled people to manage, care, feed and operate the database itself – including monitoring, keeping indexes up to data, security, governance, apps, controls, etc. The total cost of implementation is a multifaceted consideration. By embracing a data-in-place approach within a data cloud environment, some customers may find that the flexibility, scalability, convenience and performance advantages provide a compelling, valuable long-term trade-off. The ability to scale resources dynamically, access advanced analytics capabilities, and integrate with a rich ecosystem of complementary services can translate into tangible business advantages. ## **Keep an Eye on Security** Turning to security, one argument against a data-in-place ecosystem is that a CDP must have keys for accessing needed data, which opens a potential line of attack for a bad actor to access data. While true, the same can really be said for any CDP. Zero-copy/data-in-place doesn’t claim to be more secure overall, but it does provide the customer – as the owner of the database – the tools to fully control data governance and monitor everything that is happening – who is using the data, how they’re using it, etc. If you’ve set up the proper security mechanisms such as ISO 2701, etc., then it will be inherently more secure than sending your data to a vendor and relying on the vendor’s security measures. Some vendors opposed to a data-in-place framework use this security argument as an avenue to poke holes at the notion of a composable architecture, claiming that the more vendors your CDP works with, the more vendors have keys (read: access to) your data. One refutation of this point is that even a closed-system CDP has to move data in and out, with APIs, webhooks, etc. Second, in the event of a breach, it’s better to have a single shut-off mechanism than it is to ask a vendor to delete a copy of their data. Third, the whole argument about the supposed security risk is a strawman argument to begin with. May customers who choose to run a CDP in a data-in-place environment do so because the data cloud is doing the security work. That’s one of their huge selling points. ## **More Access, More Choice** And on the agility front, it’s important to keep in mind that ultimately a CDP is a means to an end. For closed-system vendors, the inescapable truth is that everything you need to consume data out of your CDP has to be connected to that vendor. For the sake of argument, let’s say it is less expensive to run your analytics dashboard in your CDP vendor’s managed data storage than an open data cloud. Well, what if you prefer a different analytics dashboard? Or have already invested in an analytics platform? Or, what if your data science team is already skilled in other tools that aren’t available directly within your CDP vendor? A [composable architecture CDP](https://www.redpointglobal.com/blog/the-pros-and-cons-of-composable-architecture/) running in a data cloud provides access to a far more robust ecosystem – hence, far greater choice – than a CDP on managed data storage. As with cost, for organizations prioritizing agility, innovation and time-to-market, the benefits of a data cloud ecosystem may justify the investment – leading to faster ROI and maintaining a competitive edge in rapidly evolving markets. Ultimately, the decision to opt for a CDP within a data cloud environment is driven by a nuanced evaluation of business priorities, technological requirements and long-term strategic objectives. While cost considerations are important, they must be balanced against factors such as performance, scalability, security and ecosystem compatibility. Redpoint’s philosophy revolves around customer empowerment and adaptability. By focusing on use cases, performance and security, Redpoint enables customers to navigate the complexities of the CDP landscape with confidence, ensuring business outcomes remain paramount while delivering maximum value over time. **Blog categories:** Composability, Customer Data Platform **Blog tags:** CDP, customer data platform, Data quality, Data-in-Place --- ### [Plot a Course for a Superior CX with a Complete Data Strategy](https://www.redpointglobal.com/blog/plot-a-course-for-a-superior-cx-with-a-complete-data-strategy/) **Published:** June 15, 2022 **Author:** Steve Zisk **Content:** Though primitive by the standards of modern navigation techniques, dead reckoning is still required training for would-be pilots. The process of using a known, fixed location and estimates of air speed and heading to plot a course to a chosen destination can be accomplished with little more than a pencil and a compass. By using a continual influx of new data points – a new fixed location, a change in velocity or drift – dead reckoning can be a lifesaver compared with the alternative of “flying blind.” It’s the same with having a data strategy in the realm of customer experience. The data is there and providing continual signals, it’s just a matter of figuring out what the data can tell us in relation to a desired outcome. If you know where you want to go, you just need to know what to look for. Whether the goal is to improve customer lifetime value (CLV), acquisition, retention, etc., once the outcome is known the next steps are to figure out what data is needed to support it, how to organize it, and – once complete – how to determine if it met the standards for success. Just as successful dead reckoning helps other pilots navigate the same route by providing additional fixed data points, a successful data strategy builds on its success by homing in on effective techniques and discarding what didn’t work. ## **Understand the Customer** For any desired outcome, customers leave data points – signals – every time they interact with a brand. One of the two key steps to achieve an intended outcome is to analyze what the data in a customer journey reveals about the propensity to reach that state. If the goal is retention, a brand will need to ask questions of the data related to that specific outcome. What is the customer’s monthly spend, is the customer active in a loyalty program, or did a customer post a product review might be some of the questions that indicate a customer’s propensity to stay with a brand. More detailed analysis might involve comparing a particular customer’s monthly spend with the monthly spend of loyal customers, or with those who churned. Analyzing how a customer proceeds through a customer journey as it pertains to the outcome in question is the customer-focused part of a data strategy. A complete understanding requires analyzing data from every source, recognizing that every bit of data may be important in forming a clear picture of a customer’s likely journey as it unfolds. ## **Understand and Map Data to the Customer Journey** The second component in a data strategy is to look at the data through the lens of the data itself. Is it fit for purpose? If the first component is to understand the customer journey, the second is to ensure that data maps to that understanding. Ensuring that data is ready for prime time, if you will, includes the standard data quality steps such as matching, cleansing, and merging, but also requires checking cadence and availability: Is data smoothly flowing in from every needed source? A high-quality [golden record](https://www.redpointglobal.com/blog/the-role-of-a-golden-record-in-providing-a-consistently-relevant-personalized-cx/) that meets the needs of the business, and is in a cadence that matches the cadence of a customer journey, is fundamental for meeting a customer with a next-best action geared to optimize the preferred outcome. For a simple example, consider a customer journey that includes interactions with customer service and returns. If a journey is optimized for retention, a brand will need connectivity into CRM history, returns process, perhaps even accounting /transaction history from a commerce system to provide reps a real-time view into the journey as it unfolds in order to take an appropriate action. A rep may see one customer more likely to churn than another. With a real-time connectivity and visibility, a rep will know for example which channels and at which frequency to engage with a customer to optimize for the intended outcome. For every possible way of analyzing a customer journey, there is a collection of data points that map to the kinds of information that reveal important details about what’s happening in that journey. There is also a collection of channels – direct and indirect – through which a brand can mediate the action that will propel the journey to its desired conclusion. Direct channels refer to actions and decisions such at what image(s) or content to display to a customer on a branded website, or deciding what advertising to buy that will best influence the desired outcome. Indirect refers more to human-mediated options, such as providing a customer-facing employee – a front-desk clerk, a customer service agent, a healthcare provider – with the needed information to help guide the customer along their journey. ## **Close the Data Loop for Continuous Improvement** A detailed analysis of success metrics is a key step for closing the loop and refining a data strategy. Brands want to be [experimental and innovative](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/) to transform not only a customer’s experience, but also to transform how the brand interacts with customers in the future. This requires a certain nimbleness, a combination of agility – the ability to move quickly – with a firm understanding of what’s happening in the moment and whether or not what’s happening contributes to the direction a brand wishes to go. The difference between mere agility and being nimble requires closing the loop on data by ensuring that all needed data is collected to form a deep understanding of a customer, and that the data is fit for purpose. Any brand is capable of agility, but moving quickly means very little if a brand is moving in the wrong direction, “flying blind” as it were. To plot the right course, a brand must know that every checkpoint is accurate. We’ve written about the [snowball effect](https://www.redpointglobal.com/blog/bad-data-snowball-effect/) in a previous blog, and interestingly enough compounding problems is the biggest drawback of dead reckoning in navigation. Any misunderstanding of a fixed point or a miscalculation of incoming data will skew results. To avoid making this mistake, a complete data strategy requires continually assessing whether a customer journey or journeys are moving to the intended outcome, figuring out what worked and what didn’t in steering them toward that goal, and knowing what data was important to ultimately produce a next-best action. Being nimble presupposes a recognition that a complete data strategy is never fixed, but rather defined by continuous improvement, whether that improvement aligns with the cadence of an individual customer, or at scale for customers with similar journeys. As Maya Angelou once said, “You can’t really know where you are going until you know where you have been.” I doubt she was referring to data quality, but the sentiment most definitely applies. ## Related Content [Trust, But Verify: Make Bold Marketing Decisions with Full Trust in Data Quality](https://www.redpointglobal.com/blog/trust-but-verify-make-bold-marketing-decisions-with-full-confidence-in-data-quality/) [The Role of a Golden Record in Providing a Consistently Relevant, Personalized CX](https://www.redpointglobal.com/blog/the-role-of-a-golden-record-in-providing-a-consistently-relevant-personalized-cx/) [Build a Comprehensive Customer Understanding Through Perfect Data](https://www.redpointglobal.com/blog/build-a-comprehensive-customer-understanding-through-perfect-data/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Identity Resolution --- ### [Personally Identifiable Information: Customer (PII) and the Value Exchange](https://www.redpointglobal.com/blog/personally-identifiable-information-customer-pii-and-the-value-exchange/) **Published:** February 21, 2019 **Author:** John Nash **Content:** ![Value of PII](https://www.redpointglobal.com/wp-content/uploads/2019/02/shutterstock_372396439-e1550256223296.jpg)In November 2018, Marriott International announced an incident where unauthorized access to a Starwood guest reservation database potentially compromised Customer PII of upward of 500 million guests. Marriott later revised the number to 400 million unique guests, with the stolen information including roughly 25 million encrypted and unencrypted passport numbers and 9 million encrypted payment cards. It was the largest known security breach since the General Data Protection Regulation (GDPR) took effect six months earlier. The European Union data protection law – which gives consumers more control over how their data is shared and used – as well as a series of breaches of consumer trust thrust PII into the national discourse. The Starwood incident further underscores the risk and responsibilities companies assume when customers provide PII. Customers understand that they are entrusting valuable information to the companies they interact with, and they expect two things in return. One, that the information is safely guarded. And two, that they receive something of value in exchange. Often, value is defined as a more personalized customer experience than one would otherwise receive without providing PII. ## **The Value of Personal Data & Customer (PII)** In the run-up to GDPR taking effect, Accenture conducted a survey that shed light on the value of PII from the consumer standpoint. Roughly 90 percent of respondents said that they planned to limit access to PII and prevent retailers from selling their information to third parties, a strong indication that consumers are aware of the value of their data. Yet about two-thirds of consumers said that they are willing to share PII in exchange for a perceived value, and one-fourth said they are willing to share PII for better service or the ability to choose which data is shared with third parties. Perceived value can take many forms. From the consumer’s perspective, it could mean being able to log into an online bank account with one less click, a retailer sending cross-channel discount notifications on an item you’ve been researching, or customized product recommendations. For many this perceived value is defined by the level of personalization. In the Sitel 2018 CX Index report, more than 60 percent of respondents said that receiving personalized communications over email, chat, and social media is important. Asked which industry they’re most likely to share personal data for a better customer experience, banking and financial services rated highest (34 percent), with travel and hospitality second (18 percent) and retail fourth (15 percent) – just below “I don’t know.” **A Matter of Trust** The [Financial Brand](https://thefinancialbrand.com/77909/digital-financial-consumer-marketing-trends-banking/) ranked the demand for value in exchange for data as the fourth biggest digital consumer trends for financial marketers in 2019 and identified trust as an important part of the exchange. The report said that this will be the start of a period where “consumers will *only* unveil their interests and personal information in exchange for value.” Consumers place so much value on a frictionless, personalized experience that they seem willing to forgive companies for an unwitting breach. Once trust is broken by a brand, recovery is often dependent on two factors – quickly repairing the trust and providing value that consumers will say is worth the risk of exposed personal information. In the Accenture survey cited above, four of 10 consumers said that their trust in a brand or company goes *up* when a breach is handled swiftly and correctly. And eight in 10 said that trust is a key driver of brand loyalty. This tells us that consumers will give the brands they like the benefit of the doubt when it comes to protecting [PII](https://www.redpointglobal.com/blog/ease-distrust-in-advertising-with-data-clean-rooms-pii-vaults/) if it’s used to enhance the customer experience. **Fragmentation, Friction, and Frustration** What customers ultimately mean when they talk about personalization is a desire to be recognized across an omnichannel journey. A customer who makes an in-store visit may expect a retailer to know about online research that preceded the visit. Or if a customer places a phone call order for a provisioned smartphone, there is often an expectation to pick up the phone at a local carrier the next day. These omnichannel experiences are not possible without the information exchange. Customers understand that enabling that seamless experience across all interaction touchpoints requires the sharing of personal data, and they will make the exchange because doing so reduces friction in the customer experience. Fragmented systems and siloed customer data impede the ability to provide the frictionless customer experience that consumers expect. Consumers faced with friction are less likely to entrust a brand, retailer, or banking institution with PII. Conversely, companies that eliminate fragmentation will be better positioned to deliver the value that their customers expect, and in return will have a greater chance of being entrusted with PII. **A Single Source of Customer Truth** Eliminating fragmentation with a unified customer profile and single point of control over customer data reduces friction by allowing a company to move at the cadence of the customer in an omnichannel journey. Whether in banking, retail, hospitality, or another industry, customers no longer move in a static, linear path. A company armed with a golden record – a continuously updated and unified customer profile – can recognize a customer’s buying patterns and preferences and can proactively engage with the customer with a relevant experience across any channel. Brands that can provide this level of personalization are raising the stakes for what consumers will demand in exchange for PII. With GDPR and other data privacy regulations taking hold, the consumer is more aware than ever that personal data is the new currency, and they are more aware that they are empowered to share and use data as they see fit. Consumers are telling us what the cost is for their business. Is your company listening? **RELATED ARTICLE(S)** [How to Personalize Retail Interactions Without Being Creepy](https://www.redpointglobal.com/blog/how-to-personalize-retail-interactions-without-being-creepy/) ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Data Quality, Real-Time Personalization --- ### [How to Personalize Retail Interactions Without Being Creepy](https://www.redpointglobal.com/blog/how-to-personalize-retail-interactions-without-being-creepy/) **Published:** July 25, 2018 **Author:** John Nash **Content:** The line between a positive brand experience and a creepy one is easy to miss, especially for marketers focused on personalizing customer engagement. Brands collect substantial amounts of consumer data to deliver individualized experiences; with that data comes the challenge of ensuring they stay on the correct side of the line between creepy and personalized. As brands establish trust and provide added value to consumers over the long term, customers start to share more data and the symbiotic cycle continues. When retailers violate the unspoken contract of that relationship by crossing the line between personalized and creepy, customers no longer see value in continuing to interact. This is further complicated by how few consumer interactions are personalized; only 12 percent [according to one study](https://www.gartner.com/smarterwithgartner/gartner-cmo-spend-survey-2016-2017-shows-marketing-budgets-continue-to-climb/). Brands seeking to increase that number must understand that they are building a long-term relationship with consumers and base that relationship on trust, transparency, and a mutual value exchange. ### **Retail Personalization Is a Value Exchange** Personalizing the customer experience comes down to a question of trust and a question of value. Consumers need to trust that retailers will keep their personal data secure, and they need to receive something of value in return for their information. This value exchange is a two-way engagement where customers provide their data and retailers provide personalized offers and messages. Retailers must build the trust that underlies this exchange of value over time; [79 percent of customers](https://www.wantedness.com) want brands to understand and care about “me,” so taking the time to build consumer trust is valuable. This desire for brands to care about their customers is why Apple has become one of the world’s most trusted brands. Apple is highly customer-centric in their product design and product security, and as a result has a high number of loyal customers. Apple is consistently ranked highly on lists of the most trusted brands because of this approach, which they have maintained for years. They have amassed a substantial amount of information on their customers as well, but their brand reputation is such that consumers trust them to use that personal data wisely. Retailers may not have the same devoted fan base as Apple, but there are other ways to build trust. One of the most direct is through being transparent about what consumer data is collected and why. One recent study found that [83 percent of consumers](https://www.accenture.com/t20180503T034117Z__w__/us-en/_acnmedia/PDF-77/Accenture-Pulse-Survey.pdf#zoom=50) will share their data in exchange for a personalized experience, but only if businesses are transparent about how the data is used and that customers retain control of their information. This is important as a basis for customers learning to trust the brand; once consumers receive something valuable in return for their personal data, retailers can ask for more information over time to build a richer profile. ### **Think of Minimums, Not Maximums** Marketers traditionally tried to collect as much data on consumers as possible in the initial interaction. This approach to data collection will not work in the current data privacy environment, with consumers increasingly concerned about keeping their personal information secure. Retailers still need to collect data to personalize their marketing, however, which requires deciding what data is truly necessary to collect. Marketers should be thinking about the minimum amount of data needed, as opposed to trying to capture all data right away. Brands must realize that some consumers might not want to share everything about themselves right up front. Spotify, for example, has different levels of permission that grow and change over time through usage of the service. They collect the least amount of information needed for each phase, adding progressively more insight into the customer’s needs, wants, and desires at each stage. At each point in the process, customers can decide what information they are willing to share to receive a more personalized experience. Through collecting data a little at a time, and providing value along the way, brands show that they are interested in maintaining the customer relationship. This is often an unusual approach, but allows customers to create their own path to purchase as opposed to traveling a pre-defined journey from awareness to sale. It also leads into brands building a golden customer record over time that can remain consistently updated as new information is added. ### **Ignore Context at Your Peril** The line between creepy and timely depends heavily on context and cadence. What customers consider creepy in one context may not be viewed that way in another. One example is sending out abandoned cart notifications. This is a best practice for engagement but, in some contexts, that can be considered overly intrusive – such as if the brand sends out the abandoned cart notification too quickly after the consumer leaves the website. Retailers need to be aware of how their engagement tactics are perceived and take customer preferences into account. According to [one recent study](https://www.accenture.com/t20180503T034117Z__w__/nl-en/_acnmedia/PDF-77/Accenture-Pulse-Survey.pdf), the three creepiest engagement tactics are: - 41 percent – Text from a brand or retailer when walking by a store - 40 percent – Mobile notification after walking by a store - 35 percent – Ads on social site for items browsed on a brand website To fully understand consumer preferences, retailers need to unify customer data into a single point of control. That is often achieved with a customer data platform (CDP), a solution that provides an always-on and always-processing single view of the customer – often called a “golden record” – across the organization. With customer data unified and accessible, retailers can readily participate in a value exchange with their customers. The underlying insight into the customer’s needs, wants, and desires then must be leveraged in orchestrating personalized interactions across channels of engagement. These communications must be done at the proper cadence, which is why unified data is so crucial. Retailers need to personalize customer interactions to drive increased revenue over time, but they also must remain aware of how best to use the data they collect. If they can be explicit in what data they collect, and transparent in terms of how they plan to use that information, they can then build long-term trust with consumers. As consumers trust retailers more readily, they will share more data and continue the value exchange to benefit both sides of the relationship. **Blog categories:** 1:1 Personalization, Data Management, Real-Time Personalization, Retail, Segmentation & Activation --- ### [Beyond Safety in Travel & Hospitality: How to Grow through Personalized Experiences](https://www.redpointglobal.com/blog/beyond-safety-in-travel-hospitality-how-to-grow-through-personalized-experiences/) **Published:** September 4, 2020 **Author:** John Nash **Content:** After withstanding disruptive business models introduced by Airbnb, Priceline and many others, the travel & hospitality industry rebounded only to be shocked again by this year’s pandemic. The industry potentially faces a long recovery, as McKinsey [estimates](https://www.mckinsey.com/industries/travel-logistics-and-transport-infrastructure/our-insights/hospitality-and-covid-19-how-long-until-no-vacancy-for-us-hotels) that it will take until 2023 for the hotel industry to return to pre-pandemic occupancy rates. Encouraging short term signs of a recovery for the travel and hospitality industry are tempered somewhat by year-over-year comparisons indicating that a full recovery to pre-pandemic levels may be a slow climb. According to the [TSA throughput report](https://www.tsa.gov/coronavirus/passenger-throughput), TSA screenings reached a pandemic peak of 863,000 daily airline travelers on August 16, capping four straight weeks of an increase. But a seven-day average of 718,000 travelers (through August 18) was 71 percent lower than in the same period for 2019. Likewise, the U.S. hotel occupancy rate is climbing, although it is still significantly lower than 2019 levels. According to a [Statista accommodation report](https://www.statista.com/statistics/1109880/coronavirus-hotel-sector-kpis/), hotel occupancy rate was 50.2 percent for the week ending Aug. 15. This was a 3 percent increase over July, but represented a 30 percent year-over-year decrease. There were also significant year-over-year decreases in average daily rate (23 percent drop) and revenue per available room (46 percent). Demand will eventually return, but how will consumer expectations change? Certainly, consumers will now expect their basic health and safety needs to be met in improved ways. But those measure will be viewed as table stakes a year from now, so how can brands create the types of customer experiences that stand out – a connected customer experience (CX) that captures more than their fair share of the market. ## **A Superior CX as the New Normal in Travel & Hospitality Experience** A slow return to customary travel patterns will likely mean fierce industry competition. Particularly with excess capacity and thin margins, the best way to gain an advantage is to provide a superior CX. Consumer expectations are rising as they see improvements in other industries – from telehealth to buy online pick-up in-store (BOPIS). Travel has the opportunity to create new connected experiences across pre-visit, visit and post-visit journeys, experiences that are highly personalized to the moment – in real time – and frictionless. Contactless hotel check-ins and other digital amenities known as [“touchless tech”](https://www.phocuswire.com/contactless-technology-solutions-hotels-covid-19) already exist, but digital transformation has just been accelerated by a matter of years. A truly differentiated experience will require that new connected experiences be customized to each customer’s preferences, based on their unique profile and journey with a brand. ## **Truly Differentiate by Knowing Everything About a Guest** Effective touchless tech that personalizes a digital experience with relevance in the context of each interaction will not only be welcomed for the enhanced safety and convenience, it will also drive new revenue because it aligns with the type of seamless, digital CX that today’s always-on, connected consumers demand. In a [2019 Harris Poll](https://www.redpointglobal.com/wp-content/uploads/2020/07/Addressing-the-Gaps-in-Customer-Experience.pdf) commissioned by Redpoint, for example, 63 percent of consumers surveyed said that personalization is a standard service they expect – and 37 percent said they will flat out not do business with a company that fails to offer a personalized experience. How, then, would a touchless experience in travel and hospitality satisfy this expectation beyond soon-to-be standard digital amenities? Some hotels, for example, are currently offering a [completely digital experience](https://www.boston.com/travel/travel/2020/06/24/contactless-stays-boston-hotel) where a guest is able to use a mobile app to pre-select a room, check in and out, control temperature, order room service, request extra in-room amenities and even use as a remote control for in-room entertainment. An even more personalized experience could include geo-fencing technology to alert the hotel when a guest is en route and preparing the room to the guest’s specific preferences for room temperature, number of towels or favorite thread count. Or sending an SMS to let the guest know the best available parking spot closest to the room. The hotel could also send push notifications letting a guest know available reservation times at a favorite local restaurant. The crux of providing a seamless CX is recognizing a customer across all channels and devices, and knowing everything there is to know about the customer to enable a brand to either respond or proactively engage in real time. Using geo-location technology, for example, shows why a real-time capability is so important; a brand must be able to engage in the context of a unique customer journey. Integration, too, is vital, which speaks to knowing everything there is to know about a customer across channels. If a guest using the app calls the front desk, a desk clerk with immediate access to all data will likely avoid introducing friction into the guest experience – and will be far more prepared to interact with relevance. ## **A Single Customer View & Real-Time Decisioning** Providing a relevant, personalized omnichannel experience that drives revenue requires breaking down all data siloes. A brand must have a single customer view that unifies customer data from every source and of every type. One differentiator of the [Redpoint Golden Record](https://www.redpointglobal.com/single-customer-view/)™ is that the single customer view is updated in real time; there is no latency between when data is collected and when it is used to orchestrate a next-best action that is always in sync with a customer’s journey. This real-time decisioning engine is the heart of [rg1](https://www.redpointglobal.com/one-platform/), the Redpoint digital experience platform with built-in automated machine learning that allows brands to personalize customer experiences at scale. Providing a single point of control over data, decisions and interactions, the platform intelligently orchestrates a next-best action for an individual customer journey. Every decision is optimized to positively influence a journey the moment it is rendered, calculating every interaction, transaction, behavior and preference that preceded it. Advanced identity resolution enables a brand to recognize a customer across every device and ID, ensuring that a seamless, personalized experience is consistent across all channels and devices. ## **A Single Point of Control in Action** [Xanterra Travel Collection](https://www.youtube.com/watch?v=r3w7_y64c_0&feature=youtu.be), which owns or manages 34 hotels (5,600 rooms) among a diversified portfolio of destination experiences across the world, uses rg1 to consolidate customer data from more than 100 data sources. With the Redpoint Golden Record, Xanterra creates a consistent guest experience for its more than 25 million annual customers, managing and automating onboarding, nurturing and conversions through managing an entire experience (pre-arrival, during and after each visit). Andrew Heltzel, Director of Marketing & CRM at Xanterra Travel Collection, attributes rg1 with helping the company deliver a uniquely consistent experience across multiple brands. The timing and cadence, he said, are “lined up perfectly to not overlap or be in conflict with each other,” despite orchestrating millions of customer journeys annually, and even with many guests on multiple journeys at the same time with different brands. “Having that comprehensive view and putting that optimized campaign or journey in place has really significantly helped deliver a better guest experience,” Heltzel said. **Blog categories:** Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View, Travel & Hospitality --- ### [Healthcare Consumers Weigh In: Personalized Experiences are a Must](https://www.redpointglobal.com/blog/healthcare-consumers-weigh-in-personalized-experiences-are-a-must/) **Published:** December 7, 2021 **Author:** John Nash **Content:** How important is personalization in healthcare? A survey that Redpoint Global conducted with [Dynata](https://www.redpointglobal.com/press-releases/80-of-patients-prefer-to-use-digital-communication-to-interact-with-healthcare-providers-and-brands/) of more than 1,000 US healthcare consumers, reveals that providers, insurers and others in the healthcare ecosystem who downplay the importance of personalizing the healthcare experience do so at their peril. A majority of consumers surveyed (60 percent) said that they would choose a provider based on how well the provider understands them, beyond basic patient data, so that the experience is relevant and personalized. Furthermore, 66 percent said that selecting a provider was dependent on the provider’s ability to communicate to them in a timely and consistent manner. As in other industries such as retail, travel and hospitality, and banking, healthcare consumers increasingly demand that organizations recognize them at an individual level across a full spectrum of engagement touchpoints and channels, and that they exhibit a deep personal understanding. Thirty-nine percent of respondents reported that a personal understanding was an expectation they had when engaging with their providers. Examples of a personal understanding include an expectation that a healthcare provider proactively contact a patient – at the right time and in the right context – to improve wellness and care (cited by 44 percent of respondents). Also, 36 percent said that they expect communications from a provider to match in-person experiences in terms of relevance, consistency and outcomes. Asked what is most frustrating when communicating with a healthcare provider, 17 percent cited a lack of personalization and patient understanding – ranking second among seven choices behind only limited doctor availability/slow response times (34 percent). ## **Digital Acceleration in Healthcare** Diving deeper into consumer expectations for consistent, relevant communications that match their in-person experiences, a majority of respondents (80 percent) said that they prefer to use digital channels (online messaging, virtual appointment, text, etc.) to communicate with healthcare providers and brands at least some of the time and 44 percent prefer digital communications the majority of the time. COVID-era influenced, digital-first communications are now the expectation and will become the standard for healthcare experiences moving forward. Over the last 18 months, 65 percent of respondents reported using telehealth and 34 percent say they want to regularly continue to do so. The results reveal an enormous cultural shift in the delivery of healthcare services. Digital acceleration, combined with solidifying expectations for personalization, pressure healthcare organizations to think strategically about patient data, [omnichannel communication](https://www.redpointglobal.com/blog/it-is-time-to-act-as-one-for-the-benefit-of-the-healthcare-consumer/) and holistic, personalized patient engagements. Examples abound of forward-looking, data-driven healthcare organizations beginning to deliver [patient-centric experiences](https://www.redpointglobal.com/blog/align-the-healthcare-experience-around-the-consumer-how-an-omnichannel-cx-delivers-improved-health-outcomes/) that deliver the personalization and understanding that healthcare consumers expect, with improved outcomes and lower costs. Additional survey results include: - A majority (57 percent) of healthcare consumers think retailers and/or financial services are better at providing personalized omnichannel experiences than healthcare - 29 percent said they expect frictionless check-in experiences across apps/phone calls/in-office - 34 percent expect data inputs in a healthcare portal (health history, surveys, insurance information, etc.) to reach providers - 24 percent of respondents said they did not utilize any sort of digital communication with providers during the pandemic - 14 percent said they had no contact with any healthcare provider during the same timeframe For more information about how Redpoint helps healthcare organizations deliver personalized omnichannel experiences that drive better outcomes, visit [Redpoint for Healthcare](https://www.linkedin.com/showcase/redpoint-for-healthcare/) on LinkedIn, or request a demo at the [Healthcare homepage](https://www.redpointglobal.com/healthcare/) on redpointglobal.com. **Blog categories:** Healthcare --- ### [Personalization Scores A Big Hit in Super Bowl LIV Commercials](https://www.redpointglobal.com/blog/personalization-scores-a-big-hit-in-super-bowl-liv-commercials/) **Published:** February 4, 2020 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/02/2-4-SB-blog-image-300x200.jpg)Three consecutive touchdown drives to overcome a 10-point deficit in the fourth quarter propelled the Chiefs to their first Super Bowl win in 50 years with a 31-20 victory over the 49ers Sunday, and in the process cemented Patrick Mahomes’ legacy as the NFL’s best quarterback. Of course, for many, the action off the field is just as compelling as the game itself, and this year was no exception. The electric all-Latina halftime show featuring [JLo and Shakira](https://www.cnn.com/2020/02/03/opinions/super-bowl-halftime-show-jlo-shakira-opinion-reyes/index.html) seemed to spark just as much discussion as the [“third-and-forever”](https://www.nytimes.com/2020/02/02/sports/football/super-bowl-mahomes-big-play.html) completion that changed the course of the game. Of course, no Super Bowl recap is complete without rehashing the commercials – what “scored” and what flopped. Here in Massachusetts, everyone is trying to read the tea leaves on the [Tom Brady Hulu ad](https://www.espn.com/nfl/story/_/id/28622447/tom-brady-says-not-going-anywhere-super-bowl-commercial) and weighing in on the “wicked” clever – clevah? – [Hyundai ad](https://www.youtube.com/watch?v=85iRQdjCzj0) for the self-parking Sonata. According to [Google trends](https://www.wfmynews2.com/article/sports/nfl/superbowl/the-top-5-trending-super-bowl-ads-according-to-google/83-92a96772-6f57-4ee3-a616-d27cd114cd31), the top five trending commercials during the game were Bill Murray reprising his role in *Groundhog Day* in an ad for Jeep, J Lo pulling double duty as performer and pitch woman in ad for Hard Rock, the NFL featuring a kid ballcarrier running past a who’s who of stars past and present, Geico’s Pinocchio bit with Joe Buck, and – the No. 1 trending ad – Sofia Vergara pitching Bounty (and a half dozen other Proctor & Gamble brands). The spot, which featured a [chili clean-up party](https://www.youtube.com/watch?v=CvUDuu58zbo), generated a ton of [pre-game hype](https://variety.com/2020/tv/news/super-bowl-commercials-2020-procter-gamble-rob-riggle-sofia-vergara-1203486753/) as a creation based almost entirely on input from customers. **Going “All Out” to Deliver Experience** The CPG giant’s 60-second spot – which cost roughly $11.2 million – is yet more in a growing body of evidence of brands’ laser focus on the empowered consumer, and a recognition that the customer demands a personalized experience across every interaction with a brand. While a brand can’t very well personalize a Super Bowl ad that reaches roughly 100 million people, P&G ceding creative control to the customer validates the notion that customer experience is king. Walmart’s [“Famous Visitors” ad](https://www.youtube.com/watch?v=suVwYyIe1nY) solicited help from a host of fictional characters from 70s, 80s, and 90s sci-fi movies – *Star Wars*, *Bill & Ted’s Excellent Adventure*, and *Men in Black*, to name a few – to showcase its “out of this world” curbside pickup grocery service. An offshoot of buy online, pick-up in-store [(BOPIS)](https://www.redpointglobal.com/blog/if-your-cx-strategy-doesnt-include-bopis-youre-doing-it-wrong/), curbside pickup is not new (Walmart ran a similar ad during last year’s Super Bowl) but it is definitely a growing channel, with one estimate that it will be a [$35 billion business this year](https://www.forbes.com/sites/pamdanziger/2019/04/07/walmart-is-in-the-lead-in-the-soon-to-be-35-billion-curbside-pickup-market/#64dd15ef199e), including $7.4 billion to Walmart (representing 33 percent of its digital sales). The service is more confirmation that brands now compete on customer experience, with personalization as a core component. In the Walmart service, for instance, the white glove treatment is made possible with the customer providing the retailer with reams of data, which the retailer uses to make the experience as seamless as possible, both when picking up the items and for all future online orders. Personalization touches includes remembering orders, serving up customized recommendations and reminders, and timely offers and discounts – as well as giving the customer control of the process to include choosing a pick-up time. **Keeping Pace with the Customer** The key for brands to create a seamless, hyper-personalized experience is data. To provide customers with the level of personalization they have come to expect, Walmart and other retailers must know everything there is to know about a customer. One Redpoint client, for example, a [do-it-yourself retailer](https://www.redpointglobal.com/wp-content/uploads/2019/05/diy-retailer-case-study.pdf), uses a single customer view (ingesting data 38 sources) to supercharge a state of the art BOPIS service. Within no more than five minutes after a customer places an online order, the retailer is ready to queue up a personalized offer across any device. With Redpoint, the client enjoys a 99 percent cycle time compression from data to action, giving it the ability to keep pace with the customer with a next-best action throughout the customer journey. As if to drive the point home, the closing tagline of the Walmart commercial is “free pickup on everything *for your journey*”, which is shown onscreen just as the hover vehicle from *Blade Runner 2049* takes off. The double entendre tagline is not lost on brands who in 2020 must know who their customers are, and deliver personalization, across any device and any channel. Now, about that Tom Brady ad … **RELATED CONTENT** [Super Bowl Commercial Scorecard: Which Brands Scored a Touchdown](https://www.redpointglobal.com/blog/super-bowl-commercial-scorecard-which-brands-scored-a-touchdown/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** AI & Machine Learning, Customer Data Platform, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Anonymous-to-Known Identity Resolution and First-Touch Personalization: A Retailer Win-Win](https://www.redpointglobal.com/blog/anonymous-to-known-identity-resolution-and-first-touch-personalization-a-retailer-win-win/) **Published:** January 28, 2025 **Author:** Renee Graff **Content:** Data-driven retailers know that customers expect personalization, and they will readily switch brands if their expectation is not met. In a recent [McKinsey study](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying), 71 percent of consumers said they expect a personalized customer experience (CX) every time they interact with a brand, and 76 percent are frustrated when this does not happen. Consumers make no distinction between a brand providing a personalized CX whether the customer is known to the brand or not. That is, both loyal customers and first-time visitors to the website expect a brand to make good on the promise of personalization. Retailers cannot afford to wait until a customer is known, in other words, before initiating a differentiated experience through personalization. According to [Storybloc](https://www.prnewswire.com/news-releases/60-of-consumers-abandon-purchases-due-to-poor-website-user-experience-costing-e-commerce-companies-billions-301706784.html), a staggering 60 percent of consumers will abandon a purchase due to a poor user experience on a website – experiences like providing irrelevant information or not understanding customer intent. The ability to provide a consistent, personalized CX across an anonymous-to-known customer journey in an effort to reduce churn and increase revenue is a key reason retailers implement customer data platforms (CDPs) as a foundational component of a MarTech stack. ## **What is an Anonymous Customer/Prospect?** A digitally anonymous customer or prospect is a person that visits a website, contacts a call center, engages a chatbot, etc., without providing any identifying information. They are typically represented by a first-party cookie ID and device information. Their digital footprint will not have any PII and may or may not be attached to a digital history – a first-time vs. repeat visitor to the website, for example. Once that digital footprint is created at the user’s first interaction, a brand can begin to attach signals to the anonymous ID such as page views, time on page, click-throughs, etc. Over time, the goal is to connect those signals to a known ID. Converting an anonymous customer ID to a known customer can be done many ways, such as through linking a credit card number used in a purchase to a known customer, when a customer fills out a form to receive a discount or to sign up for a loyalty program, or by matching a phone number, an address, or even the unique device ID to devices associated with a known customer (or household) at the same IP address. ## **What is Anonymous Personalization?** Using the example of a first-time website visitor, anonymous personalization might include showing increasingly relevant content based on how a customer (a device ID) navigates and engages with the website. A brand can build an affinity score based on pages viewed, items clicked, time on page, etc., and begin to provide a personalized experience while the online session is ongoing, such as dynamically changing out a hero image or refining product recommendations. As an example, a shopper clicking on black cotton crewneck sweaters could start to see some suggested black or gray cashmere sweaters, V-necks, or cardigans. Even before any signals are attached to the device ID, truly anonymous personalization can include performing split a/b testing on a landing page hero image, determining which image is most successful in reaching a desired outcome (sign-ups, purchases, clicks, etc.), and then using that image for future first-time visitors while continually testing different images. Anonymous mobile app visitors might receive a personalized experience if they’ve opted in to location sharing, which might then trigger notifications about a local store opening. An increasingly personalized CX becomes possible for brands that collect and analyze initially anonymous signals, and takes shape as the digital footprint expands beyond the first click or interaction with an in-store kiosk, chatbot interaction, call center, etc. ## **Anonymous Personalization with an AI Assist** Anonymous personalization can also be achieved using AI models to segment an audience based on a desired metric or outcome. Whichever way the audience divides according to the algorithm will determine the content anonymous visitors receive. Consider a model that analyzes what landing pages a group of anonymous visitors looked at, how long they interacted with a page or what products they viewed. A first-time visitor could be placed into a certain segment based on commonalities between their own expanding digital footprint and those of previous first-time visitors, and receive content that is personalized for that segment. Dynamic models can self-optimize on the fly during a browsing session. If a model predicts a visitor is on the path toward signing up for a loyalty program, the visitor may be presented with content relevant to loyalty members. ## **Anonymous to Known and a Golden Record** When an anonymous record becomes known, the digital footprint and all the signals that are attached to that footprint combine to join or build a Golden Record, a single customer view that includes all there is to know about a customer. If the anonymous record belongs to an existing customer for whom there is already a Golden Record, the signals become part of that unified record. Anonymous-to-known identity resolution and data quality are core functionality in an enterprise-grade CDP, and is a differentiating feature for Redpoint, unlike many other composable CDPs that either do not store data, or provide only basic downstream identity resolution prior to data activation. An enterprise-grade CDP should perform data quality processes as soon as data enters the system. When advanced identity resolution using probabilistic and deterministic matching takes place continuously, the resulting Golden Record will reflect a real-time understanding of a customer. Another key feature important for relying on the accuracy of a Golden Record is the use of persistent keys, which provides retailers with a longitudinal view of a customer even amid frequent changes such as a new address, email, last name, etc. The use of persistent keys is crucial for providing consistent personalization across an anonymous-to-known customer journey. ## **Differentiate with AI and Dynamic Segmentation** Dynamic segmentation using AI is another key feature of an enterprise-grade CDP, providing retailers with a predictive analytics framework that allows them to engage a customer with a relevant CX across the span of an omnichannel customer journey. Whenever or wherever a customer chooses to engage, dynamic segmentation is a key feature to ensure retailers deliver a next-best action in the cadence of the journey. A consistency of interactions as the customer proceeds along an unknown to known journey is important for building trust, providing value, and eventually driving revenue through increased loyalty and lifetime value. ## **Advance on Your Personalization Roadmap** With the Redpoint CDP, leading retailers have the needed tools to advance personalization build-out beginning when a customer presents as anonymous, ensuring a consistent CX that meets or exceeds customer expectations for relevance, even when presenting as unknown. For more on how Redpoint helps retailers ignite their customer data and provide a personalized CX across an anonymous-to-known customer journey, click [here](https://www.redpointglobal.com/retail/). **Blog categories:** Retail **Blog tags:** identity resolution --- ### [Perfect Data, Creative Marketing: A Cyclical Flow of Excellence](https://www.redpointglobal.com/blog/perfect-data-creative-marketing-a-cyclical-flow-of-excellence/) **Published:** August 4, 2021 **Author:** John Nash **Content:** Like a tangled garden hose that slows a steady flow of water to a trickle, unruly and poorly managed business data produces substandard outcomes. And identifying the root cause of bad data can be as frustrating as trying to untangle a hose, unsure which end to pull to produce a clean, steady, trustworthy stream. For marketers intent on creating an uninterrupted output of hyper-personalized customer experiences across all channels, in real time and at the cadence of an individual customer journey, any impediment has a deleterious, cascading effect. Bad data leads to poor segmentation, which leads to inferior campaign design and journeys, which in turn leads to shoddy execution. Alas, many marketers are all too familiar with the problem. Consider the findings from independent research firm Vanson Bourne from earlier this year. In [*The State of Data Management – The Impact of Data Distrust*](https://www.snaplogic.com/resources/research/state-of-data-management-impact-of-data-distrust)*,* 91 percent of IT decision makers agree they need to improve the quality of data in the organization, with 77 percent saying they lack trust in their business data. The offshoot is predictable; 76 percent said they are missing out on revenue opportunities, and 72 percent see a negative impact on customer engagement due to a lack of timely data insights. From an operational standpoint, data wrangling is also an enormous drain on creativity. The same report found that for every employee that works with data an average of four working hours is lost each week trying to resolve issues related to data prep. An older study from [Harvard Business Review](https://hbr.org/2013/12/datas-credibility-problem) had similar findings, determining that employees who work with data waste up to 50 percent of their time hunting for data, identifying and correcting errors, and seeking confirmatory sources for data they do not trust. ## **Bad Data, Bad Outcomes** What predictably happens is that marketers reluctantly accept a trade-off; with looming deadlines for segments to build and campaigns to design and execute, they move forward with less-than-perfect data knowing full well that some experiences will miss the mark. On the segmentation side, improperly matched data or substandard data governance will lead to inconsistent segment counts as different marketing teams may access different datasets. Segments will also be inaccurate, with customers improperly placed in segments that are not truly representative of what matters most to them. In addition, poor data quality prevents marketers from adding attributes to segment on; without a quality view of the customer, it is difficult to build unsupervised machine learning models where the marketer provides data and asks the model to find meaningful patterns. The same holds true for a supervised model, where marketers provide historical data for a model to find a predetermined attribute such as customer lifetime value or churn probability. In both cases, if the data is off the outcome cannot be trusted. That lack of trust become insidious; if a marketer can’t trust the accuracy of a churn probability, for instance, then what-if analysis that depends on the accuracy of the model becomes flawed all the way through to campaign design and execution. If the accuracy is flawed, potential trigger points such as days since last purchase or 30-day average spend become skewed, leading to a customer who may incorrectly be identified as a churn candidate receiving an incorrect or poorly timed message. ## **Timing is Everything** For interactive journey design and other situations that call for real-time decisions, the consequences of working with bad data are just as harmful. Think of a typical online session for a customer browsing a brand’s website. Whether an anonymous or known journey, a marketer must have confidence that a unified customer profile is completely accurate and up-to-date in order to support relevant engagement while the session is ongoing. Data is being collected as the session continues, and unless it is cleansed, matched and merged within milliseconds of it being ingested a marketer will fall behind the customer journey. The longer the lag, the greater the distance and the greater the chance to deliver the wrong message, content or action. For any type of situation that demands a real-time response – a call center engagement, a product recommendation engine, shopping cart abandonment, etc. – marketers do not have the luxury of playing out in advance all the permutations of a customer’s potential behaviors. Being perfectly responsive to a unique customer’s needs in the moment depends on trustworthy data. The alternative is to pull back on design options; instead of a personalized message or action based on the unique customer journey, a marketer works with a handful of options or paths, hoping that “good enough” carries the day. ## **Good Data, Better Decisions, Optimal Outcomes** With the cost of bad data becoming more clear, it stands to reason that perfect data elevates trust and confidence, saves wasted time and helps marketers tap into more creative and innovative design strategies, deeper analysis, better experimentation and more reliable execution. On the segmentation side, when data quality processes are completed at ingestion, new attributes can be created on-the-fly because with a more granular view of the customer, automated machine learning models detect more meaningful patterns in the behavior of a customer or group of customers. Segmenting off more attributes instills more confidence in the accuracy of predictions. Whether it’s CLV, churn potential of another variable, having more attributes takes out the guesswork. Flowing into campaign design, marketers can then base campaigns on any number of new triggers – in real time or otherwise – with full confidence that each customer’s path is optimized to a unique customer journey at the precise cadence. This will then of course translate to flawless execution, with a next-best action optimized for relevance for a specific customer, delivered at precisely the right moment. Ultimately, ensuring data quality at the point of ingestion creates a closed-loop system that leads to higher levels of trust and higher data quality; segmentation, campaign design and execution that stem from high-quality data generate better responses and/or more intended outcomes, whether higher CLV, reduced churn, more purchases, higher average spend, etc. Higher-quality responses, in turn, lead to improved segmentation and the cycle continues, with the result being more perfect customer experiences for more customers. The idea that comprehensive data quality and accurate identity resolution at the moment of data ingestion are the foundations of delivering superior customer experiences was the driving force behind the Redpoint CDP, a cloud-native CDP that delivers perfected data and resolved identities in real time, using exclusively first-party data, where the data resides. Because when 77 percent of decision makers say they don’t trust their business data, and 72 percent see a negative impact on customer engagement due to a lack of timely data insights, there has to be a better way. Every minute a marketer spends resolving issues related to data prep is a minute lost to a marketer’s ability to experiment with creative new ways to engage with a customer. Perfect data removes the all too familiar obstructions, letting marketers cultivate continually improving, highly relevant experiences with trust in the data, trust in the process and trust in the result. **Blog categories:** Data Quality, Identity Resolution, Journey Orchestration, Real-Time Personalization, Segmentation & Activation **Blog tags:** customer data platform --- ### [Personalized Patient Experience – The New Competitive Battleground for Healthcare](https://www.redpointglobal.com/blog/personalized-patient-experience-the-new-competitive-battleground-for-healthcare/) **Published:** May 25, 2023 **Author:** Steve Zisk **Content:** The pressing need to deliver a patient-centric healthcare experience was a main topic of last month’s Healthcare Information and Management Systems Society (HIMSS) conference in Chicago, where 40,000+ healthcare professionals gathered to discuss the latest in health information technology and trends. Generative AI and interoperability also took center stage, to some degree in terms of how they help support a patient-centric experience. Many of the comments from organizations attending the conference speak to the common healthcare challenge of being unable to deliver a personalized, patient-centric experience due to siloed systems or the impracticality of extracting insights from data. - “Fragmented data is our biggest challenge.” - “We have data, but we’re not able to harness it in real time.” - “We’re sitting on (a lot of) data, but it’s dirty.” - “The curation and harmonization of data in real time is critical…. Or we cause more harm than good.” - “We practice medicine as a snapshot, rather than longitudinally.” - “70% of EMR (data) is lab data, but we don’t do anything with it.” A central theme emanating from the event was that digital transformation is the key to solving data-related challenges and unlocking patient experience opportunities that span the healthcare journey. ## **A Superior Patient Experience is a Must-Have** Why is personalizing the patient experience top of mind in 2023? One reason is because the Centers for Medicare & Medicaid Services (CMS) [doubled the weight](https://medcitynews.com/2022/11/medicare-advantage-star-ratings-consumer-experience-in-2023/) of patient experience metrics to account for 57 percent of overall Star Ratings, affecting payer reimbursements as well as the ability of Medicare Advantage plans to attract and retain members. Member experience, then, is becoming a competitive battleground to attract the roughly 10,000 Americans each day becoming Medicare eligible. Another reason for the focus on patient experience is to reduce inefficiencies, such as the traditionally poor patient understanding of discharge instructions. One study from [National Library of Medicine](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8442096/) (NLM) found that difficult-to-understand instructions lead to poor outcomes, such as a noncompliance with follow-up visits of up to 67 percent. Using a one-page simplified information page tailored to an individual patient’s reading level and other personalization techniques (using pictographs, factoring in social determinants of health, etc.) helped increase patient understanding of discharge instructions by 22 percent, as measured by patient scoring. Finally, improving patient experience is becoming a top-line item because patients demand it. In a 2022 [Dynata survey](https://www.redpointglobal.com/press-releases/81-of-consumers-say-a-good-patient-experience-is-very-important-when-interacting-with-healthcare-providers/), 81 percent of healthcare consumers said that a good patient experience is “very important” when interacting with providers and health plans. In fact, more than half (57 percent) said the No. 1 aspect to consider when choosing a provider or insurer is how well they understand them as an individual beyond basic patient data. ## **More Than a Single View** The multiple incentives to improve the patient experience underline the pressing need for healthcare organizations to implement a customer data platform (CDP) to solve the data challenges that were a topic of discussion at the HIMSS event: fragmented data, data that is not fit-for-purpose and the inability to harness data in real time. The overarching requirement to deliver a patient-centric experience is to develop a single view of the healthcare consumer. A single view unlocks the personalized experiences that drive retention, revenue growth and patient satisfaction because it provides the basis for an organization to deliver a consistent experience across all channels and interaction touchpoints. A single view of the healthcare consumer entails more than simply compiling patient data from multiple sources. Storing all patient data in a data lake until it’s needed is not a single view because this approach neither resolves fragmented data into a single identity nor does it enable an organization to keep pace with a patient or healthcare consumer in real time throughout their journey. Instead, for a single patient view to be a true single source of truth for the healthcare consumer, patient data must be made fit-for-purpose at the moment of data ingestion. When data quality processes and advanced identity resolution are completed continuously as data is compiled from every source, the resulting identity graph is guaranteed to always be the most updated, complete and accurate representation of a patient. When this unified profile is then combined with a full list of data aggregates and attributes of a patient over time, marketers and business users have what’s known as a Golden Record. More than a single view, a Golden Record is the definitive single source of truth for a patient. When it is made accessible across an organization, all users have the same real-time understanding of a patient across all interaction touchpoints. They can then deliver a consistent, personalized experience in the cadence of an individual healthcare journey. Whether aiming to simplify discharge instructions with a personalized approach, [improve Star Ratings](https://healthcare.rti.org/insights/improving-patient-experience-and-cms-star-ratings) related to patient experience (targeted care for high-risk patients, benefits education, using multiple channels to increase member engagement, etc.) or simply to meet patient expectations for a provider to know them as an individual, there are many reasons to focus on improving the patient experience. Messy, fragmented data should no longer be an impediment to delivering a patient experience that drives revenue and retention and reduces long-standing inefficiencies. **Blog categories:** Healthcare --- ### [Advancing Value-Based Healthcare One Patient Engagement at a Time](https://www.redpointglobal.com/blog/advancing-value-based-healthcare-one-patient-engagement-at-a-time/) **Published:** December 3, 2019 **Author:** John Nash **Content:** The New England Journal of Medicine recently published results from a [study of an incentive-based payment model](https://www.nejm.org/doi/full/10.1056/NEJMsa1813621) which looked at changes in spending, utilization, and quality of healthcare for hundreds of thousands of enrollees of a BCBS Massachusetts program. Over the course of eight years, payers in the program paid 11.7 percent less in claims for this cohort versus spending for a control group. The study by Harvard Medical School researchers attributed the savings to several factors, including lower utilization of services such as lab testing, imaging, and emergency department visits. The study’s authors concluded that the payment model – also known as a value-based care model – was “associated with slower growth in medical spending on claims, resulting in savings that over time began to exceed incentive payments.” Most quality measures also improved for the cohort in this program, in the form of improved processes and outcomes. These quality measures are key to the continued health of a population and directly affect the incentive payments that are the backbone of the [growing VBC model](https://www.healthaffairs.org/do/10.1377/hblog20190109.546126/full/) (vs. a traditional fee-for-service basis). When savings begin to exceed the incentive payments, as they did in the study, there is an even greater financial incentive for healthcare professionals to adopt a VBC model. Implementing a scalable VBC model to yield better outcomes while reducing costs requires having the right technology platform in place. Healthcare professionals, who have traditionally been faced with siloed claims and clinical data, need a single view of the healthcare consumer that provides them with data from every touchpoint and interaction of a consumer’s complete healthcare journey. **Value-Based Care Starts with Personalization** Last year, Redpoint partnered with Lucerna Health to advance the market’s transformation to VBC through personalized engagements with the healthcare consumer. A single view is the foundational requirement for delivering personalized engagements that are in the context and cadence of an individual healthcare consumer’s journey. With every available data point about the consumer – ranging from clinical, claims, behavior to demographic data – compiled into a golden record, they have the deep understanding of each consumer required to drive the model. Combined with in-line analytics to determine next-best actions and an intelligent orchestration layer to consistently deliver messages in any channel or moment of engagement, healthcare organizations have a single point of control with which to guide an individual journey. In practice, this leads to lower costs, better patient engagement and satisfaction, and better outcomes because consumers are presented with information and recommended actions that are the most relevant to them as individuals. Inefficiencies that result from siloed data and an unintegrated system of various providers (primary care, lab, pharmacy, specialists, etc.) are eliminated, which greatly help the consumer navigate through the healthcare system. **Doctor, Heal Thyself** Lucerna Health is itself a successful case in point. Using the Redpoint Customer Data Platform for a single point of control over data, decisions, and interactions, [Lucerna Health advances a VBC approach](https://www.redpointglobal.com/wp-content/uploads/2019/11/RedPoint_CaseStudy_Lucerna.pdf) by nurturing alliances between healthcare payers and providers, and ultimately reducing inefficiency and complexity. One way it reduces inefficiency is through advanced coordination of care and services. When an individual signs up for a health plan, the new member needs to be matched with a primary care provider. Possessed with a single view, Lucerna Health matches members with providers and specialists in their area who speak the same language. By knowing everything there is to know about the consumer – and providers – Lucerna guides members to healthcare centers that have the right equipment, staffing, and services to meet the specific needs of a consumer. A single point of control also allows for advanced operational sensitivity. With real-time updates of a healthcare center’s waiting time, for instance, a healthcare consumer can be guided to a facility with the shortest wait time. Personalized engagements extend to notifying members where to find the least expensive screening or CT scan, and other coordination of care efforts. It also means communicating in the consumer’s preferred channel, sending hyper-relevant messages and reminders, and limiting the number of interactions a consumer must take to access care or services such as scheduling or billing. **Profitable Growth through Value-Based Care** Results are in line with those of the Harvard Medical School research, with the VBC model enacted by Lucerna Health using the Redpoint platform achieving significant gains. The platform has helped one client provider expand from one healthcare center serving roughly 6,000 patients to 30 clinics serving approximately 200,000 patients in just five years. Another client, a payer, needed to match thousands of patients who needed an accurate risk assessment with providers. Because data was centralized with a single point of control, the payer knew that some patients had already scheduled appointments and scrubbed 23 percent of the names from the campaign – a significant cost savings. To test the effectiveness of personalization, one subset of the remaining patients received personalized outreaches (emails, direct mailings, and a postcard) and one received static messages. The group which received personalized engagements scheduled 50 percent more appointments. The growing trend toward VBC received a major boost from the Centers for Medicare and Medicaid (CMS) this year. The [CMS Primary Care Initiative](https://www.cms.gov/newsroom/press-releases/hhs-news-hhs-deliver-value-based-transformation-primary-care), announced in April and set to begin in January, intends to “transform primary care to deliver better value for patients”, reduce administrative burdens and “empower primary care providers to spend more time caring for patients” by implementing five payment model options that are all based on paying for health and outcomes rather than procedures. By lowering costs, improving outcomes, and improving patient satisfaction, the VBC model addresses many of the underlying reasons for the cost and complexities that plague the healthcare system today. **Blog categories:** Customer Data Platform, Data Management, Healthcare, Identity Resolution --- ### [How Houston Methodist Optimizes Data to Maximize Patient Engagement](https://www.redpointglobal.com/blog/how-houston-methodist-optimizes-data-to-maximize-patient-engagement/) **Published:** July 8, 2025 **Author:** Redpoint Global **Content:** In healthcare, fragmented data remains one of the most significant barriers to meaningful patient engagement and converting patients to care. Health enterprises want a better understanding of patient entry into care scenarios and what drives them to select a health system, which requires drilling down beyond clinical data into what drives engagement. However, turning disparate data into actionable insights that can be personalized to engage patients is no small task. But with a clear strategy and the right [data readiness approach](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/), meaningful digital engagement is within reach. For a deeper dive into this topic, Redpoint Global’s Product Marketing Principal Steve Zisk, Houston Methodist’s Director of Digital Marketing Jackie Effenson, and SoftServe’s Healthcare Solutions Leader & HIPAA Security Officer Peter Burns, joined Fierce Healthcare for the Pulse Check webinar, ## **What is Data Readiness?** The panel examined the current healthcare market and how providers are challenged to retain and attract new patients through ongoing communication while speaking to their personal needs and preferences. Foundational to this endeavor is data readiness— bringing together siloed systems, or point solutions, and their clinical and consumer data to create a unified patient profile that supports patient outreach beyond the EHR. During the webinar, Effenson set the stage for Houston Methodist’s need to engage patients and prospective patients as a key objective for integrating a data readiness foundation, “We wanted to make sure we provided information that’s relevant when targeting patient populations, and what I like to call ‘pure consumers’ that have entered in their first-party data set and opted in to receive information. So, how do we do that if we don’t know anything about them?” Houston Methodist does not face this challenge alone. It’s a frequent pain point for health systems to consolidate fragmented operating systems and disparate data to create robust datasets that power omnichannel outreach and motivate patients to pursue their next care encounter. Redpoint’s Zisk expanded on his experience working with system leaders to address that challenge head-on, “In the provider world, their biggest, and common, theme is the isolation of data and different groups in the health organization. It’s hard to gain context by bringing the cross-functional departments and data together, recognizing broader organizational goals.” Zisk continued, “Tying data back to a use case is the core of what systems are trying to accomplish. The other side is the technology and methodology must tie into a system’s operating workflows, and with the other providers and practices in their network that present along the patient care journey.” SoftServe’s Burns also elaborated on this pain point, “Engaging with people and understanding them starts with data. In many cases, data from disparate systems that don’t talk with each other, which involves understanding someone not only across the continuum of care but through early stages of engagement, the community and integrating those data sources.” The goal is for health systems to find the right technology partner to produce data that’s fit for purpose. Data can enable laser-focused targeting and coordinated omnichannel outreach, while improving outcomes and building long-term resilience, paving the way for intelligent automation and artificial intelligence-driven insights. ## **Establishing a Data Readiness Hub** Houston Methodist focused on using a data readiness hub to drive effective and efficient “enter-the-door conversations” for consumers and current patients so that Houston Methodist remains top of mind as their preferred system of choice. Effenson shared, “Patient interception from different service lines and uncovering a candidate for other services is dependent on what we know about them. By utilizing insightful data sets, we understand how to communicate with these audiences differently, how they behave and interact with us, and present engaging, relevant opportunities. Putting the patient and consumer first by talking about their needs and how to meet them means having the right data and tech stack to do it as opposed to just touting Houston Methodist and its accolades.” The Houston Methodist team integrated demographic data and digital signals with patient information from Epic to enrich the consumer record and gain a more holistic view of the patient journey. By understanding how patients first engaged with the system, their preferences, other behavioral cues, and when they converted to care, Houston Methodist was better equipped to identify where and how the system could provide the most value. “A key question to ask when vetting software solutions is if they are a strong and willing integration partner. You need that flexibility, as everyone uses different tools, and you need to make sure everything flows. We wanted Redpoint to be the mothership of all the data—the source of truth. We knew we’d have different data coming from here and there, but where it became synthesized, again from either a segmentation or attribution purpose, should happen all within this one hub.” Burns expanded, “You find the art in the science. The goal isn’t to centralize the data; it’s to activate it. Understanding the outcomes as you enter the project, activating around outcomes, and interpreting those real-time signals in a way that’s consistent with your workflow. You must engage the people who are involved in the workflow to use the tools appropriately. Data readiness is the most critical to the engagement strategy but the integration into workflow and activation into further of goals fits on top of it all.” ## **Putting Data to Work for Digital Engagement** Data readiness is able to turn unified data into a solid base for segmentation, personalization, activation, and for future AI deployments. A data readiness hub with built-in tools cleans, deduplicates, resolves identities, and keeps the data accurate. Personalized engagement can be at scale and offer cleaner, more complete patient profiles and more personalized engagement and care. With the right data foundation in place, Houston Methodist continues to build brand equity, credibility, and trust while emphasizing that it’s about the patient. “We’re able to validate our story in market with data—how is media being effective, how are we driving volume that’s leading to front door encounters and further downstream revenue, and what does all that look like,” noted Effenson. When you have a good platform or partner, you’ll ease the workload, speed up results, and have secure and compliant data. “Data maturity doesn’t mean data complexity. It means data readiness. Data that’s right and fit for purpose,” shared Zisk. Interested in learning more? Check out our data readiness resources: - Webinar: [Pulse Check: Is Your Consumer to Patient Engagement Strategy Built for the Digital Age?](https://www.redpointglobal.com/resources/pulse-check-is-your-consumer-to-patient-engagement-strategy-built-for-the-digital-age/) - Infographic: [From Data Silos To Actionable Insights](https://on24static.akamaized.net/event/49/60/02/6/rt/1/documents/resourceList1750429034456/ssrpgproviderinfographicpatient360dataactivationframework1750429034456.pdf) - eBook: [Bridge the Data Gap](https://on24static.akamaized.net/event/49/60/02/6/rt/1/documents/resourceList1750428991940/ssrpgproviderebookbridgedatagapforsmarterpatientengagement1750428991940.pdf) **Blog categories:** Healthcare **Blog tags:** Data readiness --- ### [To Optimize Revenue Growth, Tune in to Customer Lifetime Value](https://www.redpointglobal.com/blog/to-optimize-revenue-growth-tune-in-to-customer-lifetime-value/) **Published:** March 4, 2020 **Author:** John Nash **Content:** A Harvard Business Review study found that personalization lifts revenues by [10 percent or more](https://hbr.org/2015/11/how-marketers-can-personalize-at-scale), and delivers between five and eight times the return on marketing spend (ROI). Because a personalized customer experience is now recognized as a key revenue driver, there is an accompanying shift in how marketers measure the effectiveness of customer engagement channels. Channel-specific performance metrics should be relegated to a secondary role as they fail to accurately measure the ROI of customer experience (CX) investments, in large part because the type of personalized CX that today’s consumer demands transcends channels. For instance, a brand may introduce personalization in a display ad campaign. With open rates as a KPI, the brand may equate an increase as proof that personalization in the specific channel was successful. But this is a narrow view that overlooks how personalization influences behaviors and actions throughout an omnichannel journey. Furthermore, brands must ensure that personalization is seamless across multiple channels, which is in line with customer expectations for consistent omnichannel personalization. In the [Harris Poll survey](https://www2.redpointglobal.com/webinar-harris-poll-cx-2019-ondemand?_ga=2.204742867.212953745.1582561726-1570104466.1540307570) commissioned by Redpoint, 43 percent of consumers claim that it’s important brands know they’re the same person across all touchpoints. And 38 percent say that offers or recommendations based on products recently purchased or viewed is a type of personalization they expect. A personalized programmatic ad, in other words, must not be a one-off – designed to influence a specific behavior in a specific channel, but is otherwise detached from a broader view for how the content and behaviors relate to a wider customer journey. ## **Customer-Centric Measurement Approaches** To achieve that broader view, brands now have a couple of holistic measurements that have become practical to implement. One is to measure ROI at a segment level e.g. \[(Segment Revenue Growth – Marketing Costs) / (Marketing Costs)\] or at a brand/business unit level. Another option is to take the future potential into account, by using customer lifetime value (CLV) as a gauge to measure the revenue impact of customer experience investments and initiatives and, ultimately, ROI. Technology now makes it possible to influence and measure the impact at the individual customer level, in effect creating segments of one with a single customer view. These approaches move away from channel-specific goals that devalue the importance of the customer journey across channels. When CLV is the arbiter of the success of a CX initiative, brands become increasingly customer-centric. And by reorganizing around the customer through omnichannel personalization, brands can better focus on the behaviors that increase lifetime value rather than focus on the channels themselves. A recent [Forrester report](https://www.forrester.com/report/Make+Customer+Lifetime+Value+Your+Polaris+For+LongTerm+Growth/-/E-RES150315) explains why CLV is now a “unifying metric” and plays a strategic role in helping firms “pivot toward becoming customer-obsessed”. Acknowledging that there are many ways to calculate CLV depending on business goals, industry, and analytical maturity, the report states that a CLV calculation allows a firm to focus on higher-cost acquisition efforts that include personalization. In this context, the report describes CLV as the “common currency” that will unite multiple lines of business in always delivering a “next-best experience” around the potential value of a customer rather than immediate (read: transactional) value. ## **A Personalized CX Raises the CLV Profile** CLV is gaining traction because a single customer view enables marketers to view the success of personalization in the context of an omnichannel journey, with campaign data then used to enhance future personalization tactics with a focus on increasing CLV for every customer. This frees marketers from having to think about lifetime value in the context of a large segment – which aligns more with a channel-centric mentality. Describing what it deems a “personalization imperative,” Marketing Insider Group research found that [78 percent of consumers](https://marketinginsidergroup.com/content-marketing/content-marketing-personalization-imperative/) reported that a personally relevant customer experience ups their purchase intent. Furthermore, in a new report from SmarterHQ, [72 percent of consumers](https://www.smartinsights.com/ecommerce/web-personalisation/consumers-personalized-marketing-engagement/) said that they will only engage with marketing messages that are personalized and tailored to their interests. Taken in consideration with the Harris Poll statistic showing that 43 percent of consumers expect a brand to know they’re the same person across all touchpoints, the evidence is overwhelming that marketers no longer having the luxury of relying on channel-centric metrics to gauge campaign success. By turning attention to CLV, marketers have a truer ROI benchmark for their CX investments in terms of how they’re driving revenue via omnichannel personalization. **Blog categories:** Customer Data Platform, Data Management, Journey Orchestration --- ### [Banks Need to Unify Their Customer Data for Omnichannel Success](https://www.redpointglobal.com/blog/banks-need-to-unify-their-customer-data-for-omnichannel-success/) **Published:** July 31, 2018 **Author:** Steve Zisk **Content:** [![Banks Need to Unify their Customer Data](https://www.redpointglobal.com/wp-content/uploads/2018/07/banks-customer-data-e1532969943509.jpg)](https://www.redpointglobal.com/wp-content/uploads/2018/07/banks-customer-data-e1532969943509.jpg)Traditional banks and credit unions are uniquely positioned to adopt an omnichannel marketing strategy. Much like retailers, banks already have a strong physical footprint and have adopted customer-facing technology designed to help customers engage where and when they want. Banks also collect extensive amounts of behavioral and transactional data – more than practically any other brand. Despite this advantage in data collection, however, traditional financial institutions are losing ground to financial technology firms, digital-first financial institutions, and non-traditional competitors like Google and Apple. Why is that? ## **Customer Data Is Locked in Silos** Although financial institutions collect a lot of data, much of it is locked in functional silos. Many of the silos are necessary for privacy compliance and other regulatory reasons, but banks recognize the problem. In [one recent study](https://www.capgemini-consulting.com/resource-file-access/resource/pdf/bigdatainbanking_2705_v5_0.pdf), in fact, 57 percent of banks said that too many silos of data is the biggest impediment to effective decision making. This is a significant barrier to success at omnichannel marketing. With [customer data](https://www.redpointglobal.com/blog/customer-data-insights-and-the-single-customer-view/) in silos, banks lack a complete view into the customer’s needs, wants, and desires. Knowing these things would change how the bank interacts, and prevent them from sending the wrong offer to the wrong customer. One example is providing a balance transfer offer to someone who doesn’t carry a balance on their credit card. If banks have a more complete view of their customers across the entire lifecycle, they can more likely avoid sending those kinds of irrelevant offers. ## **Fix Silos with a Single Point of Control** One of the best ways for banks to bridge data silos is through leveraging a [customer data platform](https://www2.redpointglobal.com/white-paper-guide-to-selecting-the-right-cdp-pr) (CDP). A CDP functions as a single point of control for customer data, bridging functional silos and connecting all types and sources of customer data in real time. This includes first-, second-, and third-party data at batch or streaming cadences. A CDP is also designed to make a unified customer view, also known as a “golden record,” accessible across the organization, which helps drive more contextually relevant interactions in the right context and at the right cadence to power customer engagement. A [golden record](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/) is a unified profile that combines all that is knowable about an individual customer, including behaviors, preferences, interests, and purchases, from multiple engagement systems and data sources into a single, 360-degree view. This unified record consists of accurate, complete, and timely data on a customer that persists over time and is continuously updated with detailed data from batch and real-time streaming data sources. This unified record is a key component of omnichannel marketing, and improves competitiveness through greater insight into customers. ## The Solution: Customer Data Unification With clean and [unified customer data](https://www.redpointglobal.com/blog/banks-need-to-unify-their-customer-data-for-omnichannel-success/) in hand, banks can more readily understand their customers’ channel preferences and more accurately contextualize their marketing. This is a key facet of deploying a successful omnichannel marketing strategy, which is especially critical in an era when banks must compete with digital-first financial institutions, financial technology firms, and non-traditional competitors alike. **RELATED ARTICLES** [How Does Better Identity Resolution Help Banks Deliver a World-Class Customer Experience?](https://www.redpointglobal.com/blog/how-does-better-identity-resolution-help-banks-deliver-a-world-class-customer-experience/) [Solution Brief – Customer-Centric Experiences in Retail Banking](https://www.redpointglobal.com/wp-content/uploads/2018/06/Solution-Brief-Banking.pdf) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![Learn How Banks Can Engage the Omnichannel Customer](https://www.redpointglobal.com/wp-content/uploads/2018/07/AD-Banner0718-02-BankingOnmichannel-730x110.jpg)](https://www2.redpointglobal.com/white-paper-retail-banking-and-omnichannel-consumer-pr) **Blog categories:** Data Quality, Financial Services, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Omnichannel isn’t Digital First, or Physical First, it’s Experience First](https://www.redpointglobal.com/blog/omnichannel-isnt-digital-first-or-physical-first-its-experience-first/) **Published:** June 21, 2022 **Author:** Mike Ferguson **Content:** When old friends reconnect, long-forgotten mannerisms such as the curve of a smile or the pitch of a laugh are instantly recognizable, causing the years and decades apart to evaporate like mist. A reunion reminds friends why they forged a bond in the first place; a shared sense of humor, common interests, easy banter. Re-establishing an old connection without skipping a beat is the task at hand for many brands that lost in-person customers during the pandemic, but are now returning to face-to-face engagements. One issue with trying to recapture the old magic, if you will, is that customers did not disappear entirely during the hiatus. Rather, they embraced digital-first and in some instances digital-only engagements when in-person encounters were not an option. ## **Embrace a Hybrid Approach** Now that in-person meetings are coming back, the challenge is to provide the same digital conveniences and amenities that customers have become accustomed to, while also providing the irreplaceable, comforting familiarity akin to a long-lasting friendship. The traditional face-to-face relationships between a brand and a customer, whether a B2B setting such as a pharmaceutical rep and a healthcare professional (HCP), or B2C such as a hotel front desk clerk and a customer, have changed for good. [PharmaExec.com](https://www.pharmexec.com/view/turning-the-corner-a-new-era-of-pharma-sales-comes-into-view) compiled a variety of survey results that analyzed the evolving relationship between HCPs and pharma reps. One from ZS Associates, a global professional services firm, found a greater push for “high-quality interactions” with a hybrid focus. In another from InCrowd, 51% of HCPs surveyed expressed a preference for hybrid. HCPs said that in-person meetings build trust, connection, and a more personalized, engaging relationship, but they are also inflexible and require a longer time commitment. Pratap, Khedkar, CEO of ZS Associates, said that reps, pressured to deliver higher-quality interactions, will “need to do everything well: physical, virtual, and digital. We’re calling it phygital.” Recapturing that deep-seated familiarity and personal understanding is less about striking the right balance between a digital experience and a human experience as it is merely recognizing that the overarching objective should be to create a seamless customer experience, by whichever collection of physical and digital channels that happen to constitute a customer’s unique journey. The in-person part is easy. The greater challenge for brands is to produce the digital equivalent of a smile. ## **Customer-Centricity and Omnichannel** In many of the recent conversations I’ve had with prospects – pharmaceutical companies among them – establishing that throughline of consistency is a priority. Forced by the pandemic into a digital-first model, these companies now embrace the opportunity to blend the best of digital with the best of in-person encounters to deliver a holistic experience across channels. The question that keeps coming up is how to deliver an [omnichannel experience](https://www.redpointglobal.com/orchestration). There is a recognition that a focus on the delivery channel is not the same as personalization, per se, but there is less of an understanding how to break free from the traditional way of thinking. As it concerns the pharma rep-HCP relationship, omnichannel is best understood as centering the experience around the HCP, rather than focusing on the channels themselves. Any decision about how to further the HCP’s journey – whether it’s education about a medication, field research, population health, etc. – must be made in accordance with whichever decision will optimize the journey at the precise moment it is rendered. That should be the only determining factor. Otherwise, setting arbitrary rules or limits around the quantity or the scheduling of content distribution – each in-person visit must be followed by this video, these two emails, one call, etc. – will introduce friction into the customer experience. A rep may think they’re offering a personalized experience, but a one-size-fits-all approach with a pre-determined combination of channels is multi-channel – not omnichannel. The best training video ever created will only replicate the familiarity and connection of an in-person visit – the digital equivalent of a smile – if it is sent in the precise cadence of a HCP’s unique journey. ## **Develop a Contextual Understanding** As a baseline, providing an omnichannel experience requires collecting every conceivable piece of data from all disparate sources and channels. An important component, to be sure, but what’s more important is to possess an in-depth understanding of the interoperability between channels. A real-time, contextual understanding is vital to remain in the cadence of a unique customer journey and deliver a next-best action optimized for the moment, irrespective of channel. Important data would include information such as how the HCP consumes videos – what device was used, how long it played, what links were clicked, images hovered, etc. Beyond that, did the HCP open an email containing similar content, and sign up for a webinar? How did an in-person visit with a pharma rep influence these behaviors? In a B2B environment, other data points providing a contextual understanding would include the sharing of data across lines of business. A fertility rep and oncology rep from the same company might have regular face-to-face meeting with the same HCP, for example, making coordination between the two important for providing the HCP with a consistent experience. Compliance issues must also be taken into consideration, such as frequency of contact, safeguarding personal data, etc. A real-time, contextual understanding that stems from possessing a unified customer profile, or [golden record](https://www.redpointglobal.com/single-customer-view/), that includes all data points and is reconciled to an individual or business entity is the key to providing an omnichannel CX. Anything less, and a pharma rep, hotel clerk, or another associate at the center of a traditional human-driven engagement is making a best guess at how to further a customer journey along to the desired outcome. By knowing all there is to know about a customer in real-time, employees who are the face of traditional human-driven interactions will always have a contextual understanding of a customer’s unique journey, and will always be primed to deliver a next-best action. ## **Related Content** [Cadence of the Customer and the Corner Store Experience](https://www.redpointglobal.com/blog/cadence-of-the-customer-and-the-corner-store-experience/) [Build a Comprehensive Customer Understanding Through Perfect Data](https://www.redpointglobal.com/blog/build-a-comprehensive-customer-understanding-through-perfect-data/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Identity Resolution, Real-Time Personalization --- ### [A New Way for Marketers to Leverage Data for Omnichannel Marketing](https://www.redpointglobal.com/blog/a-new-way-for-marketers-to-leverage-data-for-omnichannel-marketing/) **Published:** September 20, 2018 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2018/09/Iot-Data.jpg)As the world is increasingly becoming digitally connected and consumers generate quintillion bytes a day through their online behaviors, marketers now seek a holistic view of the customer journey for better engagement. The challenge is for marketers to access, analyze and activate this data using traditional RDBMS (relational database management systems). To overcome modern inconsistent and evolving data challenges, NoSQL database technologies represent innovative ways for marketers to unlock data and design more creative omnichannel customer journeys. A [recent McKinsey study](https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world) found that 42 percent of Americans use the internet frequently each day and 21 percent report being online “almost constantly”- an activity creating a rich data trail and leading the development of “big data” – wherein online behaviors such as web search, clicks, transactions and duration become fodder for understanding customer needs and intentions. To manage this complexity, volume and variety of data generated by digital interactions, NoSQL databases are a next logical step in making systems agile and scalable; handling the increasing volume; and providing high-performance analysis and activation for real-time interactions with digital-savvy customers. **What are NoSQL databases and document databases?** [NoSQL](https://www.mongodb.com/nosql-explained) stands for “Not only SQL”, and encompasses a variety of different database technologies that were developed in response to the demands presented in building modern applications. Under this NoSQL umbrella, we have document databases that are designed to store and manage data as documents such as JSON/XML. MongoDB and Cosmos DB are two of the major providers in a market that is growing exponentially with the data influx from online and mobile applications. As per [one estimate](https://www.alliedmarketresearch.com/press-release/NoSQL-market-is-expected-to-reach-4-2-billion-globally-by-2020-allied-market-research.html), the global NoSQL market is expected to grow to $4.2 billion by 2020, registering a compound annual growth rate of 35.1% during the forecast period 2014-2020. **What are the business benefits for marketers?** As firms have already established processes and systems using relational data systems, it makes business sense to add NoSQL database systems to fill in the gaps presented by these relational databases and provide business benefits to firms facing inconsistent modern data challenges (by type, volume) and also looking adapt to the omnichannel customer journey. NoSQL and document databases can provide the following benefits to marketers: **Flexibility:** To handle their customers’ data in the face of rapid digital adoption, social interactions and omnichannel shopping, businesses now need to manage data at volume, in any format, with many different customer attributes. And they need to aggregate, cleans, and analyze this data with a goal of acquiring new customers and retaining existing ones for better conversions and customer lifetime value enhancements. The schema-free NoSQL database accommodates different and rapidly changing structured and unstructured data, while still providing the performance and scalability needed for real-time interactions. **Cost savings:** Unlike high-cost and high-maintenance RDBMS servers, NoSQL databases can be spread across low-cost commodity and cloud systems with low latency and efficiency in data retrieval. **Business growth and status analysis:** With agility as the intrinsic feature of NoSQL databases, firms can now leverage the data views created real-time by pulling in data from disparate sources and different business units to extend their business information across other business areas for further growth. By supporting structured and unstructured data for unifying customer profiles and purchase histories to enable omnichannel and personalized experiences, a NoSQL database not only complements traditional RDBMS within current business ecosystems but also provides high value. The NoSQL database boasts flexibility for disparate data, scalability to manage large volumes of data, and high performance. It’s an ideal offering database for marketers who face challenges orchestrating seamless, integrated and consistent user experiences. **RELATED ARTICLE(S)** [3 Top Identity Resolution Use Cases for Marketers](https://www.redpointglobal.com/blog/3-top-identity-resolution-use-cases-for-marketers/) [Machine Learning Accelerates Real-time Customer Engagement](https://www.redpointglobal.com/blog/machine-learning-accelerates-real-time-customer-engagement/) ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2017/01/RP_eBook_Path_Omnichannel_Marketing.jpg)](https://www2.redpointglobal.com/ebook-path-to-omnichannel-marketing-ebook-pr) **Blog categories:** Data Quality, Omnichannel Marketing --- ### [Capturing Intent in an Omnichannel Customer Journey is Next-Level Marketing](https://www.redpointglobal.com/blog/capturing-intent-in-an-omnichannel-customer-journey-is-next-level-marketing/) **Published:** June 11, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/06/shutterstock_659087314-e1560192246246.jpg)With rising consumer expectations for personalization across the entire customer buying journey, the ability to understand consumers in real time is increasingly essential to the long-term success of a brand or retailer. It’s also the key for brands to demonstrate that they really understand the consumer and anticipate their needs by serving up highly personalized and relevant content and offers when a consumer is showing intent to make a purchase decision – which is demonstrated by their actions and behaviors. Understanding consumer intent is key to optimizing customer engagement across interaction touchpoints and increasing customer lifetime value. By connecting marketing interactions to consumer intent at the very moment it’s being demonstrated helps brands remain competitive. It increases the efficiency of digital advertising investments, and enables a brand to deepen a relationship with a customer at the point of interest. **Going Deep with Intent Signals** Measuring intent is highly personal and data-driven, based on correlating previous and current transactions, preferences, and behaviors to provide insights into a consumer’s interests that indicate potential intent to take action. It’s about mining a customer’s digital trail of indicators that can be leveraged as data sources (first-party, second-party, and third-party data) to detect and understand intent in the moment of interest. The challenge for brands is that the consumer buying journey is multi-event across multiple devices (desktop to mobile device to retail store to call center). To capitalize on real-time intent signals, brands must have capabilities for real-time data activation, real-time cross-channel identity resolution, and real-time data onboarding to connect offline and online data. To properly gauge intent, data must provide full context. A single customer view that combines every source of customer data (streaming, batch, structured, semi-structured, unstructured) into a golden record tells us everything there is to know about the customer, their device and channel preferences, buying patterns, and transactions. Combined with the knowledge of the customer’s precise moment in a dynamic buying journey, this provides the necessary context that gives meaning to real-time intent. **A Real-Time Response Unlocks the Value of Measuring Intent** Prioritizing consumer intent shifts the marketing mindset from pushing static messages to instead engaging a consumer based on intent signals, leading to higher relevance. It requires the continual listening and mining of digital cues to inform a real-time response as signals dictate. According to a [study from McKinsey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/marketing039s-age-of-relevance-how-to-read-and-react-to-customer-signals), traditional marketing communications that are aligned with a calendar of holidays, product launches and other marketing-defined events is an “unresponsive model organized around the company, not the customer.” The study finds that brands must organize around the customer’s circular decision-making process that consists of four phases: initial consideration, active evaluation (research phase), closure (actual purchase), and post-purchase (customer experience) period. [Omnichannel intent listening](https://www.redpointglobal.com/blog/the-art-of-listening-in-mastering-omnichannel-marketing/) connects a brand with a customer throughout each phase. It improves the customer experience while lowering the cost of interacting with a customer because it curates offerings that respond to desire (interest) rather than indiscriminate engagement. To do it right, however, entails far more than aggregating every piece of customer data from online and offline channels (in-store, website, mobile app, social). Exploiting intent signals requires a real-time decisioning engine that intelligently orchestrates a next-best action or recommendation for the customer at the moment of interaction – targeting a customer by who they are and what they are doing at a specific moment. Real-time decisioning factors intent intensity, which is a recognition that not all intent signals are the same. There are behavioral, event-driven, and variable signals, as well as those that are affinity driven, and transactional. A collection and organization of all signal types to better identify changes in frequency and intensity is known as intent monitoring; spikes in intent indicate that a customer or target account is active in the market, which marketers can exploit with pitch-perfect relevance and context in the cadence of the customer journey. **Expanding the Last Mile** Brands often limit their view of consumer intent to a single digital channel only view or to the channel that was the last mile to the consumer where a purchase was finalized. There’s a tremendous amount of value in understanding every customer interaction and how each interaction affects the next stage in the journey and the eventual purchase decision. Brands must understand the entire customer journey on the path to purchase (digital and traditional) in order to optimize it. Without [customer journey data](https://www.redpointglobal.com/blog/multichannel-vs-omnichannel-marketing-and-keeping-up-with-a-customer-journey/) that spans from anonymous to known customer engagement, there is no way to fully take into account intent or attribution to recommend the next best actions with each unique customer over time. Intent-based marketing powered by real-time decisioning generates actions that, for the customer, feel relevant and natural and give them the feeling that a brand knows them as a unique individual. It’s a powerful feeling that customers reward with a greater share of wallet. McKinsey estimates up to a [20 percent sales boost](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-heartbeat-of-modern-marketing) from data-activated marketing that is based on a person’s real-time needs, interests, and behaviors. That is a powerful incentive for brands to finally make the shift from a calendar or transaction-based marketing strategy to one that is organized around the omnichannel customer. **Blog categories:** Customer Data Platform, Data Management, Data Quality, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization --- ### [Scale New Heights with a Rules-Based Omnichannel CX Platform](https://www.redpointglobal.com/blog/scale-new-heights-with-a-rules-based-omnichannel-cx-platform/) **Published:** January 28, 2022 **Author:** Redpoint Global **Content:** The award-winning National Geographic documentary [“Free Solo”](https://films.nationalgeographic.com/free-solo) details climber Alex Honnold’s successful ascent of El Capitan, a 3,000-foot nearly vertical granite rock wall in Yosemite National Park, without using ropes or safety equipment. The 2017 climb was the essence of a “no margin for error” situation. One ill-timed gust of wind, one tenuous hold, one slip and the documentary would tell a different story. Amazingly, Honnold completed the entire climb in just under four hours, whereas it usually takes a team of experienced climbers about a week to summit using fixed ropes and other safety gear. “It’s always about excellence and perfection,” Honnold said about his famous ascent, leaving unsaid that failure was not an option. It was perfection or death. Rappelling over to the customer experience arena, to use a climbing term, more and more organizations are demanding the same perfection from customer experience (CX) platforms. Good enough is no longer good enough. Mistakes that were perhaps not *too* detrimental in the delivery of a personalized customer experience may have once been tolerated in the name of slow and steady progress. Those days are over. When excellence and perfection are possible, the payoff of an omnichannel customer experience proven to drive revenue is worth stepping outside the comfort zone. Scaling new customer experience heights is about having complete trust and faith in the process, which subjugates risk. When there is no option for failure, and precision is required, plodding a well-worn path no longer moves the needle. ## **Customers Do Not Accept Compromise** In healthcare, aerospace and other industries precision can literally mean the difference between life and death. The fact that organizations in those industries increasingly demand perfection from a CX platform is a sure sign that the technology has made the leap from a niche marketing platform to a mission-critical, operations system. As an operations system where exactness and timeliness cannot be compromised (where timeliness may equate to a millisecond response time), a key distinction between what may be “good enough” for a marketing platform is how the customer experience platform handles database extractions. The tried-and-true legacy approach is a list-based system, where – even with a perfect database and perfect counts – a list is created and extracted, from which messages, offers and communications will be generated. The list then proceeds through a campaign separated from updates and ongoing care of the database. From the moment the list is extracted, it decays and loses accuracy. It is now static, no longer updated by normal database processes. When circumstances change – and they always do – those changes are not reflected in the list. A list-based, outbound list does not allow for any new condition that is not defined prior to extract. For some marketing purposes, perhaps a 12 or 24-hour lag is acceptable. If a retailer sends an irrelevant offer because it doesn’t know you visited a store after an online session, perhaps it loses a customer. It will then have to devote time and resources on acquisition, but on an individual basis compared with other industries, the loss is manageable – to an extent. In marketing, the challenge comes with scale of personalization. However, an individual patient coping with a chronic condition, or one with a new diagnosis who desperately needs a care plan often does not have the luxury of a time lag. That said, even if an inferior customer experience may be manageable on a small scale, smart marketers are embracing the alternative, realizing that the legacy approach introduces roadblocks that needlessly frustrate customers and block revenue streams that are possible with the delivery of an omnichannel CX. A manageable loss no longer needs to be tolerated, in other words, not when customers demand perfection. In a recent [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) commissioned by Redpoint, 39 percent of consumers surveyed said they will not do business with any company – any company – that does not offer a personalized experience, or does not recognize them as a unique customer across touchpoints. ## **Apply Rules for Continuous Hyper-Precision** Instead of a list-based system, an operational CX platform that demands precision will instead pull a rule – a set of logic that defines an audience – which is applied to a campaign in place of a list and is evaluated at each point in the campaign where a list would normally be used. Because the rule is evaluated at each inflection point, it guarantees the extract happens at the latest point possible, virtually eliminating staleness and ensuring a much higher level of precision even amid changing circumstances. When a rules-based approach is coupled with an always-consolidated offer history, precision is a matter of course. The right audience is matched to the right communication (offer, message, content) every time, on any channel, inbound or outbound. A CX platform escapes the small-m ‘marketing’ yoke to become an intelligent edge, operational system that ensures precise engagement with people through every moment in time with exact data and an exact understanding of the customer. ## **Real-Time Data vs. Real-Time Decisions** There is no dearth of customer data platform (CDP) vendors that claim a real-time capability, but the important distinction is that, for many, real time is understood in the context of the database layer. They’re either referring to real-time data capture, which everyone can do, or they’re referring to a real-time data refresh on a channel-by-channel basis, with campaigns restricted to list-based processes. When a list is extracted from the database, any subsequent real-time data refresh has no bearing on the campaign. A real-time decision, then, is not based on the real-time data. Hard real-time, by contrast, fulfills the millisecond-response times demanded by organizations that operate with no margin for error. Because customers now say they will not tolerate an inconsistent experience, that same level of accountability is becoming an expectation across industries. The traditional acceptance of list-based platforms as being “good enough” for large-scale batch sending of direct mail or email communications is disappearing because it is now understood that they fall short in providing an [omnichannel customer experience](https://www.redpointglobal.com/rgone/) that dynamically alters every customer interaction based on a customer’s behavior at the precise moment of a journey. The ability to separate messages from channels across the enterprise is the key to elevating a CX platform from just marketing-oriented considerations. It recognizes that in today’s always-on, dynamic customer journeys, a customer interacts across touchpoints that include non-traditional marketing channels (customer service, call center, returns, collections, etc.). ## **Transcend the Ordinary** Returning to the rock-climbing analogy, a roped-in, careful, plodding climb is akin to the “safety net” imposed by a list-based solution; ambition and innovation are stifled because you’re limited by a fixed route. Breaking free of those limitations with a rules-based system allows for the transcendence and creativity that Honnold displayed in his magical ascent. When excellence and perfection are possible, it’s no fun settling for a second-rate experience. Conversely, when you know every step you take is the right one, you can go places no one’s ever been. **Blog categories:** Omnichannel Marketing, Real-Time Personalization --- ### [New Survey Findings Reveal Customers Grasp the Value of their Personal Data](https://www.redpointglobal.com/blog/new-survey-findings-reveal-customers-grasp-the-value-of-their-personal-data/) **Published:** July 19, 2022 **Author:** Redpoint Global **Content:** To improve customer retention, avoid sending irrelevant emails or product recommendations. That is one of the lessons learned from a recent [Dynata Survey](https://www.redpointglobal.com/press-releases/70-of-consumers-receive-mistargeted-information-from-brands-at-least-once-a-month/), commissioned by Redpoint, in which 51 percent of consumers surveyed said that mistargeted information negatively impacts their overall customer experience (CX) with a brand. Most consumers surveyed (70 percent) reported receiving mistargeted information at least once a month – unnecessary friction in the process that leaves many consumers frustrated by their experiences. More than half (52 percent) of consumers surveyed reported that less friction is a main expectation. The survey revealed that consumers understand the value of their personal data, and they expect to receive commensurate value in return for sharing it. Asked what would most influence a consumer to share data with a brand, receiving compensation in return in the form of discounts, offers or perks was cited as the top way (by 59 percent) brands can facilitate that exchange. For most consumers, giving away their data without receiving anything in return, or even knowing what it will be used for, is a non-starter. In fact, 73 percent said they either “rarely” or “never” provide data without an explicit understanding for how it will be used. For consumers who *do* share data with some expectation for a return in value, trust is an important consideration, meaning that the goodwill gained from discounts, offers or perks will be offset by a brand violating a consumer’s trust. Asked which course of action they would take if they knew their data was subject to being shared with a third-party without their consent, 48 percent of consumers surveyed said they would stop doing business with a brand altogether, and 31 percent said they would share less data – such as completing a check-out as a guest rather than log in. Presented with several ways a brand can enhance trust with a consumer, the No. 1 way – cited by 54 percent of those surveyed – was to easily allow the consumer to delete any data that has been provided. Coming in at No. 2, cited by 48 percent, was a brand being transparent over data leaks. The third choice (34 percent) was for a brand to provide the consumer with a personalized experience based on the consumer’s data – the aforementioned discounts, offers or perks relevant to the consumer and expected as a condition for sharing data. ## **Lean on First-Party Data for a Trust Relationship** The research shows the growing importance for brands to invest in customer data and customer experience at all levels of the organization, with a focus on [first-party data](https://www.redpointglobal.com/blog/go-deep-why-a-first-party-data-strategy-needs-to-incorporate-depth/) as the key to demonstrate the deep level of personal understanding that customers have come to expect. We’ve written extensively about the [value exchange](https://www.redpointglobal.com/blog/first-party-customer-data-delivers-value-and-a-personalized-cx/) between the sharing of first-party data in return for a more personalized experience. The latest survey results further validate previous findings that when brands demonstrate they value their relationships with customers outside of a transactional basis, customers respond in two ways. One, they share more data, and two, they have a higher level of trust that a brand will use their data in accordance with a customer’s permissions granted and for the intended purpose. The reason first-party data is so closely tied to meaningful experiences that demonstrate an individual understanding of a customer is that first-party data presents a more accurate window into a unique customer – based on behaviors, preferences, intent, etc. This is in sharp contrast to relying on third-party data or lookalike audiences where engagement is much less personalized. First-party data, as we’ve written, is the [fuel that drives the customer experience engine](https://www.redpointglobal.com/blog/why-trust-unlocks-a-superior-customer-experience-cx-and-builds-brand-equity/). Every company has data about its customers. The key to building a relationship founded on trust with a customer is to bring data siloed across the enterprise into a single customer view, a golden record that is in real-time, continuously updated, and lets a brand know everything there is to possibly know about a customer. The better the data, the better the personalized experience a brand delivers, and the better the outcome – for both the brand and the consumer. ## **Related Content** [Build a Comprehensive Customer Understanding Through Perfect Data](https://www.redpointglobal.com/blog/build-a-comprehensive-customer-understanding-through-perfect-data/) [There are No Third-Party Shortcuts to Understanding Customers](https://www.redpointglobal.com/blog/there-are-no-third-party-shortcuts-to-understanding-customers/) [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality --- ### [The New Reality Brings Data Transformation to the Forefront](https://www.redpointglobal.com/blog/the-new-reality-brings-data-transformation-to-the-forefront/) **Published:** June 11, 2020 **Author:** Redpoint Global **Content:** There is no shortage of eyebrow-raising statistics documenting the widespread economic disruption that has befallen the US since March. Among them, [Census Bureau](https://edition.cnn.com/2020/05/15/economy/retail-sales-record-drop-april/index.html) reports of a historic 16.4 percent drop in retail sales in April, including an 89 percent year-over-year decrease for clothing and accessories stores. [Adobe Analytics](https://www.usatoday.com/story/money/2020/03/31/coronavirus-curbside-pickup-social-distancing/2901743001/) estimates an 87 percent year-over-year increase in curbside pickup sales for the month of March. Neiman Marcus, J Crew and Pier 1 are among the most prominent retailers to file for bankruptcy protection. No statistic better encapsulates the sudden change than the U.S. Department of Commerce study that shows ecommerce penetration as a percentage of retail sales increasing more in March and April than the previous 10 years. From 2009 until the end of February, ecommerce penetration increased from 5.6 to 16 percent. In March and April, it rose to 27 percent. ![](https://www.redpointglobal.com/wp-content/uploads/2020/06/0602-E-Commerce-Surprise-Murphy-300x191.jpg) This is what is meant by [digital transformation acceleration](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/). It is a new reality marked by unprecedented, wholesale changes to consumer behaviors. To succeed in this new reality brands must connect with customers across all digital channels with relevant engagements that are in the context of a unique customer journey. **Tame Disruption with Data Management** Rising to the challenge requires more than a mere recognition that buying patterns have become more unpredictable than ever. The new reality demands more from a brand than delivering a blanket message to customers that it acknowledges the disruption. Rather, taming digital disruption requires advanced enterprise data management capabilities. Brands that succeed in the new digital-first environment will be the ones that have a handle on all of their data, as well as an understanding of how to bring the data together, secure it, and monetize it. Data security, usability, manageability and performance become even more important when data is rightly regarded as the currency that unlocks relevant, timely and personalized customer experiences throughout an omnichannel customer journey. A timely real-world example of the importance of data management is the large increase in curbside pickup service, which accounts for a sizeable percentage of surging ecommerce sales. Data privacy is just one of seemingly countless data management tasks that must be accounted for to deliver a seamless, personalized experience at scale. Adherence to GDPR, CCPA and other data privacy regulations add layers of complexity to ensure that a seamless, personalized curbside pickup experience is tailored to each customer’s preferences for how their data is collected, stored and used. **Data Management and Curbside: a Winning Match** A digital customer experience hub with a master data management component that eliminates the lag time between data ingestion, processing and calculation updates is another requirement in delivering a superior curbside pickup experience that delights a customer with relevance. Pushing data aggregates out to subscribers in real time ensures that every engagement channel and external system that subscribes to the updates is synchronized with a customer’s unique journey. With curbside pickup, this capability has direct implications for inventory, online and in-store communications, notifications, and offers, to name a few – all of which require real-time interactions to keep pace with the customer. Conversely, a platform that ingests data and calculates aggregates for a nightly batch upload to an FTP site that is later picked up by a subscriber via web services or API integrations will likely introduce friction into any curbside pickup service, either by failing to deliver relevance or failing to have the right product ready at the right time. A new reality marked by digital transformation acceleration will produce winners and losers in the battle to provide customers with a personalized customer experience that was in great demand even before the sudden disruption. Having your data in order is an all-important first step in meeting this expectation. Anyone can acknowledge the disruption. Showing true empathy for your customers, however, requires an understanding that a personalized, relevant and increasingly digital experience is only made possible when you can trust that your customer data is in the right place. **RELATED CONTENT** [Now is the Time to Set Ambitious Marketing Goals](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/) [Retailers Will Adapt of Die in the New Reality](https://www.redpointglobal.com/blog/retailers-will-rapidly-adapt-or-decline-in-the-new-reality/) [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Data Quality, Segmentation & Activation --- ### [What Marketing Clouds Don’t Get About Native CDP Functionality](https://www.redpointglobal.com/blog/what-marketing-clouds-dont-get-about-native-cdp-functionality/) **Published:** April 18, 2019 **Author:** Steve Zisk **Content:** Adobe and Salesforce both made a splash in the customer data platform (CDP) pool with recent announcements that they were bringing a product to market. Salesforce announced that it was building a CDP as an “extension of Salesforce Customer 360”, which is scheduled for general release later in 2019. Adobe dipped its toe into the water the next day, announcing at [Adobe Summit](https://news.adobe.com/press-release/experience-cloud/adobe-summit-2019-driving-future-customer-experience-management) that it would also be rolling out a CDP in tandem with the general release of the Adobe Experience Platform. With these announcements, it appears that the market is coming to the realization that a CDP is not, as one Salesforce executive said just last year, just a “passing fad” but instead is a core requirement to deliver a differentiated customer experience. Driving the evolving mindset is pressure from customers and prospects who are in the market for a marketing database and have come to the realization that a data management platform (DMP) integrated with a CRM system no longer suffice in the race to digitally transform marketing by knowing everything there is to know about a customer. ## **Competing in a Different Realm** Now we can finally dispense with the fairy tale that a CDP has ever been a fad or question its tremendous value to marketers. Underscoring the pivot from the traditional martech providers is their tacit acknowledgment that Redpoint and other pure CDP providers are ahead of the game in recognizing the transformative power of a CDP. It’s nice to have a fully deployed enterprise solution in the market rather than one tied up in design, but a head start alone would not be insurmountable for newcomers to overcome. In fact, an argument can be made they’re running a completely different race. Native CDP providers are ahead for a reason – they have a maniacal focus on having correct, standardized, matched, and keyed data which differentiate the product from the cloud-based vendors. While these vendors come to the table with bona fides in cloud data management, that capability barely scratches the surface for dealing with the data complexity at a level worthy of being called an enterprise-grade CDP. A native CDP differentiates itself from other customer data systems such as a CRM or DMP by connecting all types and sources of customer data – batch, streaming, structured, and unstructured – from across the enterprise. The [Customer Data Platform Institute](https://www.cdpinstitute.org/cdp-basics/) defines a CDP as a “packaged software solution that creates a persistent, unified customer database that is accessible to other systems.” ## **The Purpose of CDP Providers** To understand how important clean data is in the CDP construct, it helps to take a step back and look at the specific purpose of a CDP to produce a single customer view, and what separates a true CDP from a DMP in achieving this objective. Just over two years ago, when there was a lot of buzz around combining a DMP with a CRM to create a 360-degree customer view, I wrote a blog that defused this notion and explored why a native CDP stands apart. Suffice it to say the points are still valid, perhaps even more so now that marketers have more data and data sources to contend with and consumers are moving faster than ever in dynamic, omnichannel customer journeys. To recap, the many virtues of a DMP notwithstanding, treating a CRM as a source and holding all the other non-CRM sources falls woefully short in unlocking cross-channel insights that a CDP makes possible by leveraging customer data in an always-on, omnichannel environment. As mentioned before, the key difference is that ingesting data is not the same thing as making it actionable everywhere. Coping with the complexity of first-party data across various verticals is a core CDP competency; it’s not enough to aggregate data and call it a day, leaving it to customers to sort out joins and keys. The ability to impress data quality on incoming data no matter the source is critical. The alternative is a low-quality threshold of matched data, which warps a single customer view. ## **A Single View, a Single Point of Control** What makes a CDP-generated single customer view important for marketers is the fact that, unlike a solution cobbled together from a DMP and CRM, it is continuously updated in real time. There’s no latency. The golden record is an always-on, always up-to-date unified customer profile that lets a marketer know everything there is to know about a customer across anonymous-to-known journey states. High-precision identity resolution is the foundation for marketers to have a single point of control over data, decisions, and interactions. Because a [CDP](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/) takes that last, important step of cleaning and managing the data, marketers with a single view of the customer are not only making decisions in real time in the context and cadence of a customer journey, but they’re also making decisions based on real-time data for a segment of one. A marketing cloud solution may enable real-time decisioning, but the key difference is that the underlying data may itself be old; marketers may be pushing duplicate records to a third-party platform, for example, but won’t know that a program is compromised and will have little confidence in the results. ## **Don’t Sleep on Data Privacy** In addition, data privacy is now a top concern for marketers. Data curation, data quality, and persistent keys are prerequisites for a solution to be compliant with regulations such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). A cloud-based solution may indicate they’re compliant with these and other privacy regulations, but those assurances put the onus of data cleansing on the customer; provide them with clean data with proper keys assigned, and they’ll collect opt-in, opt-out information. Despite assurances, an account or a customer ID are not – as some claim – sufficient to ensure GDPR or CCPA compliance. A native CDP ensures data compliance without additional manipulation that a cloud vendor may require. If there remains confusion about what a true CDP is – and what it isn’t – I welcome readers to take a look at the [CDP Institute directory](https://www.cdpinstitute.org/find-a-vendor/) and explore the shared traits from all the named vendors, which includes Redpoint Global. We welcome all newcomers to join the club, providing, of course, they meet the CDP Institute’s definition for what [constitutes a real CDP](https://www.cdpinstitute.org/news/customer-data-platform-institute-launches-realcdp-to-reduce-cdp-confusion/). The overdue acknowledgment that a CDP is not a passing fad is a great start, but just saying you have a CDP shouldn’t be enough to warrant membership. **Blog categories:** Customer Data Platform, Data Management, Identity Resolution, Omnichannel Marketing, Real-Time Personalization --- ### [Multichannel vs. Omnichannel Marketing and Keeping up with a Customer Journey](https://www.redpointglobal.com/blog/multichannel-vs-omnichannel-marketing-and-keeping-up-with-a-customer-journey/) **Published:** August 31, 2020 **Author:** Steve Zisk **Content:** What is [multichannel marketing](https://www.redpointglobal.com/redpoint-global-moves-up-in-challengers-quadrant-of-the-2020-gartner-magic-quadrant-for-multichannel-marketing-hubs/)? Broadly defined as interacting with customers via multiple direct and indirect channels in order to sell them goods and services, multichannel marketing is about engaging with customers in their channel of choice. [Global multi-channel marketing solutions](https://www.redpointglobal.com/blog/multichannel-vs-omnichannel-marketing-and-keeping-up-with-a-customer-journey/) make all channels available to the consumer (in-store, web, mobile, social, phone) for the purposes of engaging with a brand. Marketers have traditionally pursued a multichannel marketing approach for several reasons: 1. *It’s cost-effective*: If an organization knows that a customer prefers to engage on the mobile app, it makes sense to devote resources to market to that customer on that channel and, conversely, to avoid spending marketing dollars on a channel the customer avoids. 2. *It’s personalized (to an extent….)*: A multichannel marketing approach that engages with a customer on their channel of choice checks off the box for high-level personalization because it’s not technically a “one size fits all” approach. The brand is making a concerted effort to take the customer’s personal preferences into account. We’ll contrast this with more advanced personalization techniques later in the blog. 3. *It expands brand awareness*: A CPG company that relies solely on its retail partners or in-store promotions to expose customers and prospects to the product closes off a slew of opportunities to engage with customers via digital channels. 4. *It humanizes a brand*: A multichannel marketing approach provides opportunity for a brand to solidify its message and craft a narrative about its corporate values. With a presence on social channels, for example, a brand can more easily tout its sustainability programs or eco-friendly initiatives. ## **Global Multi-Channel Marketing Solutions Requirements** To achieve the above outcomes, the most important shared requirement is that a marketer know something about the customer. More specifically, it requires knowing a customer’s channel preferences and frequency, purchases and behaviors – at a starting point. If, for example, a customer visits the (fictional!) Wellesley Outfitters website and browses the landing page for mountain bikes, it behooves Wellesley Outfitters to know not only that the customer prefers to shop online to be ready with content when the customer appears, but also to know the customer’s transaction history and preferences. Presenting the right content (color, size, brand, etc.) or offer (free tune-up, accessory discount, local trail maps) depends on having a single customer view, which is the key to providing each interaction with relevance and context. A [customer data platform (CDP)](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/) facilitates a multichannel marketing strategy by ingesting data from all sources, retaining full detail of all ingested data, storing ingested data indefinitely, converting the data into unified customer profiles and making those profiles available to all external systems. Multichannel marketers cite a single customer view, improved predictive modeling and recommendations, and improved message selection and personalization as among the core CDP benefits. ## **Multichannel Marketing Limitations** The need for a single customer view to engage with today’s always-on, connected consumer across an omnichannel journey highlights one limitation of multichannel marketing; the purpose of developing a single customer view is to understand how a customer interacts with a brand across all channels, not on a channel-by-channel basis. Customer journeys are dynamic and becoming more digitally focused with customers engaging in multiple channels, on multiple devices. The key limitation to multichannel marketing is that it cannot match the dynamic nature of today’s customer journeys; it kneecaps itself, in other words, by focusing on a one channel/one customer approach. Consider the customer browsing mountain bikes at Wellesley Outfitters. The customer’s experience is enhanced when the company presents content or offers that are relevant to the customer’s journey, which will often extend beyond that one channel. But in a multichannel marketing approach, despite all the channels being available to the customer, the channels themselves are not integrated. If the mountain biker leaves the website but then goes to the mobile app, with a multichannel marketing approach Wellesley Outfitters may consider the app experience as a separate interaction. The reality, of course, is that as far as the customer is concerned it is all part of one holistic engagement with a brand. A lack of channel integration hampers personalization efforts for the same reason. Without real-time insight into how a customer is moving through various channels, brands lose the relevance and context required to delight customers with a personalized CX optimized for each interaction. If personalization stops at engaging with a customer in the channel of their choosing, minimal benefits are easily outweighed by introducing friction into the customer experience by providing a customer with an irrelevant experience that ignores the entire, holistic customer journey. ## **Mutlichannel vs. Omnichannel Marketing** While a CDP can certainly support a multichannel marketing strategy, the technology is more suited to support omnichannel marketing, which integrates channels and eliminates data siloes and data latency to enable an organization to move at the pace of the customer throughout a dynamic, non-linear [customer journey](https://www.redpointglobal.com/blog/capturing-intent-in-an-omnichannel-customer-journey-is-next-level-marketing/). An omnichannel approach connects a customer’s digital and traditional experiences, which increases customer satisfaction and lifetime value. Research from IDC shows that omnichannel shoppers have a [30 percent higher lifetime value](https://www.thinkwithgoogle.com/marketing-resources/omnichannel/omni-channel-shoppers-an-emerging-retail-reality/) than customers who use a single channel. And an Aberdeen Group survey shows that companies with an extremely strong omnichannel customer engagement have an [83 percent customer retention rating](https://lmistatic.blob.core.windows.net/document-library/boldchat/pdf/en/omni-channel-customer-care.pdf) compared to 53 percent for companies with weak omnichannel customer engagement. Omnichannel marketing more accurately reflects the way today’s consumers engage with brands across both physical and digital channels and online and offline touchpoints. The strategy enables a personalized, relevant experience that is always in the cadence of the customer journey wherever a customer chooses to engage. For more on the Redpoint omnichannel approach that puts customers in charge, and puts you in control, [click here](https://www.redpointglobal.com/omnichannel-personalization/). *Editor’s Note: A follow-up blog post will examine [omnichannel personalization](https://www.redpointglobal.com/omnichannel-personalization/) in more detail, including exploring how real-time customer engagement that is consistent across all enterprise touchpoints leads to outsized revenue gains*. ## Frequently Asked Questions \[ultimate-faqs include\_category=’omni-channel-marketing’ \] ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Outrun the Competition: Best Practices for Modernizing Your Marketing Platform](https://www.redpointglobal.com/blog/outrun-the-competition-best-practices-for-modernizing-your-marketing-platform/) **Published:** March 27, 2025 **Author:** Renee Graff **Content:** In competitive running, for weekend warriors and pros alike, the carbon plate “super shoe” is all the rage. Lighter, more dynamic, and more responsive, carbon-plated shoes regularly shave upward of 5 percent off race times. Imagine a 5 percent performance improvement from making one change, with everything else being equal. You don’t have to work harder. Keeping the same approach, you’re instantly faster, more efficient, and more effective. It’s hard to justify *not* making that change, especially when an end-of-life replacement is a certainty regardless. Sometimes we don’t even realize our equipment might be limiting. When you’ve worked with a software solution for a long time, you get used to what they offer – and it might not occur to you that there’s something else on the market that might give you better performance and bears consideration. It’s one of those “if it’s not broke, don’t fix it” situations. For marketers frustrated with legacy marketing campaign tools that are holding them back, there is a super shoe equivalent that allows you to modernize your marketing campaign platform while keeping customer data behind the company firewall. Tolerating a lesser system is no longer acceptable, particularly in such a fast-paced environment where seconds matter, and where precision marketing has a proven measurable impact on conversions, loyalty, retention, and lifetime value. ## **Check Your Gear: Are you Staying Ahead of the Game?** Periodically it’s worth checking on your software solutions to see if they’re still measuring up. Looking specifically at campaign automation platforms, recent advancements brought faster processing times for even the largest workflows, sophisticated segmentation that produces updated lists whenever segments are included in downstream audiences, no-code API connections to other tech, and real-time feedback loops. It’s not just about making your process easier, because these advancements do. It’s also about what you could be delivering in terms of campaign results, and what you could add to the mix to elevate your game. What are your opportunity costs? Let’s break it down. **First, processing times.** This is fairly self-explanatory. How fast can your current system process data, and is that slower than what other providers can deliver? Do you experience lag time when processing large data sets? It’s possible something more modern could speed that process for your team. **Next, segmentation.** Is your current segmentation static or dynamic? Modern segmentation technology includes dynamic capabilities. What makes segmentation “dynamic” is the ability for the system to automate segments once they’re created, with audiences moving into and out of outlined segments during the natural progression of customer journeys. For example, a segment that specifically excludes customers that have already completed an online application would automatically pull in an updated group of customers, as more complete the application over time. Another key element of dynamic segmentation is the ability to create a segment once and re-use it with different functional teams and across channels, even as the segment is dynamically updated. **Third, file transfers.** While batch file transfers were once the norm, today’s modern systems utilize native API connectors whenever possible. Native API connectors offer much faster and tighter connections than legacy hard-coded batch transfers that are not nearly as fast or as easy to set up. But API connectors don’t just benefit the IT teams that do the coding to “plumb” the connections. They give marketers the ability to rapidly launch, test and measure campaigns, and pivot toward the best results. If your team is still using batch transfers to send data to and from your CRM, email service providers, advertising platforms and social media, you may not be moving as quickly as you could. **Fourth, fast, real-time feedback.** A continual feedback loop that provides real-time insights that are immediately fed back into the system improves segments on-the-fly and optimizes campaign metrics. The result? Speed and greater efficiency with keeping up with your customers in the cadence of their customer journeys. No more frustrating data siloes that cloud your visibility into what a customer is doing in the moment. No more having to rely on data scientists to create offline models for segmentation that become outdated faster than you care to admit. No more having to wait a few hours to adjust your abandoned cart strategy because you’re still waiting on a/b test results. Real-time insights help you put your finger on the pulse of your customers and be ready with next-best actions at the individual level, at scale. ## **Consider the Possibilities** It can be difficult to grasp the magnitude of a change when you’re still moving forward with incremental gains, or if you’re able to show month over month improvements. But in accepting those small gains as status quo, you’re essentially benchmarking against yourself. Change starts by rethinking what’s possible. Here are some tips for narrowing the field when considering what a modern solution can deliver: - Consider solutions that have a similar feel, such as a visual workflow-based process, that would make it easier for your teams to adapt to a new platform. - Review implementation and data storage and processing against your security requirements. If your highly sensitive data is best managed behind your own firewall, seek solutions that don’t require moving your data to their cloud. - What new features or processes could reduce your costs or improve results? For example, could the inclusion of data cleansing and unification as core competencies of a marketing platform provide better downstream campaign results? Could clean audience lists reduce advertising and direct mail waste or consumption-based costs from messaging vendors? Could better segments and cross-team sharing of audiences streamline your campaigns across the organization? - Could you deprecate ancillary plug-in software solutions (and their costs) with a full-featured system that eliminates the need for them? - Review onboarding, training and support options. Does the new software provider have ample resources to help smooth your transition and achieve ongoing success? - Consult with your internal IT team. Are there maintenance processes related to your current solution that would be easier with the new vendor? What time and cost savings could be achieved over time? Concerns over a move become harder to justify when you start to see what’s possible with a modern marketing automation platform. ## **Lace Up for a Faster Future** Outdated marketing technology is like a comfortable old running shoe. It’s familiar and it keeps you moving, but it’s not going to help you win any awards. Legacy systems simply can’t match the speed, precision, and efficiency of a modern platform designed for today’s dynamic customer journeys. By upgrading to a modern customer engagement platform, you’ll move faster, respond with agility, and can gain a measurable edge over the competition. Unified customer data, real-time insights, and seamless execution across channels all become a reality – all while keeping your data securely behind the firewall. Don’t settle for incremental gains when transformation is within reach. To learn more about how Redpoint can help you reach your goals, click [here.](https://www.redpointglobal.com/landing_pages/upgrade-from-unica-campaign-to-redpoint/) **Blog categories:** Financial Services **Blog tags:** Data quality, Data readiness, Data-in-Place --- ### [Marketing Data is Too Important to Cede Control of the Security Perimeter](https://www.redpointglobal.com/blog/marketing-data-is-too-important-to-cede-control-of-the-security-perimeter/) **Published:** January 3, 2020 **Author:** Redpoint Global **Content:** Safeguarding customer data while delivering omnichannel experiences is a choreographed dance between an enterprise and marketing cloud vendors. With GDPR, CCPA, and other state data privacy laws cropping up, and with consumers demanding real-time, personalized experiences that are consistent across channels, the delicate push-and-pull is now under a glaring spotlight. It’s a challenging time. An enterprise must be compliant, yet still provide customers a personalized customer experience that has been shown to drive revenue. Consider, for example, a recent Merkle study where [66 percent](https://marketingland.com/consumers-indicate-experience-not-price-as-top-conversion-factor-263286) of consumers rated experience over price when making a purchasing decision. A recent study by [Scott Brinker](https://www.linkedin.com/posts/sjbrinker_saas-martech-activity-6615980397761179650-B99n) shows a scant 19 percent of executives believed that their cloud providers were even 75 percent compliant with their security practices. With increasing scrutiny and penalties for violations and the unspoken premise for SaaS (bypass your internal IT), it’s a real question whether SaaS models are even viable in the enterprise unless they seriously up their security game. ![](https://www.redpointglobal.com/wp-content/uploads/2020/01/Security-1024x768.jpg) How to strike the right balance brings the issue of the cloud security perimeter surrounding data to the forefront. When an enterprise moves its customer data to a vendor’s cloud solution, it puts its faith in that vendor’s security perimeter. With tightening privacy regulations, potential risks to reputation and capital are so great that companies are increasingly reluctant to make this trade-off, barring full indemnification – a non-starter for any SaaS vendor. In addition, omnichannel customer engagement requires the ingestion of customer data from multiple sources and channels. A typical cloud environment with multiple SaaS applications introduces risk by requiring that data move between applications, and between the client and vendor. Decisions on how to balance data privacy with functionality differ for every company based on size and industry, but the answer for large-scale enterprises is to own the cloud security perimeter. Particularly for enterprises with a strategic imperative to provide a personalized customer experience, this ensures visibility and control over all data that sits inside the perimeter. A previous [blog in this space](https://www.redpointglobal.com/blog/the-cloud-advantage-control-your-own-security-perimeter/) examined the Redpoint cloud advantage, a hybrid cloud solution that can sit inside of a company’s security perimeter. The deployment model preserves the control and integration needed to provide a hyper-personalized customer experience that drives revenue while mitigating risk and providing enterprise-grade security. Being able to control customer data inside your own security perimeter provides enormous advantages beyond security, especially pertaining to providing a differentiated customer experience. ## **Control at a Granular Level** The benefit of keeping all customer data “under one roof” is immeasurable, but it’s also important to have controls in place to ensure proper data management. In a crowded customer data platform (CDP) market, not every vendor can claim a data layer that does more than ingesting data from multiple sources. While most every CDP will have data flowing from external websites, internal enterprise sites, and third-party sites, there are many data requirements that direct the appropriate connections for the ingested data. Tasks such as data encryption, encryption in transit, multi-factor authentication, and audit measures ensure that data permissions are met. While the fulfillment of these data requirements are common for enterprise software outside of the marketing purview, the functionality is all too often missing for a CDP that sits in a public cloud. CDPs with advanced data management provides unmatched flexibility while policing data management guidelines to satisfy various requirements. A CDP with this functionality that sits inside the cloud security perimeter is unfettered by the typical restrictions that surface when customer data must cross boundaries between various applications. Data integration is simplified, it is accessible to those applications that need it (and accessible in real-time), and there is only one set of data security measures that the enterprise fully controls. The secure environment allows for a consistent, holistic approach to data privacy and compliance, while also giving marketers the flexibility needed to create innovative and differentiated customer experiences. ## **Best of All Worlds** The only catch for this type of environment – if it can be called that – is the business is going to have to collaborate with IT, which runs counter to the SaaS argument that the IT function can be outsourced along with data management. Deploying software within an enterprise’s private cloud gets the best of all worlds: marketers are empowered to market creatively, IT is empowered to protect the enterprise’s data assets, and the enterprise can take advantage of new innovations from a CDP software vendor. Another important point is that when an enterprise entrusts customer data to an external vendor, it usually must sacrifice records that would likely be useful if kept in a CDP within the security perimeter. A SaaS, in other words, will for the most part only handle short data records for channels and applications such as email, CRM, and other marketing records. A true, complete 360-degree customer view, however, is only possible by ingesting customer data from any source and retaining even the smallest detailed data for that customer’s lifetime. Safeguarding data privacy and providing a personalized customer experience are too important for enterprise companies to entrust their data to an external vendor. With an enterprise-grade CDP that sits inside its own security perimeter, an enterprise has the best of both worlds: enterprise-grade security and a mission-critical solution that gives marketers complete access and control over customer data to drive revenue growth. --- ### [How MilliporeSigma Masters Customer Identity and Maximizes Marketing Impact with Data Readiness](https://www.redpointglobal.com/blog/how-millipore-sigma-masters-customer-identity-and-maximizes-marketing-impact-with-data-readiness/) **Published:** October 17, 2025 **Author:** Redpoint Global **Content:** Fragmented customer data, spread across countless systems, channels, and geographies, can seem insurmountable for organizations. For global enterprises like MilliporeSigma, unifying that data to build accurate, actionable customer profiles is critical to delivering meaningful engagement. In a recent [webinar](https://www.redpointglobal.com/resources/mastering-identity-maximizing-impact-a-roadmap-for-data-readiness/) hosted by Redpoint Global, MilliporeSigma’s Customer Data Platform Product Owner Marc Brauch and Tim Perry, Co-Owner of consulting firm [Solvenna](https://solvenna.com/), joined Redpoint’s Chief Marketing Officer Beth Pfefferle to discuss how MilliporeSigma built a global data readiness foundation to overcome customer identity fragmentation, reduce reliance on third-party data, and accelerate the impact of effective marketing campaigns. ## **Building a Foundation for Data Readiness** As Brauch shared, [MilliporeSigma’s](https://www.sigmaaldrich.com/US/en) data challenges reflected those of many large organizations: siloed systems, inconsistent APIs, and no single view of the customer. The team sought to reduce dependency on third-party cookies while creating a unified first-party data strategy that could support multi-channel engagement and ensure compliance across dozens of markets. “Without a unified customer profile, we couldn’t effectively link activity to one customer or account,” said Brauch. “We needed a data readiness strategy that would let us connect fragmented systems and power a comprehensive, multi-channel approach.” To achieve this, MilliporeSigma partnered with Redpoint Global and Solvenna to achieve data readiness with the [Redpoint Data Readiness Hub](https://www.redpointglobal.com/data-readiness-hub/), with a mandate to consolidate and standardize global data while maintaining the flexibility to meet evolving privacy and governance requirements. ## **From Fragmented Data to Unified Identities** According to Perry, the first step was establishing a clear framework that aligned people, processes, and technology. “This wasn’t a lack-of-data problem. It was a data-everywhere problem,” he explained. The team tackled six core pillars: - Use case definition: Focusing on real-world applications to guide priorities. - Data architecture: Mapping which systems to integrate and when. - Data quality: Profiling and cleaning information to ensure reliability. - Unified customer profile: Resolving identities across complex B2B and B2C models. - Governance and compliance: Maintaining oversight across international regions. - Skill alignment: Training teams to adopt new tools and workflows. The process required overcoming challenges ranging from inconsistent address formats and duplicate identities to regional data privacy variations and cultural naming conventions. “The goal wasn’t just centralization, it was activation,” said Perry. “We wanted data that was not only clean, but context-ready and trusted across the enterprise.” ## **Scaling Across Regions and Teams** Operating in more than 60 countries, MilliporeSigma adopted a phased rollout of the Redpoint Data Readiness Hub to ensure compliance with each region’s data privacy requirements. “As much as this was a technical challenge, it was also a people challenge,” noted Brauch. “Change management, training, and identifying champions in each region were essential.” By emphasizing transparency and collaboration, the company fostered user adoption through regular training sessions, office hours, and success-story sharing. “Our goal was to turn this platform into an accelerator…something that boosted confidence, streamlined work, and helped teams achieve their goals faster,” Brauch added. ## **Turning Data into Marketing Impact** Once data quality and identity management were in place, the marketing team unlocked entirely new capabilities. Instead of one-off campaigns, they could now deliver orchestrated journeys that were multi-touch, multi-channel, and measurable. Key new capabilities included: - Intelligent long-tail customer engagement, reaching accounts not covered by sales reps. - Abandoned browse programs, re-engaging potential customers who viewed products online. - Predictive lead scoring, combining offline and online data to prioritize customer leads, and improve alignment between sales and marketing. “Once the data was clean and trusted, everything changed,” said Perry. “Campaigns became smarter, journeys became more personalized, and the business could scale engagement without losing precision.” ## **Real Results: From Trust to Tangible ROI** The results have been significant. With unified data powering lead scoring, marketing and sales teams have greater confidence in prioritizing outreach, resulting in faster follow-up times and stronger alignment. By comparing campaigns activated through the data readiness hub against control groups, MilliporeSigma saw higher attributed revenue, reduced reporting time, and faster attribution cycles. The team also expanded its reach to previously inactive customers, driving measurable online revenue growth. As Brauch put it, “No one measures the ROI of electricity, you just know you need it. Our data readiness platform has become that foundational layer that powers everything else.” ## **What’s Next** With a trusted data foundation in place, MilliporeSigma is focused on continuous optimization, expanding personalization, integrating with CRM systems, and advancing AI-driven segmentation and targeting. “We’ve moved from migration mode to operation mode,” said Perry. “Now we’re executing campaigns, harnessing the data, and driving more revenue.” By mastering identity and building a scalable data readiness hub, MilliporeSigma has not only improved marketing performance but also set the stage for the next era of intelligent, AI-enabled engagement. Their success highlights how data that’s fit for purpose – accurate, unified, and actionable – creates real business advantages. Explore more Redpoint resources to continue your data readiness journey: - [Why You Need Data Readiness in a Modern Data Cloud](https://www.redpointglobal.com/blog/why-you-need-data-readiness-in-a-modern-data-cloud/) - [Driving Patient Engagement Through Data Readiness: A Prescription for Improved Health Outcomes](https://www.redpointglobal.com/blog/driving-patient-engagement-through-data-readiness-a-prescription-for-improved-health-outcomes/) - [What is Data Readiness?](https://www.redpointglobal.com/resources/what-is-data-readiness/) **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [Metadata: The Unsung Hero of Data Quality & Data Readiness](https://www.redpointglobal.com/blog/metadata-the-unsung-hero-of-data-quality-data-readiness/) **Published:** August 22, 2025 **Author:** Steve Zisk **Content:** In the pursuit of delivering personalized customer experiences (CX) at scale, organizations are rightly focused on the **quality, completeness, and accessibility** of customer data. But beneath every clean dataset, accurate match, or seamless data flow lies something just as critical – metadata. Often described as “data about data,” metadata is more than a supporting player; it’s the silent enabler of data readiness, helping organizations ensure that data is not only right but also *fit for purpose* across a wide range of enterprise use cases – including predictive modeling, journey optimization, and emerging generative AI applications. As AI and machine learning become more deeply embedded in CX strategies, the role of metadata becomes even more pivotal. GenAI models need context to produce relevant outputs. Predictive models need clarity on input fields, freshness, lineage, and permissions. Without rich metadata, even the most advanced AI initiatives are likely to stumble on the fundamentals: **data trust, usability, and compliance**. Data readiness is a guiding principle – a dynamic, agile framework for ensuring that customer data is continuously made complete, accurate, and available to power enterprise goals. Within this framework, metadata is foundational, enabling t**ransparency, consistency, governance, and actionability** across every stage of the data lifecycle, and as a bridge to enabling trusted, scalable AI. ## **Metadata’s Role in the Data Readiness Lifecycle** Customer data moves through multiple stages on its path to becoming activation-ready – ingestion, cleansing, identity resolution, segmentation, orchestration. At each stage, metadata plays a vital role in providing context, meaning, and control. ###### At the Connected Data Layer: - Metadata informs how structured, unstructured, and semi-structured data is cleansed, standardized, and merged. It allows systems to recognize and treat a ZIP code differently from a currency field or a product SKU, applying default rules and validations to ensure consistency. Metadata also governs how privacy-related information is handled – marking sensitive data types like credit card numbers or medical information for encryption and access controls. ###### At the Analytics Layer: - Metadata enables the proper application of predictive models and segmentation strategies. It tells data scientists which fields are reliable inputs, when models were trained, how fresh the data is, and what permissions exist on each field. Without metadata, even the most sophisticated machine learning algorithms are operating blind. ###### At the Orchestration Layer: - Metadata ensures that customer engagement decisions respect timing, permissions, and priorities. It supports the measurement of performance and attribution, helping marketers know not just *what* worked, but *why*. This is key for building trust, maintaining compliance, and driving continuous optimization. ## **Customer Data Readiness Best Practices: What to Ask of Your Metadata** To support a data readiness strategy that is resilient, compliant, and scalable, organizations must ask critical questions about how metadata is captured, managed, and applied: ###### Is metadata automatically updated and pushed downstream? - Real-time systems require real-time metadata. Delays in propagating metadata updates – such as schema changes, data quality scores, or source availability – can lead to downstream errors, misattributions, and compliance risks. ###### Is metadata consistently applied to critical data assets? - Inconsistency breeds doubt. Organizations must ensure that semantic rules, validation logic, and enrichment standards are applied uniformly across systems, channels, and use cases. ###### Do we have the contextual, structural, and semantic metadata to support our use cases? - Can your systems reliably distinguish between a date of birth and a transaction date? Between a billing address and a shipping address? Metadata should clarify meaning at every level – field-level definitions, relationships between entities, data lineage, and operational performance. ###### Does metadata clearly define access levels, usage rights, and consent requirement? - Governance doesn’t happen in a vacuum. Metadata should encapsulate not only what data is collected, but also how and by whom it can be used. Consent, opt-ins, regulatory flags, and audit trails must be embedded and enforceable via metadata rules. ## **Why It Matters: Metadata and the Trust Equation** At the heart of a robust data readiness methodology is trust – trust that the data is **accurate, timely, and usable** for its intended purpose. Metadata underpins this trust. It makes data **transparent, traceable, and auditable**. It tells a story: where the data came from, how it’s been transformed, what it means, and how confident you can be in using it. More than ever, customers expect hyper-personalized, real-time engagement that feels relevant but not invasive. Marketers need to walk the fine line between knowing and overstepping. Metadata makes that possible. It **applies the rules, enforces preferences, and provides the guardrails needed to execute personalization responsibly and compliantly**. ## **Metadata Readiness = Data Readiness** The success of a data readiness hub or any modern engagement architecture doesn’t begin with customer data. It begins with metadata – the invisible yet essential layer that makes data intelligible, trustworthy, and actionable. Redpoint’s approach to data readiness doesn’t treat metadata as an afterthought. It promotes metadata as part of data readiness methodology and embeds metadata intelligence throughout the [Redpoint Data Readiness Hub](https://www.redpointglobal.com/), making it easier for organizations to achieve speed-to-value, personalize with precision, and ensure compliance without friction. In a data-driven world, metadata isn’t just behind the scenes – it’s behind every successful customer experience. **Blog categories:** Data Readiness, Master Data Management **Blog tags:** Data quality, Data readiness --- ### [Now is the Time to Maximize the Efficiency of a Digital Front Door](https://www.redpointglobal.com/blog/now-is-the-time-to-maximize-the-efficiency-of-a-digital-front-door/) **Published:** March 16, 2023 **Author:** Sarah Lull **Content:** Patients aren’t just patients anymore, they are consumers, meaning they are perfectly willing to change providers and health plans if their expectations aren’t met. At the same time, providers and payers are facing a shortage of skilled technical workers, making it even more difficult to resolve lingering issues around data silos, patient engagement and more. Add in an uncertain global economy, intensifying competition and an ongoing pandemic, and it’s clearly time to make the Digital Front Door as effective as it possibly can be. Here’s how to set your organization up for Digital Front Door success. ## **Understand What Patients \*Really\* Want** Today healthcare consumers have very high expectations of their interactions with a provider and this plays directly into a Digital Front Door’s success. Digital-savvy consumers know what personalized communication looks and feels like. Thanks to shopping on Amazon and browsing through Hulu, Peloton and other consumer websites, they’re unlikely to settle for less. In fact, the [Redpoint 2022 Healthcare Perspectives on Consumer Engagement Survey](https://www.redpointglobal.com/resources/2022-healthcare-perspectives-on-consumer-engagement/) found 65 percent of patients want any communication to be relevant to their needs and 91 percent said personalization was very or somewhat important to them. One-third want all their communications with a provider (in person, on the phone or online) to be exactly the same in terms of relevance, consistency and outcomes. What happens if your Digital Front Door can’t pull this together? Over 40 percent told us they’d switch providers because engagement is poor, and 38 percent would leave due to lack of personalization or patient understanding. ## **Unlock the Digital Front Door** An omnichannel Digital Front Door is more than a patient portal; including data from all digital and offline sources, it should offer a real-time view into a patient’s activities, appointments, medications, condition, etc., for a single patient view across an omnichannel journey. A Digital Front Door is a comprehensive effort that stretches well past a patient portal to include actionable data, insights, and a well-choreographed omnichannel experience. Yet siloed data, fragmented engagement systems and privacy concerns make getting to that point easier said than done. While phone calls are still the primary way patients communicate with providers, our survey found close to 80 percent of patients have used a web portal for communication. That said, gaps remain on the provider side due to a lack of comprehensive data and analytics around the consumer and their digital behaviors. This single customer view, which needs to be activated in real time, is very difficult to achieve, but critical for achieving a superior customer experience. Most providers do offer some type of Digital Front Door today, but struggle to consolidate data and communicate with one voice across the platforms. Many could certainly do better informing and educating patients about what services, providers, and channels are available and guide them to the next best step on their care journey. So while a fully-functioning and entirely cohesive Digital Front Door is the goal, the provider team’s first step must be to take a fresh look at what services are being offered, which make sense to automate and offer through a Digital Front Door, and finally to consider how best to let patients know what is available. As patient satisfaction improves through self-service, the organization will reap the benefits including: - Improved data quality - Real-time care path optimization - Reduced burden on healthcare professionals - Bridging care and equity gaps - Better patient and member retention ## **Start With Data** Data has to be the core of the Digital Front Door, but it’s also the most difficult to gather, merge and optimize for success. A single patient could have data in dozens of places, and often the data is fragmented and incomplete. A Digital Front Door strategy must include a unified customer profile that can be updated in real time so the provider team can confidently and accurately identify and communicate with a patient. While gathering all the health-related data in a single spot is critical, it’s also important to ensure non-healthcare data is included and is as robust as possible. Does the patient have transportation? Is there access to nutritious foods? What is the language preference? Is the patient open to a telehealth experience? How would the patient prefer to be contacted – text, email, phone? There is no question that casting the data net this broadly will be a significant mindset shift for many teams used to viewing medical encounters as discrete events. But the benefits of bringing everything together in a single place significantly enhances patient experience and becomes quickly apparent, especially when dealing with medically or socioeconomically complicated cases. ## **Connect the Pieces** Once every ounce of patient data has been collected, it’s time to make it actionable. This step requires the same open mindset as with data gathering: how can a team personalize the Digital Front Door experience? Being able to process and analyze the data to identify the next best action allows the patient to have a thoughtful, coordinated and easy experience whether it’s booking an appointment, paying a bill or trying to arrange for in-home care. Every interaction, virtual or in person, should offer the same access to information, services, records, and more (remember, patients are likely comparing a digital front door to shopping on Amazon). This allows for a true omni-channel experience, rather than a disconnected multi-channel experience. Having relevant and consistent patient communication increases engagement and ultimately improves health outcomes. ## **Orchestrate the Journey** With the data and the connections in place, patients coming to the Digital Front Door can have a personalized experience, which research shows is critical to patient satisfaction. A [2022 survey from CVS Health](https://www.hcinnovationgroup.com/population-health-management/consumerism/news/21273993/survey-consumers-desire-a-more-personalized-healthcare-experience) found 83 percent of consumers believe having all their healthcare providers interconnected is vital to their health, and 71 percent said it was “somewhat” or “very” important to have personalized reminders and alerts about their healthcare. Also, a Digital Front Door should be more than personalized: it can be *guided* by the provider or payer team so a patient is getting access to relevant information, next steps, care options and more. For example, a pre-diabetic patient can be offered articles on diet and prevention or be given the option to enroll in an exercise program. A solidly constructed Digital Front Door will also allow providers to reach out to patients proactively with news about new treatment plans or a walk-in clinic with a short wait time. The bottom line: a successful Digital Front Door strategy will lead a patient along a frictionless and customized care path. A Digital Front Door is an opportunity to guide patients on a path that is mutually beneficial: health outcomes and engagement will improve while the burden on providers will be greatly reduced. Providers successful in creating a unified patient record and embracing an omnichannel communication process will be well placed to leverage their Digital Front Doors and also be better prepared to accommodate future change. *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization --- ### [Rethink Master Data Management for Customer Engagement Success](https://www.redpointglobal.com/blog/rethink-master-data-management-for-customer-engagement-success/) **Published:** February 13, 2018 **Author:** Redpoint Global **Content:** Master [data management](https://www.redpointglobal.com/customer-data-management) (MDM) has gained a poor reputation over the years. This largely stems from traditional MDM projects taking a long time to produce a return on investment. As a technical and procedural discipline, however, MDM is immensely valuable. Master data technologies enable the entire enterprise to work from a single canonical database that hosts the “single source of truth” about multiple data types. The ability to centralize data and keep it current is powerful in an age of fast-growing data volumes. The canonical database created from master data management empowers brands with consistent information about consumers across organizational silos. Working from this single optimized record means that each customer can receive the right message through the right channel. In the age of the always-on customer, having and maintaining this kind of high-quality central database makes a difference. [Dun & Bradstreet recently](https://www.dnb.com/content/dam/english/business-trends/netProspex_2015_state_of_marketing_data.pdf) found that companies that regularly maintain their database can see 66 percent higher conversion rates than those that don’t. A high-quality database also leads to being able to recognize consumers across channels, in real-time. [Acxiom recently found](https://www.acxiom.com/resources/) that 70 percent of marketers can’t recognize their customers like this, which is a problem in an era where customer experience is a strong point of brand differentiation. The canonical database that MDM projects create enable this responsiveness, but only if the right tools are in place and the data is accessible where it needs to be. ## **From the Center to the Edge: An MDM Evolution** Because IT has historically led the drive toward canonical data, the traditional start point for an MDM initiative is to pick a small dataset and use it as proof of concept. This small dataset is often not the most valuable to the business at large, and beginning the process this way means it will take a significant amount of time before IT reaches datasets that are more valuable. The slow pace tends to foster a poor opinion of MDM among executives, despite its substantial value to the enterprise. How then to speed up the creation of the canonical database and make it accessible to the wider enterprise? For the process problems, I advise adopting an agile approach, which pushes the MDM technology out to the edge, not the center of the organization. This is a fundamental change in the approach and role that MDM plays in an organization. It’s also a vital one. Moving master [data management](https://www.redpointglobal.com/customer-data-management) technology to the edge of the organization increases access to the unified customer profile that MDM creates. This empowers business users, such as in marketing, to leverage master data in their customer engagement. All without needing to make requests of IT, which frees up data managers to focus on other tasks. By doing this, MDM becomes more central to day-to-day operations. This centrality would go a long way toward rehabilitating the perception of MDM in the business, which is why an agile approach is so powerful. ## **Agile MDM is Useful MDM** Master data management technology positioned at the edge of the organization is more focused on the direct needs of the business. This is a good thing. By emphasizing the needs of the business, agile MDM projects highlight the data that is most valuable to the larger enterprise. For marketers, this means cleansed and centralized customer data. With customer data organized, cleansed, matched, *and accessible*, marketers can react to customer behaviors more readily. Marketers that react to customer behaviors more readily drive better business results – and that helps everyone. Agile master data management has a broad day-to-day impact because of its openness and accessibility. With MDM more accessible at the edges of the organization, more departments are able to make better decisions because they have the opportunity to curate the data to their business specifications. As the use of agile MDM grows throughout the organization, a virtuous cycle is created that further proves the value of MDM to the organization at large. ## **The Future of Master Data Management** As data volumes increase and [data types](https://www.redpointglobal.com/blog/redpoint-introduces-enhancements-to-data-management-and-security-with-rollout-of-version-9-0/) change, it becomes more necessary to change how MDM functions in the enterprise. MDM technologies must be shifted from a centralized approach to the edges of the business to be valuable. And that can only be done with an agile approach to the solution. Brands that adopt an agile approach stand to gain a canonical customer record faster, and can maintain it more easily, versus the traditional MDM pathway. Moreover, an agile approach means that master data is curated to the needs of the business. This empowers business users to leverage the data they need when they need it, and focuses the attention of data managers on the most valuable data assets first. In this way, taking an agile approach to MDM empowers enterprises to be where preparation meets the opportunity to succeed. **Blog categories:** Customer Data Platform, Data Management, Data Quality, Master Data Management --- ### [Master Personalization Capabilities Across Anonymous Touchpoints](https://www.redpointglobal.com/blog/master-personalization-capabilities-across-anonymous-touchpoints/) **Published:** September 25, 2019 **Author:** John Nash **Content:** Today’s online-savvy, continuously connected consumer demands a personalized customer experience (CX) in every channel, and brands have – for the most part – willingly obliged when it comes to addressable channels, because they know that relevance drives revenue. Addressable channels such as email, SMS, direct mail, and call centers are where targeting based on personally identifiable information (PII) is permitted. However, privacy regulations require that many types of consumers’ online behavior data, such as ad targeting, exposure, and response data are never linked to an individual’s identity. For display on the open web, brands are never permitted to link ad campaign data to a specific consumer – regardless of the consumer’s status with the brand. The dilemma for a brand is how to deliver high-touch personalization in non-addressable channels to satisfy the consumer demand for personalization across every channel and touchpoint. Consider a recent Harris Poll survey, commissioned by Redpoint, where 63 percent of consumers said that personalization is a standard service they expect, with 43 percent saying they expect a brand to know them as the same customer across all online and offline touchpoints. Brands do not have the luxury of waiting to deliver a personalized experience until the customer appears in an addressable channel. In the Harris Poll survey, 37 percent of consumers said they will stop doing business with a company that fails to offer a personalized experience. **Digital Acquisition Platform** An enterprise-grade customer data platform (CDP) makes it possible to work with anonymized data and create a personalized customer experience in non-addressable digital channels. The Redpoint Digital Acquisition Platform, powered by LiveRamp, gives clients a privacy-compliant environment for working with anonymized data. Through our partnership with LiveRamp, Redpoint clients can onboard PII-based records to LiveRamp, which removes all PII and anonymizes the data. Clients can then activate these audiences across any of the 500+ destinations LiveRamp supports. In addition to activating audiences across the AdTech ecosystem, LiveRamp can also return anonymized audiences to a Redpoint client’s auxiliary anonymous database. Using this anonymous version of their CDP, clients connect different data types such as transaction, promotional history, and impression data to the anonymized records to give a more robust picture of advertising audiences. Anonymized records are never re-identified in the Redpoint Customer Data Platform. Companies can apply many of the same marketing capabilities they use with PII-based records such as modeling and segmentation. Brands interested in personalizing a customer experience across all touchpoints can now create and operationalize models for acquisition, reactivation, retention, and suppression – activating them in non-addressable channels to optimize the customer experience for specific business goals. **Recognize and Personalize for First-Time Website Visitors** Some brands have struggled with personalizing content for first-time visitors to their website. Consider research from Quicksprout, which found a [benchmark average bounce rate](https://www.quicksprout.com/how-to-decrease-your-bounce-rate/) between 40-60 percent for content websites and up to 40 percent for retail sites. Given the significant acquisition costs associated with driving traffic to your site, there’s significant ROI to reducing the bounce rate. Relevant content from the very first visit accomplishes this. Thanks to real-time engagement from Redpoint clients can use an IdentityLink to personalize site content. When a visitor arrives without a cookie, a pixel requests an IdentityLink from LiveRamp. Within milliseconds, the site takes the IDL, locates information associated with it within the anonymized database and customizes content based on attributes or preference data. This means a sporting goods retailer could show racquets to a tennis player and golf clubs to a golfer who’ve never visited the site before. A generic experience, on the other hand, will not move the needle. According the a recent report from Merkle, [52 percent of consumers](https://marketingland.com/consumers-indicate-experience-not-price-as-top-conversion-factor-263286) surveyed said that they have left a website while shopping because of a poor site experience. **Know Everything There is to Know About a Customer** From the customer’s perspective, seamless personalization across addressable and non-addressable channels reduces friction and enhances loyalty. A first-time visitor to a website presented with personalized content has a higher propensity of becoming a second-time visitor. Brands that extend consumer insights gained from their CDP into the anonymous ecosystem not only reduce waste and improve relevance, they’re also rewarded with the profitable growth that comes from loyal customers. Personalized inbound marketing has its challenges, but the challenges do not outweigh the customer’s expectation for personalization across a dynamic customer journey. Taking the easy way out by waiting to deliver personalization until a customer appears in an addressable channel is no longer tenable in an environment where the consumer has control over the customer journey and expects it to be unique according to their preferences and interests. Consumers say that they value [personalization over price and product](https://marketingland.com/consumers-indicate-experience-not-price-as-top-conversion-factor-263286), and brands need to take this desire to heart to differentiate in today’s hyper-competitive market. **Blog categories:** Anonymous to Known, Customer Data Platform, Identity Resolution, Real-Time Personalization --- ### [Does Your MarTech Stack Measure Up? Risk vs. Reward in the Age of Customer Experience](https://www.redpointglobal.com/blog/does-your-martech-stack-measure-up-risk-vs-reward-in-the-age-of-customer-experience/) **Published:** February 13, 2020 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/02/2-13-zisk-blog-300x225.jpg)Reassessing any part of the MarTech stack is always a game of risk vs. reward. This holds true really for any enterprise technology, but it seems the burden weighs particularly heavy on the shoulders of marketers and the CMO. The acute need to create a personalized customer experience demands a modern, integrated customer engagement platform. This fact may take marketers outside their comfort zone as far as technology limitations they might otherwise countenance. Seen in this light, an honest reassessment will accept more of the common risks of upheaval associated with people, processes, and technology change. This is particularly true if the end result is to build and deploy a unified view of the customer that will empower marketing to deliver personalization, real-time customer interactions, and next-best actions across an omnichannel customer journey. The power of a unified view – a persistently updated golden record that provides a 360-degree customer view – should erase any doubt whether an existing platform that fails to deliver the capability is worthy of replacement. Consider the [2019 BRP Unified Commerce Study](https://brpconsulting.com/download/2019-unified-commerce-survey/), where 87 percent of consumers surveyed said that it is important to receive “a personalized and consistent experience across channels.” Or a 2019 [Merkle report](https://marketingland.com/consumers-indicate-experience-not-price-as-top-conversion-factor-263286) where two-thirds (66 percent) of consumers surveyed rated customer experience over price when it came to making a purchase decision. **Benefits and Opportunity Tilt the Scale** A recognition that delivering a personalized customer experience drives revenue should make some of the inherent uncertainty around a new software implementation more palatable, particularly if a solution being replaced is graded against the new possibilities. With typical nagging doubts out of the way, the primary organizing principle during a reassessment must be an ironclad understanding of specific goals and expectations. If the objective is, as outlined here, to possess a single customer view in the form of a golden record, marketing must have a defined use case in mind. The question, then, becomes less about the short-term effect of the transition and more about assessing which customer data platform (CDP) is right for the business. Infrastructure change becomes another important factor because of a CDP’s role as the centerpiece of an engagement platform. We’ve outlined previously the [five core requirements](https://www.redpointglobal.com/blog/key-questions-to-ask-when-evaluating-an-enterprise-cdp/) a CDP must include, which including ingesting data from all sources. Satisfying these requirements involves a host of secondary considerations when weighing potential risks – because of the CDP’s reach and potential to transform marketing operations. By ingesting data from all sources, a CDP provides enormous ancillary benefits having to do with better flexibility and scalability – as well as enterprise attributes to include performance, security, and privacy. In that regard, an assessment should weigh whether a change will set the MarTech stack up for much easier changes and enhancements in the future. A CDP that fulfills the five core requirements, in other words, doesn’t just set marketing up with operational flexibility in delivering a superior customer experience, it also future-proofs the business. By ingesting customer data from all sources, the right CDP renders complicated multi-wave integrations obsolete and minimizes frustrations of bringing people and processes up to speed with every new implementation. Think of a CDP with an open garden architecture as the hub of a spoked wheel; new components shouldn’t create people, process, and technology uncertainties when the “hub” is done right. The architecture provides a single point of operational control over data, decisions, and interactions – regardless of the designated use case. When business objectives change, the bespoked configuration abstracts the day-to-day user from complexity while maintaining the single point of control. **Making a Good First Impression** Of course, a full ex post facto ROI accounting will determine the accuracy of a risk vs. reward calculation, which makes it especially important to mitigate as much risk beforehand as possible by securing more than just vendor assurances when selecting a CDP. When performance metrics are determined up front, it will be revealed very quickly post-implementation whether a CDP is delivering on its promises. Those metrics might relate to real-time performance, or whether advanced identity resolution is really bringing together online and offline facets of the customer record to create a true and accurate 360-degree view. Or those metrics might be more directly related to integration – whether the CDP delivers on a promise of open garden connectivity that truly delivers a single point of control. And if the CDP falls short of expectations, the delivery of a personalized customer experience is too important to either wait for things to improve, point fingers, or – worst of all – ignore what’s happening. When [63 percent of consumers](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) say that a personalized experience is a standard service they expect (according to a Harris Poll survey commissioned by Redpoint), it’s far better to cut bait early than late. If you’ve already made the determination that your existing MarTech stack falls short in transforming customer engagement, know that there is a CDP out there that is worth the short-term risks. **Blog categories:** Customer Data Platform, Data Governance & Security, Data Management, Data Quality, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Why Marketing Must Take the Lead on Creating a Personalized Customer Experience](https://www.redpointglobal.com/blog/why-marketing-must-take-the-lead-on-creating-a-personalized-customer-experience/) **Published:** June 13, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/06/Marketing-Lead_587384861-e1560366817890.jpg)IT has long been a valuable partner to marketing. Particularly for multi-channel B2C marketing programs, marketing has leaned heavily on IT to aggregate, clean, and segment data and to deploy technology to connect with customers. Needing some type of structure to facilitate ongoing management of these efforts, businesses have traditionally organized around product lines, channels or business functions. In rare cases, they are structured around customer segments, though these segment lines are typically based on one or two dimensions and are thus drawn broadly. This has proven to be an efficient way to run a business in an era where *profits* are king, and *products* provide a high degree of differentiation. Something else is called for in today’s era where *customer experience* is the key source of differentiation, and *profitable growth* is king. According to Gartner, fully [81 percent of companies](https://www.gartner.com/en/marketing/insights/articles/key-findings-from-the-gartner-customer-experience-survey) expect to compete mostly or completely on the basis of customer experience. With a personalized, differentiated customer experience now a strategic imperative for brands looking to increase revenue and customer lifetime value, the long-standing partnership between marketing and IT is evolving. To achieve this level of differentiation, brands must now coordinate resources with a primary emphasis on the customer, not the business. The good news is that current technology enables a “virtual” organization around the customer, even if the formal organization remains as-is. The cornerstone technology elements are ones that meet the empowered customer’s expectations for a seamless experience in an omnichannel environment. A recent [Harris Poll survey, commissioned by Redpoint](https://www2.redpointglobal.com/white-paper-customer-experience-harris-poll?_ga=2.223423323.1761186708.1559569484-1570104466.1540307570), underscores the importance of personalizing the customer journey and highlights the gap between customer expectations and what marketing currently delivers. While just 18 percent of consumers polled said that brands deliver an excellent customer experience, 34 percent of brands said that they do. More than one in three customers said they are very frustrated when a brand fails at personalization staples, including sending an offer for something they recently purchased, an offer that is irrelevant, or when a brand does not recognize them as an existing customer. Close to 40 percent said that they will stop doing business with a company that does not offer a personalized experience. A recent [Accenture study](https://www.accenture.com/us-en/insight-exceed-expectations-extraordinary-experiences) also put a price tag on personalization failures, estimating that they cost US firms $756 billion and $2.5 trillion globally. **Personalization Investments on the Rise** CMO investments indicate that personalization is more than a passing fad. According to the most recent [Gartner Spend Survey](https://www.gartner.com/en/marketing/insights/articles/8-top-findings-in-gartner-cmo-spend-survey-2018-19), which analyzes marketing technology spending and key budget trends, “personalization has emerged as a strategically important marketing capability, given the increased focus on customer experience and the fight for customer attention.” The survey reports that CMOs spend an average of 14 percent of their budgets on personalization, with double-digit averages across all industries and business models. In addition, new research from exec search firm Spencer Stuart reports that the average CMO tenure is dropping, falling to an [average of 43 months](https://www.marketingdive.com/news/study-average-cmo-tenure-drops-a-month-while-minority-chiefs-lose-ground/556400/), down from 44 in 2017. Marketing Drive posits that one reason for the decline is that CMOs, already overseeing all branding, marketing, advertising activity, and related technology, are now also expected to be responsible for customer experience, personalization, and company revenues. With the recognition that personalization drives revenue, marketing becomes the chief stakeholder for creating personalized customer experiences that are relevant for a consumer in the context and cadence of each individual’s customer journey. Marketing is better positioned than IT to take the lead because marketing sits closer to the consumer and has an “ear to the ground” for what motivates the continuously connected consumer. In this dynamic, marketing does not have the time or luxury to request new or different customer data from IT or to build static product-based programs that fail to recognize each customer as a segment-of-one. As marketing shifts from a support function to a mission-critical line of business and a revenue-generating engine, IT shifts from focusing on efficiency and infrastructure to enabling revenue and innovation. **Don’t Be Left Behind** In the same Harris Poll survey referenced above, 76 percent of marketers surveyed acknowledge that they have “significant room for improvement” in delivering a consistently exceptional customer experience. Asked to expand on the challenges inherent in delivering this experience, roughly half reported that real-time engagement and customer understanding posed the biggest challenges. The cause of these challenges, marketers said, was not a lack of data but rather fragmented engagement systems that fail to connect or deliver a unified view of the customer across touchpoints. Fragmentation is the reason why, according to the Harris Poll, 63 percent of marketers are unable to execute their customer experience strategy very well. For marketers to take the lead in delivering an exceptional customer experience that customers demand, they must resolve these key challenges with available technology. To accept limitations is to either ignore the truth that personalization drives revenue, or to take the chance that your business will remain competitive with marketing remaining a support function. **Give Marketers the Right Capabilities** The root cause of the customer engagement gap is a capabilities gap, as evidenced by how many brands struggle to execute their strategies. The first order of business for marketing to lead on is to create an enterprise-wide, easily accessible customer data capability because it is the key to personalization at scale. Personalization is impossible without real-time access to all first-party, second-party, and third-party customer data that make up a [single customer view](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/). Marketing needs to spearhead the creation of this golden customer record as the foundation for creating a hyper-personalized customer experience. Data siloed by marketing channels is, for all intents and purposes, useless for marketing teams that must fashion a real-time response to a customer in an omnichannel buying journey. A real-time response at scale requires AI and machine learning models that serve as a data activation layer, and intelligent orchestration of next-best actions no matter which channel an individual customer shows up in. As the primary stakeholder for personalization, marketing must oversee the models, testing, tuning, and tweaking as necessary with support from IT. Marketing’s bona fides as a creative force underscores the need for marketing to lead a top-down personalization initiative; they can better interpret what’s working and why and more quickly spin up new models that reflect the changing pulse of the consumer. For marketers to steer the personalization ship across the enterprise without having to combat data siloes that are the bane of keeping pace with the customer, they need to have a single point of control over data, decisions, and interactions. A single point of control combines data of any source, type, or cadence with in-line analytics and an intelligent orchestration layer to offer a personalized next best action to an individual at any phase of a customer journey. Effectively accomplishing this will lead to breakthrough levels of differentiation and revenue growth. **RELATED ARTICLES** [Data as a Revenue Engine: Monetize Your Data with a Single Point of Control](https://www.redpointglobal.com/blog/data-as-a-revenue-engine/) [Addressing the Gaps in Customer Experience: Redpoint Global/Harris Poll Benchmark Survey](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![cx ebook cover image](https://www.redpointglobal.com/wp-content/uploads/2019/04/CompetingonCX-ebookcover-1024x768.png)](https://www2.redpointglobal.com/ebook-competing-on-basis-of-cx) **Blog categories:** Customer Data Platform, Data Management, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization --- ### [Ambitious Marketing Goals Deserve an Ambitious Marketing-IT Partnership](https://www.redpointglobal.com/blog/ambitious-marketing-goals-deserve-an-ambitious-marketing-it-partnership/) **Published:** June 19, 2020 **Author:** Steve Zisk **Content:** Research from Gartner shows that best practices in customer-centricity often yield greater than [20 percent increase](https://www.gartner.com/smarterwithgartner/is-your-organization-customer-centric/)s in response rates. Best practices include using transactional, preference and historical data along with behavior across devices, IoT and sentiment analysis, and data of every type and from every source in order to deliver a personalized, omnichannel customer experience. Firm evidence that customer-centricity drives new revenue explains why marketing is increasingly viewed as a mission-critical organization, aligned with overarching enterprise goals. Digital transformation acceleration spurred by short-term and long-term disruptions make it even more important to personalize the customer experience, and further elevate marketing as a partner to the enterprise. As such, marketing technology – particularly mission-critical, customer experience software – is no longer relegated to marketing as the traditional sole user/buyer. Rather, a strong collaboration between IT and marketing is a necessary foundation for creating a digital experience platform. ## **Be Ready When Disruption Strikes** Because customer data has always been associated with high value and high risk, a marketing-IT collaboration is not an entirely new proposition. A myriad of disruptions that alter the customer experience landscape and bring customer experience to the forefront simply increase the urgency. Disruptions include Amazon’s imprint on traditional brick-and-mortar retail, a shift to a service economy, GDPR, CCPA and other privacy compliance mandates, and the demise of the third-party cookie, among other factors. Overshadowing all of these challenges is the immediate disruption caused by COVID-19. Each of these factors heighten the need to interact with customers with relevance and consistency across multiple digital channels, while using data in accordance with an individual’s preferences and recognizing the customer as the ultimate authority of the customer journey. In this context, the capabilities of a data-driven, digital customer experience platform must clearly exceed tasks traditionally assigned to “small m” marketing – the shiny new toys, if you will, that might offer incremental, single-channel or single-campaign enhancements. ## **New Partners, New Roles** If we accept the premise that a digital experience hub is becoming a mission-critical platform, then this clears up some questions around how IT and marketing should partner to deliver on the promise and opportunity of a digital customer experience. With marketing now operating on equal footing with the enterprise and no longer independently pursuing departmental or local means to its own ends (stitching together identities, scattershot collection of customer data, etc.), IT’s role is to cement the new partnership by owning the enterprise aspects of the underlying customer experience platform. This includes tasks and capabilities such as reliability, security, locality and universality that ensure that a digital experience hub is properly designed to deliver high availability, scalability and high performance. IT must also own the quality parameters and customer-facing metrics of the business. With customer experience in the enterprise fold, IT must be responsible for making sure that those previous independent tasks (data collection, etc.) have an enterprise purview, and will thus meet customer requirements for a consistent, seamless experience beyond traditional marketing interactions. IT becomes the ultimate arbiter of ensuring that the quality, consistency and performance of information used for such purposes are adequate to the task. ## **Collaboration Unlocks Ambition** What role does that leave for marketing as a “first among equals” organization largely responsible for driving new revenue? With IT delivering the data-driven customer experience hub, marketing is better equipped to seek out innovative use cases. With tools, people, and processes in place to enable digital transformation, marketing can dive into what it really means to understand the cadence of the customer, to map the omnichannel customer journey, and to build the automated machine learning models that will be necessary to meet the particular goals of each innovative use case. Marketing must, in other words, break out of a “business as usual” mindset, shifting its primary focus to gaining a deep understanding of a customer’s needs and wants as they relate to an omnichannel journey. Operationally, this will – or should – replace traditional spray and pray strategies with more thoughtful, more experimental and more measurable ways of interacting with a customer with consistent relevance. This “agile marketing” mindset also requires that IT itself goes beyond “Big IT” project to deliver a fast, flexible and customer-friendly development and deployment model to avoid forcing marketing to slow down innovation. The need for IT and marketing to partner in driving a digital customer experience stems from an acceleration of short-term and long-term digital disruption, which alters long-standing roles. The partnership strengthens when each player adopts somewhat new roles; IT’s responsibility is to ensure the underpinnings and the customer, organization and jurisdictional requirements of the digital experience are met, and marketing’s responsibility is to become more forward-looking, agile and flexible as it makes the delivery of a consistently relevant, personalized customer experience its main focus. By bringing these elements together, an enterprise’s digital transformation initiative will meet customers where they are through the current disruptions, and through disruptions to come over the next decade. Redpoint recognizes the challenges brought on by an emergence into a data-driven, digital-first new reality, which is why the [Redpoint CDP](https://www.redpointglobal.com/cdp/) was built from the ground up as the enterprise-ready, digital experience platform of record that fosters a strong partnership between marketing and IT in pursuit of ambitious customer experience goals. **Blog categories:** Omnichannel Marketing, Real-Time Personalization --- ### [Marketers Beware: CRMs Plus DMPs Don’t Equal a Customer Data Platform](https://www.redpointglobal.com/blog/marketers-beware-crms-plus-dmps-dont-equal-a-customer-data-platform/) **Published:** February 8, 2017 **Author:** Steve Zisk **Content:** Acquiring and maintaining a true 360-degree customer view is perhaps the most desired goal in the entire marketing field. It’s also one of the most valuable to customers, so every provider of marketing technology products attempts, at some point, to make the case that they can provide that 360-degree view. And for several years we’ve all been told that we can just dump all that data in a data management platform (DMP), connect it together, and we now have a marketing database source of record. “Don’t worry,” we were told, “it’s all good and you’ll love it.” But lately, it’s become more and more clear to the market that this is not exactly correct. To those of us who actually look at corporate data sources, it was always curious to note that missing part about making sure the data was correct, standardized, matched, and keyed. The latest version of attempts to continue pretending to be a marketing source of record suggests that pairing a DMP with a customer relationship management (CRM) solution can provide a 360-degree customer view throughout the entire anonymous-to-known customer interaction chain. DMPs can handle any type and structure of data, so the story goes, but CRM interfaces provide the best possibilities for sourced data because they allow you to link web behavior data with known customers and track it. As the old adage goes, “If it sounds too good to be true, it probably is.” Well this DMP/CRM pairing is no exception. The strengths of a [customer data platform (CDP)](https://www.redpointglobal.com/cdp/) are separate and distinct from that of a DMP and CRM. Chaining these two solutions together won’t provide the kind of cross-channel insights, or intelligent orchestration, required to fully leverage customer data in the always-on, multichannel world that marketers inhabit or provide the 360-degree customer view. And certainly not to the highest quality a CDP does. Having the DMP treat a CRM as a “source” while then holding all the other non-CRM sources does not a CDP make. ### **DMPs Don’t Have the Same Data Powers as CDPs** DMPs have told the same story for years: they can handle any data no matter the structure or cadence, can perform any attribution, send emails, perform advanced segmentation, handle personally identifiable information (PII), interface with demand-side (DSP) and supply-side platforms (SSP), conduct look-alike extensions, do organic retargeting … and so on. Essentially, making the case that they can do anything you need from a data perspective. That’s not entirely true. DMPs can’t perform nearly the amount of functions that are intimated, and the proof in this is the number of customers with current DMP-centered stacks out on the prowl for a way to get their data from the silos (one of which is the DMP itself) and into a single place – reversing the trend and turning the DMP back in to what it is – another channel. The DMP is a channel that can do amazing things, and the DMP itself interacts with specialized channels. What DMPs can do, and do exceptionally well, is typically interface to DSPs to buy and serve up advertising, perform look-alike extensions for audience acquisition, and facilitate organic retargeting. All of this is largely centered on interactions based on cookie/ID data (whether “pooled” or not) that identifies devices instead of individual customers and hopefully matching some percentage to first-party data. Sure, DMPs can on-board PII (sort of), but there’s a fundamental truth that we in the [data management](https://www.redpointglobal.com/solutions/redpoint-data-management/) business understand: *ingesting data isn’t the same thing as making it actionable everywhere*. First-party data is used as the “jumpstart” into onboarding processes, but is separately handled (and can take days), and thereafter is distinct from second- and third-party data. And it’s only actionable in the connection span of the DMP. The first-party data is tagged in the DMP repository, but unless regular (read: batch) updates happen, it stays the same, except for what is in the connection span of the DMP. In contrast, the CDP tracks *both* anonymous and known interactions across the entire universe of interaction channels and identifies customers in addition to devices. Given the same data, plus DMP audience information as an inbound channel, the CDP can make real-time decisions across the entire spectrum, can do it using the very most current state and status information and can make decisions of arbitrary complexity, rather than simple “audiences.” DMPs may be able to ingest PII, but they don’t necessarily serve it back to you or, for that matter, tell you how it all ties together, or improve its quality – fundamental elements to any business serious about putting their data to work. Furthermore, the data locked in a DMP also can’t be leveraged elsewhere in the business, which creates another data silo that prevents true business insight. On the other hand, the CDP absorbs any variety of data and links it, allowing the full spectrum of the business beyond marketing, from manufacturing and supply chain to store manager, to easily and readily consume it without further ado. ### **What About Adding a CRM? (DMP VS CRM)** What happens when you consider adding a CRM to the mix? CRMs contain profiles of known customers, so it stands to reason that linking a CRM and a DMP would solve the problem of tracking customer behavior from anonymous to known across channels, right? Not so much. CRMs are largely focused on customer transactions. If a customer calls, emails, or buys, most CRMs record the transaction and allow access to the entire transaction history, so you know how best to respond to an inbound customer interaction. As a result of this set up, CRMs are strongest when deployed in support of contact centers, for sales reporting, or for order/service tracking. Again, it isn’t that CRM solutions aren’t valuable in the decision domain, but only as one more input to (and one more output from) the CDP. A CRM isn’t sophisticated enough for 360-degree marketing, which is why there’s a host of other marketing solutions available for activities like email, social marketing, advertising, and related marketing tasks. In addition, they are notorious for creating and exacerbating data quality issues like record duplication. Moreover, CRM systems don’t have the MDM ability needed to produce a truly coherent series of persistently keyed records representing people, devices, cookies, DMP identifiers, voice of customer data, ratings information, weather, IoT data points, in-home micro-inventory management and so on. Only a properly deployed CDP can really handle all this complexity in a consistently seamless way. To truly support the business, these are things that just must be done. Data lineage is not accomplished by dumping CRM data into a “schema on read” style repository. In other words, CRMs can’t reconcile multiple profiles of a single person, device, or visitor, which is one of the most powerful (and valuable) functions of a [CDP](https://www.redpointglobal.com/blog/the-role-of-a-cdp-in-multi-touch-attribution/). Even when you link a CRM and a DMP together, there’s little to no integration of a return trip to serve up a new offer based on an action, or lack of one, or communication with other channels, especially if they aren’t the favorite integration channel of the CRM product chosen. It is simply trading one set of handcuffs (silos in the DMP-centered stack) for another (a CRM solution, with silos in the DMP-centered stack). In essence, the DMP-centered stack product is NOW saying “well, we do care about good quality data sources now, and we choose a CRM system for that.” But the handcuffs are the same, just with a few more sparkly things to distract from the actual issue with having a purported system of record without a fully integrated data solution. ### **Get a Customer Data Platform for a 360-Degree View** If you really want a 360-degree view of your customer, and most marketers do, you need to leverage [a customer data platform](https://www.redpointglobal.com/customer-data-platform). The Redpoint CDP is designed to sit behind all the channels of interaction and provide a global view of customer behaviors, actions, and data to drive cross-channel orchestration and conduct hyper-personalized campaigns across anonymous and known interactions. *DMPs and CRMs are channels of interaction*. Channels that perform their core tasks exceptionally well, but channels all the same whether inbound, outbound, or both. CRMs are customer-focused to the extent that they can be and are valuable in their designed space, but they can’t be used to enable marketing and the rest of the business in the same way a CDP can. Similarly, DMPs are vital to managing certain online interfaces and do lookalike extensions exceptionally well, but they can’t track customers along the full anonymous-to-known interaction cycle while also being a data source of record. They don’t give any finished data back to the business for use outside of their designed space either. Contrast this with CDPs, which ingest multiple types of data of any type from multiple channels, integrate it all together, and allow for high-level visibility that can be used to orchestrate cross-channel customer engagement and serve and enhance wider business functions beyond marketing. With a CDP, you’re able to serve business users that require data at any level of detail—across anonymous and known interactions—and no matter if you’re looking at online or offline engagement or “n” party data. That’s the power of a CDP, and why the DMP/CRM pairing just can’t compete. So, if you’re looking for a platform that can give you a true 360-degree view of the customer and provide cross-channel insights paired with intelligent orchestration, then you need a customer data platform —not a cobbled together solution that tries to pass off as a pseudo-CDP. **Blog categories:** Customer Data Platform, Single Customer View --- ### [Map Your Own Personalization Journey with the Redpoint CDP Builder](https://www.redpointglobal.com/blog/map-your-own-personalization-journey-with-the-redpoint-cdp-builder/) **Published:** March 19, 2021 **Author:** Liam Eckert **Content:** One of the hottest new trends for marketing technology vendors is to refer to their product as a customer data platform (CDP). According to the CDP Institute’s industry January update, the industry added 13 vendors and $250 million in funding in just the second half of 2020, and 2021 revenue is expected to reach $1.55 billion, a 20 percent increase over 2020. However, not all CDPs are created equal. To help you find exactly what functionality you need in a CDP – and what you don’t – Redpoint designed a [custom CDP builder app](https://www.redpointglobal.com/#molecule) that enables you to discover the specific capabilities your CDP will need based on your industry and use case(s). The CDP builder is referred to as the #Molecule. Like the scientific version – a group of atoms representing the smallest unit of a chemical compound that can take part in a chemical reaction – the Redpoint CDP builder enables you to visually map the granular functionality that you will need to meet your use case or the specific business goal you’re trying to accomplish. The “chemical reaction,” in this case, being a personalized, hyper-relevant experience that is always in the proper cadence of an individual customer journey. The intention is to get the conversation started by showing visitors to the website what they need at a tactical level. Often, terminology seems to exist in a vacuum, making it difficult to understand how functionality is tied to solving for a specific use case. With the Redpoint solution finder, terminology is matched to the business problem at hand, helping users understand the key steps and functionality needed to accomplish the desired goal. Easy to use, the #Molecule will help you map a CX “DNA” that is unique to your company’s vision for a differentiated customer experience. For instance, marketers may understand the value of a [single view of the customer](https://www.redpointglobal.com/single-customer-view/), where customer data of every type and from every source comes together to form a golden record. But fewer marketers understand that [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/), real-time persistent updates and a [real-time decisioning engine](https://www.redpointglobal.com/orchestration/real-time-decisions) are among the key features that give a golden record its power to deliver a hyper-personalized CX. By showing marketers the core components that they need, we can show them how Redpoint can help at a tactical level – making it easier to understand the value proposition vs. what other CDP vendors may deliver. ## **How it Works** The customer CDP builder is easy to use. Website visitors are asked just two questions: What are you trying to accomplish, and what is your industry. Use cases include real-time [personalization](https://www.redpointglobal.com/omnichannel-personalization/), [journey orchestration](https://www.redpointglobal.com/orchestration) and data integration. A user who selects real-time personalization and retail/CPG (**see Figure 1**) will see the logo for the rg1 customer experience platform at the center of the molecule, with the attaching “atoms” of DM (data management), RPI (Redpoint Interaction, aka intelligent orchestration layer) and AML. ![Redpoint CDP Builder ](https://www.redpointglobal.com/wp-content/uploads/2021/03/molecule-real-time-personalization-300x226.png)**Figure 1**: The Redpoint solution finder showing the molecule for real-time personalization for a retail/CPG company. The “atoms” show the components of Data Management that yield real-time personalization: Real-time data layer, identity resolution, data enrichment and prebuilt data connectors. This screenshot highlights the DM components integral to real-time [personalization](https://www.redpointglobal.com/omnichannel-personalization/). Hovering over AML or RPI on the solution finder will similarly highlight the key functionality of those components. The purpose of providing a simple visual representation is to help users quickly identify the functionality and capabilities needed to accomplish their business goals, and to break down and explain that functionality clearly and concisely. It also helps users quickly find relevant content about rg1 and its capabilities pertinent to their specific use case. The color-coded atoms (an orange outline for RPI, etc.) correspond to an option on the page to learn more about the components within. Choosing DM, for instance, will present a drop-down menu for each of the four components. After selecting an option, a user is presented with a detailed explanation of benefits along with any applicable explanatory content – videos, case studies, solution briefs, white papers, etc. Explore the [\#Molecule](https://www.redpointglobal.com/#molecule) today to find out how Redpoint can help you build a CDP that solves for your specific use case(s) and delivers superior, hyper-relevant experiences across an omnichannel customer journey. ## **Related Content** [A CDP Implementation Reference Guide: What You Need to Know](https://www.redpointglobal.com/blog/a-cdp-implementation-reference-guide-what-you-need-to-know/) [Prescriptive, Predictive & Insight: Tools for a CDP as a Digital Transformation Engine](https://www.redpointglobal.com/blog/prescriptive-predictive-insight-tools-for-a-cdp-as-a-digital-transformation-engine/) [How Does Your CDP Stack Up? The Real-Time Difference](https://www.redpointglobal.com/blog/how-does-your-cdp-stack-up-the-real-time-difference/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Identity Resolution, Real-Time Personalization --- ### [Make the Best Use of Your Customer Data Platform (CDP) with GenAI](https://www.redpointglobal.com/blog/make-the-best-use-of-your-customer-data-platform-cdp-with-genai/) **Published:** July 26, 2024 **Author:** Steve Zisk **Content:** According to the latest Gartner Technology Marketing Survey, 14 percent of martech leaders are already deploying generative AI (GenAI), and 63 percent plan to within the next two years. Those who have deployed GenAI have reported increases in productivity across several proven use cases, including content creation and next-best action optimization. GenAI, says Gartner, will evolve rapidly in both capability and commercialization, enabling brands to effectively reach prospects and customers as an “essential part of the fabric of marketing.” What does this mean for the use of [GenAI](https://www.redpointglobal.com/ai/) specifically in the context of a customer data platform (CDP) for the purposes of simplifying a marketer’s job or to improve upon core functionality of collecting and storing data, performing data cleansing, enrichment and [identity resolution](https://www.redpointglobal.com/identity-resolution/), building a Golden Record, [segmentation and activation](https://www.redpointglobal.com/segmentation-activation/)? If the overall purpose of a CDP is to develop a better understanding of a customer to enable a more personalized customer experience (CX), GenAI helps accomplish that goal in several ways. First, GenAI can be a valuable tool in helping to build, enhance and maintain a Golden Record, working with machine learning models to understand the context and relationships between data points, analyzing customer data details, and leveraging a deep understanding to improve match and merge accuracy. ## **Simplified Segmentation with GenAI** Second, GenAI augments the democratization of customer data available and accessible across the organization. It does this by significantly reducing both the amount of time and number of iterations for building audience segments. Even without GenAI, a robust, enterprise-grade CDP should already provide marketers with a no-code environment with which to build and refine dynamic segments – minimizing the dependency on data scientists and IT teams. With GenAI, the process is simplified even more through a natural language interface and the use of large language models (LLMs) to allow marketers to ask questions about their customer data. A marketer might use a conversational interface to generate an audience, for instance, by saying “Build me a segment of Midwesterners who expressed an interest in an outdoor gas firepit over the last six months.” The marketer might also ask probing follow-up questions. Which of these customers also expressed an interest in patio furniture? Which have an average annual spend of over $1,000? How many have a natural gas line into the house? Perhaps the marketer, failing to see anything unusual or interesting in the dataset, asks the model to probe further, “tell me something about this audience that will maximize value in an email campaign that offers a free set of decorative lava stones for a purchase of a firepit over $500.” Using GenAI simplifies the identification of patterns, trends and correlations that a marketer might otherwise miss, and it does so in a fraction of the time it would take versus either having to write code, or even use a standard drag-and-drop UI. Generating segments and producing better segment insights is a prime GenAI use case for a CDP, which falls into what Gartner deems high-value, high-feasibility in its GenAI use case prism for marketing in the category of content co-pilot. ## **An Activation Assist Using GenAI** Third, when it comes to data activation, GenAI can be indispensable in helping to generate personalized content for different segments and ensuring that the chosen messaging resonates. Familiar content assets include email content, social media posts and website personalization, and a marketer might ask a GenAI framework to “create five email subject lines for our fire pit campaign that fit our brand voice and would appeal to Gen X buyers.” Similarly, GenAI can be a valuable tool in predicting the right channel and the right time to engage with a customer, and in generating real-time recommendations such as product recommendations and upsell or cross-sell opportunities. On the activation front, GenAI can accomplish these tasks in an automated way, or through natural language conversations with a marketer using GenAI to help refine chosen content, analyze response data, or create follow-up messages. The throughline with using GenAI to augment core CD functionality is that better data yields better results. Whether it’s building a more trustworthy Golden Record, generating dynamic segments or activating data to different end channels, GenAI ensures that marketers are working with cleansed, accurate and fit-for-purpose data as well as a deeper understanding of the customer. To learn about how the Redpoint CDP leverages GenAI to generate a deeper customer understanding and to help marketers more easily perform their day-to-day tasks, [click here](https://www.redpointglobal.com/ai/). **Blog categories:** AI & Machine Learning **Blog tags:** CDP, customer data platform, GenAI --- ### [Make Bold Marketing Decisions with Full Confidence in Data Quality](https://www.redpointglobal.com/blog/make-bold-marketing-decisions-with-full-confidence-in-data-quality/) **Published:** March 27, 2024 **Author:** Vin DelGuercio **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Blog categories:** Data Quality --- ### [Luxury Fashion Retailers Drive Value with the Redpoint Customer Data Platform (CDP)](https://www.redpointglobal.com/blog/luxury-fashion-retailers-drive-value-with-the-redpoint-customer-data-platform-cdp/) **Published:** August 24, 2023 **Author:** Mike Ferguson **Content:** The difference between a wishful browser and a loyal brand advocate can mean a huge difference to a brand’s bottom line – but identifying the differences and being able to act on this information in a timely manner is not always easy. Luxury fashion retailers are not that dissimilar to other retailers in that they are all under pressure for higher sales, increased basket size and more loyal advocates. However, the value of a loyal customer in the luxury fashion market can be multiple times that of your average retail customer. Ultimately the luxury fashion retailer wants loyal brand advocates who will buy their brand even if there is a better deal to be had elsewhere. These customers undoubtedly feel special when they proudly wear the brand’s label, but the key is to make them feel special when they are interacting with your brand either online or in-store. ## **Seamless Shopper Experience** Brands want to provide **seamless shopper experiences** regardless of where or how their customers shop. A high valued customer expects a continuous experience whether she is shopping for a dress in her local store in London or visiting a store in Milan for some shoes while on a business trip. For many, a brand experience starts with a visit to the brand’s website. This is initially an anonymous visit, but it does not mean that the marketer cannot gain information from this first interaction. A/B content splits can automatically be tested to see which piece of content performs best and then automatically present the best performing as the favoured content, improving the effectiveness of the website. ## **Unknown to Known** Loss of third-party cookies means that marketers need to make the most of first party data. As unknown customers browse your website, experiences can be personalised based on their browsing behaviours. When an unknown website visitor shows an interest in the shoe section, this insight can be used to personalise their web journey by focussing the content on shoes. If they abandon the session, first party cookies collected from that session can be used to recognise the anonymous visitor and present them with relevant content on their return visit. As your unknown customer engages further and hopefully makes a purchase, they begin to share PII with you. This information should then persist against the customer record and provide the marketer with a more holistic view of the new customer and their previous activities. ## **The Struggle to Identify Customers** Unknown customers are just one of the challenges for the fashion retailer when identifying customers. Customers will often use different email addresses such as business and or personal when completing a purchase or registering an account. High value customers may also have multiple home addresses and provide different data in different locations. These customers will also visit stores which then can add offline data to the mix. Although this data is captured by various systems, it presents a challenge for the marketer who needs to have all this rich information in one place and be accessible in real-time. This is where a [Customer Data Platform](http://www.redpointglobal.com/cdp/) is necessary, to build a Single Customer View or [Golden Record](http://www.redpointglobal.com/data-quality-and-data-ingestion/), providing the marketer with a holistic view of the customer with clean, real-time information. The data needs to be captured, cleansed and merged to ensure all data is accurate and up-to-date. A combination of probabilistic and deterministic matching is required to join records with differing email addresses and multiple house addresses. Without this the marketer will be challenged to identify high-value customers. The Golden Record will also enable identification of household members which is extremely valuable when providing a personalised message or household offers. ## **Reducing Ad Spend** Identifying customers and their purchases as early as possible is where fashion retailers can quickly gain a return on their investment through reduction in ad spend. Because the Golden Record is updated in real time, marketers can identify customers who have made a purchase online or in-store, and use this information to suppress social and Google ads for the products they have just purchased. Apart from the reduction on spend through irrelevant ads, the brand also demonstrates a personal understanding of its customers. There is nothing more frustrating to a customer than seeing an ad or promotion for an item they have just purchased. ## **One and Done** Not all shoppers are created equal. There are those that will only purchase once despite your marketing efforts. The trick is to separate the “wheat from the chaff” and concentrate your efforts on where you best return will be. Some customers will only purchase when there is a discount to be had – these customers will never turn into your loyal advocates. It is therefore important for the marketer to identify those “bad eggs” as soon as possible and prevent wasting valuable marketing budget on targeting them. These “one and done” customers could be a useful target group for ‘end of season discounts’ but are best avoided for more expensive activities such as in-store personal shopping experiences. ## **Marketers in Control for Speed-to-Value** Most fashion retailers are adept at capturing customer information. In fact they have lots of data, but it is just difficult to get hold of in a timely manner. Often, the marketer has to rely on their IT team for insight and selection of customers for campaigns. This causes a bottleneck within the process and usually results in delayed (irrelevant) campaigns being run based on “cold” data. The CDP needs to provide the marketer with a “no code” interface that allows the marketer to action customer insight in real-time and engage with customers when they are ready to shop. ## **Clienteling** Providing your in-store assistant with relevant information about your customers is key to providing an exceptional shopping experience. The CDP should be able to provide your stores with a view of the **Golden Record** with the latest information on purchase history, browsing behaviour and product recommendations – **Next Best Offer.** ## **The Benefits** - 80% reduction in time to build segments - 50% increase in match rates - 28% increase in return on marketing investment - Real-time Next Best Offers - 40% reduction in interaction costs These are just some of the benefits Redpoint Global customers are seeing. **Redpoint Global** empowers brands to deliver highly personalized, contextually relevant experiences that optimize engagement and value. Redpoint solutions unify control over all customer data, determine next-best actions in real time, and orchestrate interactions across all touchpoints. **Blog categories:** Customer Data Platform, Retail --- ### [Limitless Data Integration Unlocks a Superior Customer Experience](https://www.redpointglobal.com/blog/limitless-data-integration-unlocks-a-superior-customer-experience/) **Published:** June 23, 2020 **Author:** Steve Zisk **Content:** Scott Brinker’s iconic [“Martech 5000” slide](https://chiefmartec.com/2020/04/marketing-technology-landscape-2020-martech-5000/), published every year since 2011, counts the number of martech solutions on the market. The 2020 supergraphic, released in April, lists 8,000 solutions, representing a 13.6 percent increase since 2019, which rises to 24.5 percent when discounting the churn rate. The steep increase is not an outlier; the number of new solutions since just last year (1,575) is almost equivalent to *every* solution that comprised the 2015 slide (1,876). For marketing organizations striving to accelerate digital transformation efforts in the wake of unprecedented change, the vast number of vendors presents quite a few challenges, not least of which is the daunting task of having so many options from which to consider and select the right solution. No less important are the integration considerations; how will a new point solution integrate with the existing martech stack and make it easier for marketers to ultimately better understand the customers they’re trying to reach? Because certainly the opposite can also be true; a new solution that does little more than create a new data source and data extraction point may in effect just be creating a new data silo. To overcome these challenges, data integration and data quality rise to the forefront for organizations that know a single point of operational control is vital for compiling a deep understanding of a customer at the individual level. With customer journeys becoming even more dynamic and unpredictable, there are no valid excuses for failing to deliver a hyper-personalized customer experience. A connected consumer, in other words, will not forgive data siloes and unintegrated processes and technologies that get in the way of such an experience. **What is Data Integration** Seamless data integration and superior data quality are must-haves for any digital transformation. Data veracity allows companies of any industry to establish the foundation that allows them to start executing their visions of the future today, not tomorrow. A digital experience platform that delivers unfettered data integration must meet a few conditions. First, it must welcome any and all data; data from every source and every type all hold important clues about a customer’s behaviors, preferences and patterns – which paints a complete picture of an individual customer. Volume alone, however, is not enough to ensure an accurate unified customer profile, which is where automated tasks come into play for managing even the most complex data. Cleansing, matching, [advanced identity resolution](https://www.redpointglobal.com/customer-data-management), persistent keys and enrichment are all vital for returning a single customer view that combines every customer identifier with complete behavioral and transactional data. Finally, a platform that can deliver on digital transformation objectives out of the gate must be able to do all of the above at the speed of the business. To keep pace with a connected consumer throughout an omnichannel journey, this often means milliseconds not hours or days. Data integration loses significant value if marketing is unable to extract actionable insight in the time needed to delight a customer with a personalized offer or otherwise next-best action based on the precise moment of their customer journey. **Data and a Superior CX** Unmatched data integration without data restrictions, and with real-time automation of cleansing, matching and identity resolution capabilities produce several key benefits for data-driven marketers who recognize the importance of having a deep understanding of the customer. First, automation and ease-of-use allow for an optimal, effective use of resources; operational marketers can automatically connect, normalize and enrich all customer data. Data silo roadblocks that cloud a single customer view are eliminated, freeing marketers to pursue and test innovative customer engagement strategies. Second, low latency guarantees the access of precise, integrated and comprehensive data at the speed of the business, which is vital for delivering a relevant, next-best action in the cadence of a customer journey. Third, peerless data integration and a digital platform that offers a single point of operational control facilitates oversight, making it easier for marketers to define workflows that notify data stewards when they need to approve, reject or discard changes to customer data. **Trust in a Digital Experience Platform** Inaccurate data can undermine business initiatives and decrease productivity across an entire organization. Risks include lost revenue, damage to brand reputation, loss of competitive edge and an inability to make strategic, data-driven decisions. To mitigate these significant costs, the [Redpoint rg1 platform](https://www.redpointglobal.com/one-platform/) was built from the ground up with unmatched data integration and data quality capabilities, providing marketers with the trust and confidence in customer data that they need to deliver a personalized, contextually relevant customer experience at scale that is always in the cadence of a unique customer journey. **Related Content** [Redpoint Global’s rg1 Platform Empowers Ambitious Business Leaders to Drive Real-Time Customer Engagement](https://www.redpointglobal.com/blog/rgone-is-how-ambitious-marketers-lead-markets/) [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) [Satisfy the Customer Expectation for a Personalized Experience with Intelligent Orchestration](https://www.redpointglobal.com/satisfy-the-customer-expectation-for-a-personalized-experience-with-intelligent-orchestration/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Member-Centricity: The Key to Driving Engagement and Improving Outcomes in Healthcare](https://www.redpointglobal.com/blog/member-centricity-the-key-to-driving-engagement-and-improving-outcomes-in-healthcare/) **Published:** August 5, 2024 **Author:** Steve Zisk **Content:** According to an [Accenture study](https://www.accenture.com/us-en/insights/health/difference-between-loyalty-leaving), more than half of healthcare plan members who switch plans do so because they are dissatisfied with the experience they receive, such as inaccurate or inconsistent information, a poor digital experience, unsatisfactory customer service and discomfort with how payers use their personal data. A poor experience, in fact, drives more members to switch plans than unhappiness with benefits or coverage. As healthcare consumers, many of us are all-too familiar with the kinds of negative experiences that cause us to consider finding a new healthcare plan. Irrelevant communications, such as receiving a reminder to schedule a colorectal screening that we’ve already taken care of. Untimely or lacking communications, such as spotty appointment reminders. Or even a disjoined website experience where it’s difficult to find the information we need. Our healthcare plans market to us consistently, but too often what comes through is a “collision of messages” and irrelevant communications that undermine member trust and satisfaction. ## **Break Through to Member-Centricity** Despite trying their best, healthcare plans find it challenging to meet the modern healthcare consumer’s expectations for a personalized, relevant experience. With data systems and infrastructure centered on employers, on a specific channel, or on a specific metric, what gets lost in the shuffle is the member. Because healthcare plans have not been incentivized to differentiate one member from another in terms of a member’s individual healthcare journey, preferences or behaviors, it is difficult to overcome the barriers that are caused by legacy infrastructure and data siloes to become member-centric. Challenges include: - Difficulty identifying the right populations and activating them individually - Orchestrating personalized programs in a coordinated way - Measuring and optimizing member engagement at a strategic level ## **Consistent Experience, Greater Trust, Better Outcomes** As consumer choice and regulation have increased, it becomes mission-critical for health plans to adopt a member-centric approach and find new ways to engage and empower members to drive better health outcomes while they focus on cost optimization. The benefits in doing so include improved health outcomes from members who are more engaged, reduced costs through better care coordination and utilization, and higher member satisfaction and loyalty, leading to stronger growth and a lower risk of churn. In the Accenture report, plan members are 3X more trusting of a healthcare payer when the plan provides consistent and accurate information. > One healthcare plan discovered that the No. 1 barrier for women taking an at-home osteoporosis exam was the question of whether she had to dis-robe. By sending communications that address that specific point, the plan saw a 35 percent year-over-year increase in completed examinations. Another plan saw a 2X increase in diabetic screenings by personalizing outreach according to historical reasons for non-compliance by age cohort. What does a member-centric approach look like in practice? Consider the colorectal cancer screening gap. Many plans market to an age group, sending a static message or reminder based on little more than a member’s birthdate. Family history, a member’s proclivity to schedule annual screenings, reasons for noncompliance and other factors are usually ignored. But what if, for example, a plan knew that one member had a history of delaying routine care because she was always pressed for time, while another member had previously put off the same screening because he didn’t like the prep work (or didn’t receive a reminder to complete it). Tailored messaging that addresses an individual member’s concerns and history is proven to produce better outcomes, i.e., more care gap closures. One plan, for example, discovered that the No. 1 barrier for women taking an at-home osteoporosis exam was the question of whether she had to dis-robe. By sending communications that address that specific point, the plan saw a 35 percent year-over-year increase in completed examinations. Another plan saw a 2X increase in diabetic screenings by personalizing outreach according to historical reasons for non-compliance by age cohort. ## **Actionable Insights Through a Deep Member Understanding** Achieving a level of member-centricity that moves the needle on better outcomes, lower cost and improved member satisfaction starts with data. That is, using data to derive a deep understanding of an individual plan member, to build meaningful segments and to deliver pitch-perfect communications that are in the context of a plan member’s healthcare journey. All communications are relevant to the member’s experience and are delivered at the right time and on the right channel. Furthermore, a deep understanding of an individual member unlocks new opportunities to build satisfaction and foster trust. A unified member profile that includes social determinants of health, for example, might indicate that a member is challenged with accessing nutritional foods. A plan might offer educational materials, or offer a discount on a food delivery service – providing a personalized experience while also yielding better outcomes. To deliver a consistent experience that reflects a deep understanding of a member, a unified member profile should be available and accessible to the entire organization to ensure a single brand voice, a single set of decisions and single set of experiences across an omnichannel member journey. Beyond having a deep level of understanding of who the member is through their data, the other component of using it to enhance the member experience is to operationalize that data flow effectively across your technology stack. A member-centric approach consists of three core capabilities: - **Profile unification and activation**: Consolidating member data from various internal and external sources into a unified, accurate, and up-to-date profile (a Golden Record) that can be used to power personalized engagement. This includes using identity resolution to connect anonymous and known member data. - **Segmentation and Orchestration**: Creating dynamic, intent-based member segments and orchestrating consistent, omnichannel experiences across all touchpoints. This requires high levels of automation to scale personalization efforts. - **Inbound, Synchronous Interactions**: Updating member profiles in real-time to deliver personalized experiences during in-the-moment interactions on websites, portals and apps. This requires tight integration of data, context and decision-making. By investing in these capabilities, health plans can break down silos, eliminate irrelevant communications, and deliver more meaningful, personalized engagement that drives improved outcomes and lower costs. The key is to start small, prove the value, and then scale these capabilities across the organization. - For more on personalizing, harmonizing and optimizing engagement and outcomes through a member-centric approach, join Engagys Co-Founder and Managing Partner Kathleen Ellmore and Redpoint Global Chief Marketing and Strategy Officer John Nash in an [on-demand webinar](https://event.on24.com/wcc/r/4638262/9532A5BF273E87D35EBF6492291BC75D) with a focus on how healthcare plans can knock down the barriers to personalized engagement. - To see how you can start transitioning to a member-centric approach using your own data, Redpoint and Engagys are [running a POC](https://www.redpointglobal.com/landing_pages/healthcare-payer-poc/) to help health plans accelerate on the path to member-centricity **Blog categories:** 1:1 Personalization, Healthcare --- ### [Is There a Data Fabric in Your Marketing Automation Future?](https://www.redpointglobal.com/blog/is-there-a-data-fabric-in-your-marketing-automation-future/) **Published:** September 9, 2022 **Author:** Steve Zisk **Content:** In a perfect world, marketers would have information at their fingertips about whether enterprise customer data is fit-for-purpose. Metadata-driven automations that reveal something about how active data is flowing through a system provide some information of value. Harnessing this value to take a prescribed action brings us into the realm of a data fabric and its role as a marketing automation tool, including for marketers intent on optimizing customer data to differentiate on customer experience. Almost by definition, dynamic, active metadata that presents a live view of what’s happening inside of a system is always changing. At a basic level, the automation of tasks to understand those changes is a data fabric. As an example, consider a data management automation that continually assessed a data quality index for a set of columns and paused a workflow if the index dropped below a preset level. The automation of data processes can range from simple to extraordinarily complex. In the customer data realm, a good example of data fabric automation might be using active metadata to extend a CDP, perhaps when a new database comes online, automatically querying its content and mapping customer data to the CDP. Conceptually, a data fabric may have an expanding role within a CDP. At its core, a CDP is an automation platform for customer experience. It stands to reason, then, that harnessing active metadata through the automation of multiple data processes will directly contribute to customer experience enhancements. Automation based on data is already prevalent, such as the automatic suppression of emails to an audience that opened a previous email within a campaign. Automating a task based on metadata carries this a step further, such as initiating a retention campaign if an NPS score falls below a certain level. Or, if an NPS average dips below an acceptable number diverting resources to address the root cause. As an automation platform for customer experience, a robust CDP can be an important component of a data fabric architecture in both directions; feeding metadata in the form of inferences, propensities and decisions into a data fabric to make automations occur, and conversely using automations managed by the fabric to effect change. In this context, a robust CDP is still laser focused at the data level on customer data required to drive a superior CX, which can be an important component of a data fabric, but the CDP is not operating as the entire underlying data infrastructure. ## **Data Fabric: An Ally in Improving CX** If a data fabric is at its core about automation using active metadata, data mesh is about governance, i.e. setting up rules for metadata to improve outcomes, such as determining the acceptable NPS average, for instance, or when to pause a workflow. Both data fabric and data mesh share the goal of using automation for better outcomes through metadata, with the key difference that a data mesh is centered around building governance into systems. From the marketer’s standpoint, it’s primarily a difference of semantics; both involve use cases intended to become smarter about using metadata. What, then, does this all mean for the day-to-day operational marketer? In the short term, I would argue not much. Again, if we were to look at an ideal world in which a marketer had unlimited information about how consumer data is fit-for-purpose, that might manifest itself in something like a trust index, where an underlying data fabric contributes to a continuous assessment of data’s readiness, thus giving marketers confidence to make bold decisions. In the longer term, as the data fabric design matures, existing systems that may now simply share metadata might become more active participants in a data fabric architecture, adapting to alerts and recommendations generated by the fabric through AI, for example. At its core, though, a data fabric is about metadata-driven automation, particularly active metadata. And the reason it should be on the radar for marketers in the CX arena is that there an endless number of marketing CX initiatives that can be improved by metadata-driven automation. ## **Related Redpoint Orchard Blogs** [What to Know About Metadata at the Data Layer](https://www.redpointglobal.com/blog/what-to-know-about-metadata-at-the-data-layer/) [What We Mean When We Talk About Data Quality](https://www.redpointglobal.com/blog/what-we-mean-when-we-talk-about-data-quality/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality --- ### [Satisfy the Customer Expectation for a Personalized Experience with Intelligent Orchestration](https://www.redpointglobal.com/blog/satisfy-the-customer-expectation-for-a-personalized-experience-with-intelligent-orchestration/) **Published:** April 29, 2020 **Author:** Steve Zisk **Content:** *What is intelligent orchestration? Intelligent orchestration is the consistent delivery of relevant, contextually aware, and hyper-personalized next-best actions across all customer journey stages and all enterprise touchpoints.* Intelligent orchestration is analogous to the conversationalist who always says the right thing at the right time, rather than coming up with the perfect response an hour later on the way home from the party. In customer engagement terms, it is always seizing the right moment of interaction with a customer. For a retailer suddenly experiencing a [surge in ecommerce](https://www.digitalcommerce360.com/article/coronavirus-impact-online-retail/), intelligent orchestration is the basis for always displaying the right product and always making the right offer, accounting for every decision and every behavior the customer makes up to and including a visit to a specific web page. Likewise, for an [uptick in curbside pickup](https://www.usatoday.com/story/money/2020/03/31/coronavirus-curbside-pickup-social-distancing/2901743001/) or buy online, pick-up in-store (BOPIS) activity, intelligent orchestration coordinates online, physical, and mobile channels to guide a customer journey and ensure for a consistent, seamless experience. Logistically, it ensures product availability matches inventory at a customer’s given location, or coordinates pick-up between the customer and a specific store location. It ensures that an offer for a complementary product is made on the right device before the customer arrives for pick-up. **Drive Relevance, Drive New Revenue** These customer-facing actions are where the rubber meets the road in guiding customer journeys with hyper-personalized experiences that drive new revenue. They exemplify responsive execution of real-time analytics to drive relevant experiences. Behind the scenes, the intelligent orchestration engine at the heart of the Redpoint platform takes into consideration a user’s updated preferences, actions and inactions, and whether they should be included in a segment. A customer’s propensity to buy, lifetime value, likelihood to churn, and other considerations are calculated and updated in real time, optimizing a proper flow of data from the source to every orchestration touchpoint. Advanced orchestration guarantees there is no under-use or over-use of customer data. This general misuse of data is why [73 percent of customers](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) in the 2019 Harris Poll commissioned by Redpoint said that brands do not meet their expectations for a personalized omnichannel experience. The expectation is for a friction-free experience where the customer feels the brand knows them as an individual. In the same Harris Poll survey, 43 percent of consumers defined a personalized experience as a brand knowing them as the same customer across all touchpoints (in-store, email, mobile, social, website, etc.). **Inner Workings of Orchestration** Intelligent orchestration is a foundational requirement to compete on customer experience. Orchestrating a customer journey begins with orchestrating customer data. Cleansing, matching, merging, data transport, and feeding data into automated machine learning algorithms are [necessary steps](https://www.redpointglobal.com/blog/no-data-left-behind-analytics-orchestration-and-making-data-work-for-you/) to guarantee that a customer-facing action is always in concert with the customer in the precise context and cadence of a unique journey. These steps ensure that marketers have the right data at the right time and the right place. The process automates a next-best action based on the totality of a customer’s behaviors, preferences, and lifetime transactions – and how they relate to one another – in real time. A real-time component with a [rules-based platform](https://www.redpointglobal.com/blog/top-10-benefits-of-rules-based-orchestration/) is what sets Redpoint intelligent orchestration capabilities apart. Real time in this context not only refers to making real-time decisions, but also real time as it refers to the behind the scenes orchestration machinations. It is real-time decisions based on real-time data. Advanced orchestration is not bound by a traditional batch cadence, where analytics is done offline from customer interaction, resulting in data latency that minimizes the effectiveness of analytic models. This method might be effective for surface level analytics of segmented customers, with the understanding that keeping pace with an omnichannel customer journey is far beyond its purview. In-line analytics and a real-time decisioning engine free marketers from a reliance on customer engagement strategies that segment customers into geographic, demographic, or other cohorts. A seamless BOPIS experience, or a website visit where a brand calculates consumer intent based on a totality of behaviors to include that visit, are the types of experiences today’s always-on, continuously connected customer expects. Providing a personalized experience in an omnichannel environment may be a challenge, but the empowered customer awards no points for a good faith effort. Rather, they will reward a consistent, personalized experience with greater wallet share. In a 2019 Merkle study, [66 percent of consumers](https://marketingland.com/consumers-indicate-experience-not-price-as-top-conversion-factor-263286) rate experience ahead of price when making a purchasing decision. Intelligent orchestration does not take out the inherent complexity of optimizing the use of data from the source to a next-best action, but done correctly it hides that complexity from the consumer. What’s left is a delighted customer, who has a far greater chance of becoming a repeat customer. **Blog categories:** Customer Data Platform, Data Management, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization --- ### [No Data Left Behind: The Importance of a Closed-Loop Cycle for Intelligent Data Orchestration](https://www.redpointglobal.com/blog/no-data-left-behind-the-importance-of-a-closed-loop-cycle-for-intelligent-data-orchestration/) **Published:** August 7, 2019 **Author:** Steve Zisk **Content:** *![](https://www.redpointglobal.com/wp-content/uploads/2019/08/shutterstock_1054825253-300x200.jpg)Editor’s Note: This is the second blog in a two-part series exploring the importance of continual data infusion, orchestration, and a closed-loop data cycle over the complete customer data lifecycle.* [*Part 1*](https://www.redpointglobal.com/blog/no-data-left-behind-analytics-orchestration-and-making-data-work-for-you/) *examined analytics and data models as use cases for data orchestration.* A hyper-personalized customer experience at scale is the holy grail for retail and brand marketers who aim to keep pace with today’s always-on consumer throughout all digital and physical touchpoints. To handle customer journey complexity, marketers must deploy an engagement strategy that continuously delivers the right offer in the context and cadence of that journey. This is the core job of intelligent orchestration. One obvious place to start is with a next-best action for a customer, the “tip of the spear” in orchestration, which happens at the moment of engagement with a single customer through a specific channel. But a single action – an offer or an ad, a piece of content, or a human voice – is just one step in an orchestrated journey. Behind the scenes, orchestration – like the customer journey itself – is a process, not a destination. Recognizing orchestration as an ongoing process underscores the importance of orchestrating the customer data along with the customer journey. [Data orchestration](https://www.redpointglobal.com/blog/no-data-left-behind-the-importance-of-a-closed-loop-cycle-for-intelligent-data-orchestration/) means continually managing the customer data stream – driving new decisions, retraining and retesting machine learning models – while also recognizing an actual customer at the center, demanding both relevance and delight. **Playing the Right Note** The orchestration engine must continuously adapt and recalculate audience, channel, and action. Whether reaching out or reacting to the customer, the engine is choosing content, measuring response, and interacting in accordance with multiple small decisions about a customer during a precise moment of their journey. The importance of feedback is illustrated with an examination of the seemingly infinite permutations of a set of actions in a customer journey. Did a customer open an email? How did the customer respond? How soon after opening the email did the customer visit a physical location? Did they post a photo on Instagram? Orchestration dictates the next note for a brand to play to ensure that every interaction is “in tune” with the chords the customer plays throughout a buying journey and, ultimately, throughout the entire customer lifecycle. Feeding every piece of customer response data back into the orchestration engine in real time ensures that the brand stays in sync with the connected consumer. With the right data at the right time and in the right place, marketers eliminate guesswork that creeps in when they’re working with stale data or old data models built to answer questions that may suddenly be irrelevant to a customer’s current experience. **Orchestration is a Stool with Many Legs** Retraining machine learning models to stay in tune with a customer is a necessary but not entirely sufficient part of the closed-loop data cycle. The marketers’ intent or preference is another component that requires a continual data refresh. Real time product availability, for example, will also determine engagement – such as a product recommendation model that operates at the intersection of what a customer desires, what a marketer intends, and what a brand can offer. Dynamic open time email offers present another example, where offers are made – and changed in real time – depending on whether a predetermined threshold is met. Machine learning models represent the intersection between customer desires, marketer intent, and organizational constraints. Each requires continuous retraining to make sure the right data is in place to keep pace with a dynamic customer journey, and to ensure that a journey is not derailed by ignoring or devaluing one part of the intersection. The combination of desires, intent, and constraints offers a broad canvas for experimentation. Because the customer journey is open-ended, permutations on the journey may seem infinite (or at least daunting). Selecting a short, well-defined path for testing and optimization allows marketers to manage a specific outcome. Feeding results back into the [data orchestration](https://www.redpointglobal.com/blog/no-data-left-behind-the-importance-of-a-closed-loop-cycle-for-intelligent-data-orchestration/) engine to test a new model’s effectiveness can then inform a decision whether to tweak or reboot a model to explore a new path. **Customer Preferences Take Center Stage** Another consideration is customer preferences for how their data is collected, stored, and used in accordance with regulations such as GDPR and CCPA. A consumer’s right to be forgotten, for instance, requires marketers to act in accordance with a customer’s opt-in and opt-out preferences throughout the customer lifecycle. A customer might opt-out of marketing emails, but welcome marketing to them on the website – or vice-versa. Customer preferences that govern how and when a brand is allowed to engage must also be considered as part of the intersection between the customer, the marketer, and organizational constraints. Intelligent orchestration is far more complex than one machine learning model generating a next-best action depending on where a customer appears in a dynamic customer journey, or depending on the previous interaction with the customer. It is a continual, multi-layered, and bi-directional process that requires continuous data infusion and model retraining to ensure that the intersection of the customer, the marketer, and organizational constraints is always free of any obstacle in the way of engaging with the customer with a hyper-personalized experience. **Blog categories:** AI & Machine Learning, Data Management --- ### [How Insurance Companies Differentiate with a Single Customer View](https://www.redpointglobal.com/blog/how-insurance-companies-differentiate-with-a-single-customer-view/) **Published:** February 23, 2023 **Author:** Mike Ferguson **Content:** With rising costs, supply chain disruptions and other economic factors weighing heavily on multi-line insurance companies, customer retention becomes a business priority. If all insurers pass cost increases onto the consumer in the form of higher quotes on price comparison websites, one way for insurers to differentiate from the competition and achieve retention goals is to provide added value to the consumer. This blog will focus on how value is accomplished through developing a deep understanding of the customer, incenting customers to renew and being more open to cross-sell and upsell opportunities, particularly with all else being equal (i.e., price). ## **Minimize Risk, Increase Value with a Deep Customer Understanding** The insurance industry is all about trying to minimize risk, of course. Multi-line insurers assume risk by providing quotes on aggregate sites knowing only basic, self-provided (and often inaccurate) information about a customer. Increasingly diversified product portfolios make it more difficult to develop an in-depth knowledge of a consumer, with the risk that policies or deductibles may not maximize profit. For insurers, there are many challenges with developing an understanding of an individual consumer. Beyond the common lifestyle changes (employment, physical address, marital status, children), marketing and operational challenges include situations such as a mailing address different than a property a customer or prospect wants to insure. Or when a single policy may cover multiple people, such as with a family auto policy. Developing a deeper understanding of a customer or household accomplishes multiple objectives. Risk is minimized because an insurer knows a customer’s precise needs, and can thus tailor coverage on an individual and/or household level. There is a cost savings component by eliminating marketing inefficiencies, i.e., using the right email/address for a policyholder, knowing the needs of a household, etc. A third accomplishment, and equally important as mitigating risk and cost from a retention standpoint, is that an insurer delivers value to the customer. Ideally, a customer who has built a relationship with an insurer based on a first-party data exchange will trust that an insurer knows his or her needs, translating to a far superior customer experience than one would receive on a free-for-all price comparison website. ## **A Personal Understanding, a Personalized Experience** A personalized experience that drives loyalty will entail relevant product offers, timely renewal notifications, and attractive cross-sell and upsell offers. A consumer who intuits that an insurer values their business will provide more first-party data, trusting that it will be used to further enhance a frictionless experience. Perhaps the insurer will automatically adjust a renewal quote based on a new household dynamic, as an example. Or an insurer that knows a customer has an open claim will put a hold on marketing activities until the claim has been resolved. The key for an insurer to maximize the use of first-party data is to take all customer data into consideration in developing a single customer view that is accurate, trustworthy and up-to-date. The Redpoint Golden Record is a unified customer profile that compiles customer data in real time from all sources, combining all possible identifiers and a complete transactional history into a fully resolved identity of a customer or household. Using probabilistic and deterministic matching and persistent keys, the Golden Record provides a contextual understanding of a customer or household over time. Because the rg1 platform makes the Golden Record accessible to all users and all applications, the unified view can be shared across marketing and operations, giving the business a single point of operational control. With a single point of control, an insurer can interact with a customer (or household) with a consistent voice across multiple product lines, reducing friction while also helping to guide a customer journey. Does the customer have a policy up for renewal? Is there an open claim? Does the customer have a new driver in the household? Is the customer in the process of moving? A call centre agent who accesses a Golden Record during a call with a customer will have all information about the customer at his or her fingertips. With this updated single customer view, the agent will see the complete portfolio, know what is up for renewal and be able to advance cross-sell or upsell offers that are hyper-relevant to the customer’s situation in real time. By using the Golden Record to interact with consistent relevance across an entire portfolio, and in the precise cadence of a unique customer journey, a brand demonstrates to the customer that he or she is more than just a number. Mutual value is created from building and strengthening a relationship based on first-party data, a value impossible for a price comparison website to equal. If price was the only factor customers cared about, insurers would only care how low they could price a quote and still remain profitable. But as with any other industry with a brand-consumer dynamic, the reality is that customers want to be understood, and they expect that understanding to be reflected in a personalized experience. By investing in the customer through a purpose-built Golden Record, insurance companies prove ROI through reduced costs, more efficient marketing, a single point of control and, importantly, a loyal customer base appreciative of being treated like an individual. ## **Related Redpoint Orchard Blogs** [Take a Personalized CX to the Next level with Advanced Identity Resolution](https://www.redpointglobal.com/blog/take-a-personalized-cx-to-the-next-level-with-advanced-identity-resolution/) [A Bountiful Harvest: Continual Identity Resolution and a Pristine, Profitable Golden Record](https://www.redpointglobal.com/blog/a-bountiful-harvest-continual-identity-resolution-and-a-pristine-profitable-golden-record/) [Do You (Really) Know Who I am? Why Advanced Identity Resolution is Vital for Omnichannel Customer Experiences](https://www.redpointglobal.com/blog/do-you-really-know-who-i-am-why-advanced-identity-resolution-is-vital-for-omnichannel-customer-experiences/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Identity Resolution --- ### [Feelings Meet Facts: Infusing Customer Experience with Meaning will Define 2021](https://www.redpointglobal.com/blog/feelings-meet-facts-infusing-customer-experience-with-meaning-will-define-2021/) **Published:** November 9, 2020 **Author:** Dale Renner **Content:** One could look back at our [2020 predictions column](https://www.redpointglobal.com/blog/2020-predictions-expect-personalization-to-gain-a-distinctive-edge-for-the-next-decade/) and conclude that we were right that “marketers will have their hands full in 2020”. Unfortunately, their hands were full with a deeper set of challenges than we thought probable. While no one of course could have predicted the scale of disruption, we were optimistic that ambitious marketers can successfully overcome their challenges, and did in fact hit the mark on the importance of a personalized customer experience – a trend that I believe in light of the events of this year is rapidly becoming even more prominent. With the same spirit of optimism, I see better days ahead for the new calendar year. I think one of the main things 2020 taught us is to appreciate what is truly important, both in our personal relationships and in how we conduct business. For the latter, this means that building a relationship with a customer becomes even less about price or product than it does providing a meaningful experience. One in which value is not measured by a price tag, but rather by respect, empathy and understanding of an individual customer’s unique needs and wants. ### **Re-Examine the Value Proposition** A top prediction for 2021, and one that will resonate for any industry that interacts with customers, is that the need to deliver a meaningful experience will force businesses to re-examine their core value propositions. Successful brands will clearly articulate what they stand for and what they offer, because what a brand represents will become just as important, if not more so, to a customer than its products and services. I’m reminded of a line from “Hamilton” when Alexander Hamilton says to Aaron Burr, “If you stand for nothing, Burr, what’ll you fall for?” This is the time, in other words, for companies to clearly deliver experiences that are aligned with what they stand for – their value proposition. Correctly identifying and going to market with a successful value proposition will largely depend on a company showing its customers that it shares the same values. Getting this right will require a keen understanding of what matters to a customer; how the customer defines and extracts meaning from the brands and companies it interacts with. My argument is that the need to establish an emotional connection with a customer is heightened. With price and product largely commoditized and in the wake of a disrupted economy, customers are taking a deeper look at whether the companies they do business with share their values on a political, social or environmental basis. When these values align, customer relationships strengthen with an emotional connection. Even though it may appear counter intuitive, developing such an emotional connection at scale requires marketers to abandon gut feelings or intuition about what’s meaningful for a customer, and avoid using guesswork to craft personalized customer experiences. A meaningful experience for one customer may not be meaningful for the next. By relying instead on data and analytics to compile a single customer view, marketers will be able to capitalize by infusing meaning, personalization and relevance into every customer interaction. ### **Customer Behaviors are Changing** Developing a keen understanding of an individual customer brings us to another challenge for businesses that will dominate 2021, which is the pressing need to pivot to changing customer circumstances. Preferences, attitudes and behaviors have been significantly upended. In healthcare, financial services, retail and other industries, there will be completely new ways of interacting with a particular customer, a patient or a member. Perhaps a dedicated in-store customer who used to keep a dressing room attendant busy is now buying online and simply using the store as a fulfillment center, to use one example. A [U.S. Department of Commerce study](https://www.ga.agency/en/blog/ecommerce-sales-growth-retail-united-states-2020) perfectly captures the sheer scale of disruption in the retail industry. In March and April of this year, ecommerce penetration as a percentage of retail sales climbed more than it had in the 10 years prior. From 2009 until the end of February, ecommerce penetration increased from 5.6 to 16 percent. By the end of April, it rose to 27 percent. That is an unprecedented amount of change in such a condensed timeframe, and it has serious implications for brands that need to quickly adjust to meet new customer behaviors, while also delivering an emotional connection imbued with meaning. To make this a successful transformation, brands will invest in personalization, machine learning and automation underpinned by a data platform having the best most up to date view of the person to create differentiated customer experiences. It will be the only way to scale a personalized customer journey that honors each customer’s preferences and behaviors to deliver meaning and value with every interaction. Brands that do not make this journey and embrace an all-out digital acceleration will be challenged to thrive and survive. This is why forming a sustained emotional connection based on trust tops the list of 2021 predictions and flows through the rest of the predictions in this column. ### **Fine Tune a CX Data Strategy** Once organizations grasp the importance of strengthening the emotional bond with customers, they will take a long, hard look at their customer data and some will come to the realization that they lack the proper foundation for a complete customer data strategy. To crystalize the prediction somewhat, I anticipate that half of digital transformations already in progress will stumble because their customer data is not in order. It’s one thing to understand the importance of customer experience, and quite another to create differentiated, innovative experiences that ultimately drive revenue. The latter requires that all customer data be integrated – from every source and of every type – making it possible to know all tht is knowable about a person. Furthermore, there can be no barriers or data siloes between when data is collected, cleansed, prepared and made available to create hyper-personalized experiences at scale. Another CX data strategy roadblock will be incorporating precise decisions in real-time, because even with a complete data picture, the delivery of a personalized customer experience requires that a brand move in the cadence of the customer. By delivering a perfectly timed action, offer or message in the right channel a brand demonstrates that it possesses a deep, ongoing understanding of an individual. It shows, in other words, that it cares. It cares about what’s important to the person at every point in the relationship journey. That capability derives from having a comprehensive CX data strategy which, given the new reality, will be a must-have for all consumer-facing companies. To develop innovative customer experiences that create and sustain an emotional connection, companies no longer have the luxury of treating customer data as an afterthought. ### **Let the Machines Take Over the Minute Details** When companies examine their readiness to transform digitally to infuse emotion and meaning into every customer interaction, they will recognize AI and machine learning – really, the entire set of advanced technologies including IoT, block chain, big data, etc. – as a powerful ally in creating a differentiated customer experience. Another prediction for 2021 is there will be a deepening of the trend that advanced technologies will take over minute tasks, freeing marketers to focus on the more creative aspects of the job. Companies have been straddling the inflection point of viewing AI as the shiny new toy vs. a key cog for revenue-driving business use cases. 2021 will bring into focus the need to use advanced technologies to optimize data and analytics for building meaningful connections with customers. We will start to see organizations using AI and other advanced technologies for the tasks that require attention to the fullest and smallest details, e.g. which exact message to send to a specific individual customer at a specific moment in time and through which touchpoint. This frees up marketers to deliver greater value for the customer, by identifying macro trends, crafting new content, defining segments and programs, etc. The trend is all part of the making the customer experience more closely aligned with individual customer needs and with the brands value proposition while making it simpler for the brand to achieve a meaningful connection. By paying attention to a particularly detailed set of circumstances – who the person is, what they have done in the past, what they are likely to do in the future – makes each customer feel understood by the brand and eliminates the friction that so often derails a seamless CX. Deploying advanced technologies to bear responsibility for the entire context of a customer circumstance – down to the smallest minutia of data – will be at the core of delivering superior customer experience at scale. ### **A New Year, New Opportunity** In summary, I believe that there will be a reckoning of sorts come 2021. Unforeseen circumstances in 2020 exposed which side of the customer experience gap companies were on, and also made clear that closing the gap will require companies to reassess what really matters to their customers. Just as an indecisive Burr suffered from his indecision, empowered customers will punish a failure to act by taking their business elsewhere. Brand reputation will also suffer. Conversely, a brand that clearly demonstrates that it shares a customer’s values and treats that customer in accordance with a customer’s individual preferences will be rewarded. Like any new year, 2021 will be filled with opportunities for a fresh start. Prior to the disruption of 2020, brands with their eyes closed to the importance of a superior customer experience may have gotten away with the status quo – at least for a little while. That luxury is gone. Using data and analytics to infuse a meaningful, emotional connection into every customer experience will be a make or break proposition in 2021. **Blog categories:** 1:1 Personalization, Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Machine Learning Accelerates Real-Time Customer Engagement](https://www.redpointglobal.com/blog/machine-learning-accelerates-real-time-customer-engagement/) **Published:** January 23, 2018 **Author:** Vin DelGuercio **Content:** ![machine-learning-real-time](https://www.redpointglobal.com/wp-content/uploads/2018/01/machine-learning-real-time-engagement.jpg)Contextually relevant interactions, at the customer’s moment of truth, are the key to maximizing revenue and customer satisfaction. A relevant, high-impact recommendation is up to 50 times more likely to trigger a purchase than a low-impact recommendation according to [research from McKinsey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-new-way-to-measure-word-of-mouth-marketing). Taking action at that moment of truth often requires responding in real time to customers’ cues. Real-time marketing spans a spectrum that extends from ad hoc (e.g., one-time targeted communications) to automated (e.g., triggered, dynamic, adaptive) – but the most important aspect is that it needs to be real time from the perspective of the recipient of the message. Consider: Even a Tuesday batch message email blast is still considered real time to the recipient at the time it is opened. Consequently, marketers should incorporate a variety of real-time tactics to improve message relevance. Not doing so leaves companies open to delivering poor customer experiences. For instance, I recently renewed my home owner’s insurance and three days later received a call reminding me that I was up for renewal. Clearly, my renewal wasn’t a real-time interaction for the company, which is most likely replete with siloed systems and might outsource the calls to a third party. It may be overwhelming to think about a seemingly endless array of next best action choices needed for real-time, contextually relevant customer interactions. But in practice, real-time marketing is not too complex to layer into an existing marketing strategy or execution process – with some readily available tools and techniques. Plus, many of the analytical capabilities required for this approach are now available to marketers from within their current customer engagement tools — as long as they use those tools, and up-to-date data in a cohesive way. ## **The Technology That Will Take You There** Forrester Research asserts that more firms will start to assemble “contextual marketing engines” to create self-perpetuating cycles of insight-driven interactions across channels. Today, however, most organizations excel at only one or a few channels and aren’t capable of a next-best-interaction journey across touchpoints. Advanced real-time personalization requires machine learning and advances in computing capacity that will allow marketers to tap into on-the-fly insights that become triggers for contextually relevant communications. Applying the algorithmic scalability and advanced modeling functionality of machine learning is the only way to effectively scale real-time engagement and make it successful in the long term. Further, machine learning is the most scalable way to understand the circumstance (timing and context) for an interaction with a customer and determine the next best action (engagement). As marketers move from basic personalized engagement to advanced real-time interactions, they can adapt technologies that will help prepare them to get the most from machine learning. ## **Crawl, Walk, Run for Real-Time Customer Engagement Success** Begin with a focus on real-time marketing through one channel. For example, use business rules that suppress message volume and offer repetition, run cart abandonment campaigns, build customer segments, and test dynamic personalization in outbound channels. Technologies that support this basic approach include campaign management, email marketing tools, and customer data platforms – solutions that bring together first-, second-, and third-party data, probabilistic and deterministic record matching, and identity resolution to generate a holistic view of the customer. I can’t overemphasize the importance of properly managing message cadence. As machine learning allows for greater automation, marketers may be tempted to contact customers more frequently. Adding more messages simply because you can will only speed customer fatigue. Marketers need to use analytics tools such as machine learning not only to determine next best actions, but also to determine the optimal frequency, message volume, and offer repetition. Graduate to incorporating more than one channel in an orchestrated way. Introduce and test multichannel campaigns that comprise at least two channels. Add retargeting and event-based trigger campaigns to the marketing mix. And, establish a customer preference center to inform future campaigns. Technologies that support this more advanced approach include ad tech, content management and e-commerce platforms, and next best action/offer tools, which use advanced analytics such as machine learning algorithms, as well as business rules, to automate decisions across the customer journey. Progress to more contextually relevant real-time marketing by embracing advanced algorithms and modeling. Begin to coordinate inbound and outbound campaigns. Adopt an attribution model for optimization. Use closed-loop measurement and automate customer lifetime value scoring. Technologies that support contextually relevant real-time marketing include customer engagement hubs, optimizers for targeting and testing, product recommendation engines, and advanced machine learning. Indeed, machine learning is present all along the journey from basic to advanced contextually relevant real-time marketing. Marketers can use machine learning techniques for customer segmentation, to learn how items such as behaviors and channels affect each other, and to see what patterns are present. They can use machine learning to predict something, find the most important variables, and cut out the “noise” of the information that’s not relevant—all of which helps to personalize campaign elements such as offers and pricing, or encourage current and prospective customers to take an action. Another aspect of machine learning is optimization. There are myriad ways to segment customers, run campaigns, etc., and there may be multiple “best ways,” each with different positives and negatives. Marketers can use machine learning to optimize on top of those insights. ## **Keep the Momentum Going** Moving along the path to contextually relevant real-time marketing takes time, but it doesn’t require wholesale changes to a company’s marketing stack. It’s possible to layer real-time capabilities into an existing infrastructure. The new breed of customer engagement tools, for example, behave like a hub between existing marketing tools, connecting disparate systems, centralizing disparate data, and allowing marketers to trigger next best actions across a variety of systems and channels. But futureproofing customer engagement technology will require marketers to adopt more sophisticated analytical capabilities, such as machine learning and advanced modeling, from within their customer engagement tools. Any organization can benefit from real-time customer engagement efforts, no matter how basic, or advanced, their current practice may be. But making continued progress means that marketers should be thinking about ways to layer manual, triggered, dynamic, and adaptive real-time engagement into their marketing mix. This means embracing new technologies, connecting disparate data, and applying more sophisticated analytical capabilities such as machine learning to enable real-time contextual interactions. *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Real-Time Personalization --- ### [Improve CAHPS Scores with a Customer Data Platform (CDP) and a Single Member View](https://www.redpointglobal.com/blog/improve-cahps-scores-with-a-customer-data-platform-cdp-and-a-single-member-view/) **Published:** September 12, 2024 **Author:** Steve Zisk **Content:** The Consumer Assessment of Healthcare Providers and Systems (CAHPS) scores are a vital component in evaluating the quality of healthcare services. They impact Medicare Advantage plans’ star ratings and associated bonuses, and also serve as an essential measure of member satisfaction and trust. Improving CAHPS scores requires a nuanced understanding of member-centric engagement strategies, which starts with developing a deep understanding of an individual member. By demonstrating such an understanding through consistent, personalized interactions across all channels and touchpoints, health plans build strong, trust-based relationships with members – a key metric for enhancing CAHPS scores. ## **Trust as a Foundational Element of Higher CAHPS Scores** Beyond the financial incentives associated with the bonuses from higher Medicare Advantage star ratings, CAHPS scores are an important gauge of overall satisfaction and trust in healthcare services. Trust, in turn, is beneficial because a member who trusts his or her health plan is more likely to engage in healthier and cost-saving behaviors such as scheduling preventive screenings or opting for home delivery services. Plans build trust through positive interactions with members. Every phone call, automated message, online session and in-person visit is an opportunity to influence a member’s perception. But more than just stringing together a series of positive interactions, trust is solidified through consistent personalization. The member should be made to feel that a health plan is communicating consistently, addressing the member’s individual needs, independent of channel. A high CAHPS score reflects that consistency, because it is a measure of overall satisfaction versus a single set of communications. It is the member’s positive reaction to consistent personalization across the member journey. ## **Build Member Trust Through Data** The pressing need for health plans to deliver a personalized CX across the member journey calls for a robust and up-to-date single view of the member as well as more advanced omnichannel capabilities. For health plans and healthcare organizations, the most comprehensive solution is a [customer data platform (CDP)](https://www.redpointglobal.com/cdp/). A CDP provides a single view of the member by aggregating member data from every source, continually applying [data quality processes](https://www.redpointglobal.com/data-quality-and-data-ingestion/) as soon as new data is being brought in, [dynamically segmenting audiences](https://www.redpointglobal.com/segmentation-activation/) and activating member cohorts through seamless connections to every end channel. Personalizing the member experience using a single member view that is updated in real time gives health plans the ability to meet a member with a relevant, personalized experience the moment a member engages regardless of the channel. The interaction will reflect a deep understanding of the member journey because the single view contains all relevant member data. Far more than claims information and a medical history, the single view or Golden Record includes all known devices and IDs, preferences, behaviors, social and even social determinants of health (SDoH) data. Everyone with access to the updated Golden Record will know, for instance, whether the member is due for a screening, has an open claim, has a change in dependents or any other relevant information – at the moment of engagement. Everyone who interacts with a member – a care navigation coordinator, a call center rep, a billing agent, a marketer – has a precise understanding of the contour of a member journey and is able to use that knowledge to help guide the journey to a mutually beneficial outcome. The member is satisfied and is guided toward better outcomes. The health plan reduces costs and enhances trust. A personalized journey, for example, might show empathy for how a member is coping with a chronic condition. Or by acknowledging a past health event such as a child’s injury. Or anticipating that a member needs help with transportation for a certain preventative screening of medical procedure. ## **Improve Scores Through Dynamic Segmentation** A CDP serves another important function. It provides health plans with the ability to identify and analyze reasons for a low CAHPS scores and take preventive measures. By building segments of members who have provided low ratings, a health plan can use a CDPs automated machine learning capabilities to analyze areas of dissatisfaction, and then test different personalization tactics to see where gains can be made. CAHPS scores are more than just a measure of member satisfaction; they reflect the quality of interactions and trust between healthcare providers and their members. By adopting a member-centric approach, focusing on personalized communication, and utilizing data effectively, healthcare organizations can enhance their CAHPS scores and build stronger relationships with their members. This not only improves financial outcomes but also fosters a more positive and trusting environment for healthcare delivery. Redpoint Global has partnered with [Engagys](https://www.engagys.com/), a healthcare consumer engagement consulting and advisory services firm, to assist health plans with maximizing the value of their member data. We are bringing together data-driven technology with best-in-class, personalized engagement solutions to help increase member satisfaction – and improve CAHPS scores. For more on how Redpoint and Engagys can help you maximize member engagement, using your own data, click [here](https://www.redpointglobal.com/landing_pages/healthcare-payer-poc/). **Blog categories:** Healthcare --- ### [Identity Resolution Takes Center Stage in Upcoming Webinar Series](https://www.redpointglobal.com/blog/identity-resolution-takes-center-stage-in-upcoming-webinar-series/) **Published:** July 20, 2022 **Author:** Redpoint Global **Content:** Increasing privacy restrictions and tracking mechanisms like the third-party cookie becoming obsolete are thrusting [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) into the spotlight as a core requirement for personalization, as well as for user-level targeting and measurement of marketing campaigns. Because many marketing technology and service sectors offer variations on identity resolution, there is buyer and marketplace confusion about what identity resolution is, what it does, and why [perfected data quality](https://www.redpointglobal.com/customer-data-management/data-quality/) is an essential component of advanced identity resolution, needed for the creation of personalized, [omnichannel customer experiences](https://www.redpointglobal.com/omnichannel-personalization/). To more clearly define identity resolution and address confusion in the marketplace, Redpoint will host a [four-part webinar series](https://event.on24.com/wcc/r/3873263/56CF34B557633E7E15112490E25CB65C) beginning July 26 and continuing through September 7 in which we address the key questions and issues. The first webinar (July 26) will focus on the meaning and types of identity resolution. The August 10 session will center on why identity resolution is a core component of any customer data platform (CDP) for building an accurate golden record. On August 25, subject matter experts will discuss various use cases for identity resolution and examine how identity resolution using first-party data honors a customer’s values for transparency, privacy, security and preferences. The closing September 7 session will focus on data quality, examining how identity resolution helps build a complete, accurate golden record. ## **What are Identity Resolution Use Cases?** Identity Resolution is the process of finding, cleansing, matching, merging and relating all the disparate signals (martech touchpoints, enterprise systems, databases/lakes) about a customer to produce an accurate, complete and up-to-date view of the customer. Identity Resolution is used by marketing and other business functions to analyze, deduplicate and relate customer records in order to provide consistent Customer Experience. Privacy regulations and the end of the third-party cookie increase the need for reliable customer identities, spanning many use cases and approaches. Common use cases for resolving an identity to an individual, household or entity include: - Eliminating audience duplication – Using a combination of deterministic (rule-based) and probabilistic (analytics-based) matching, advanced identity resolution minimizes or eliminates interacting with duplicate records and other waste. - Better understanding of a customer (household/entity) – Identity resolution in the building of a complete, accurate golden record is key to understanding channel preferences and behaviors of customers in order to target them more effectively. - Implementing controls – Frequency capping and suppression rules placed on a unified customer profile helps eliminate sending messages, offers or content that annoys or confuses customers. - Extending relationships to new devices and contexts – identity resolution that brings in updated, real-time data ensures a golden record always reflects an up-to-date, complete profile of an individual/household/entity, allowing for consistent, personalized experiences across channels and devices, applied consistently and in line with customer expectations – online and offline. ## **Identity Resolution & Data Quality** Omnichannel personalization as an outcome of advanced identity resolution is becoming a competitive differentiator in the experience economy. Recent [McKinsey research](https://www.mckinsey.com/business-functions/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) on the value of personalization found that 71 percent of consumers expect companies to deliver personalized interactions, and 76 percent become frustrated when this doesn’t happen. The value of personalization as it pertains to retention and [customer loyalty](https://www.redpointglobal.com/blog/customer-loyalty-and-an-emotional-connection-for-the-new-reality/) underscores why data quality is a critical element of identity resolution, and why simply matching records is just the tip of the iceberg for resolving identities across all touchpoints, records, devices, channels and identifiers. Because inaccurate matching, overmatching or undermatching all result in the likelihood of introducing friction into a customer experience with irrelevant or unwanted offers or communications. With a firm understanding of the importance of data quality, and perfecting data the moment data is ingested from multiple data sources, it becomes clear why identity resolution is a core component of a CDP. Herein lies some of the market confusion, in that many companies may think of a CDP as primarily a data aggregation system meant to automate some personalization. The reality, though, is that a CDP is really only as good as its data quality processes – and when it tackles those data quality processes – in creating a complete, accurate golden record on the back of advanced identity resolution capabilities. The webinar series will explore each of the core issues in greater detail. To sign up for the first webinar, [click here](https://event.on24.com/wcc/r/3873263/56CF34B557633E7E15112490E25CB65C). **Blog categories:** Data Quality, Identity Resolution, Real-Time Personalization --- ### [Data Matching and Identity Resolution: Keys to Hyper-Personalization](https://www.redpointglobal.com/blog/data-matching-and-identity-resolution-keys-to-hyper-personalization/) **Published:** January 14, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/01/Twins_360752891-e1547222617575.jpg)A [PwC study](https://www.pwc.com/us/en/advisory-services/publications/consumer-intelligence-series/pwc-consumer-intelligence-series-customer-experience.pdf) revealed that consumers will pay more and share more data with companies that can provide a relevant, personal customer experience. In the study, a staggering 73 percent of consumers said that customer experience is a factor in their purchasing decisions, yet only 49 percent of U.S. consumers said that companies can provide a positive experience. One reason for the discrepancy is that many brands fail to grasp the level of personalization customers have come to expect, or even understand the full spectrum for what’s possible. It’s not about addressing a customer by name in an email. Rather, it’s about recognizing a customer’s preferences, patterns, and behaviors in real time and shaping the customer journey with next-best action recommendations that are always relevant and in the right cadence at any touchpoint. Identity resolution is required to close the experience gap. We explored many of the business drivers for identity resolution in a recent blog. Here, we’ll focus more on the challenges that advanced [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) presents. While identity resolution or identity matching is sometimes misunderstood to mean finding a name, contact details, or even a person, its primary goal is to gain a better understanding of customers by reconciling different records for a customer across different engagement systems. For example, an email address and a digital certificate on a smartphone could be proxies for the same customer residing in different systems. Reconciling the two, along with any other identity proxy, helps build an anonymous-to-known record and provides marketers with the ability to better predict a customer’s behavior and begin to have an approximate measure of customer intent, resulting in the delivery of a more personal, relevant experience. Marketers must resolve identity proxies across different systems while faced with an explosion of data sources and types – streaming, batch, structured, semi-structured, unstructured and third-party data – all of which are potential sources of customer data to be reconciled across different engagement systems. An obstacle to getting there is trying to process and match all customer data in the timeframe needed to be able to engage with a customer at their pace and speed. A brand that can gauge positive social sentiment and link it via a device identifier to a recent e-commerce transaction can proactively shape a customer engagement in a segment-of-one approach – but only if it can do so according to the customer’s timetable. Data matching at the pace of the customer adds context to interactions, and without this context a brand may lose the opportunity to provide a next-best action in the proper cadence. **Identity Matching: It’s All About the Data** Today, there are countless ways for a customer to interact with a brand. An increasing number of touchpoints and devices has contributed to the dissolution of what was once a predictable customer journey into an indirect path-to-purchase without a defined beginning, middle, or end. As customers take charge of their journeys, they also expect real-time, context-aware responsiveness throughout their discovery and purchase lifecycle as they shift between devices and anonymous-to-known interactions. Creating a 360-degree view of the customer across online and offline channels and all marketing, point of sale, and all enterprise interaction touchpoints is also known as a golden record. The golden record is an always-on, always-updating aggregation of an individual entity that continuously builds and maintains a record of linking information across an anonymous-to-known lifecycle. It provides marketers with the tools and capabilities needed for strong identity resolution, including advanced data matching, which adds an important element of context that is necessary for recommending relevant and personalized next-best actions. **Identity Resolution and Householding** Context is an important element in the evolution of householding, which [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) helps take to the next level. With more data, the complete customer profile that results from identity resolution does two things. First, it expands the opportunity for marketers to tie identity proxies to the same physical address beyond such basic markers as a shipping address. Second, a complete profile allows for more targeted messaging. For instance, while a marketer can benefit from knowing that two customers live at the same address, benefits increase exponentially if identity resolution lets that marketer know who the head of household is, if they’re single or married, or how many children they have. Understanding all purchasing dynamics within that household enables a marketer to provide a relevant, personalized customer experience to increase the customer lifetime value. A financial services company, for example, might correlate tax roll information with other customer data to make a determination if a residence is a single or multi-family unit and score the relevance of sending a re-finance offer to the head of household. Solving the challenges and complexities of [data matching](https://www.redpointglobal.com/customer-data-management/data-matching/) and data latency are the keys to identity resolution, which is vital to keep pace with the customer across increasingly disjointed customer journeys. Matching customer records from a multitude of data sources across multiple systems of engagement are the key to providing the personalized experiences that today’s empowered customer demands. **Blog categories:** Customer Data Platform, Data Management, Data Quality, Identity Resolution, Master Data Management, Omnichannel Marketing --- ### [Does Your Customer Data Have Enough Context? Here’s How to Tell](https://www.redpointglobal.com/blog/does-your-customer-data-have-enough-context-heres-how-to-tell/) **Published:** October 30, 2025 **Author:** Steve Zisk **Content:** Having a contextual understanding of a customer often refers to having situational awareness of a customer journey. It is about leveraging data readiness to compile and analyze enough signals about a customer to understand real-world relationships – what the customer cares about, the customer’s place in a household or business, how the customer interacts with a brand. In addition to real-world, situational context that comes from recognizing and understanding customer signals, another aspect of context is the context of the data itself. Contextual metadata – the data universe context – is just as important for truly understanding and engaging with customers with a relevant customer experience (CX). Contextual metadata helps marketers and data engineers understand, trust and responsibly use the data. Here, we’ll break down what each means, why they matter, and how they work together to power smarter marketing. ## **Situational Context: Understand the Person Behind the Data** Situational context is gleaned from collecting and analyzing signals about a customer and a customer’s relationships with other people, with brands, products, and even to themselves over time. These signals reveal intent, affinity, and preferences. Examples include social media activity (likes, reviews, etc.), loyalty program participation, purchase history, store visits, search behavior and subscriptions. All of this real-world, situational context helps marketers deliver a relevant CX. Putting signals together yields a deep, personal customer understanding that marketers use to deliver a next-best action, to understand whether the customer is more likely to churn or convert, to know the preferred communication channel, etc. Customer context is **dynamic** and **situational** – it changes as the customer’s behavior and environment evolve. ## **Data Context: Understand the Data Behind the Customer** Metadata provides another layer of context to customer data, providing an understanding of data as it relates to other data. This data context enables data engineers to better understand what matters for marketers, which helps them build and deliver data products for downstream business users This additional data context helps evaluate whether data is [ready for business use](https://www.redpointglobal.com/blog/enterprise-data-readiness-the-key-to-filling-the-gaps/), i.e, complete, accurate, timely, actionable, trusted, and compliant. It also helps determine whether data is semantically clear. Metadata that provide data context include elements such as data lineage, data quality, timelines, meaning, compliance and usage – anything that helps us understand the data itself. For instance, whether data feeds are complete, missing, or delayed. Or whether there is data drift, i.e., unexpected changes in volume, accuracy or timeliness. Data may become more reliable or less reliable; either way, this type of contextual understanding about the underlying data is a key element of data trustworthiness – which underlines data readiness. Some elements of data context may not even relate directly to the customer. For instance, context may include how much it costs to acquire a piece of data. Or how often the data is uploading, an important metric for calculating the cost to keep the data reliable. Semantic understanding means providing additional detail about data so that people, applications, and AI can understand meaning, constraints, and usage for that data. This may include textual definitions, allowable values or rules, data types and relationships. For example, semantics provides a common understanding for the value of a number – does the number “5” refer to a size, an amount, a unit of currency, etc.? If the number denotes a purchase amount, a comparison can be made between purchases in dollars and euros provided a known exchange rate. The data context provided by metadata lets data engineers and data agents do their job reliably and consistently. But this context also lets business users understand the meaning, relationship, and usage of data for better insights and CX. ## **Why You Need Both: The Power of Combined Context** There is ample crossover between situational context and data context that underscores the importance of both. For instance, imagine trying to predict churn based on email engagement – only to find out the customer opted out of email months ago. That’s a situational signal that changes how you interpret the data. On the one hand, you have a situational signal about a customer (channel preference). On the other, that data point is important context about the data source itself, letting you know that you can’t make conclusions or calculations based on how often that customer interacts with emails. Likewise, if a significant percentage of customers have opted out of email, perhaps you don’t calculate a churn prediction based on email interactions. In this and similar examples, the real-world, situational context for the customer (channel preference) is tied back to an element of the data universe. Conversely, there are many elements in the data universe context that tie back to situational context. For instance, a data engineer training AI models needs to make sure the model understands the meaning of individual data elements, e.g., this number represents a shirt size, this number represents a purchase in dollars. These and other semantic understandings are crucial for the AI model to make the correct recommendations or predictions for both direct and indirect interactions with customers. ## **What Does This All Mean?** Marketers well understand the value of capturing situational signals that reflect customer behavior and intent. Doing so is foundational to providing a relevant CX. Making this context clear for data engineers and developers lets them create visualizations, APIs, applications, and models that handle the whole customer context. Capturing metadata and developing a deep understanding of data as it relates to other data is just as important for extracting the optimal value from data. No longer just an issue for IT and data engineers, it is key to ensuring data is trustworthy, timely and well-understood. The [Redpoint Data Readiness Hub](https://www.redpointglobal.com/data-readiness-hub/) bridges the gap by providing both types of context, helping marketers see top-level signals without having to dig into raw data. It automates the translation of context into actionable insights, validating recommendations and predictions through the delivery of accurate, relevant experiences. The reason context is king is because the best marketing decisions come from understanding both the customer – and the data that represents them. **Blog tags:** Data readiness --- ### [Marketing’s Star Turn: How to Secure Enterprise Buy-in for a Customer-Centric Approach](https://www.redpointglobal.com/blog/marketings-star-turn-how-to-secure-enterprise-buy-in-for-a-customer-centric-approach/) **Published:** July 7, 2021 **Author:** John Nash **Content:** As the sudden, unexpected surge in a remote workforce took hold last year, there was concern among executives that it would result in lost productivity. The fear proved to be ungrounded. In a [PwC remote work study](https://www.pwc.com/us/remotework?WT.mc_id=CT10-PL102-DM2-TR1-LS3-ND30-PR4-CN_ViewpointHighlights-), 52 percent of employers said that employees have been *more* productive working remotely. As of January, 83 percent of employers said a shift to remote work has been successful for their company, up from 73 percent since June, 2020. The lesson learned is that preconceived notions based on little more than an adherence to tradition are not always the most efficient ways of doing things. It is analogous to a long-held belief among various enterprise business units that success metrics must be department- or channel-specific. But with revenue growth increasingly tied to the ability to deliver a seamless customer experience (CX) across channels, it is high time for the enterprise to dislodge some entrenched beliefs in favor of a customer-centric approach, which recognizes marketing as a key partner and the driving force in orchestrating a differentiated CX. While customer experience indeed [transcends marketing](https://www.redpointglobal.com/blog/a-superior-customer-experience-cx-transcends-marketing/), because the customer looks at a relationship with a brand through the prism of an entirety of interactions, marketing must leverage their depth of customer understanding to take the lead as a single point of operational control for CX. The reason for a customer-centric approach is that it delivers profitable results. According to [research from Deloitte](https://www2.deloitte.com/content/dam/Deloitte/ie/Documents/Strategy/2014_customer_centricity_deloitte_ireland.pdf), customer-centric brands that imbed customer centricity into their organization’s DNA increase profitability by up to 60 percent. This goes beyond brands being “persona-centric” to truly understand customers at the individual level. From a data perspective, this requires going beyond using transactional, preference and historical data to understand customers to also looking at behavior across devices, analyzing device usage, IoT and sentiment analysis, and analyzing all data – structured, unstructured, first-party, third-party, etc. – across an entire anonymous to known customer journey. With that as the objective, it is easy to see how conventional business and channel siloes with independent reward systems or metrics fall short of providing a seamless CX. A call center, for example, that awards bonuses based on satisfaction surveys is not incented to further enhance a customer’s journey beyond a single interaction. Furthermore, call center agents may lack the necessary data from other channels that could help further the customer relationship and lifetime value. Likewise, an email or website marketing team judged on click and open rates has no skin in the game for what happens next. But, just as a reluctance to embrace a remote workforce took a trial by fire for minds to change, so too will it take hard data for various business units to appreciate marketing taking a leading role in crafting a personalized CX to the benefit of not just the customer – but the entire company. ## **For a Customer-Centric Approach, Use Customer-Centric Metrics** To convince all business functions to change metrics in adopting a customer-centric approach is difficult, but it need not be a leap of faith. Instead, proving it on a segment basis allows for a seamless transition and an eventual full switchover once the efficacy of using customer-centric metrics is proven. One national retailer moved from management incentives that were aligned with operating margin by channel to revenue by segment, while also controlling costs by setting overall company margin targets. The reason for the change was two-fold. First, the previous operating margin by channel system reinforced channel siloes. Teams were not invested in the success of any other channel, nor in the success of the company as a whole. Second, the system inadvertently created a fractured experience for the customer, who, moving in and out of various channels, was presented with a disparate set of inconsistent interactions. The revenue by segment reward system, by contrast, is a rising tide that lifts all boats; every team is vested in creating a holistic experience for an entire segment audience. By creating new or different segments, organizations can essentially do a/b testing at scale, comparing the segment approach to existing success metrics. An airline, for example, with standard silver/gold/platinum loyalty tiers that uses channel-based success metrics might decide to peel off a new business traveler segment, and tie rewards on how the segment performs compared against existing metrics. Instead of judging, say, how a direct mail campaign performed in each of the three loyalty tiers, a new segment might be measured against the totality of the customer journey and include every interaction – what the customer did online, a call with a booking agent, the return of a satisfaction survey, etc. ## **Use a Single Point of Operational Control** Limitless experimentation is possible with the right technology, along with the willingness for different business units to cede to seamless customer experiences oriented by marketing. A customer experience platform that provides marketers a single point of operational control, and with an open garden architecture, allows for the creation of endless segments and 24/7 in-line testing without disrupting operations and without requiring an unwelcome business realignment. Leading organizations in retail, healthcare, finance, insurance and other industries use the [Redpoint rg1 platform](https://www.redpointglobal.com/one-platform/) to lead digital transformation initiatives that include a crossover to a customer-centric approach. Embedded automated machine learning enables fleets of inline, self-training models to test for any business metric. Segments can be created to test any hypothesis and create personalized experiences that can then be measured on a segment basis across channels. With an [open garden architecture](https://www.redpointglobal.com/blog/new-redpoint-capabilities-embrace-the-open-garden-approach-to-marketing/), rg1 connects with an existing architecture. Teams familiar working within the constraints of a single system can continue to work as they always have, with the difference being that rg1 is the single point of operational control. More than a repository for every source of customer data, its real-time decisioning engine intelligently orchestrates a next-best action for an individual customer or segment based on what a machine learning model calculates is optimized to produce the desired result. We have outlined before that now is the time to [set ambitious marketing goals](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/). Customers engage in increasingly unpredictable, omnichannel, digital-first journeys, moving in and out of channels at will. Metrics set up to reward teams based on the performance of a specific channel, or actions taken within a channel, are mis-aligned with today’s modern customer journeys. Unfortunately, the consequences fall on the customer. Moving toward a customer-centric approach by testing new segments and using metrics tied to overall customer experience will secure enterprise buy-in and new partners in recognizing marketing’s leading role in deliver a superior CX that is proven to drive new revenue. ## **Related Content** [Data as a Revenue Engine: How to Monetize Customer Data with a Single Point of Control](https://www.redpointglobal.com/blog/data-as-a-revenue-engine/) [How Can CMOs Meet the Connected Customer?](https://www.redpointglobal.com/blog/how-can-cmos-meet-the-connected-customer/) [Customer Journeys are Dynamic: Your Engagement Technology Should Be as Well](https://www.redpointglobal.com/blog/customer-journeys-are-dynamic-your-engagement-technology-should-be-as-well/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Journey Orchestration, Segmentation & Activation --- ### [Data as a Revenue Engine: How To Monetize Customer Data with a Single Point of Control](https://www.redpointglobal.com/blog/data-as-a-revenue-engine-how-to-monetize-customer-data-with-a-single-point-of-control/) **Published:** March 21, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/03/Data-revenue_662430661-e1553125415716.jpg)Astute brands know that the exchange of legal tender when a customer makes a purchase is not necessarily the most valuable part of the transaction. Throughout each step in a customer journey (or journeys), a customer provides something of equally high value to the brand over the course of the brand-consumer relationship: data. Data drives revenue because it is the key ingredient in the delivery of a hyper-personalized customer experience that ultimately increases revenue lift. With an approach that elevates data to a revenue-driving engine, a single transaction is a part of a bigger picture, one in which every conceivable data point contributes to a single customer view and the lifetime value of the customer. Social sentiment, behavioral triggers, weather conditions – everything that contributes to a fuller understanding of the customer can be viewed as a potential revenue source. Customer data provides the key to a treasure map leading to greater riches for the marketing organization, turning data clues into a hyper-personalized experience that creates value for the consumer and the brand. Unfortunately, data by itself is about as useful as buried treasure. It needs to be activated by in-line analytics that turn it into the next best action for any one customer, whether that action is in the form of a message, offer, or content. Value is fully realized when these actions are delivered to the customer at just the right moment, seamlessly orchestrated into their omnichannel journey. In this sense, data is a vital revenue-driving engine for any brand with the desire and capability to turn data into insight and results. ## **Customers Will Pay for Personalization** The potential rewards for brands with a desire to monetize data are immense. Analysis by the Boston Consulting Group revealed a potential revenue increase of up to [10 percent](https://www.bcg.com/en-us/publications/2017/retail-marketing-sales-profiting-personalization.aspx) by brands that create personalized experiences by integrating advanced digital technologies and proprietary data for their customers. Conversely, inaction is more costly than status quo. Frost & Sullivan estimates that companies globally lose more than [$300 billion each year](https://www.prnewswire.com/news-releases/omni-channel-customer-experience---not-an-option-but-a-strategic-necessity-300303866.html) due to poor customer experience – with more than two-thirds going directly into the coffers of competitors. Capturing revenue lift from customer data begins by ensuring the data is precise, accurate, and accessible. The kind of hyper-personalized experience that drives revenue demands it; otherwise, customer data loses marketing value. With accurate and easily accessible data, a marketer can keep pace with a connected customer who is accustomed to engaging across multiple interaction touchpoints on multiple devices throughout a self-directed digital and physical customer journey. Machine learning is the activation engine that turns customer data into real-time, actionable insights at scale and at the pace of the customer. Machine learning algorithms continually optimize a customer experience by unlocking next-best action recommendations in real time that are in the context and cadence of the customer and result in the hyper-personalization that drives the needle for a consumer. In one survey, nearly [80 percent of consumers](https://marketinginsidergroup.com/content-marketing/content-marketing-personalization-imperative/) said that personally relevant content (offers, notifications, messages) positively influenced their intent to purchase. Machine learning is essential because of the nearly infinite permutations of a dynamic, omnichannel customer journey. Applying intelligent orchestration and real-time decisioning to a next-best action recommendation is the final step to the ultimate monetization of customer data. Intelligent orchestration produces the relevant content that provides revenue lift by personalizing every interaction across all channels, devices, and touchpoints. Done right, such a well-placed, well-timed offer or interaction is the “wow” factor that customers reward with wallet share and loyalty, impressed by a brand that meets them wherever they are in the journey at the right moment of time. ## **A Single Point of Control Unlocks Data Value** A single customer view, advanced analytics with machine learning, and an intelligent orchestration layer that touches the consumer constitute a single point of control over data, decisions, and interactions. In the context of monetizing customer data, a [single point of control](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/) is the printing press that turns otherwise worthless paper, in this case data, into currency. Many marketers chasing the pot of gold of the modern, hyper-personalized customer experience make the mistake of over investing in the last mile to the consumer, believing that the next transformative social channel, mobile app, or other touchpoint to the customer will bring untold riches and differentiate from the competition. The problem with this approach is that comes at the expense of a consistent omnichannel experience across channels, which is what customers expect. More than half of consumers in a [Boston Retail Partners survey](https://brpconsulting.com/download/2018-digital-commerce-survey/) said that a personalized experience across all digital channels within a brand is important. A new mobile app that offers the best user experience in the world is great, but if you’re only chasing the shiny new object you run the risk of introducing customer friction by not being able to recognize a customer across all channels at the point of interaction. A single point of control over data, decisions, and interactions is the key to these types of frictionless experiences consumers now expect, while also setting up marketing to optimize their return on investments. The speed and scale is now available to deliver the perfect recommendation for each individual consumer, leading to optimal results and freeing marketing from difficult investment decisions. It is now possible to get past the state of today’s market where some may over invest in collecting data without using it, over invest in off-line analytics without in-line analytics, or over invest in siloed, last-mile processes rather than the orchestration of each channel relative to the potential in the market. The relative low cost of data storage makes this an opportune time for marketing organizations to rethink their investment strategy to better capitalize on a single point of control. Previous decisions on how to allocate precious resources are being rendered obsolete by open garden technology that allows marketers to leverage their existing investments in engagement systems and data. Combined with capabilities to deliver a real-time platform for personalized engagements with perfectly aligned relevance across channels, now is the perfect time to fully monetize data. Robust customer engagements hubs with an open garden approach provide marketers with the capabilities they need to achieve this single point of control. This is also future proofs their investments, enabling them to continually innovate customer experiences and giving marketers operational control over an effective use of all data – past, present and future. From a revenue standpoint, this is like having a seat in the treasury to watch as data transforms into currency with hyper-personalized experiences that customers reward through their pocketbooks. **RELATED ARTICLE(S)** [Data and the Empowered Customer](https://www.redpointglobal.com/blog/data-and-the-empowered-customer/) ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Data Quality, Omnichannel Marketing --- ### [How to Define Value when Buying a Customer Data Platform (CDP)](https://www.redpointglobal.com/blog/how-to-define-value-when-buying-a-customer-data-platform-cdp/) **Published:** March 17, 2025 **Author:** Stephanie LaFazia **Content:** I’ve seen It time and again – companies get excited about implementing a customer data platform (CDP), expecting immediate transformation. The reality, though, is that technology alone doesn’t solve problems. The real power lies in how you use it. Do you know what your business outcomes are? ## **Use Cases First – They Define Everything** A focus on use cases from the outset is strategic approach to maximizing the value of a CDP: - Your businesses’ use cases determine the capabilities your vendor must have (not just the buzzwords). - Use cases dictate how you prioritize data sources. If a vendor tells you to ingest *all* your data upfront, be cautious – it’s a sign they’re selling tech, not outcomes. Start with the data required for high-impact use cases first. - They help you assess vendors—not just on features but on their real-world execution and industry expertise. ## **Clearly Define Your Goals During the Vetting Process** To decide on use cases that are specific to your business, think about the metrics by which you’ll measure success: - Before selecting a vendor, ask yourself: What problem are we trying to solve? What outcomes do we expect? - A use case without a clear business goal is just an idea. You need to know why you’re implementing it and how success will be measured. Is it to improve ROAS? Reduce churn? Increase up-sell conversions? Create awareness? Build Loyalty? - Vendors should help refine and prioritize use cases, but they can’t do that effectively unless you’ve defined your business drivers first. A strong vendor will challenge your thinking and help you align on high-impact, achievable goals. ## **Paid Media Use Cases & How to Measure Success** Many CDP buyers begin with paid media use cases because they deliver quick, measurable wins that prove value early and set the stage for broader CDP adoption**.** - **Suppression for Smarter Ad Spend** **Use Case:** Suppress existing customers from acquisition campaigns on DSPs, Google, and social media to reduce wasted spend. **Success Metrics:** Lower cost per acquisition (CPA), improved ROAS, and higher conversion rates on net-new prospects. - **Lookalike Audience Expansion** **Use Case:** Use first-party data from high-LTV customers to build more precise lookalike audiences on Meta, Google, and programmatic channels. **Success Metrics:** Higher match rates, increased conversion rates, and improved customer lifetime value (LTV) from acquired customers. - **Retention & Upsell Campaigns** **Use Case:** Target existing customers with personalized upsell or cross-sell offers based on recent purchase behavior and engagement signals. **Success Metrics:** Increased repeat purchase rates, higher AOV (average order value), and improved customer retention rates. - **Churn Prediction & Win-Back Campaigns** **Use Case:** Identify at-risk customers based on inactivity, declining engagement, or other churn signals, and re-engage them with targeted offers. **Success Metrics:** Reduced churn rate, higher reactivation rates, and improved customer lifetime value. - **Real-Time Personalization in Paid Media** **Use Case:** Deliver dynamic creative variations in ads based on real-time customer attributes (e.g., last browsed product, past purchases, loyalty status). **Success Metrics:** Higher CTRs, improved engagement, and better ad relevance scores. ## **CDP Adoption is a Journey – Start Pragmatic, Then Scale** To recap, focusing first on use cases helps companies maximize the value of their CDP. Paid media is a common starting point for its demonstrable wins, setting the stage for what’s possible. - The fastest path to value often starts in paid media – suppression, retargeting, and lookalike modeling can drive immediate efficiency. - Build internal proof of value before expanding the CDP across teams – momentum matters. - Governance is critical – monthly reviews to track expansion across teams keep the strategy aligned. A CDP isn’t just a database – it’s a tool to drive business outcomes. Success isn’t measured by how much data you collect, but by the use cases you activate and the value they create. --- ### [Application Rationalization: How to Balance CX & Corporate Goals](https://www.redpointglobal.com/blog/application-rationalization-how-to-balance-cx-corporate-goals/) **Published:** February 18, 2021 **Author:** Steve Zisk **Content:** Martech selection – purchasing, updating, and eventually retiring marketing software – is a critical and complex task for ambitious marketing teams. Every organization has to strike balances: between best-of-breed and integrated stacks; between current needs and future desires; between “sky’s-the-limit” transformed customer experiences and concrete limits on tech budgets, personnel costs, and organizational change. [Customer engagement software](https://www.redpointglobal.com/customer-engagement-hub) will help brands to compete with a personalized, omnichannel customer experience, but making sure all the pieces work together for both the marketing team and the target customers can be fraught. A reading of the tea leaves for what might be needed in a year or two in response to evolving customer expectations is as much a part of the calculation as what’s needed today. Application rationalization – defining what the new customer engagement software will be used for and deciding what it will displace and what it will interface with – makes choices much harder. Further complicating matters, the balancing act must also consider corporate goals along with marketing goals, recognizing that the two may often be – if not in juxtaposition – at least not perfectly aligned. Assembling the right martech stack when the end goal is a personalized CX is like trying to hit a moving target. The number of channels and data sources are expanding, customer behaviors are changing and expectations for a holistic experience are deepening. Customers make no distinction between interactions with marketing, service, a call center, billing or another department. The experience a brand delivers must be seamless, and a martech stack must reflect this new reality. Siloed data and a network of unintegrated point solutions are chief culprits in creating a disjointed customer experience that will alienate customers. According to research from PwC, 90 percent of consumers would increase their likelihood of a purchase with a more personalized experience. Conversely, 52 percent are *less likely* to engage with a brand because of a poor digital experience. ## **Omnichannel vs. Multichannel** With consumers crystal clear that they will not tolerate a subpar experience, having a customer engagement application for each channel is no longer sustainable. The technology itself, however, is just one consideration for successful application rationalization. People and process issues must also be taken into account. A company that consolidates its martech stack and successfully eliminates data siloes between channels will still introduce friction into a customer experience if the teams that manage those channels continue to operate independently. A team of call center agents blind to the activities of the team managing email campaigns may be without pertinent information when fielding a customer inquiry, for instance. And lack of coordination may be exacerbated when goals and KPIs are not aligned across teams and channels. For instance, reducing Average Handle Time (AHT) in the call center has been used as a success metric, but this may be directly opposed to understanding customer needs and working to resolve any questions or issues. Consolidating technology, people and processes to better meet the expectations of an always-on, connected consumer reflects an understanding that becoming omnichannel is different than merely being multichannel. The former implicitly recognizes that the consumer is in charge of a nonlinear, nonsequential customer journey. Interacting with a customer with precision and relevance requires collecting and acting on customer data in real time. A [single view of the customer](https://www.redpointglobal.com/single-customer-view/) that is updated and accessible in real time helps ensure a frictionless experience. With a single view, every customer-facing team has the same updated, real-time view of where the customer is *right now* in a unique customer journey and can deliver a next-best action that is true to the customer’s current situation. A multichannel approach, by contrast, may optimize for a specific channel but without integrating the technology, people and processes it will be hamstrung – always one step (or more) behind a customer likely to flit between channels. ## **Measurement Dimensions to Consider** Turning toward the technology part of application rationalization, every company faces the problem of obsolescence, which applies to both software and the corporate and/or marketing goals that were in vogue at the time the software was implemented. An honest accounting of technology needs that will meet customer expectations for personalized experiences today – and hopefully in the future – will first determine what its new CX goals are and try to align what matters to the customer with what matters to the C-suite. A customer, for instance, will not have the slightest interest in a corporate objective to reduce churn, but a delightful experience may produce an identical result. To align what may be competing objectives, it becomes important to identify key metrics and determine how to measure those on a consistent basis, meaning that there is assurance that measurements are an “apples to apples” comparison both over time and on a channel-by-channel basis. Another element of measurement, particularly important with the moving target of a personalized CX, is measuring a default state against a proposed possible outcome – a wide lens approach to a/b testing that encompasses the totality of goals that pertain to customer experience. A product recommendation engine optimized for a personalized approach, for example, would presumably be required to produce a result at least marginally better than recommending the top 10 best-sellers. ## **Not All CDPs Are the Same** Application rationalization is never routine when it comes to customer engagement technology, as Scott Brinker’s iconic [Martech 5000 graphic](https://chiefmartec.com/2020/04/marketing-technology-landscape-2020-martech-5000/) makes clear. As of April, there were 8,000 solutions listed – a 13.6 percent increase since 2019. With such an over-populated and diverse landscape, a rationalization or acquisition team has to look hard at team and corporate goals when assessing technology needs – and projecting how those needs will impact people and processes. In the crowded customer data platform (CDP) market, especially, the task will require that a company have detailed descriptions of intended use cases, as well as a firm understanding of the distinctions among various CDPs. The CDP Institute provides a handy guide on [CDP basics](https://www.cdpinstitute.org/cdp-basics), and its [RealCDP™ Initiative](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/) offers a set of five features a “real” CDP must include. Is a CDP that promises data orchestration, for example, really just segmenting an audience, moving customer data into one channel or another and then “orchestrating” an experience for the segment within that one channel? This distinction of how much of this “last mile to the customer” is orchestrated and automated by an engagement platform cuts to the core of what it means to be omnichannel vs. multi-channel. A real time component, as mentioned above, is another key distinction. Another is the determination for how to bring customer data together to form a single customer view, which entails questions related to probabilistic and deterministic matching, householding, and other data quality considerations. There are also a host of non-functional requirements related to privacy, compliance, auditability, latency, scale, timeliness, etc. Each of these considerations should balance corporate goals and customer-focused goals, delivering a superior customer experience that meets the lofty expectations of today’s always-on, connected consumers. Customers demand excellence, which makes a detailed application rationalization of a customer engagement landscape worthy of the exercise. **Blog categories:** Customer Data Platform, Data Quality, Identity Resolution, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [How Right-Sizing Data Helps Deliver a Better Customer Experience (CX)](https://www.redpointglobal.com/blog/how-right-sizing-data-helps-deliver-a-better-customer-experience-cx/) **Published:** October 24, 2023 **Author:** Redpoint Global **Content:** In the crowded customer data platform (CDP) market, there is a misconception that more is better, at least from a data standpoint. If, after all, the purpose of a CDP is to generate a unified view of the customer, it would seem on its face that the more data a company has about a customer, the more detailed the unified view. This is generally true, with a few caveats. Because while there is a possibility of not having enough data to support a unified view or a deep understanding of the customer, there is also the possibility of having too much. It’s like throwing a dinner party where you carefully plan the guest list, considering both the right number as well as the dynamic between invitees. Too few and your “party” loses pizzazz. Too many, or the wrong mix, and the risk becomes a lack of control. The additional expense and clean-up aside, the worry is you still have a house full of people when it’s time to go to bed. ## **What is Right-Size Data?** The concept of right-size data refers to a company having policies and procedures in place for a strict accounting of what data the company will collect about a customer. A subset of a broader data governance structure, right-size data considerations include rules for what type of data to collect, its shelf life, rules for discarding, etc. In the house party analogy, it would be like having someone at the door checking invitations. There are three primary countervailing forces to the “more is better” school of thought: 1. Risk of Knowing Too Much About the Customer: The [infamous example](https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/?sh=6bacaa1c6668) here is the department store that used predictive modeling to determine – correctly – that a teenage customer was pregnant. The store started sending coupons for cribs and baby clothes, which the unknowing teen’s father intercepted – and confronted the store. “Even if you’re following the law, you can do things where people get queasy,” said the statistician who predicted the pregnancy. The rule of thumb to avoid this type of situation is to only collect customer data that will be used to enhance the customer experience (CX) in ways that delight the customer, not make them feel like a stalking victim. Customers will share data for specific purposes – or even for a specific amount of time – and violating a customer’s trust by the misuse of data is a surefire way to drive a customer away. Conversely, being open about why data is being collected and how it will be used – and abiding by those conditions – will generate trust with a customer. In a [Dynata survey](https://www.redpointglobal.com/press-releases/80-of-consumers-more-likely-to-shop-with-brands-that-show-they-understand-them/) of retail consumers, 85 percent said they are more likely to shop with a brand that is transparent with how they collect and use personal data. 2. More Data Creates More Risk: Having too much data creates additional exposure for data breaches and data loss. It makes little sense to collect and indefinitely store data that will never be used without a justifiable business need, i.e., using it provide a more personalized customer experience. A determination of what to keep may also factor in how long to keep it, with different rules for different types of data, such as a credit card number vs. data from a browsing session. A potential for exposure also involves privacy concerns. This concern overlaps somewhat with knowing too much about a customer, with consequences that may exceed the fallout from sending mistargeted communications. The state of New York, for example, just joined at least one other state from [banning advertisers](https://www.adweek.com/programmatic/new-york-bans-geofencing-near-health-care-facilities/) from using geofence technology near health care facilities. Earlier this year, the FTC fined two companies close to [$10 million](https://www.arnoldporter.com/en/perspectives/blogs/enforcement-edge/2023/03/ftc-announces-betterhelp-fine#:~:text=Just%20one%20month%20after%20the,consent%20order%20with%20BetterHelp%2C%20Inc.) for sharing customers’ sensitive health information. 3. More Data = More Clutter: While there are valid business reasons for holding onto specific customer data, such as purchase history for a specific timeframe for warranty fulfillment and/or returns, the rule of thumb about having a valid business need for collecting or holding onto data applies to more than exposure to risk. It is simply good business practice for developing a deep understanding of a customer. Recency matters, and “cleaning house” on occasion might make it clear that there is no valid reason to hold onto a 10-year-old loyalty club application. In fact, keeping useless data might even be detrimental to providing a customer with a hyper-relevant experience because the old data may no longer be accurate, or it might simply not be important to how the customer interacts with the brand today. A regular audit will help ensure that existing policies and procedures for how data is collected and used are effective, up-to-date and optimized for delivering a better customer experience. In a [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/) on customer experience, 51 percent of customers identified friction in interactions with brands because the data used in an attempt at personalization was inaccurate or out-of-date. ## **Right-Sized Data and the Value Exchange** Beyond the CX implications, right-sizing data also reduces a company’s overall footprint, storage costs, and network and compute costs. The school of thought that says to throw all customer data in a data lake and figure it out later also ignores those pratfalls in addition to being susceptible to the three potential consequences outlined above. A final thought on the importance of right-sizing data is that it aligns with the expectations of consumers who are increasingly aware of not just their data footprint, but that the data they provide has value. The Dyanta survey result, in which a large majority of consumers say they are more likely to shop with a brand that is transparent about data collection, validates that consumers know they are giving up something of value. In return, they want brands to honor their preferences for how their data is used. With GDPR and other data privacy regulations, more and more consumers elect to police brands on the use of personal data. Brands that honor consumer preferences [build trust, loyalty and brand equity](https://www.redpointglobal.com/blog/why-trust-unlocks-a-superior-customer-experience-cx-and-builds-brand-equity/). For many reasons, the right-sizing of customer data is good business practice. Ignoring it with the thought that more is better exposes a company to the risk that its customers will “right-size” it and terminate the relationship, to borrow the human resources connotation of the phrase. To learn more about three hot martech trends, including-right-sized data, you can access an on-demand webinar where Redpoint Global’s Ian Clayton, Kris Tomes and Steve Zisk discuss how to ensure that you have exactly the data you need to impact the experience your customer wants. To access the webinar, [click here](https://event.on24.com/wcc/r/4372973/76B6AEB378EB040C21C16C760161CA4E?partnerref=rpgblog). **Blog categories:** Customer Data Platform --- ### [Retailers Will Rapidly Adapt or Decline in the New Reality](https://www.redpointglobal.com/blog/retailers-will-rapidly-adapt-or-decline-in-the-new-reality/) **Published:** May 12, 2020 **Author:** John Nash **Content:** Amid all the speculation on the lasting impact coronavirus will have on the retail industry, a common denominator among customer-facing changes is likely to be a greatly enhanced focus on digital customer experience – already a priority for retailers looking to drive new revenue with highly personalized experiences across omnichannel customer journeys. Consider, for example, a Harris Poll sponsored by Redpoint, where [63 percent of customers](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) surveyed said that personalization is part of a standard service they expect. What was an imperative for many forward-thinking retailers has now become an imperative for all: the ability to provide a consistent, cohesive and personalized experience across channels. This capability will likely determine which retailers survive the new reality marked by rapidly evolving social interaction rules resulting in more dynamic, unpredictable and mostly digital customer journeys. ## **The New Customer Journey** There is a general acceptance that changing consumer behavior will, in some shape or form, [entail physical distancing](https://www.businessinsider.com.au/how-retail-could-change-in-the-wake-of-coronavirus-2020-4?r=US&IR=T): a shift to contactless payment, curbside pickup, buy online and pay in-store (BOPIS) with no human interaction and grocery delivery to name a few. A newly reimagined in-store experience may mean wider aisles, customer limits or appointment-only visits. There is also speculation about what the future of retail means for [ecommerce](https://www.nytimes.com/2020/04/15/business/economy/coronavirus-retail-sales.html), and how changes will impact CPG brands and the shift to a direct-to-consumer (DTC) approach. New reporting shows that consumer behaviors are already shifting. According to [Sense360](https://azbigmedia.com/business/the-future-of-retail-and-america-in-the-post-covid-world/), 31 percent of US households used a grocery delivery service over the last four weeks, up 18 percent over a four-week period last summer. Walmart reported $900 million in online grocery sales in March, up 21 percent from February – and up 99 percent from March, 2019. According to [Rakuten Intelligence](https://www.digitalcommerce360.com/2020/03/19/coronavirus-is-changing-shoppers-relationship-with-grocery-retailers/), the dollar volume of BOPIS orders for all kinds of retail (grocery and non-grocery) grew 111 percent year-over-year for a period of time in March. ## **Changing with the Times** A recent [blog in this space](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) discussed the acceleration of digital and omnichannel engagement amid this shifting and uncertain landscape. While every industry is being impacted, retailers may be those most deeply affected by changing consumer preferences, simply by the prospect of having to evolve on two fronts – digital and physical, with physical distancing measures in addition to an enhanced online experience. Compounding this is unprecedented changing consumer behaviors. These behaviors are highly unpredictable at the moment, and current behaviors are disconnected from past history. The key is to understand and act on changing consumer behavior in the moment, which almost guarantees that retailers will have to evolve to prepare for this new reality. Evolution will likely take one of three forms: - **A shift to a digital-first/digital-only model** – A [Business Insider article ](https://www.businessinsider.com.au/coronavirus-could-trigger-retail-bankruptcies-and-mass-store-closings-2020-4?r=US&IR=T)examines the potential fate of the brick-and-mortar store. The CEO of PVH Corp. (Tommy Hilfiger, Calvin Klein) predicts that up to 25 percent of US stores will close in the next two years. Retailers will face tough choices on the value of reconfiguring physical stores vs. devoting those resources to creating a better online shopping experience. - **Leveraging digital and physical assets** – Some retailers who were in front of changing consumer behaviors are now well-positioned to create differentiated customer journeys that prioritize customer experience. For example, Walmart, Kroger, Target and Best Buy are among a group of successful retailers already pioneering curbside pickup, which Cowen [estimated would be a $35 billion channel](https://www.forbes.com/sites/pamdanziger/2019/04/07/walmart-is-in-the-lead-in-the-soon-to-be-35-billion-curbside-pickup-market/#7cfe44f9199e) this year. While that estimate was made before coronavirus upended the industry, the service offers a model for others to follow to create a new experience for consumers who may be wary of physical interaction, yet eager for the convenience of same-day pick-up and easy returns. - **A complete re-invention –** CPG brands in particular will likely become less dependent on retail as a last mile to the consumer, and continue to invest in creating innovative direct-to-consumer (DTC) channels. According to analysis from Nielsen and Rakuten, for the week ending April 11 online sales of CPG products rose [59 percent](https://www.digitalcommerce360.com/article/coronavirus-impact-online-retail/) compared with the same 2019 timeframe. A [“doubling down on digital channels”](https://www.imd.org/research-knowledge/articles/unilever-buys-dollar-shave-club/) includes CPG behemoths investing in digital-native start-ups, and coming up with other innovations to increase brand loyalty that reduce a reliance on traditional retail partners. ## **Start with a Deep Understanding of the Customer** Whichever path a retailer takes, the most complete, freshest and accurate understanding of the customer is a foundational requirement to enable a superior customer experience. True before, the need becomes more urgent as behaviors change and new channels emerge. A state-of-the-art curbside pick-up service, for instance, has a lot of moving parts. Each interaction – web browsing, shopping cart fulfillment, a transaction, pick-up logistics – offer an opportunity to delight the customer with a personalized experience. Conversely, there is ample opportunity to fail – a failure to recognize the customer across multiple devices, a poor website experience, miscommunicating pick-up details, ill-timed offers, etc. Mistakes do nothing but create friction with the customer and result in sub-optimal outcomes for the retailer. Orchestrating a seamless, personalized experience across an omnichannel journey begins with having a single customer view. The Redpoint Golden Record provides brands and retailers with every source of customer data – structured, unstructured, semi-structured, first-party, second-party, third-party, IoT, sensor, etc. – for a 360-degree view of the customer. A continually updated record – which includes a customer’s preferences, behaviors and transactions combined with a rich contact graph – enables marketers to keep pace with a customer journey in real time across all channels and devices, and meet each interaction with a perfectly timed and relevant next-best action. By delivering relevance at every interaction, retailers and brands demonstrate that they know a customer as an individual, aligning with customer expectations for personalization. In the Harris Poll sponsored by Redpoint, [52 percent of customers](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) surveyed said that they define personalization as a brand sending them special offers that are only available to them, and 43 percent said that it is when a brand knows them as the same customer across every touchpoint. The changing retail landscape will make the need to deliver real-time personalization within the context of a dynamic customer journey a necessity. Customers have already stated that they expect personalization as a standard service. It is now a strategic imperative for retailers and brands to fulfill this expectation or be left behind in the new reality. **Blog categories:** Retail --- ### [Align AdTech and MarTech to Avoid Over-Targeting and Supercharge Personalization](https://www.redpointglobal.com/blog/align-adtech-and-martech-to-avoid-over-targeting-and-supercharge-personalization/) **Published:** February 25, 2020 **Author:** Steve Zisk **Content:** Disconnected AdTech and MarTech systems often have differing goals and run list-based campaigns for audiences that are defined at a set moment in time. From the consumer’s perspective, such an approach has the potential to introduce friction into the overall customer experience by creating a splintered experience. Today’s dynamic customer journeys deserve a more cohesive, customer-centric approach. Providing the open web as a channel available to a direct marketer is an enormous opportunity in the quest to provide every customer with a hyper-personalized experience throughout an omnichannel customer journey. The always-on, continuously connected consumer expects this type of an experience with a brand – across all digital and physical channels. In the [Harris Poll survey](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) commissioned by Redpoint, 43 percent of consumers define one aspect of personalization as a brand knowing that they’re the same customer across all touchpoints. Notably, consumers in the survey do not make a distinction between addressable and un-addressable channels. Marketers know that meeting the expectation for personalization seamlessly across all channels that span known and unknown customer journeys is hard to do. According to the CMO Council report on data-driven customer strategies, just [7 percent of marketers](https://www.cmocouncil.org/thought-leadership/reports/empowering-the-data-driven-customer-strategy) say that they’re able to deliver a real-time, data-driven engagement across digital and physical touchpoints. Fragmented and disconnected systems are cited as a chief roadblock. The traditional separation of AdTech and MarTech stacks is responsible for some of the disconnect, clouding direct marketers and advertisers from having a unified, real-time view of the customer across both ecosystems. Common personalization failures that result include serving an existing customer a digital ad for a recently purchased product, engaging with the customer in the wrong channel, or sending too few or too many touches. **Optimal Channel, Optimal Outcome** A platform that runs multi-touch ad campaigns on the same rules-based platform that manages direct, PII-based communication equips marketers to manage and personalize a customer journey across all channels – in known and unknown environments – from a single point of control. Marketers define an audience once, and the campaign runs according to rules that determine which audience segment receives a touch and when – whether that touch is PII-based (email, direct mail) or targeted to an unknown record (programmatic ad). Keeping unknown records in a separate, anonymous database that strips a record of PII ensures privacy compliance, but the single campaign flow allows for that audience – prospects, for example – to be stratified in the rules-based campaign along with lapsed or existing customers. In practice, one use case could be to develop a canvas on a single interface with the intent to send a rich offer to eligible customers (purchase last 60 days, or lapsed within 90 days, or web visit last 30 days, etc.). Preset rules and thresholds suppress more active existing customers or other audience segments, and a customer’s record is updated according with behaviors in the campaign flow. Accomplishing this on a single platform ensures that marketers are engaging with an audience in the optimal channel to solicit the best outcome. It eliminates having to define an audience multiple times and run separate, list-based campaigns using two or more systems that are typically not connected. A list-based restriction risks introducing friction into a customer journey by not being in synch with the customer. For instance, pulling a list of lapsed customers to receive a programmatic ad but not being ready with the rich offer when a lapsed customer self-identifies on the website. **Personalization Across the Board** In the Harris Poll referenced above, 37 percent of customers say they will avoid doing business with a brand that fails to offer personalization. By bringing the MarTech and AdTech ecosystems together in a single campaign flow, the Redpoint platform enables unique personalization opportunities. In addition to being able to, say, offer tennis content to a tennis player – as one example of basic personalization – the platform’s single campaign flow enables more nuanced personalization by automating messaging in the optimal channel. With rules set to trigger an action based on a customer’s or prospect’s behavior within the campaign, automated machine learning models optimize a journey in concert with both the customer’s pace and with how the customer is responding – the real-time actions they’re taking. Drilling down, this fully supports suppression as a personalization method – more than channel and content optimization, it’s optimization characterized by what a target customer or prospect *doesn’t* see. Will a display ad drive an intended result if another communication already triggered an action? In a Forrester [“Future of Omnichannel” report](https://www.forrester.com/report/The+Future+Of+Omnichannel+Advertising+Must+Be+Customer+Obsessed/-/E-RES141337), nearly 100,000 internet users who used an ad blocker in the past 30 days were asked their reasons for doing so. Not surprisingly, “too many ads” was the top reason cited (48 percent), quickly followed by “ads are annoying or irrelevant” (47 percent), and “ads are too intrusive” (44 percent). Consumers expect better. With the open web as a channel available to direct marketers, seamless personalization in pitch perfect sequence with an omnichannel customer journey is possible. Sometimes less is indeed more, and aligning the AdTech and MarTech stacks in pursuit of a common goal takes into account frequency as an underrated and underutilized personalization tactic. **Blog categories:** Customer Data Platform, Data Governance & Security, Data Management, Real-Time Personalization --- ### [AI Inside: How Redpoint Embeds Intelligence to Drive Smarter CX](https://www.redpointglobal.com/blog/ai-inside-how-redpoint-embeds-intelligence-to-drive-smarter-cx/) **Published:** July 16, 2025 **Author:** John Nash **Content:** Artificial Intelligence (AI) has quickly evolved to become an integral part of modern business solutions, revolutionizing how companies operate and engage with customers. Redpoint’s approach to AI starts with getting the data right. In the first part of this series, we explored how the Redpoint Data Readiness Hub ensures data is [complete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/), [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/), [timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/), and fit-for-purpose—making it AI-ready. This post highlights the next step: **AI Inside**. Here, we focus on how Redpoint embeds AI directly into the platform to enable intelligent automation, advanced personalization, and continuous optimization – while giving businesses the flexibility to bring their own models and tools to tailor AI to their unique needs. ## **AI Possibilities Unleashed** Whether it’s through embedding native algorithms or integrating with third-party platforms like Databricks and Snowflake Cortex AI, Redpoint’s software leverages AI to deliver a wide range of functionality. From predictive models and machine learning to embedded reporting and visualization, these capabilities empower companies to make data-driven decisions and optimize their processes. AI-prompt based workflows and embedded data visualization tools like Sigma further enhance ease of use, making sophisticated AI tools accessible even to those with limited technical expertise. In addition to these capabilities, Redpoint also implants AI to tackle difficult edge cases such as address standardization. This is accomplished using a methodology built upon decades of experience in learning about and solving these challenging scenarios, effectively baking that knowledge into AI-fueled processes. ## **AI Innovation Made Real** From simple to complex, there are an endless number of ways to use AI to improve CX through the use of software products and solutions. As a starting point, perhaps the initial objective is to simply integrate with a third-party innovation to produce a more human-like chatbot. Advancing on the AI maturity curve, it is also possible to infuse AI in a way that brings out-of-the-box predictive models and machine learning into workflows to create next-level personalized journeys and campaigns – with optimal journeys and campaigns where the AI is properly fed with AI-ready data. Embedding AI innovations into software unleashes a full spectrum of AI-driven decisioning, from descriptive analytics and evaluation to recommendations and next-best actions. Infusing customer engagement technology with AI is a closed-loop process, where integrating AI into processes generates a better lift in performance – from detecting anomalies in data to improving data match rates. When those results are fed back into a system that makes the data right and fit-for-purpose, the methodology behind a process like identity resolution is continually fine-tuned. It’s a form of captured intelligence, an AI sidecar that continuously improves both inputs and outputs. ## **AI Your Way** This embedded intelligence is made possible by the way Redpoint’s software is architected. Designed with openness and flexibility in mind, the Redpoint platform is entirely platform-agnostic, built to work with any cloud, any data warehouse, any database, and any security or deployment model. It fits seamlessly into the broader AI ecosystem without locking you into a single vendor or approach. Whether you’re using AI capabilities from Salesforce, Adobe, Databricks, or your own proprietary models, Redpoint makes it easy to integrate, orchestrate, and scale without compromising control or transparency. A key advantage of Redpoint’s architecture is the option it provides to keep your data where it is most secure – behind your firewall – while still making it accessible for advanced analytics, machine learning, and AI. This means your approaches to managing and protecting data stay intact, even as you evolve your AI capabilities. Redpoint’s engines are highly configurable and built with standardized APIs, making it easy to expose functionality and automation across a wide range of roles, systems, and use cases – from simple to sophisticated. Its object-oriented design allows individual components to be tuned for specific channels, databases, attributes, or roles. For AI applications, this translates into a powerful environment where AI agents can operate with fine-grained control, adjusting and optimizing across multiple dimensions. ## **AI Transparency & Trust** Unlike rigid or black-box solutions, Redpoint was built not from the UI down but from the engine up – giving AI agents access to the levers that matter. That transparency is critical: not only can AI optimize decisionsthat may span data quality, identity resolution, and campaign orchestration, but the outcomes are understandable and tunable. Analysts and data teams retain visibility into how decisions are made, creating trust in both the models and the data that powers them. This balance of robustness and manageability puts Redpoint in a unique position. Where isolated systems might use AI to perform one or two tasks with little flexibility or explainability, Redpoint thrives in bringing simplicity to the complexity inherent in modern CX and AI use cases. It gives your teams – and your AI agents – the tools to optimize outcomes intelligently, securely, and at scale. This series on data readiness for AI will continue with a post that expands on the Redpoint approach to Agentic AI. For more on the Redpoint approach to AI, click [here](https://www.redpointglobal.com/). --- ### [Humanizing a Customer Experience without Humans: Let Machine Learning Take Control](https://www.redpointglobal.com/blog/humanizing-a-customer-experience-without-humans-let-machine-learning-take-control/) **Published:** March 14, 2019 **Author:** Redpoint Global **Content:** In aviation, senses can play tricks on a pilot. A loss of visual reference can lead to a sensory illusion where a pilot’s perception is the opposite of actual conditions. The next time you board a flight, you can take comfort that your pilot has an instrument flight rating (IFR) and has been trained to rely on an instrument panel even if the readings are counterintuitive to all of their instincts. Many marketers fall prey to a similar type of illusion when it comes to implementing machine learning. They harbor the mistaken belief that delivering a personalized or “human” experience to a consumer requires human input. As counterintuitive as it might seem, the truth is that the more automated the process, and the more it’s driven by analytics, the closer marketing gets to the humanization of the experience for the consumer. Putting one’s full faith and trust in machine learning is an imperative for marketers to take off and soar with transformative customer engagements that capture the promise of digital transformation. ## **A Corner Store Experience Across Channels** One of the biggest marketing use cases for machine learning is to build operational models that bring a personalized experience to the consumer, akin to what one might expect from a visit to the corner mom and pop store where the proprietor knows your tastes and preferences inside and out and always seems to have the perfect recommendation every time you visit. Dynamic, multi-channel, and non-linear customer journeys across physical and digital channels have upended the brand-consumer relationship, but the expectation for personalization remains. One recent study found that [86 percent of shoppers](https://www.infosys.com/newsroom/press-releases/Documents/genome-research-report.pdf) said that personalization had at least “some impact” on what they purchase. The expectation is not limited to a single interaction. BRP’s [2018 Digital Commerce survey](https://brpconsulting.com/a-personalized-brand-experience-across-digital-channels-is-imperative/) revealed that 51 percent of shoppers say it is important to get a personalized experience “across all digital channels within a brand.” Marketers who cling to the mindset that a human experience requires human input will hire armies of data scientists to build offline models that they think will produce the personalization customers expect, but they’re actually doing the exact opposite; they’re introducing friction because they’re not keeping pace with the customer. There is also the opportunity cost to contend with, as an offline model construct fails to address the influx of data in the time it takes to operationalize the model. Machine learning provides marketers with the tools that have been missing to manifest the personalized experience at scale, and at the speed necessary to keep up with a consumer across physical and digital touchpoints. ## **Contextually Relevant Messages at Scale** Machine learning, at its core, is about applying analytics to every shred of customer data. It begins with a single view of the customer. Advanced machine learning models that work with a persistently updated golden customer record will ensure a next-best action recommendation across channels at the cadence of the customer. Working with contextual data adds relevance to the messages, offers, and even non-offers that are eventually delivered to a customer. This is another important area where human intervention falls short; the smartest data scientists in the world cannot account for all the permutations of a dynamic customer journey. The context that produces one result one minute – a discount offer, an email, a notification – may produce an entirely different result the next minute or even the next second depending on the customer’s next action, new data, or based on a prediction of the customer’s intent. Humans may think they have a firm grasp on how a customer will behave based on their own experiences as a shopper, a banking customer, and an all-around consumer, but the reality is that every interaction influences the next interaction, and because manual algorithms can’t account for every permutation they must resort to segmentation by cohort. Contrast that with the capabilities enabled by machine learning – in-line operational modeling marked by 24/7 testing, self-training and continually optimized models with zero human intervention, and a built-in mechanism that detects and incorporates changes to data into subsequent production models. These capabilities turn a clunky manual process into a seamless, sophisticated process that brings marketers much closer to a personalized experience precisely when it will have the most impact. Analytics turns what is essentially a shot in the dark, a guess, into a targeted, personal, and contextually relevant message that will resonate with a consumer. As the models drive the tactical steps of managing relationships, the marketer is freed to manage at a more strategic level. While this may seem a minor change, it is the very reason that marketers report striking increases in revenue directly attributable to the platform. This is a growing and sometimes surprising effect of stepping back from the tactical control of the day-to-day marketing and enabling a platform that has an environment that fosters analytically driven interactions at scale. ## **Testing will Yield Quick Wins** Because data is the lifeblood of machine learning, clean, accurate, and updated data is a requirement before implementing machine learning capabilities. Another fundamental requirement is the ability to perform cross-channel marketing with a single view of the customer. With these in place, marketers are well-positioned to begin testing models and validating results irrespective of data or execution issues. Continual testing and optimized models with machine learning ensure big wins. The only thing needed to get started delivering a relevant, personalized message to every customer with the right context and cadence is to trust the machine over the human, as counterintuitive as it might seem. Trust the instruments, and you’ll guarantee a fast arrival at your destination. **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Management --- ### [Here’s How Location-Based Marketing Can Enhance a Personalized Customer Experience (CX)](https://www.redpointglobal.com/blog/heres-how-location-based-marketing-can-enhance-a-personalized-customer-experience-cx/) **Published:** March 1, 2023 **Author:** Steve Zisk **Content:** You walk by a storefront and immediately receive a flash sale notification on a product you’ve had your eye on. On vacation, you’re lounging at the hotel pool and your phone buzzes with a two-for-one lunch special at the cabana grill. You arrive at a doctor’s appointment and receive a request to log into your patient portal, where you find an estimated wait time and a check-in questionnaire. Welcome to geotargeting use cases for improving customer experience (CX). Location-based experiences are becoming popular in retail, travel, healthcare and other industries for helping guide consumers through more personalized customer journeys. When a customer breaks a GPS-based geofence or nears a beacon, a triggered action will be relevant to a customer’s physical location and, used judiciously, has a high likelihood of enhancing the customer experience presuming the action is within the context of an individual customer journey. ## **Location-Based Experiences and Customer Signals** Geotargeting and the use of beacons wonderfully complement a personalized, omnichannel customer experience, with the caveat that a customer’s current location is only one signal about a customer. A retailer using a store beacon as a flash sale trigger event will ideally tailor the offers based on the entirety of an individual customer’s behaviors. Having neared the beacon or crossed the geofence, the brand knows the customer is in the store, but the trigger becomes much more powerful when used in the context of a customer [Golden Record](https://www.redpointglobal.com/single-customer-view/). A brand that knows the customer has recently purchased a wool coat, for example, perhaps sets the trigger up to offer a matching scarf to match a customer’s recent browsing activity. Or the location-based trigger is only set up for customers who haven’t made a purchase in the last month. Maybe a personalized offer is made to customers who have downloaded the mobile app in the last week. When a customer’s location is considered in the context of an individual customer journey, a location-based experience can be used to both guide the journey as well as accomplish business goals such as lifetime value, reducing churn, retention, optimizing inventory, etc. This type of segment-based personalization of a location-based experience is referred to as geotargeting, in contrast with standard geofence advertising or marketing where an offer or ad is shown to everyone who breaks a geofence. To be sure, there are countless use cases where breaking a geofence triggers the same experience for everyone, which may also align perfectly with a customer’s journey. Curbside pickup is one such example, where a geofence may alert store associates when a customer arrives. A mobile app check-out offer is another use case, where a brand offers the service for everyone who has downloaded the app and is currently in a physical store. ## **Using Location to Better Understand a Customer** While geofencing and beaconing differ slightly in that the former uses GPS while the latter requires control of the physical space, their activation both require knowledge of the customer’s location, usually through a mobile device. The location-based experience, however, does not necessarily have to instantaneously trigger upon the breaking of the geofence, nor does an offer or message have to be delivered through the mobile app. A car dealership might, for example, set up a geofence around a competitor to find out whether anyone who has visited its lot within a certain timeframe also visited its competitor. Knowing a customer took its luxury SUV for a test drive, the dealer sends an email undercutting the competitor’s price for its luxury SUV. Burger King employed a similar tactic (albeit on the mobile app) in a clever ploy to steal McDonald’s customers. In the [\#WhopperDetour campaign](https://newgrove.com/burgerkinggeofencing/), the restaurant offered a $0.99 Whopper for customers who broke geofences set up within a 600-foot radius of McDonald’s locations. The offer, delivered through the mobile app, also included directions from the broken geofence to the nearest Burger King. A restaurant executive said the campaign was 20 times more effective than any previous app offer. A [Lawless Research study](https://s3.amazonaws.com/factual-content/marketing/downloads/Factual-2019-Location-Based-Market-Report.pdf) commissioned by Factual, a global location data company, found similar rates of success. In the survey, large majorities of companies said that using location-based data helps increase sales (89 percent) and better engage with their customer base (84 percent). Further, 83 percent said that using location-based marketing has provided them with a deeper knowledge of customers’ needs and interests. Having information about a customer’s real-time location certainly qualifies as possessing a deep knowledge, but when it comes to providing a hyper-personalized customer experience, location-based experiences are best utilized when location is leveraged in the context of the overall customer journey. Well-timed, location-based triggers that align with a customer journey are welcomed and valued by customers appreciative that a brand demonstrates a personal understanding. *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Retail, Travel & Hospitality --- ### [Data or Die: How High-Quality Data Fuels AI-Driven Agile Marketing](https://www.redpointglobal.com/blog/data-or-die-how-high-quality-data-fuels-agile-marketing/) **Published:** May 6, 2024 **Author:** Ian Clayton **Content:** In the past decade, there has been a remarkable shift from product-centric to customer-centric marketing as brands in every industry recognize the importance of providing a personalized customer experience (CX). Coincident with that shift, the concept of agile marketing has arisen and evolved so that agility is now about improving CX rather than just selling more product. That is, marketing must consistently produce meaningful and relevant CX outcomes – which depends on rapid course changes to keep up with dynamic customer changes. Agile marketing is a strong driver of the [customer data platform (CDP)](https://www.redpointglobal.com/customer-data-platform/) space, particularly with the ascendance of artificial intelligence (AI), Large Language Models (LLMs), and generative AI (GenAI) as key marketing trends. This blog will break down the elements that factor into an AI-driven agile marketing landscape best suited for producing optimal business outcomes. ## **Data, Data, Data** The core of agile marketing is a focus on understanding and responding to customer needs based on customer data. First-party data is of particular importance with Google eventually eliminating third-party cookies on Chrome, following Safari and Firefox. But a focus on first-party data does not mean simply accumulating as much data as possible. To support agile marketing, i.e., rapid response to constant customer change, first-party data must be cleansed, validated, and ready for business use: Data quality rather than quantity. An [enterprise CDP](https://www.redpointglobal.com/cdp/) that supports an agile marketing framework should perform all data hygiene tasks as data is ingested into the system. Any data – even data that might not spring to mind as being important today – may become critical to quickly support and respond to emergent use cases. This is one area where AI is both a source of – and reason for – an agile approach. As an example, AI can help identify data sources that marketers might overlook as being pertinent to a specific use case. Conversely, high-quality and relevant data might become relevant to launch a new GenAI capability, such as an automated customer service assistant. Clean, pristine and fit-for-purpose first-party data is essential to fuel a virtuous cycle of AI-driven personalized experiences. When clean data is a constant, marketers are better positioned to think about tomorrow’s use cases today. So when the next disruptive trend burst on the scene, they’re not wasting time with data prep before pouncing. ## **Give Data Quality its Due** Because clean and fit-for-purpose data is so critical, it is worth noting that not all CDPs treat [data quality](https://www.redpointglobal.com/data-quality-and-data-ingestion/) with the same level of importance. Some bring data together and rely on a third party to perform data quality tasks somewhere downstream. Or they consider identity resolution or simple validation a stand-in for data quality, claiming that bringing records together is a form of data hygiene. But if we think about agile CX use cases that depend on clean, high-quality data, data hygiene at ingestion is critical. It’s the difference between understanding a customer as a unique individual (or in the context of a household) vs. having a muddled view and, ultimately, an inferior CX. The former – a deep understanding – ultimately depends on having data that is complete, accurate and matched appropriately for a specific use case. One consequence of putting data quality off until later is the potential for false matches, such as failing to match addresses that were entered incorrectly, creating duplicate customer records. When data quality is embedded into the identity stitching process as data comes into the system, marketers are confident that a unique customer profile is just that – unique. A customer ID assigned to a customer is guaranteed to be the intended customer for any business use case. ## **Eliminate Data Bottlenecks** Perfecting data at data ingest is also key for marketers to stay in the cadence of a customer journey. Raw data eventually requires processing. If processing occurs only at the point data is needed for business use, latency is introduced. The amount of latency will depend on several factors, among them how many processes, systems and channels are impacted. Different processes for different systems and/or channels will also introduce inconsistencies. The result of latency and inconsistencies is a strong potential that a customer journey outpaces the ability of a marketer to keep up. In a similar vein, putting off data hygiene makes it almost impossible to create an omnichannel CX. With multiple channels, different cadences, inconsistent data and new data always entering a CDP, the issue when data is cleansed on the way out but not on the way in is that every channel has to manage its own process and queues. The data flow stops at the channel, with large amounts of data sitting at a channel waiting to be cleansed and aggregated, creating a bottleneck and impacting downstream CX. There is no cross-channel consistency. For a real-world example, consider having to delay outbound emails because you’re waiting on real-time updates from your digital channels. ## **An AI-Driven Virtuous Data Cycle** With customer data as the common denominator in producing a personalized CX, [AI](https://www.redpointglobal.com/ai/) can be seen from two sides of the equation. That is, as AI and machine learning models become more accessible, less expensive and out-of-the-box (without the need for data analyst teams) more data (models, aggregates, predictions, and responses) can be made available to the CDP. On the flip side, for an AI framework (driven by ML and LLMs) to coach a company with better customer insights and suggested responses, the AI needs accurate and up-to-date data. LLMs work best when they know more about what a user is trying to achieve, and that knowledge stems from cleansed, fit-for-purpose data. The constant for both sides of the equation is customer data. With pristine data as a foundation, as both a destination and source, an AI-driven virtuous cycle is created where data drives better outcomes, which in turn creates better models and better insights. In this context, the importance of data quality becomes clear; models are only as good as the data that’s fed into them, and feeding actual results – human responses to those models – back into the cycle ensures that the next models are better. Without data quality, models will degrade and measurements become skewed. In a virtuous cycle, models collectively become better, but without good data, they become progressively worse. ## **A Composable, Agile CDP** The convergence of the growth of agile marketing with the demand for high-quality, first-party data explains the popularity of agile CDPs as the foundational technology for capitalizing on emerging trends such as AI to deliver differentiating CX. In this context, an “agile CDP” or “composable CDP” is a short-cut reference to a broader composable architecture – a modular approach to integrating best-of-breed technologies that does not force a company to duplicate the functionality of components they may have already invested in, or to replace things they want to keep using (e.g.., an analytics system or a data cloud). When agile marketing is the goal, it’s important to ask a few questions about your CDP: 1. Does it support the connectivity (data sources, engagement channels, AI frameworks) you need, or will you have to engage in a long and complex services journey? 2. Can it run in your [chosen data environment ](https://www.redpointglobal.com/configurations/)(on premises or cloud), or will you have to move or replicate your vital customer data? 3. Does it allow you to license and use the specific business capabilities you need, or force you to manage a landscape of overlapping, superfluous, and technically-oriented tools? 4. Does it offer the flexibility and configurability you will need as martech, other enterprise tools and frameworks, and your data environment itself change in the future? A CDP that supports a modular approach becomes an orchestration engine, geared toward improving CX and business outcomes. And that CDP should be able to grow with you as you your use cases, CX, and outcomes evolve. The Redpoint CDP supports agile marketing in a few different ways. First, Redpoint meets companies where they are. A unified customer profile can be built on top of an on-premises database a private cloud, a data cloud or on top of something Redpoint helps a company set up. Second, Redpoint offers unmatched [data observability](https://www.redpointglobal.com/data-observability/) and measurement features. From data ingestion through segmentation and activation, Redpoint lets users see any potential data problems before they reach the customer. Full transparency over the health of data providers users with confidence that data flowing through the system is clean, accurate and ready for use. Third, Redpoint provides the foundation of a consistent, accurate and real-time customer profile by completing all [data hygiene at ingest](https://www.redpointglobal.com/data-quality-and-data-ingestion/), fueling AI models and providing deeper insights and outcomes (i.e., a superior, personalized CX) that are then fed back into the CDP. Clean, fit-for-purpose data, embedded AI and a composable architecture elevate Redpoint as the only enterprise CDP ready to meet you where you are today to help you accomplish your business and CX goals now and in the future, whatever they might be. **Blog categories:** AI & Machine Learning **Blog tags:** Data quality --- ### [Dynamic Offer Management Can Be Customer-Centric: Here’s How](https://www.redpointglobal.com/blog/dynamic-offer-management-can-be-customer-centric-heres-how/) **Published:** December 29, 2020 **Author:** Steve Zisk **Content:** What is dynamic offer management? The traditional definition – the dynamic altering of the contents of an offer to better manage supply and demand – tells only half the story. Managing supply and demand or margins, such as an airline trying to fill seats, certainly justifies engaging in dynamic offer management, but that’s mostly for the brand’s purposes. A more modern definition also accounts for and elevates the customer as an equal. Through that lens, dynamic offer management is delivering contextually relevant information on the right channel at the right time, based on an individual customer’s behaviors and preferences. Efficient management of inventory and margins, or delighting a customer with a personalized next-best action – an offer, a promotion, a message, etc. – all contribute to new revenue. The personalized next-best action, however, has the added advantage of optimizing customer lifetime value which, one could argue, is potentially far more beneficial than a one-time generic offer made to fill a flight, or to clear excess inventory. Dynamic offer management is, at its core, is about maximizing relevance for an individual customer. And relevance, we know, is what today’s always-on, connected customer demands from the brands they engage with. Consider research from the [Harris Poll sponsored by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/), where 63 percent of consumers surveyed said that a personalized experience is now a standard expectation. Asked to cite examples of personalization they expect, 52 percent of consumers said it was when a brand sent special offers available only to them, 43 percent said it was when a brand recognizes them as the same customer across all channels, and 38 percent said it was receiving recommendations based on purchase or viewing history. ## **One-Size-Annoys All?** A discount that is sent to every customer not only fails to meet customers’ expectation for personalization, it has a high likelihood of introducing friction into a customer experience. We all know the drill; an inbox cluttered with generic “25 percent discount” offers for items you’re not interested in, or – worse – that you recently purchased at full price. In a traditional understanding of dynamic offer management, friction is a price brands are willing to pay for more efficient inventory management. Sure, the brand might annoy a certain percentage of customers, but it clears shelves to make room for its winter line. The same approach in loyalty programs runs the risk of alienating a brand’s most loyal members. Sending a blanket 25 percent discount to everyone in the loyalty program may do more than introduce friction, it can also remove the incentive to remain a member. Why sign up if I’m not receiving special offers? Or why should I spend more to be a “platinum” member if I’m receiving the same offer as a “gold’ member? A “dynamic” offer that is squarely focused on the brand’s bottom line, and not the end customer’s individual behaviors and preferences, does not align with rising customer expectations to be recognized as an individual. ## **Dynamic Offers and Real Time** To be successful, dynamic offer management must rest on a [golden record](https://www.redpointglobal.com/single-customer-view/), a real-time, continuously updated single view of the customer that includes data from every source and of every type. A golden customer record is built on advanced identity resolution capabilities that ensure an accurate customer match. Knowing everything there is to know about a customer, in real time, is the secret behind dynamic offer management where the offer or action is personal and relevant to the customer’s journey at a precise moment in time, in whichever channel the customer chooses to engage. Consider, for example, the difference between receiving an email offer for 25 percent off a winter coat you just bought or an offer for matching hat and gloves. The example highlights the need to have integrated customer data and [real-time decisioning](https://www.redpointglobal.com/orchestration/real-time-decisions) as key components of dynamic offer management. A brand may take an initial step of sending an email offer for the coat to a certain audience. But if a customer then goes online and purchases the coat – before opening the email – dynamic offer management requires the brand to have the ability to change the content of the email up to the moment the customer opens it. An offer for the matching hat and gloves might be determined to be the next-best action if the customer browses accessory items, for example. But if the customer takes a different action, [dynamic offer management](https://www.redpointglobal.com/real-time-interactions/) ensures that the next interaction will be in the precise context of the customer’s individual journey. With a golden record and real-time decisioning, dynamic offer management does not even necessarily have to be an offer. Perhaps the brand’s intent is to have a customer download a mobile app. With open-time email, the brand can dynamically change the content to whatever the next-best action is – for that specific customer – based on the entirety of the customer’s behaviors. Every interaction is completely relevant at the precise moment the customer is interacting with the brand, for the channel they’re on. ## **Hands-Off Dynamic Offers with AML** A blending of traditional and modern dynamic offer management – pushing inventory or managing margins that also reflect an individual customer’s behaviors, preferences, and transactions – requires advanced analytics. [Automated machine learning](https://www.redpointglobal.com/machine-learning) (AML) can handle endless permutations of offers and audiences. Code-free, self-training models that are optimized for a specific metric or business result can be programmed to strike the optimal balance between the customer and business goals, ensuring that the decision rendered accounts for any variable. Automated machine learning eliminates the need for human judgment. Audience segmentation that relies on human input is inadequate to account for the vast differences in characteristics that make up a “look-alike” segment. Women ages 18-35 who live in the Midwest might all have a need for a winter coat based on a geographical segmentation, but that segmentation ignores other traits more indicative of a person’s characteristics, tastes and traits. A marketer may consult a spreadsheet to view a woman’s recent purchases, her size, and color and style preferences, but only AML can take this information in the form of a golden record that also includes viewing habits, channels and pages visited, time on page, social sentiment and other behaviors and actions to generate a real-time decision. Fleets of machine learning models are tuned a desired outcome, and when optimal conditions are met based on the various offers at hand, the customer’s behaviors and other variables, a winning model will render a decision optimized for maximum impact. Dynamic offer management dressed as next-best actions are always in the cadence of the customer, delivered in the precise moment of a customer’s journey when they are most likely to positively influence and guide the journey to the desired outcome. A dynamic offer management approach that focuses on supply and demand may achieve short-term objectives. One that puts the customer first and consistently delivers personalized, relevant information is built for long-term success. **Blog categories:** 1:1 Personalization, AI & Machine Learning, Customer Data Platform, Data Management, Real-Time Personalization, Single Customer View --- ### [The Rise of AI Agents: How Data Readiness Powers Next-Level CX](https://www.redpointglobal.com/blog/the-rise-of-ai-agents-how-data-readiness-powers-next-level-cx/) **Published:** May 13, 2025 **Author:** John Nash **Content:** Agentic AI is the next frontier of artificial intelligence. Improving customer service interactions has been an early popular use case, where AI agents are deputized as part of a company’s workforce, intended to personalize customer service beyond standard conversational AI. Using an example of a customer initiating a return, a GenAI-based chatbot interaction might involve asking how to print out a prepaid mailing label, inquiring about options for how to apply a balance, or other straightforward questions that can be answered without the model having to know much, if anything, about the customer posing the question. Agentic AI essentially dials it up a notch, automating more complex tasks. A returns inquiry, for example, might entail the agent providing a customer with options, waiting for the customer’s decision, and then scheduling a pick-up time. Or letting the customer know the closest drop-off location that also has the product the customer wants in stock. The agent might then help the customer complete the transaction, while also arranging with the store to retrieve the item for pick-up. ## **Agentic AI Depends on High-Quality Customer Data** As agentic AI becomes more enmeshed in customer service, companies will begin to entrust more processes to agents, transitioning from live agents whenever possible. When agentic AI becomes part of the everyday fabric of how customers engage with brands, customers will naturally have higher expectations for AI agents – viewing transfers to live agents as a dreaded last resort. Heightened expectations will require that AI agents know everything there is to know about a customer. To schedule a pick-up for a product return, for instance, the agent should have a customer’s updated address. To help the customer apply her new balance, it should have access to real-time product inventory. A comprehensive, real-time understanding of a customer will also include a customer’s payment information, her preferences, her website activity and other behaviors – even social media. For an AI agent to provide a relevant experience, it will require real-time updates to a unified customer profile as well as real-time access to the unified profile. An AI agent’s overall effectiveness, in other words, is highly dependent on the data it’s fed being complete, accurate and timely. ## **The “Double Agent” Concept: Agents Speaking with Agents** Expanding the concept of a company having its data ready for agentic AI, it might soon become commonplace for brands and customers to be mutually transparent for how agents are involved in CX. It will be an open secret, in other words, that a brand is using agentic AI almost as a personal concierge to help guide an individual customer journey. The customer, in exchange for receiving a more personalized experience, will provide an agent with what it thinks the agent needs to meet the customer’s expectations. Taken to the extreme, agentic AI as a CX tool could even involve a customer creating their own personal agents for different brands – with the more trusted brands receiving more detailed data and preferences from the customer’s agents. > Agentic AI is poised to redefine customer engagement, shifting from basic chatbot interactions to fully autonomous AI agents that manage complex tasks and anticipate customer needs. As brands integrate these advanced AI systems, the key to delivering seamless, personalized experiences will be real-time, high-quality customer data. In this “double agent” concept where the consumer gives its own agents permission to interact with a brand’s agents, an extendable use case may even evolve to a customer using an agent to start a customer journey. In lieu of a product search on a website, for example, a customer might set rules for its agent and then send it off to negotiate with one or multiple agents for the best deal. Perhaps a customer is interested in booking a family vacation. Because it’s a loyal customer of one travel brand in particular, the customer has shared personal data with the brand’s agent – the ages of the traveling children, the type of accommodation needed, the preferred on-site activities, and other personal details that the agent uses to create a personalized experience. The customer initiates the entire experience through its agent. Data from one engagement is fed back into the agent to improve future experiences. ## **Agentic AI’s Sidekick: Data Readiness** A consistent data feedback cycle that creates a more robust agent – i.e., it continually expands its understanding of a customer – has the potential to all but eliminate common CX friction. As a digital stand-in for a customer, an AI agent becomes a customer’s biggest champion. It will advocate for and anticipate a customer’s needs as it works with a brand’s agent to optimize the customer journey. Real-time relevance will depend largely on the quality of the data used to feed the agent – the brand’s and the customer’s – underscoring data readiness as a foundational requirement. [Data readiness](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/) takes a holistic approach to data quality as an enterprise principle, a foundational requirement of a data-driven approach to accomplish business and CX goals. Data readiness ensures that enterprise customer data is right (it is complete, accurate and timely), and is fit-for-purpose (actionable, trusted and compliant). ## **The Future of AI-Driven Customer Experience** Agentic AI is poised to redefine customer engagement, shifting from basic chatbot interactions to fully autonomous AI agents that manage complex tasks and anticipate customer needs. As brands integrate these advanced AI systems, the key to delivering seamless, personalized experiences will be real-time, high-quality customer data. The “double agent” concept – where both businesses and consumers deploy AI agents – will further shape a future where interactions are more efficient, transparent, and tailored than ever before. To stay ahead, companies must prioritize data readiness, ensuring AI agents have the insights needed to drive meaningful, frictionless customer experiences. Those that embrace this shift will set a new standard for customer engagement, turning AI-driven interactions into a strategic advantage. **Blog categories:** Agentic AI, AI & Machine Learning, Data Readiness **Blog tags:** Agentic AI, Data quality, Data readiness --- ### [Churn Isn’t Inevitable: How Contextual Data Changes the Game for Banks](https://www.redpointglobal.com/blog/churn-isnt-inevitable-how-contextual-data-changes-the-game-for-banks/) **Published:** November 21, 2025 **Author:** Renee Graff **Content:** For years, retail banks have faced a consistent uphill battle: customer churn. The numbers haven’t changed much over time, with an annual churn rate of about 15 percent becoming the norm. It can almost feel like an unavoidable cost of doing business. ## **Why the Old Playbook Isn’t Working** Common churn mitigation strategies target improved onboarding, personalized product recommendations and loyalty programs. But when those methods don’t seem to be moving the needle, it can be largely because of a one-size-fits-all approach, or because the personalization that IS incorporated isn’t tied to specific behaviors that signal a potential break. Customer “stickiness” is important but it’s not the same as a proactive churn prevention strategy that’s continuously watching for subtle signals that a customer might be on their way out, and then acting fast. Customer churn signals span a customer’s entire relationship with the bank, well beyond transactions: - Are there fewer logins? - Is there a decline in card activity? - Did a customer post a negative branch review on Yelp? These signals tell a story for the banks that are listening. And banks don’t need *more* data to hear it, they need *better* data. Accurate, unified and contextual data unlocks insights that help to interpret customer behavior in context, rather than isolation. ## **Data Readiness Brings Game-Changing Results** Recognizing these nuanced customer signals through sophisticated analytics can help cut churn by more than a third, [according to one study](https://thefinancialbrand.com/news/bank-onboarding/the-churn-challenge-four-big-ideas-for-banks-and-credit-unions-looking-to-drive-down-attrition-182528) that also touted personalized engagement as a pillar of churn prevention. But that personalized engagement needs to address a specific indicator of churn, not just satisfy everyday expectations for a “Hello \[First Name\]” greeting. ## **The Right Data, the Right Churn Signals** Shifting the churn prevention mindset is more than a marketing tweak, it’s an enterprise-wide effort. - **Marketing teams** design and execute triggered actions and journeys based on churn signals. - **Data teams** build and train models that predict churn based on every relevant customer signal. - **Customer experience** teams ensure every interaction feels seamless and empathetic, turning insights into more meaningful engagements. It’s about pulling in the right data – data that’s just as important as financial transactions in providing contextual understanding that reveals what’s important to each customer. When you understand more of the why behind the churn, it becomes easier to create campaigns that address the real root causes, rather than relying on intuition or gut feelings. ## **Churn Prevention is a Data Collaboration** The reason churn prevention should ideally be a collaboration between marketing and data teams is because churn indicators are revealed through two distinct groups of data that when combined yield an understanding of the larger data story: - **Contextual customer data,** the page views, email opens, channel interactions and other signals familiar to marketers, and - **Contextual metadata,** the data lineage (sources, recency, history), quality (accuracy and completeness), and meaning (definitions, semantics, relationships), which is typically under the purview of data teams. Together, this collaboration is what creates *data readiness* – the organizational capability to [unify, cleanse, and interpret all signals in context](https://www.redpointglobal.com/data-the-defining-difference/) so that marketing and CX teams can act on them in real time. The combination of situational awareness and knowledge about the details of the data itself allow marketers – and systems – to understand, trust, and responsibly use the data. ## **Fight Churn with Data Readiness** When data is clean, unified and contextual, it unlocks the ability to [orchestrate customer interactions](https://www.redpointglobal.com/data-orchestration/) that are not just personalized, but *relevant to the moment*. It’s what gives teams the ability to not only spot all the churn signals, but to also *make sense of them and then formulate and quickly respond.* Staying one step ahead of the game when it comes to identifying and mitigating churn requires [data readiness](https://www.redpointglobal.com/data-readiness-hub/), which means - [Cleaning, normalizing and standardizing](https://www.redpointglobal.com/automated-data-quality/) data at ingestion - [Building a real-time, accurate unified profile](https://www.redpointglobal.com/identity-resolution/) through advanced identity resolution - Understanding customers in the context of households or business accounts. The reality is that fewer logins or a decline in card usage might mean churn for one customer, but not for another. Minimizing churn depends on being able to tell the difference, because a one-size-fits-all triggered action could backfire and end up creating more churn than it prevents. Preventing churn starts with knowing your customers in context, and that starts with data readiness. [Redpoint’s Data Readiness Hub](https://www.redpointglobal.com/data-readiness-hub/) helps retail banks unify, clean, and contextualize data across every channel, so you can spot churn signals early and act decisively. To see how Redpoint can help you develop a data-driven churn prevention strategy, using your own data, click [here](https://www.redpointglobal.com/financial-services/). **Blog categories:** Financial Services **Blog tags:** Data Observability, Data quality, Data readiness, identity resolution --- ### [There’s a Model for That: How Automated Machine Learning (AML) Tackles Any Business Use Case](https://www.redpointglobal.com/blog/theres-a-model-for-that-how-automated-machine-learning-aml-tackles-any-business-use-case/) **Published:** July 14, 2021 **Author:** Steve Zisk **Content:** There are many business use cases for artificial intelligence that a customer may consider as augmented processes, but that have little to do with machine learning. Chatbots or conversational AI that use text analysis to quickly answer a question. Facial recognition software for hassle-free access or authentication. Patient triage or prescription auditing systems for the healthcare consumer. Across all industries and departments, there are interesting, diverse and growing sets of use cases for enhancing certain tasks in an automated fashion using intelligent systems. But for business users trying to understand, map and improve customer journeys in ways that they could otherwise not accomplish, the primary focus of using AI tooling is to build and optimize machine learning models. An interesting irony here is that using [automated machine learning (AML)](https://www.redpointglobal.com/machine-learning) to enhance customer experience (CX) will usually never overtly reveal itself to the end consumer. Unlike a chatbot that customers likely recognize as using AI, but that does not materially improve their end-to-end experience, a customer on the receiving end of a consistently relevant, [hyper-personalized CX](https://www.redpointglobal.com/blog/the-role-of-a-golden-record-in-providing-a-consistently-relevant-personalized-cx/) throughout an entirety of a customer journey is usually unaware that machine learning is largely responsible for the behind-the-scenes magic. Yet even if the accolades go to AI, machine learning does the hard work of truly understanding a customer. It is why marketers turn to machine learning modeling to analyze customer cohorts, single customer view, behavioral and transactional patterns and other numerical data sets to provide a better understanding of who a customer is and what they want. Arriving at a better understanding of a customer journey is something marketers have been doing for decades. Compiling and analyzing average monthly spend, lifetime value, average transaction value, time on a web page and other statistics about a customer are not new, of course. They have and will continue to have tremendous value to a marketer. But those statistical calculations do not, for the most part, require a machine learning model. Where AML separates itself in shedding light on the intricacies of a customer journey is the ability to take large amounts of data that have no obvious, simple correlation between the question being asked and the information in the data and figuring out an answer. In other words, if you cannot simply calculate it, aggregate it or average it, you likely have a very good business use case for AML, especially if it needs to be at scale. ## **Two Types of Machine Learning Models** A key benefit of using AML to help guide customer journeys to the desired conclusion is that marketers do not have to get their hands dirty, if you will, by becoming involved with the sequence of steps needed to build models, from [data prep](https://www.redpointglobal.com/customer-data-management/data-matching/) through continual measurement of the model. Nor do they need to interact with data scientists or data engineers to develop an understanding of the sequence interplay. But to be able to develop applicable use cases for what they’re trying to accomplish, they should at the very least have a rudimentary understanding of how machine learning works, and how automation reduces the need for human involvement. There are effectively two different classes of models that marketers will build – unsupervised and supervised. The former entails a marketer providing data, and asking the model to figure out interesting patterns in the data and what the data might say about a customer or segment of customers. The latter – a supervised model – is when there is a predetermined attribute a marketer wants to find, and where the marketer provides historical data to build a model that will sort through the variables to find what may be a good predictor of the attribute – whether it’s [customer lifetime value (CLV)](https://www.redpointglobal.com/blog/to-optimize-revenue-growth-tune-in-to-customer-lifetime-value/), propensity to churn or another characteristic that says something about the customer. Data on who increased basket size in a cross-sell/upsell campaign, for instance, is fed into a model to predict who may buy more of a product or a more expensive product in the future. The reason it is important for marketers to have a base level understanding of the distinction between supervised and unsupervised models is that ultimately they are the ones asking questions of the data, and they have a sense of what data they have on-hand that might contribute to building a successful model. They may know they have to turn over a certain number of rocks, but they lean on machine learning to determine which ones. ## **Uncovering an Audience with an Unsupervised Model** An example of each model type may better frame the picture in terms of how a marketer poses questions of data. A simple unsupervised model might be where a marketer has an audience, and knows they want to put the audience through a multi-step, multi-channel campaign – but first they need to understand how the audience divides out. Breaking down the audience into subsets – on classifications determined by the machine learning model – will help the marketer decide who receives a certain offer on a certain channel. Here, the model discovers data correlations that reveal something interesting about the audience. In these types of scenarios, unsupervised models are the perfect use case for [audience segmentation](https://www.redpointglobal.com/segmentation-activation/). Rather than an artificial, manual segmentation based on intuition or arbitrary cut-offs (men 18-34, Massachusetts residents, income over $75,000, etc.) the model will segment an audience based on what – according only to the data – is important, interesting or unique about a particular audience. Embedded automated machine learning that is part of the Redpoint CDP goes beyond segmenting an audience to let marketers know *why* it arrived at a conclusion. With an understanding of the decision points – why an audience member was put in one segment vs. another – marketers not only have a better sense of the data they already have on-hand, but they are then able to develop and test new hypotheses. In other words, the answers provided by the machine learning model lead to new and different questions. ## **Finding Out a Value with a Supervised Model** An example of a supervised model is when a marketer has a value or attribute they’re trying to find, and they provide a model with historical data that has a broad range of calculations for the value. To try to find out a customer’s lifetime value, for example, the model will start with the known value for a big enough sample size of existing customers to predict lifetime value for a new customer or prospect. Building the model is easy enough; provide historical data, point out the variable of interest, and let the model work its magic to make a prediction. In executing the model – putting it into production – a few things have to happen. First, testing will determine the model’s accuracy. By feeding the model historical data that has been held in reserve, a comparison can be made between the predicted CLV and what a marketer already knows about lifetime value – the better the correlation, the better the model. Another more obvious kind of model execution is using the model to control rules about what to do with a group of customers, much like any other set of campaign-based rules. With CLV, for instance, a marketer may want to run campaigns for high, medium and low lifetime values. Setting that up as part of an AML algorithm is another task taken off the plate of marketers. In this type of situation, the integrations made available in Repoint mean that models automatically set up and managed inside rg1 are also available for other applications that may care about the model. A call center, for instance, could make a call through the web server and add a pop-up window showing agents the CLV of a caller. Or it could route calls to different agents based on a customer’s CLV. ## **AML Makes Marketers Smarter** Both unsupervised and supervised models shorten a marketer’s to-do list for turning raw data into customer insight, but that is not to say that automated machine learning replaces or minimizes the value that marketers bring to the table. In fact, AML can augment a marketer’s capabilities just as a chatbot can act as the first-line to allow call center agents to focus on the more complex questions. Automating time-consuming processes with AML frees up time for marketer that is better spent focusing on their expertise – the customer. Also, by unlocking previously unattainable insights into customer data, AML allows marketers to ask better questions and make better decisions. The more they know about a customer or segment, the more they can design customer experiences that are consistently relevant throughout a customer journey. **Blog categories:** Segmentation & Activation --- ### [Align the Healthcare Experience Around the Consumer: How an Omnichannel CX Delivers Improved Health Outcomes](https://www.redpointglobal.com/blog/align-the-healthcare-experience-around-the-consumer-how-an-omnichannel-cx-delivers-improved-health-outcomes/) **Published:** November 18, 2021 **Author:** John Nash **Content:** In a recent survey Redpoint Global conducted with [The Harris Poll](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) on consumers’ and marketers’ perceptions of customer experience across various industries, consumers consistently ranked healthcare third (behind retail and financial services) in terms of providing a consistent experience that demonstrates a thorough understanding of the customer. Yet when consumers were asked which industry *should* provide the most consistent experience encompassing a through customer understanding, healthcare polled first. The gap between reality and expectations can partly be explained by the all-too common frustrations of a healthcare journey – confusing processes, difficulty scheduling appointments, long wait times, and providers/insurers lacking up-to-date, accurate healthcare consumer data. In a survey Redpoint conducted with [Dynata](https://www.redpointglobal.com/press-releases/75-of-u-s-consumers-wish-their-healthcare-experiences-were-more-personalized-redpoint-global-survey-reveals/), 54 percent of consumers surveyed said that providers and insurers lack contextual information necessary for providing a personalized healthcare experience, with 71 percent reporting poor overall experiences. The root cause of frustrations with the healthcare experience can be attributed to siloed data, siloed processes and siloed business functions where various stakeholders in the health of a consumer may have different strategies, or different financial incentives, for promoting better health outcomes. At the very least, the various healthcare organizations lack a single view of the healthcare consumer that is a requirement for delivering a seamless experience across channels that reflects a thorough understanding. ## **Put the Healthcare Consumer First** At Redpoint, we believe there is a better way to meet healthcare consumer expectations for a consistent experience. The path forward is the adoption of a healthcare consumerism model, one that recognizes the consumer as being in control of the experiences that encompass a holistic healthcare journey, across all physical and digital channels. Gartner frames the healthcare consumerism model as [three distinct generations](https://www.redpointglobal.com/resources/setting-the-stage-for-the-second-generation-healthcare-customer-experience/) of consumer engagement, laying out a roadmap for healthcare organizations to advance to a customer-centric state. In Generation One – where most healthcare organizations are today – organizations use customer data to optimize one or more consumer journeys. This initial phase is more a mindset shift – a recognition that putting a customer at the center of the experience requires determining a use case, breaking down entrenched siloes, and beginning to coordinate engagement across two or more channels. Generations Two and Three advance an organization’s readiness to put the customer at the center of a holistic journey, first across an entire organization and then across an entire healthcare ecosystem. ## **A Single View of the Healthcare Consumer** The[ Redpoint CDP](https://www.redpointglobal.com/cdp/) was designed to help companies deliver an omnichannel customer experience by providing a single point of operational control over data, decisions and interactions. For healthcare organizations, the Redpoint CDP is purpose-built to advance organizations along the three stages of consumer engagement. [Lucerna Health](https://www.redpointglobal.com/wp-content/uploads/2024/04/Redpoint-Global-Enables-Lucerna-Health-to-Transform-Healthcare-One-Patient-Engagement-at-a-Time.pdf), for example, uses the Redpoint CDP to further a value-based care (VBC) operating model that integrates data, analytics, processes and services into a single end-to-end platform to improve efficiencies, lower costs, and deliver better health outcomes. As opposed to a fee-for-service system, a [VBC approach](https://www.redpointglobal.com/blog/advancing-value-based-healthcare-one-patient-engagement-at-a-time/) provides payers and providers with financial incentives for better health outcomes. Redpoint activates Lucerna’s VBC healthcare data and analytics system via the Redpoint CDP to deliver personalized omnichannel patient experiences. Use cases include delivering messages in a consumer’s preferred channel and leveraging provider operational sensitivity that gives payers real-time access to a provider’s services ability and capacity. ## **Coordinated Care with an Omnichannel CX** Other clients who use Redpoint include a world-renowned healthcare provider treating patients with chronic conditions. With nearly 100 years of expertise, the provider wanted to leverage its extensive experience to help patients with individualized treatment plans along the entire healthcare journey – from diagnosis, care plans, appointments, education, communication, etc. The provider understood that by designing hyper-individualized treatment plans, it could more effectively answer patients’ questions, help reduce stress and anxiety associated with fighting a chronic condition and, ultimately, deliver better outcomes by keeping patients engaged in an end-to-end health plan across the journey – from the moment of diagnosis or even before, to current patient to post-patient, which for many could be a lifelong treatment plan. Having a single view of the healthcare consumer through the Redpoint CDP is instrumental to delivering this type of holistic experience. Is the patient genetically pre-disposed to a certain chronic condition, for instance, and how might that determine educating the patient about what to expect? Because Redpoint provides a single operational platform, the provider coordinates care across an extensive provider network. With every provider, lab, testing facility, clinic, specialist and other caregivers having a real-time awareness into a patient journey, the patient experience becomes as frictionless as possible. Not only does a patient knows what to expect at every stage of the journey, but communication and education are optimized and delivered by channel preference, as well as optimized in the cadence of a unique journey. Based on the latest visit, for example, would the patient benefit more from a video, a phone call, or a telehealth consultation? Does an immediate family member help direct care, and what is the next-best action for that caregiver? > At the heart of it what we’ve done is connect the pieces of the consumer/patient journey. These independent points used to be siloed. Now all the points of the journey are monitored and visible through the Redpoint CDP. – VP Marketing Platforms & Analytics, Non-Profit Healthcare Organization Coordinated care is about the patient (pre-patient, patient, post-patient) experience as much away from the hospital as inside. It’s about the healthcare organization providing a branded experience that communicates with the patient with a consistent voice throughout the entire journey. With the understanding that a chronic diagnosis is a life event, the provider sought to eliminate as much of the confusion, anxiety and uncertainty as possible, and Redpoint helps them do it with a single point of operational control that provides that single voice. One interesting aspect of this use case is that it really has little to do with marketing outreach. While marketing is certainly a part of it – when to send an email, an SMS, appointment scheduling, etc. – at heart the use case for having a single point of operational control is simply to provide a better experience. Anyone who has ever coped with a chronic condition can attest to the relief one feels from having a dedicated caregiver in your corner who seems to always have the right information and make the right recommendations. Better health is certainly the top priority, but reducing the stress that comes from coping with a chronic condition is also key. ## **Driving a World-Class Healthcare Experience** Another Redpoint client is a non-profit healthcare provider with an extensive network of caregivers and healthcare facilities. With a mission to deliver a world-class patient experience, the provider partnered with Redpoint to help it move from a doctor-centric to a consumer/patient-centric model. A “Healthcare 2.0” initiative led by the CMO looked to completely revamp how the organization engaged with the healthcare consumer, beginning with how it even found consumers looking to access care, and how it matched them with the right provider. Before partnering with Redpoint, its brand marketing efforts were typically not personalized. It relied heavily on billboards, for instance, and other methods that made it difficult to prove ROI and demonstrate value. Using the Redpoint CDP, the provider sought to move lower down the marketing funnel, switching from mass media outreach and difficult-to-measure campaigns to highly personalized, relevant and measurable campaigns, increasing spend efficiency by targeting much smaller groups of potential at-risk healthcare consumers. “To do this, we knew we had to leverage data, and give marketers access to first-party patient data to build out those campaigns,” says the organization’s VP of marketing platforms and analytics. “We had an EMR system and financial systems that were completely siloed and couldn’t reconcile data.” The first step, she says, was to bring all consumer data together, which required a significant mindset change. “It was a difficult conversation to have, particularly with clinicians and doctors who have a sacred relationship with patient data,” says the VP. “But those disparate siloes prevented us from delivering a unified patient experience. With assurances that the data would be protected, we were able to change that conversation and prove that it would be a net benefit for both the healthcare consumer and providers. Fixing the data problem creates value for everyone in the organization, and it aligns with our mission and our values of providing that world-class patient experience.” ## **A Single Platform, An Improved Experience** By targeting a smaller audience with personalized messaging, the provider can better predict the likelihood of care for a specific diagnostic use case. Rather than use a billboard, for instance, the provider targets a much smaller audience of potential patients who may need a knee replacement in the next six months, and engages with that audience in a targeted fashion enabled by a single customer view. “The platform connects the right consumer with the right message at the right time,” she says. “It’s all HIPAA compliant, it’s targeted, relevant, personalized, faster and it’s at scale. “At the heart of it what we’ve done is connect the pieces of the consumer/patient journey. These independent points used to be siloed. Now all the points of the journey are monitored and visible through Redpoint. They’re all connected. Even if you’re anonymous and you visit the website, that data becomes visible over time to create a view of an individual consumer. The model now has the patient at the center of the experience.” For more information about how Redpoint helps healthcare organizations deliver personalized omnichannel experiences that drive better outcomes, visit [Redpoint for Healthcare](https://www.linkedin.com/showcase/redpoint-for-healthcare/) on LinkedIn, or request a demo at the [Healthcare homepage](https://www.redpointglobal.com/healthcare/) on redpointglobal.com. **Blog categories:** Data Quality, Healthcare, Identity Resolution, Master Data Management --- ### [Can Your CDP Deliver Perfect Data? How a Need for Perfection is Shaping the Market](https://www.redpointglobal.com/blog/can-your-cdp-deliver-perfect-data-how-a-need-for-perfection-is-shaping-the-market/) **Published:** July 23, 2021 **Author:** Steve Zisk **Content:** The [customer data platform](https://www.redpointglobal.com/customer-data-platform) (CDP) market continues to run hot, with many customer deployments and a flurry of vendor activity. [ The estimated market growth](https://www.prnewswire.com/news-releases/the-customer-data-platform-cdp-market-size-is-projected-to-grow-from-usd-2-4-billion-in-2020-to-usd-10-3-billion-by-2025--at-a-compound-annual-growth-rate-cagr-of-34-0-301025352.html) from $2.4 billion in 2020 to $10.3 billion in 2025 appears in reach as the market continues to invest in these capabilities. Fortunately, there are several external forces at work that are helping crystallize a CDP’s must-have features and functionalities for providing a differentiated customer experience (CX), enabling marketers to make smart choices. This is particularly relevant in light of the drastic changes in customer behaviors and flight to digital-first customer journeys that have taken root over the past year-plus, as brands need the right capabilities to keep up with consumer changes and current expectations. ## **Coping with Complexity: Requirements Evolve** Those behavior changes and digital-first journeys are responsible for the biggest spotlight on the CDP marketplace as far as underlining the requirements needed to deliver a seamless omnichannel experience. Over the past year customer journeys have become far more complex; curbside pick-up, digital returns and online comparison shopping are among some of the newer behaviors that together represent consumers taking more control over their journeys and solidifying their expectations for a seamless experience across channels. In healthcare, too, there has been an acceleration of the [healthcare consumerism trend](https://www.redpointglobal.com/blog/it-is-time-to-act-as-one-for-the-benefit-of-the-healthcare-consumer/) where the end consumer is taking greater control of a holistic journey with a growing digital component. Along with the changing behaviors, consumers are raising their expectations for how a brand engages with them. Despite the growing complexity, consumers expect not only that a brand recognize them across every channel, but that [every interaction is relevant](https://www.redpointglobal.com/blog/financial-impacts-of-reacting-to-customer-intent-in-real-time/) to where they are in a customer journey. Marketers grasp that more than just integrating all customer data sources, a CDP must now help guide comprehensive, multi-channel, multi-touch journeys to meet the needs and expectations of customers amid the growing complexity. To do this, there is a growing recognition that an [accurate, real-time customer profile](https://www.redpointglobal.com/single-customer-view/) is essential for driving decisions across multiple channels at the cadence of the customer. ## **The Role of a CDP in Managing Compliance** Another force at play is the slate of worldwide data privacy laws that are either in effect or on the horizon. In addition to GDPR and CCPA, Brazil’s [Lei Geral de Proteção de Dados](https://iapp.org/news/a/an-overview-of-brazils-lgpd/) (LGPD) took effect in September 2020, with administrative sanctions expected to be enforced beginning next month. The China Personal Information Protection Law (PIPL), drafted last year, is expected to roll out before the end of the year. In the US, even without a national data privacy law other states are following California’s lead, including Virginia with the [Consumer Data Protection Act](https://iapp.org/news/a/virginia-passes-the-consumer-data-protection-act/) (CDPA), which will take effect in 2023. While a CDP is not intended to be the go-to compliance platform, data privacy considerations do give CDPs in general a more prominent seat at the table as far as managing the implications on an individual customer basis. A CDP enables brands to implement the necessary compliance processes on behalf of its customers and have a way to handle those processes correctly. A customer may invoke the right to be forgotten, as an example. Rather than simply throw a record away, the process must recognize that certain data must be kept. Recent purchase history may be important for returns and warranties, for example. Or credit card transactions to handle a fraud claim. ## **First-Party Data Drives the Need for Perfection** Lastly, the phasing out of the [third-party cookie](https://www.redpointglobal.com/blog/the-demise-of-the-third-party-cookie-opens-new-methods-of-engagement/) is a big factor in driving the CDP marketplace. Certainly the most well-publicized, it is putting a premium on the importance of the accurate collection and unification of [first-party customer data](https://www.redpointglobal.com/blog/the-secret-to-a-customer-centric-approach-is-hiding-in-plain-sight-first-party-customer-data/), accurate identity resolution and comprehensive data quality. If easy access to unified customer data is the baseline requirement and [expectation for a CDP](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/), the growing importance of first-party data raises the bar for what a unified profile must look like. The need for perfect data and complete transparency become essential. Marketers can no longer tolerate imperfect or outdated records, or accept half measures for resolving a customer’s identity regardless of device, channel or other variables. Perfect data is a common thread among the external forces that, together, shape the direction of the CDP marketplace. Growing complexity in digital-first customer journeys, rising expectations for a seamless customer experience, an increase in data privacy laws and the disappearance of the third-party cookie all make it essential that brands work with perfect customer profiles. ## **Data, Insight & Action: CDP Must-Haves** With perfect data the ultimate objective for the delivery of superior customer experiences, prospective CDP buyers should take careful note of vendor claims about the features and functionalities of their specific products. If the collection of data, data prep and identity resolution represent baseline expectations, within those categories there is a broad spectrum of competencies. A vendor may, for instance, cross ‘identity resolution’ off a checklist, but at what level? Is it using both heuristic and probabilistic matching, solving for any device or ID, householding? Any cutting of corners for basic [customer data platform](https://www.redpointglobal.com/customer-data-platform) requirements will have significant downstream implications; a real-time decisioning engine applied to an imperfect or outdated customer profile will produce imperfect or outdated actions. A perfect, next-best action requires a real-time component to enable the business to operate at the cadence of the customer. Just as damaging as engaging with the wrong customer because you’re working with imperfect data is engaging with the right customer but at the wrong time or on the wrong channel, or otherwise not aligned perfectly with where the customer is on a customer journey. Prospective buyers need ensure that a CDP is capable of real-time decisions that meet the requirements of the business, whatever that definition of real time may be for specific business use cases. ## **Some Lead, Some Follow: Stay Ahead of the Market** With the need for perfect data rapidly becoming a requirement for meeting customer expectations for a seamless omnichannel experience, it’s a safe bet that we will see many vendors in the short term offer new features and functionalities that promise unified customer profiles. Marketers should look past the surface to make sure a [customer data platform ](https://www.redpointglobal.com/customer-data-platform)satisfies their ambitions for the future, in ways that will deliver tangible ROI. For information on how Redpoint’s solutions provide a scalable, unified single point of control where all customer data is connected to form a golden record and every customer touchpoint is intelligently orchestrated, we welcome you to include a Redpoint [demo](https://www.redpointglobal.com/request-demo/) as part of your evaluation process. **Blog categories:** Customer Data Platform, Data Quality, Identity Resolution --- ### [Beyond the Martech Stack: How a Center of Excellence Drives Competitive Advantage](https://www.redpointglobal.com/blog/beyond-the-martech-stack-how-a-center-of-excellence-drives-competitive-advantage/) **Published:** November 22, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/11/blog-11-22-COE-300x200.jpg)A Gartner report from December 2018 does not mince words referencing the importance of an integrated digital platform to optimize customer experience, among other objectives. “An unintegrated digital platform is simply an oxymoron” the report states, insisting that to make “customer experience, IoT, ecosystems, intelligence, and IT systems” work together, a digital platform must connect the capabilities at scale in a manageable way. The report recommends that a managerial focus is just as important as technology investments to pursue a strategy that overcomes considerable integration challenges. Technology alone, in other words, will not eliminate the data and operational siloes that prevent marketing from delivering the hyper-personalized customer experience that has been shown to drive revenue. The Harris Poll survey commissioned by Redpoint makes this clear, with 37 percent of consumers saying they will stop doing business with a company that fails to deliver a personalized experience, and a full 63 percent claiming that a personalized customer experience is a standard service they now expect. **Break Down Departmental Siloes** Forward-looking companies that recognize that personalization is an imperative are forming digital marketing centers of excellence (CoE) to optimize digital transformation efforts. The COE ensures integration is not limited to the martech stack, but also breaks down departmental siloes that can hamper productivity. One Redpoint customer, for example, attributes a CoE to a more than 800 percent increase in revenue per email. By assigning responsibility for segmentation, A/B testing, dynamic templates and tailored creative treatments to the CoE, the company not only developed a central strategy for more relevant offers but also prepared the operating model that the business-as-usual marketing teams used to quickly deploy personalization in their respective Business Units. The whole marketing organization was able to get up a steep learning curve much more quickly, leading to revenue lift of 73 cents per email, compared with eight cents for previous average campaigns. **What is a CoE, Really?** A Center of Excellence is a team apart; consider it special forces for your digital transformation. The CoE blazes new trails and circulates learnings to the rest of the organization, supercharging change. According to Gartner, a CoE (or what it calls an “empowerment team”) has five key attributes: the design and evolution of an integration strategy; implementation of a core platform; set up and enforcement of overall governance policies; training, support and help desk services to multiple personas, and the running of what it calls a “community of practice”. These attributes distinguish leaders from laggards in blazing a fast path to differentiation as a digital-first, dynamic organization. A CoE puts even small or midsize enterprises on equal footing with a multinational with its far greater resources for organizing around digital principles. Smaller organizations or companies with lower levels of marketing maturity retain a CoE as a managed service. A team of experts embeds in your organization, establishes the innovation “lab” and trains the marketing team on new technologies, techniques and how to get results quickly. A CoE cuts through internal politics that have deep-sixed many a technology installation. Department infighting, budget wars, egos, and stubbornness to adopt new technology or change processes can all stand in the way of a successful implementation, and a CoE – with backing and support from the C-level – helps smooth over the operational potholes that can easily throw a project off course. **Does My Company Need One?** Gartner provides a handy [checklist](https://www.gartner.com/en/marketing/insights/articles/what-makes-a-marketing-center-of-excellence) for organizations to determine if a CoE is right for their companies. Conditions that warrant a CoE, according to the list, are when specific capabilities are needed, specialized knowledge is required, knowledge is difficult to acquire, and the capability is important to the business, among others. For many marketing organizations, a mandate to deliver a hyper-personalized customer experience across channels certainly qualifies as a condition that would benefit from a CoE. According to a much-referenced research from [Frost & Sullivan](https://inform.tmforum.org/data-analytics-and-ai/2016/08/customer-experience-overtake-price-product-differentiator-2020/), by the end of next year it is anticipated that customer experience will overtake price and product as a key brand differentiator. Delivering on this expectation requires marketing organizations to break the mold, eliminating the data and operational siloes that were organized for a bygone era. Organizations have continued to make technology investments to modernize their martech stacks to keep up with the personalization mandate but doing so without a CoE is akin to buying a Ferrari but keeping it in the garage because you don’t know how to use its awesome power. A CoE takes the machine out on the road with confidence. **RELATED CONTENT** [How to Future-Proof Your Omnichannel Approach](https://www.redpointglobal.com/blog/how-to-future-proof-your-omnichannel-approach/) [3 Obstacles to Maximizing the Value of Customer Data](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2019/03/Harris-Cover-032719-300x169.jpg)](https://www.redpointglobal.com/blog/how-does-your-cdp-stack-up-the-real-time-difference/) **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [What’s So Hard About Customer Data? The Hidden Challenges You Can’t Ignore](https://www.redpointglobal.com/blog/whats-so-hard-about-customer-data-the-hidden-challenges-you-cant-ignore/) **Published:** December 9, 2025 **Author:** Beth Pfefferle **Content:** At its core, data may be strings of ones and zeroes, but not all data are the same. Customer data is different because it’s personal. Behind customer data is an actual customer, and every customer has a story that’s worth knowing. Worthwhile, that is, for the enterprise that stands to gain revenue from deciphering what the data reveals about a customer. Understanding a customer’s desires, intent, behaviors, preferences and relationships both at a household and business level is important for driving a relevant customer experience (CX), as well as smart AI. Because customer data is so different, with underlying complexities and unique traits, maximizing the value of your customer data depends on understanding the complexities, and how to solve for its unique challenges. Here are the five hidden challenges you can’t ignore: 1. **Identities Are Complex and Ever-Changing** Unlike product SKUs or vendor IDs, customers are people. They move, change phone numbers, update emails, and even change names. They use multiple identifiers across channels such as social handles, loyalty IDs, different nicknames, and more. They generate a constant stream of dynamic, contextual behavioral signals like clicks, purchases, and engagement patterns. They identify as individuals, as household members or perhaps as employees of a business. This variability makes identity resolution a constant challenge. A single customer might appear as five different records, and merging them incorrectly can lead to embarrassing and costly mistakes. Stakes are even higher because of the real time, dynamic nature of a customer journey. A relevant customer experience (CX) at the moment of interaction requires real-time decisioning, which demands that identities are resolved continuously and in real time as new data enters the system. 2. **Data are Scattered Across Silos** Customer data rarely lives in one neat system. It’s fragmented across CRMs, marketing platforms, websites, billing systems, and support tools. Each system captures different attributes, often with inconsistent formats. The result? Duplicates, gaps, conflicting records, data quality issues, and ineffective use. One recent survey shows that while 87 percent of companies actively gather data, just 25 percent say they use it effectively. Data siloes are a huge culprit responsible for the massive gap, and the organizations on the winning side of the gap are those that have largely eliminated data siloes. 3. **Real People = Higher Stakes** A wrong merge or misclassification isn’t just an operational hiccup. Because there are real people involved, mistakes can damage trust, ruin personalization, and even trigger compliance violations. Imagine sending a high-value customer a discount meant for new buyers, or failing to honor an opt-out request. Unlike inventory data, mistakes in customer data have direct human consequences that impact your brand reputation. 4. **AI Authenticity Counts** Your customers enjoy the convenience of AI but authenticity matters. In a [Dynata survey](https://www.redpointglobal.com/press-releases/73-of-consumers-believe-ai-can-have-a-positive-impact-on-their-customer-experience/), 73 percent of respondents said that AI can positively impact customer experience (CX), but by the same token 76 percent say that they are less likely to trust and continue engaging with a brand if they sense disjointed communications across channels. Your customer data has to be “AI ready” which is different than having your data ready for analytics. AI-ready data is contextually complete, continuously updated, and unified across touchpoints. 5. **Privacy and Consent Are Non-Negotiable** Customer data is subject to strict regulations like GDPR and CCPA. Brands have to track consent, honor opt-outs, and manage “right-to-be-forgotten” requests. Other entity data rarely carries this legal weight. Treating customer data casually can lead to fines, lawsuits, and reputational harm. Compliance isn’t optional, it’s foundational, and this is particularly true for companies in regulated industries with a greater risk exposure. ### **Solve for the Hidden Challenges with Enterprise Data Readiness** Solving for these challenges is where data readiness comes into play. [Data readiness](https://www.redpointglobal.com/data-the-defining-difference/) treats customer data as an enterprise asset. As such, it focuses on making sure that data is right and fit for purpose for every conceivable use case. Data readiness focuses not on generic data quality, but data quality suitable for how the business intends to use customer data. It is about trust, timeliness, and context. If you recognize any of the hidden challenges outlined above, the chances are that your customer data is holding you back from next-level CX and effective AI. By re-examining your customer data foundations and making data readiness an enterprise discipline, you will be one step closer to joining those organizations who have already experienced the payoff that comes from making data readiness a priority: smarter AI, insights tailored to specific use cases, and more human customer experiences. To learn more about how Redpoint can help you develop a solid data readiness foundation – using your own data – click [here](https://www.redpointglobal.com/request-demo/). **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [Healthcare Survey Shows a Need for Consistent & Relevant Engagement](https://www.redpointglobal.com/blog/healthcare-survey-shows-a-need-for-consistent-relevant-engagement/) **Published:** October 4, 2022 **Author:** John Nash **Content:** A recent survey by Dynata, commissioned by Redpoint, shows a pronounced gap in the experience healthcare consumers expect to receive from their provider and what is provided. More than [90 percent of patients](https://www.redpointglobal.com/press-releases/81-of-consumers-say-a-good-patient-experience-is-very-important-when-interacting-with-healthcare-providers/) say that it is either “important” or “very important” to receive relevant communications from their provider and healthcare plan that accurately reflect where they are in their healthcare journey, yet just half (50 percent) of patients say they are very satisfied with the relevance of the communication they receive. The disparity is cause for alarm for providers when considering the consequences of a poor experience. Asked the top reasons why they would switch providers or healthcare plans, respondents ranked a poor experience No. 1 (41 percent), followed closely by a lack of personalization and patient understanding (38 percent). The results mirror those of the [2021 Accenture Health and Life Sciences Experience](https://www.accenture.com/us-en/insights/health/digital-adoption-healthcare-reaction-or-revolution) survey, in which 34 percent of respondents said that a poor experience would cause them to either switch medical providers or make them less likely to access care in the future. ## **Healthcare Consumerism and a Single Patient View** The elevation of experience in how a patient assesses quality of care relates to the healthcare consumerism trend, where patients accustomed to personalized, omnichannel experiences in other industries expect the same from healthcare. Those expectations include an ability to engage seamlessly on physical and digital channels, for providers to have a personal understanding of them across channels (even outside of a clinical setting) and to have more control over their healthcare journeys. More broadly, healthcare consumers expect all their caregivers to possess the same consolidated view to ensure a frictionless healthcare journey. To achieve a consolidated view, healthcare plans, providers (primary care, specialists, urgent care, etc.) and pharmacists must all work with the same consumer data, a unified customer golden record that allow for consistency across channels and touchpoints. The challenge, though, is that data is often siloed across multiple systems and processes which makes it difficult to compile a consistent, real-time view of a consumer in the context of an individual journey. A provider, for example, may have clinical data but lack insight into a patient outside of a clinical setting, such as social determinants of health. When engaging with a patient, a lack of a single patient view makes it difficult to communicate with relevance. But as the Dynata survey shows, healthcare consumers expect relevant communications that span the entire healthcare journey. Consider that 64 percent of respondents said that an important factor when choosing a healthcare provider is the provider’s ability to communicate in a timely manner, and 57 percent claimed an important factor was how well the provider understands you as a patient and creates a personalized experience. ## **rg1 and a Single Point of Operational Control** Redpoint rg1 addresses the challenges in meeting demands for a holistic, personalized experience by enabling a single patient view and a single point of operational control. With rg1, providers, payers and other healthcare professionals communicate at the right time and on the right channel, optimizing engagements that guide patients through an individual, relevant healthcare journey across digital and physical channels. Leading healthcare organizations trust rg1 because its approach to privacy, data security, compliance, segmentation and omnichannel orchestration is unlike other customer data platforms (CDPs). As an example, unlike pure SaaS vendors, the rg1 platform can be deployed behind a healthcare organization’s security perimeter in its own private cloud. Because PHI data, PII data, consumer, claims and clinical data are integrated in a single platform, healthcare professionals can segment audiences at a granular level to vastly increase relevance in patient engagements. When running a campaign to close a care gap, for instance, rg1 makes it possible to segment based on health condition, channel and content preferences instead of merely sending static content based on a patient’s age, location or another demographic variable that may be less relevant to the individual patient’s current healthcare journey. For more on why rg1 is the preferred choice for leading healthcare organizations, visit [Redpoint for Healthcare](https://www.linkedin.com/showcase/redpoint-for-healthcare/) on LinkedIn, or to see how rg1 can solve your company’s unique business challenges and improve outcomes, click [here](https://www.redpointglobal.com/healthcare/). **Blog categories:** Data Quality, Identity Resolution --- ### [To Stanch Revenue Loss, Healthcare Providers Need to Embrace a Digital Mindset](https://www.redpointglobal.com/blog/to-stanch-revenue-loss-healthcare-providers-need-to-embrace-a-digital-mindset/) **Published:** October 12, 2020 **Author:** John Nash **Content:** A recent [blog in this space](https://www.redpointglobal.com/blog/to-be-more-effective-closing-care-gaps-the-healthcare-industry-needs-a-single-view-of-the-consumer/) focused on how coronavirus has affected the healthcare industry, particularly for closing gaps in care and managing chronic conditions. Both situations contribute to what a recent survey reports is the trend expected to have the biggest impact on the healthcare industry – the loss of revenue for hospitals and other healthcare providers. In a [Definitive Healthcare survey](https://www.healthleadersmedia.com/finance/hospital-revenue-loss-most-important-healthcare-trend-going-forward) of healthcare professionals, 36 percent of respondents said that the biggest trend is losses sustained by providers because of the widespread cancellation of elective surgeries. The implications of delayed care (24 percent), increased telehealth usage (23 percent) and a decline in commercial insurance enrollment (10 percent) also ranked as top trends. An [American Hospital Association](https://www.aha.org/issue-brief/2020-06-30-new-aha-report-finds-losses-deepen-hospitals-and-health-systems-due-covid-19) (AHA) report from June estimated a minimum of $120.5 billion in losses through the end of 2020 related to the pandemic, with most incurred due to lower patient volumes. This does not include the $202.6 billion in losses suffered between March and June, bringing the total loss to $323.1 billion, a figure that the AHA says will cause “immense financial strain” for hospitals, healthcare providers and health systems. The report was careful to state that the total loss figure does not account for potential increase in coronavirus case rates. The effects of financial distress that, in some cases, result in up to a [50 percent reductio](https://abcnews.go.com/Health/covid-19-effects-hospitals-foresee-financial-distress/story?id=70511495)n of emergency visits and in-hospital stays include layoffs, furloughs, overworked doctors and nurses and insolvency. ## **Combat Revenue Loss with a Digital Acceleration in Healthcare** There are many strategies to capture lost revenue, and most of them will have a digital component to them. A Manatt Health study published in June, [“Emergence From COVID-19: Imperatives for Health System Leaders”](https://www.manatt.com/Manatt/media/Media/PDF/White%20Papers/Healthcare-White-Paper-Emergence-From-COVID-19_Imperatives-for-Health-System-Leaders-June-2020.pdf), examined short-term and long-term ways to recoup losses. A long-term focus it said, “will be on building the next-generation distributed, highly interconnected, community engaged and extensively digital system of care, which will be the lynchpin of a resilient health system.” The report argued that by taking this step, hospitals will achieve a better connection with patients and the community and aid in a recommitment to a [value-based care](https://www.redpointglobal.com/wp-content/uploads/2024/04/Redpoint-Global-Enables-Lucerna-Health-to-Transform-Healthcare-One-Patient-Engagement-at-a-Time.pdf) approach, lowering costs through improved patient satisfaction and greater efficiencies. The study listed several priority questions providers and hospital administrators should be asking. In the category of “Re-wiring the Organization for a Post-Pandemic World”, which the report referred to as the “new normal”, questions included: - Where are the siloes that we need to break down to optimize our ability to fully recover and thrive in the “new normal”? - Have we successfully managed the cultural transition to a highly digital system, or will we rapidly revert once the emergency fades? ## **A New Normal Will Require a Single View of the Healthcare Consumer** We’ve covered in this space the [new reality](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) in terms of digital excellence as it pertains to other industries post-COVID. In healthcare, just as with other industries, it will increase the need for payers and providers to completely understand, influence and measure each patient engagement. As the Manatt Health study suggests, this will require breaking down institutional siloes that are common in healthcare, such as the separation of clinical and claims data that cloud visibility into a single view of the healthcare consumer. This is the tip of the iceberg, as demographic, IoT device and behavioral data are also siloed. The “cultural transition” the study references pertains to meeting healthcare consumers’ increasing preference for digital channels of engagement – for scheduling appointments, messaging, looking up provider information or medical history, directions to a lab, etc. Often, these mundane tasks are incumbent on the healthcare consumer to navigate, which introduces friction into the healthcare journey and creates an [overall poor healthcare experience](https://www.redpointglobal.com/blog/survey-says-healthcare-personalization-has-room-to-grow/). When institutional siloes are broken down and the single view of healthcare consumer takes shape, hospitals and providers are positioned to drive revenue gains by providing hyper-personalized experiences across an omnichannel healthcare journey. According to [Gartner research](https://www.gartner.com/smarterwithgartner/is-your-organization-customer-centric/), organizations that use transactional, preference and historical data while also looking at a consumer’s behavior across devices, analyzing device usage, IoT and sentiment analysis, and first-party, second-party and third-party data across a complete anonymous to known record produce a conversion lift of 20 percent or more. These organizations, deemed “customer-centric” produce roughly twice as much lift as “persona-centric” organizations, that stop short of the single view – forming a personalization strategy that only using transactional, behavioral and historical data. A single view of the healthcare consumer, or [Golden Record](https://www.redpointglobal.com/single-customer-view/), produces optimal gains because it is continuously updated with real-time data. With no data latency, hospitals and providers always have the most updated, complete and unified consumer profile, which ensures that every message or communication – a next-best action – is always in the cadence of each consumer’s unique healthcare journey. ## **Reduce Costs and Increase Revenue with a Healthcare Consumerism Approach** The [Redpoint partnership with Lucerna Health](https://www.redpointglobal.com/press-releases/redpoint-global-and-lucerna-health-join-forces-to-advance-consumerism-in-healthcare-for-payers-and-providers/) demonstrates that the “customer-centric” approach as described above has proven to lower costs, achieve higher revenues and elevate the patient experience with an interconnected, digital-first platform. A joint solution combines the Redpoint [rg1 customer experience platform](https://www.redpointglobal.com/one-platform/) with the Lucerna Healthcare Data Platform (HDP) to advance a healthcare consumerism approach that prioritizes a holistic consumer experience across digital and physical touchpoints. The partnership delivers results by creating innovative, consumer-centric experiences that recognize an empowered consumer in charge of a healthcare journey. One client saw a 4X in closing care gaps for high-risk patients, a 3X improvement in new patient wellness visits and a 320 percent increase in program enrollment for on-demand telehealth services. ## **Take Charge of the Patient Experience** The way forward for hospitals and providers to recapture lost revenue and drive new revenue is to start by identifying every source of healthcare consumer data, realizing that a “customer-centric” approach that drives gains requires far more than just traditional provider data such as medical records. Once hospitals and providers begin using a single view of the healthcare consumer to deliver personalized experiences, they will be able to measure the ROI of the rg1 customer experience platform with vastly improved patient satisfaction rates, improved health outcomes and lower costs. With expectations for significant revenue loss and the prospect of a long recovery, hospitals must explore all options for reversing the slide and take aggressive measures to rebound. The healthcare consumerism trend that puts the healthcare consumer first was already accelerating pre-COVID, in large part because it has proven to lower costs and increase revenue through an improved patient experience. In the “new normal” of a post-pandemic world, the trend will continue on a growth path and it will become more important than ever to embrace patient experience as a defining metric. ## **Related Content** [The Data-Driven Acceleration of the Consumer Healthcare Journey](https://www.redpointglobal.com/blog/the-data-driven-acceleration-of-the-consumer-healthcare-journey/) [A Dynamic Healthcare Journey Deserves a Coordinated, Personalized Approach](https://www.redpointglobal.com/press-releases/redpoint-global-and-lucerna-health-join-forces-to-advance-consumerism-in-healthcare-for-payers-and-providers/) [Advancing Value-Based Healthcare One Patient Engagement at a Time](https://www.redpointglobal.com/press-releases/redpoint-global-and-lucerna-health-join-forces-to-advance-consumerism-in-healthcare-for-payers-and-providers/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [A Dynamic Healthcare Journey Deserves a Coordinated, Personalized Approach](https://www.redpointglobal.com/blog/a-dynamic-healthcare-journey-deserves-a-coordinated-personalized-approach/) **Published:** May 7, 2020 **Author:** John Nash **Content:** *Editor’s Note: This is Part 2 of a blog series on digital transformation acceleration in the healthcare industry.* [*Part 1*](https://www.redpointglobal.com/blog/the-data-driven-acceleration-of-the-consumer-healthcare-journey/) *examined the growing trend toward healthcare consumerism and a digital-first, personalized healthcare experience, especially with today’s empowered consumer changing daily behaviors.* Medicare Advantage (MA) programs provide a window into the healthcare industry transitioning to a value-based care (VBC) reimbursement model that prioritizes preventive medicine and managed care. The VBC approach conceptualizes a consumer’s health, lifestyle, relationships, and access to healthcare as a dynamic, holistic journey. By recognizing it as such and helping to guide that journey, the VBC model strives to lower overall costs and improve health outcomes and satisfaction. Consider the Medicare Advantage adoption of [new flex benefits in 2019](https://www.faegredrinker.com/en/insights/publications/2019/1/medicare-advantage-plan-adoption-of-new-flex-benefits-in-2019), which include meal delivery, transportation, and home safety improvements for eligible beneficiaries, usually those with a chronic condition. Supplemental benefits based on a VBC model help explain the growing popularity of MA plans, which now count roughly 22 million beneficiaries, roughly double the number of beneficiaries of a decade ago, and about a third of all Medicare recipients. With a [capitation reimbursement](https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Downloads/RegionalRatesBenchmarks2019.pdf) of roughly $1,000 per month per MA plan consumer – and the average member staying with a plan for six years ($72,000) – the competition among private insurers which offer MA plans to acquire and retain members is fierce. There is also clear financial incentive to keep members healthy over the long-term, with plans becoming vested in the consumer’s healthcare journey. **Meet the Dynamic, Uncertain Healthcare Journey Head-On** I bring up this background in the context of the seismic upheaval to the healthcare industry caused by COVID-19. Overwhelmed hospitals and a sudden, general disruption change the calculus for how to market to and otherwise engage with confused and anxious healthcare consumers who are trying to chart a new journey amid an uncertain landscape. For MA plan marketers trying to acquire market share of the roughly 10,000 people available to be acquired each day, either by aging in (turning 65) or those switching plans, traditional methods of engagement – seminars, breakfast meetings, and other in-person events – have come to a halt. Seemingly routine flex benefits – transportation to an appointment, in-home meal delivery – become complicated. These new challenges impact the entire healthcare community, and further accelerate the digital transformation in the industry and the transition to healthcare consumerism [covered in the first blog](https://www.redpointglobal.com/blog/the-data-driven-acceleration-of-the-consumer-healthcare-journey/) in this series. These challenges further widen the gap between the healthcare experience a consumer expects, and what payers and providers deliver. Overwhelmed providers must deal with the current health crisis, reallocating resources normally devoted to onboarding, care gap management, and education. Payers and providers are both challenged with communicating new information to the healthcare consumer, who often struggles with navigating benefits on a good day. Typical challenges – siloed data, unintegrated processes, a confusing matrix of access touchpoints – are exacerbated with an unprecedented health crisis that makes it difficult to know where to turn. **Foster New Partnerships with a Single View** To guide the healthcare consumer through new, dynamic situations requires data. A [single view of the healthcare consumer](https://www.redpointglobal.com/wp-content/uploads/2018/06/SB-CUSEXPHCUS0318-02-CusCentricHealth-hi-res.pdf) is the foundational requirement that provides payers and providers a capability to form new partnerships that break down the traditional data and process siloes largely responsible for a disjointed consumer journey. With a single point of control over claims and clinical data, as well as behavioral and preference data, healthcare professionals are empowered to guide a healthcare journey with a focus on achieving the VBC goals of improved health outcomes and satisfaction with lower costs. The ability to deliver a personalized consumer experience is one advantage of possessing a single view of the healthcare consumer. By knowing everything there is to know about the consumer, healthcare professionals can communicate in the right channel, optimize engagement, and become a more trusted caretaker able to walk hand-in-hand with the consumer throughout a dynamic journey. **The Tangible Advantages of Personalization** I brought up Medicare Advantage at the outset to showcase the example of one Redpoint client which ran a pilot campaign to gauge the effectiveness of personalization, with significant results. For this MA acquisition initiative, the client partnered with a health plan and value-based care provider, using relevant data from both the payer and provider side to match MA prospects with providers that were the best fit for an individual consumer, based on the consumer’s profile. It matched, for example, Spanish-speaking consumer with Spanish-speaking providers by closest location. It matched prospects with specialists treating the consumer’s specific condition. Personalized communications (emails with videos, postcards, direct mail) were sent directly from the provider, rather than the usual genericized messaging from a payer that normally only tout plan benefits and offer little in the way of differentiation. The payer ran this innovative pilot program for a select few zip codes, resulting in a significant ROI and an increase in acquisitions. By combining traditionally siloed data, this one pilot program forged an innovative payer-provider partnership to the benefit of both, and importantly resulted in a better experience for the healthcare consumer, in this case the new MA members presented with an innovative onboarding experience. Having a provider reach out directly is a compelling message, but it can only be done with the right data in place. Increasing dynamism in the consumer healthcare journey provides a powerful incentive for healthcare professionals to close the gap between what the consumer expects and what is currently being provided. Redpoint and Lucerna Health will co-host a webinar, “Payer Marketing in a Challenging Environment” on May 13 that will cover many of the topics addressed in this blog. The second in a series on healthcare consumerism, the webinar will touch on the impact of social distancing on this year’s marketing campaigns, the impact on providers, and how to optimize personalized offer journeys in an increasingly digital marketplace. Register [here for the webinar](https://redpointglobal.zoom.us/webinar/register/5115861996489/WN_taf2fWfDRoiRiHbD9PnV7w), which will be held May 13, 1 p.m. EDT. **RELATED CONTENT** [Reduce Gaps in Healthcare and Drive Better Health Outcomes with Personalized Engagement](https://www.redpointglobal.com/blog/reduce-gaps-in-care-and-drive-better-health-outcomes-with-personalized-engagement/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Quality, Healthcare, Real-Time Personalization --- ### [For Health Plans, Member Retention Starts with a Unified Profile](https://www.redpointglobal.com/blog/for-health-plans-member-retention-starts-with-a-unified-profile/) **Published:** August 27, 2024 **Author:** Steve Zisk **Content:** Member retention is a pivotal focus for health plans. With a growing emphasis on member satisfaction and loyalty, for inside and outside of the Medicare population, health plans must adopt strategies that address retention by fostering deep, meaningful relationships with their members through relevant, personalized experiences. Among the most common reasons why healthcare members switch plans, a lack of consistent personalization is frequently cited by members as a main reason for exploring alternatives, joining poor quality of care, lack of trust and ineffective pricing. In a [McKinsey study](https://www.mckinsey.com/industries/healthcare/our-insights/the-role-of-personalization-in-the-care-journey-an-example-of-patient-engagement-to-reduce-readmissions) on the role of personalization in the healthcare journey, members were more than **2X likely** to switch health insurance plans if their existing plan did not meet their expectations concerning their particular condition. In addition to higher potential churn, member dissatisfaction also had negative effects on Medicaid and Medicare Advantage payers’ Star ratings and Consumer Assessment of Healthcare Providers & Systems (CAHPS) scores. According to Kathleen Ellmore, Co-Founder and Managing Partner of [Engagys](https://www.engagys.com/), a healthcare consumer engagement consulting and advisory services firm, several factors are increasing pressure on payers offering Medicare Advantage plans to focus on retention. Included among them are tighter operating margins that are predicted to result in a reduction in supplemental benefits, intense competition, and an expected increase in the number of members shopping for and eventually switching plans. Ellmore counsels that it is wise for plans to think about every interaction as an opportunity to improve retention, and not just in the months and weeks leading up to the Medicare Annual Enrollment Period (AEP), which falls from Oct. 15 to Dec. 7. “Plans need to get ahead of it,” she said. “You can’t just flip a switch and start retention. It has to be baked into every interaction that a plan has with a member.” At a basic level, plans must proactively set expectations beginning with the onboarding process. There should never be any surprises, for example, about whether a provider is covered in-network, or that there is a provider shortage that might affect scheduling availability. Early transparency and forthrightness are essential for setting expectations, building trust and establishing a positive relationship, as studies show that likelihood of churn increases significantly if members are not engaged with effectively over the first three months. ## **A Personalized Member Experience** Being transparent about coverage and pricing is a good start, but an effective retention strategy must do more to personalize the experience for a member, aside from providing clear communication about coverage and benefits, which are largely the same for each member. Member-centric personalization, by contrast, uses data to develop a full understanding of the member, and then applies that knowledge to provide relevant interactions across a member’s healthcare journey. Incorporating medical history from claims data, consumer behavior, preferences and even social determinants of health (SDoH) into a single view of the member is essential for tailoring communications and messaging at an individual level. Personalized communication, based on individual attributes and historical interactions, significantly enhances engagement and retention by building a relationship with the member. > In an [Accenture study](https://www.accenture.com/us-en/insights/health/difference-between-loyalty-leaving) on patient loyalty in healthcare, 50 percent of members who switched health insurance plans did so because of a poor experience with their existing plan. And members are 3X more trusting when their plan provides them with “consistent and accurate information.” An Engagys program for example used data-driven insights to power a year-long text messaging campaign for members with chronic conditions including diabetes, asthma and hypertension. As part of the program a member with high blood pressure received personalized texts based on the results of a weekly home test. Depending on the numbers, the text might be congratulatory, offer encouragement, provide recommendations or additional resources, etc. By the end of the program, 50 percent of members living with hypertension had brought their numbers under control, a significant improvement over traditional static campaigns. Other examples of proactive communication relevant to a specific member include knowing that a member needs an MRI or lab work and providing a list of local facilities, or facilitating appointment scheduling. A personalized experience might include notifying the member of optimal times at a particular facility. Or, if a plan uses SDoH in a member profile, understanding that the member might have an issue with transportation and arranging for a shuttle pick-up or offering a telehealth option, if available. By using an updated member profile to deliver a consistent, relevant experience, healthplans not only drive better outcomes, they reduce costs by not sending wasteful, irrelevant or duplicative information such as reminders to schedule for members who have already scheduled. ## **The Technological Backbone of Retention** Many data-driven health plans are turning to a customer data platform (CDP) to power their retention strategies through the development and activation of a single member view, and for a good reason. A composable solution such as the Redpoint CDP enables member-centric engagement strategies and aligns efforts from different business and care navigation units to streamline the member journey. A good CDP creates a robust data foundation by fully integrating all internal and external data sources and applying advanced identity resolution to create a unified member profile. Using AI and dynamic segmentation capabilities, users can build dynamic audiences that can be used across channels to personalize communication at scale. Using a GenAI-powered interface, users can create, visualize and test audiences without having to write a single line of code. This allows health plan leaders to give their undivided attention to what matters for members. Member retention is a multifaceted challenge that requires a strategic approach structured around member-centric engagement and the right supporting technology. By addressing key challenges, staying abreast of trends, and implementing effective strategies, health plans can improve retention and build stronger relationships with their members. As the healthcare landscape continues to evolve, focusing on personalization, proactive service, and thoughtful engagement will be crucial for maintaining member loyalty and satisfaction. For more on how Redpoint and Engagys can help you maximize member engagement, using your own data, click [here](https://www.redpointglobal.com/landing_pages/healthcare-payer-poc/). **Blog categories:** Healthcare --- ### [Hawaiian Airlines Partners with Redpoint to Deliver a Personalized CX with the Redpoint CDP](https://www.redpointglobal.com/blog/hawaiian-airlines-partners-with-redpoint-to-deliver-a-personalized-cx-with-the-redpoint-cdp/) **Published:** July 18, 2023 **Author:** Redpoint Global **Content:** Hawaiian Airlines’ customers board with expectations – not just to arrive safely to their destination, but to have a seamless, connected experience across every engagement with the brand from pre-travel to post-travel. Every passenger’s unique journey spans far more than the flight destination, and Hawaiian knows that creating a loyal customer depends on having a deep, personal understanding of a customer’s behaviors, preferences and general travel portfolio. Does the customer travel mostly for business or pleasure? Do they book online? How far in advance do they search for fares? Do they prefer the aisle or a window seat? Are they active in the rewards program? To meet evolving customer expectations for omnichannel personalization, [Hawaiian Airlines partnered with Redpoint Global](https://bit.ly/46O04tv) to create a single view of the customer with [the Redpoint CDP](https://www.redpointglobal.com/rg1/). The airline cited Redpoint’s data management capabilities, the creation of a Golden Record and the ability to target new segments as key factors in the decision-making process. ## **Better Data Management, a Better Experience** In its 95th year of service, Hawaiian Airlines’ more than 60 aircraft have carried about 10 million customers – and counting – on daily routes from the state of Hawaii to every major city on the West Coast as well as scheduled service to Boston, Orlando, Austin and the American Samoa. The airline had been facing several challenges related to disconnected customer data and siloed marketing technologies that prevented a single customer view. Its marketing team was relying on manual processes to achieve basic personalization and segmentation, resulting in expensive and time-consuming custom implementations – as well as static offers that were not personalized to each customer’s updated travel portfolio. Existing customer records were sometimes outdated and inaccurate, making it difficult to maintain and enhance customer relationships. The airline’s marketing team also faced delays in communicating with groups of customers through digital channels. Hawaiian Airlines selected Redpoint because of the its proven success in meeting the airline’s main goals, which were to reduce duplication in technology, create stronger data management and governance practices, support a more streamlined omnichannel marketing engine and create a more flexible and connected infrastructure for marketing and IT systems. ## **Flexibility and Control** With real-time data quality checks and continuous data cleansing, Redpoint will ensure that Hawaiian Airlines’ customer database remains accurate and up-to-date, a key factor in reducing duplicate records that will result in higher ROI and better customer engagement. The Redpoint Golden Record serves as a single source of truth for the customer, providing Hawaiian Airlines with an accurate, real-time and persistently updated, unified customer record that lets the airline know everything there is to know about a customer. With the platform’s [real-time decisioning](https://www.redpointglobal.com/real-time-interactions/) and [intelligent orchestration](https://www.redpointglobal.com/journey-orchestration/) capabilities providing a single source of operational control, next-best actions – inbound and outbound – will be delivered in the cadence of each Hawaiian Airlines customer throughout an omnichannel journey. Another driver for the project was for Redpoint to serve as a hub for Hawaiian Airlines’ future Martech platform evolution. While a core capability of the Redpoint platform is integrating data from various sources to build a complete customer profile, rg1 also offers flexibility with a composable architecture framework that allows companies to quickly bring in additional data sources and enterprise technology – such as a generative AI application for instance. Or to quickly switch out a database to cost-effectively achieve performance benchmarks. Another goal for Hawaiian Airlines was to enhance its ability to personalize and target new segments. The airline had been facing issues with basic personalization and segmentation due to its disconnected customer data and marketing technologies. With Redpoint, Hawaiian Airlines creates more sophisticated customer segments based on a wider range of attributes and facets resulting in more personalized and relevant marketing campaigns, increased customer engagement and loyalty. **Blog categories:** Travel & Hospitality --- ### [The State of the CX Gap: Harris Poll, Redpoint Revisit Benchmark Survey](https://www.redpointglobal.com/blog/the-state-of-the-cx-gap-harris-poll-redpoint-revisit-benchmark-survey/) **Published:** September 22, 2021 **Author:** John Nash **Content:** In “Revisiting the Gaps in Customer Experience,” the second Harris Poll survey commissioned by Redpoint, marketers and consumers remain split in their perceptions regarding the delivery of an omnichannel CX. The survey, [released today](https://www.redpointglobal.com/resources/harris-poll/), reveals that while 51 percent of marketers believe they are delivering an “exceptional” CX, only 26 percent of consumers say the same. That 25-point differential is down from 30 from the initial 2019 survey. More shared agreement is certainly good news, but it also comes with heightened demands that an omnichannel customer experience may be a requirement for doing business with a customer. Consider that 39 percent of consumers surveyed said they will not do business with a company that fails to offer a personalized experience. The extensive research also explores reasons for the disparity, identifies what marketers and consumers consider the most important aspects of CX, examines CX from an industry standpoint, looks at technology’s role, and reveals some of the biggest hurdles marketers must overcome to meet the heightened demands. The survey also looks at CX through the lens of the vastly changed landscape since 2019, particularly the impact of COVID-19 and digital acceleration as it pertains to customer journeys. ## **Elevation of the Omnichannel CX** A digital acceleration of customer journeys, marked by an increase in digital-only or digital-first engagements, is reflected in the survey findings where, unlike in 2019, consumers ranked omnichannel consistency – meaning a brand recognizes them as the same customer across all channels, and delivers a consistent experience – as the most important dimension of CX, up from a second-place ranking. A majority (61 percent) reported being frustrated if that recognition is lacking, with 31 percent saying that it would make them *less likely* to do business with the brand. A large majority of both marketers and consumers agree that the pandemic has made it more important for brands to know customers’ individual needs and preferences. Marketers say it has changed their CX strategy, with a majority citing an increased emphasis on flexible delivery systems, a focus on integrating digital and physical channels, and an embrace of new channels. ## **Closing the Gap with Data Quality** On the technology side, marketers were asked to rank the top barriers preventing their existing marketing solutions from accomplishing their strategic goals. A lack of data integration between channels and the use of a closed marketing cloud were each cited by 41 percent of marketers as the top two barriers to managing an omnichannel CX. To break through this logjam, 91 percent of marketers said that martech investment is a key initiative (up from 89 percent). Asked to identify specific areas of investment, 41 percent cited the ability to create personalized, relevant experiences as the top purpose for martech investment. There was agreement between the two groups that data quality – or a lack thereof – is also an area of opportunity. Marketers cite customer data being inaccurate, fragmented and incomplete as their biggest data quality challenges. Meanwhile, half of consumers say brands are failing to deliver personalized experiences because data is not accurate or up to date. Both groups agree that accuracy is the area most in need of improvement as a conduit to create the type of personalized CX consumers demand. For the consumer, an inaccurate representation of who they are – likes, preferences, behaviors, etc. – because data is not up to date explains why roughly half of all consumers surveyed said they feel undervalued and unseen by the brands they engage with. Because marketers seem to acknowledge where they fall short and the steps they need to take address and close the CX gap, there is an expectation that customer experience will remain a priority as a key for driving revenue growth. Additional CX enhancements will certainly help, but they will also serve to reinforce consumer expectations that a consistent personalized experience is a base-level requirement for engaging with a brand. *Download the full whitepaper, [“Revisiting the Gaps in Customer Experience”](https://www.redpointglobal.com/resources/harris-poll/) for more detail.* ## Related Content [Addressing the Gaps in Customer Experience: Redpoint Global/Harris Poll Benchmark Survey](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) [The Role of a Golden Record in Providing a Consistently Relevant, Personalized CX](https://www.redpointglobal.com/blog/the-role-of-a-golden-record-in-providing-a-consistently-relevant-personalized-cx/) [Application Rationalization: How to Balance CX & Corporate Goals](https://www.redpointglobal.com/blog/application-rationalization-how-to-balance-cx-corporate-goals/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* --- ### [Harness the Power of Digital Technology as a Revenue-Driving Engine](https://www.redpointglobal.com/blog/harness-the-power-of-digital-technology-as-a-revenue-driving-engine/) **Published:** July 16, 2019 **Author:** Redpoint Global **Content:** *This is the first blog in a two-part series that explores how to use advanced digital technologies to create revenue lift. Part two will take a deeper look at technical considerations, use cases, and benefits.* The first and only real consideration for any business looking to deploy artificial intelligence (AI) or machine learning is whether the application or model will produce revenue. Will inserting advanced digital technologies into the continuous business process cycle of data, insight, and action monetize opportunities that arise from turning deeper insights into action? Many companies deploy machine learning models to improve the customer experience, but most fall short of becoming true revenue-generating engines. Fast food restaurants, for example, are racing to introduce [AI-powered menu boards](https://www.nrn.com/technology/sonic-drive-demonstrates-voice-ai-pilot-nra-show) that recommend add-on items based on current selection, restaurant traffic, or conditions such as the weather, time of day, or trending items in the area. Upsell may be a noble pursuit, but this use case in present form is more of a novelty than a mission-critical system that will boost the bottom line. Likewise, AI-powered chatbots are exponentially more intelligent than even a few years ago, but other than providing a more pleasant customer experience they are unlikely to move the revenue needle. ## **Move Beyond Personalization for Personalization’s Sake** For AI and machine learning to truly ascend as a top revenue-generating engine for the business by providing a differentiated customer experience, advanced analytic models must be embedded across the complete customer lifecycle and every channel or touchpoint. Otherwise, models do little more than scratch the surface of possibilities. Personalization for personalization’s sake, such as seeing your name on a menu board when you order a hamburger, is vastly different than personalizing a customer experience for a segment of one, in real time, based on a customer’s behaviors, interests, and intent across an omnichannel buying journey. Consider again the AI-powered chatbot that helps a customer resolve a service issue. The customer may be pleasantly surprised by the user-friendly experience and awed by the technology, but the experience will not translate into direct and measurable revenue gains because it is restricted to a single channel. While the chatbot can intelligently respond and interact with a customer, it will not know anything about the customer beyond the specific issue, or beyond the available data that resides in that channel. Perhaps it goes so far as to recommend a product in the color and size that matches previous online transactions. The customer may later purchase the item in-store, but if the data is siloed by channel the brand will not have visibility into the journey, clouding any direct impact of the chatbot conversation and depressing the value of the advanced technology. A one-off sale is not akin to direct revenue lift, which can be significant. According to Boston Consulting Group, there will be an [$800 billion revenue shift](https://www.bcg.com/en-us/publications/2017/retail-marketing-sales-profiting-personalization.aspx) to the 15 percent of companies that get personalization right over the next five years in three sectors alone – retail, healthcare, and financial services. Personalization also drives retention, which astute brands know is more profitable than acquisition. According to the Aberdeen Group, companies that have an extremely strong omnichannel engagement strategy have an 83 percent customer retention rating, vs. 53 percent for companies that do not. The “Profiting from Personalization” article states that “brands that create personalized experiences by integrating advanced digital technologies and proprietary data for customers are seeing revenue increase by 6 percent to 10 percent … two to three times faster than those that don’t.” ## **Unlock Channel Constraints to Move the Revenue Needle** To produce revenue lift that makes a difference, advanced technologies must automate intelligence to dynamically engage with a customer across every interaction and touchpoint in an omnichannel buying journey. Moreover, self-training models must be built on a unified customer profile or single customer view that captures customer data of every source and type in real time. Embedded intelligence supported by a 360° view of the customer has the power to recommend an algorithm-produced next-best-action to a segment of one the moment the customer appears next in a dynamic customer journey. This is transformative. Consider a traditional marketing strategy bound by channels. A message or offer is sent to a segment of customers, with success measured by click rates and conversions. A customer who doesn’t respond may then show up anonymously on the website; without any link between the email and the cookie, the customer will likely receive an inconsistent message. Like the chatbot example, without synergy between channels across a complete buying journey, the power of AI is muted because it will fail to recommend a next-best-action in the context and cadence of the customer. With embedded advanced analytic capabilities unbound by channels and data siloes, marketing will know in real time everything there is to know about the customer – a call center agent or chatbot will know that the customer who received a specific offer then went to the website anonymously before making the call. Within milliseconds, a machine learning model will not only produce real-time information about the customer, it will also produce a next-best-action based on the consistently updated single customer view. ## **Ensure Consistent Messaging with Lights-Out Modeling** Advanced digital optimization ensures a consistent, personalized message and journey for a customer irrespective of channel or any other variable or condition such as day or time. Embedded AI doesn’t have to wait for data scientists to build models. Rather, lights-out modeling runs 24/7 looking for opportunities in the unified customer profile. Simulation engines constantly watch models, and will move new models into production that predict better outcomes based on predetermined metrics. Linking predictive rules, despite what many marketing automation tools claim, is not AI. The fact is, predictive rules are not dynamic; models built on them will become stale over time. An in-line optimization engine that works with real-time customer data is immune from this problem, and thus does not require human intervention to refresh models – a common practice that is often only done after many missed opportunities at monetizing data. Automated embedded intelligence enables hundreds or thousands of models to run concurrently, all with a single-minded purpose of exploiting revenue opportunities according to any metric the business proposes. Personalization is the key that unlocks any opportunity to monetize customer data with a differentiated customer experience. **Blog categories:** AI & Machine Learning --- ### [Why the Right to Be Forgotten is One of the Hardest Parts of GDPR](https://www.redpointglobal.com/blog/why-the-right-to-be-forgotten-is-one-of-the-hardest-parts-of-gdpr/) **Published:** September 4, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/09/shutterstock_1064933888-300x183.jpg) *Editor’s Note: This is the first of a two-part series on the Right to be Forgotten, also known as the Right to Erasure. Part two will focus on how marketers can solve GDPR complexities, and forestall issues by establishing a consent-based relationship with the customer.* Marketers tend to think of the right to be forgotten primarily in the context of the General Data Privacy Regulation (GDPR), the sweeping data privacy regulation that dictates how organizations that operate in the European Union (EU) or with customers in EU process, hold, and treat personal data. The concept of the right to be forgotten, however, had little to do with marketing in its initial development. The case of [Google Spain v. AEPD & Mario Costejo Gonzalez](https://epic.org/privacy/right-to-be-forgotten/) helped codify what “the right to be forgotten” entails. In 2010, Gonzalez – a Spaniard – filed a claim before the Spanish Authority for Personal Data Protection (AEPD) arguing that Google violated his privacy by including, in a search for his name, a link to an old newspaper article indicating his home was being repossessed to pay off debts. AEPD agreed, and Google lost an appeal to the Court of Justice of the European Union (CJEU) which ruled for Gonzalez in 2013, deciding that data subjects have a legitimate interest to deny the disclosure of personal data. By establishing a legal precedent for the right to be forgotten for data subjects, the decision reverberated for data controllers internationally. This right was institutionalized in Article 17 of GDPR, which refers to it as the [right to erasure](https://gdpr.eu/right-to-be-forgotten/). The scope extends beyond search, codifying that data subjects “shall have the right to obtain from the controller the erasure of personal data … without undue delay”. The regulation and others like it, such as [California Consumer Privacy Act (CCPA)](https://oag.ca.gov/privacy/ccpa), have significant implications for marketers, who must balance the many facets of compliance with the need to deliver a personalized customer experience. The complexity of compliance is compounded by the ongoing evolution of GDPR. Because it is relatively new (it took effect in May 2018), organizations are still coming to terms with what it means from a legal standpoint. They also struggle with compliance on a global scale, needing to handle, for example, what the right to erasure means for customers in different jurisdictions, or how to satisfy potential conflicts between different regulations. **The GDPR Balancing Act** Much of the complexity, though, relates to the sheer number of steps marketers and their organizations must take to ensure compliance, which explains why many organizations are now hiring data privacy officers or GDPR compliance officers to handle the Article 17 nuances. These steps include: - Recording a request for erasure by a consumer (“data subject”) - Confirming the request for erasure comes from the data subject - Informing the data subject whether the organization will honor the request and, if not, why - Deciding what information needs to be erased and how to erase it (archived, without a trace, etc.) - Removal of the information - Notification of internal systems - Notification of external partners - Recording each of the erasure steps taken If this sounds complex, it is. Marketers must comply with the right to erasure, while essentially having to “remember” (via documentation) that they “forgot” a consumer. Business processes must be put in place to strike the fine line between the requirements and the circumstances that dictate those requirements. The right to be forgotten, in other words, is not an absolute right, and Article 17 stipulates where an organization’s right to possess someone’s data overrides the right to erasure. Reasons include: - The data is being used to exercise the right of freedom of expression - The data is being used to comply with a legal ruling - Data is used to perform a task carried out in the public interest, or it represents important information that serves the public interest where erasure would impair progress toward the public interest (scientific or historical research, etc.) - Data is used for establishment of a legal defense Organizations must have procedures in place to determine how to balance what may at times seem to be competing interests. They must be vigilant in documenting how they handle each instance of a request for erasure, how they recognize the legitimacy of the request, and how they implement the request. In addition, a strategy that is in place for an erasure request must above all be adaptable based on the changing interpretations of GDPR that are still working their way through the court systems and regulatory bureaucracies, both domestically and internationally. **What GDPR Means for a Brand Marketer: Stay Tuned** Creating a personalized customer experience is a top priority for brand marketers. According to a [Walker study](https://www.walkerinfo.com/knowledge-center/featured-research-reports/customers-2020-a-progress-report), customer experience will overtake both price and product as the key brand differentiator as early as next year. To deliver on the expectation for personalization, marketers must know everything there is to know about a customer – likes, preferences, buying patterns, and transaction history included. With Article 17 of GDPR, marketers must also factor in each customer’s right to erasure history. And, like the sometimes-competing interests of the regulation itself, a right to erasure request can conflict with the effort to deliver a hyper-personalized customer experience. An upcoming blog will focus on how marketers can address these potential conflicts and forestall future issues while still ensuring GDPR compliance. **Blog categories:** Customer Data Platform, Data Management, Omnichannel Marketing --- ### [Google is Keeping the Cookie, but Don’t Bank on its Future Utility](https://www.redpointglobal.com/blog/google-is-keeping-the-cookie-but-dont-bank-on-its-future-utility/) **Published:** July 24, 2024 **Author:** Renee Graff **Content:** In a surprising announcement, Google now says the third-party cookie isn’t crumbling from Chrome browsers after all. Many advertisers breathed a sigh of relief. But before you start celebrating, let’s look at what this looks like in reality, and why you shouldn’t abandon your first-party data plans. ## Catching Up on the Crumbling Cookie - Google originally announced they would end Chrome browser support for third-party tracking cookies back in 2000, with a 2022 cut-off date. We’ve seen three extensions on the original 2022 deadline, with the latest just a few months ago in April. - Over the years, Google took multiple paths toward replacing cookies, but none of those methods gained widespread adoption in the ad industry or regulatory approval. - Following the initial announcement, the AdTech industry scrambled to adapt ahead of the original deadline (and subsequent revisions), developing and implementing alternative methods (“Universal IDs”) that rely on hashed email and phone number data, in an attempt to achieve similar targeting and reporting results as the longstanding cookie-fed processes. - The Trade Desk’s UID 2.0 emerged as a widely accepted alternative for privacy-focused advertising across the “open web,” which is the internet outside of the “walled gardens” of Meta, Google, Apple, Amazon, etc. - Marketers prioritized first-party data collection to learn as much as they could about their customers and develop a data set that would work within the new frameworks, both inside and outside of those walled gardens. CDPs, identity graphs and data clean rooms became key components in core retention and acquisition strategies. ## The New Google Plan Google’s current plan is to maintain support of third-party cookies, but give users the choice to allow or disallow tracking themselves, at any time: “Instead of deprecating third-party cookies, we would introduce a new experience in Chrome that lets people make an informed choice that applies across their web browsing, and they’d be able to adjust that choice at any time,” Privacy Sandbox VP Anthony Chavez wrote in the announcement blog post. If this setup sounds familiar, it’s similar to what Apple did back in April, 2021, when iOS 14.5 privacy options gave iPhone users the option to prevent advertisers from using their device IDs for targeting. By August of that year, [some advertisers saw their addressable iPhone audiences drop](https://mediamattersww.com/mmww-blog/the-impact-of-ios14-5-and-app-tracking-transparency-att/) by 10-30%. That October, [AppsFlyer reported only 38%](https://www.cnbc.com/2021/11/13/apples-privacy-changes-show-the-power-it-holds-over-other-industries.html) of users opting-in to device tracking for ads, with 62% opting-out. Using Apple as an example, if Chrome users were to be prompted with an opt-in or opt-out message, it’s likely that there will be even fewer live cookies over time. ![Cookies in a row of jars with each jar emptier than the last](https://www.redpointglobal.com/wp-content/uploads/2024/07/Cookies_in_jars_2179641509.jpg) ## Our Advice: Stay the Course with Your First-Party Data From Google to Apple, across browsers, and on computers to mobile devices and beyond, what we’ve witnessed is an overall degradation of signals from digital audiences. *Even while cookies were still in play*, segmentation and targeting became more challenging than ever, channel and ad type preferences shifted within the media mix, and analytics reports evolved to look different today than they did four years ago. What’s more: - Privacy regulations are continuing to change state-to-state, at the federal level, and worldwide. Even though Google’s keeping the third-party cookie, there are a lot of influential external forces that will likely render it less and less reliable and useful in the future. - User behavior has fundamentally changed and become more privacy-focused. A May, 2023, Pew Research Center survey reported that [67 percent](https://www.emarketer.com/content/majority-of-us-adults-will-turn-off-cookies-manage-privacy-online) of US adults turn off cookies or website tracking to protect their privacy. *That’s right – around 70 percent of the U.S. internet doesn’t have an active third-party cookie anyway.* ## Summing Up, Here’s Your Action Plan With all those forces acting on digital advertising, it’s vitally important to take steps to future-proof your strategy. Here’s what your action plan should be in light of this cookie news: - **Continue collecting permissions-based data about your customers.** Learn as much as you can about them so you can market to them in a way that resonates. The more data you own moving forward, the better you will be able to adapt as the advertising landscape changes. - **Bring all that data together, scrub it, and enrich it so it’s usable.** A CDP connects your disparate systems and pulls all your data in to one place so you can activate it across channels. A truly useful and good CDP will clean that data for you, eliminating duplicate records, filling in the blanks with identity resolution, and making it actionable. The cleaner your data is going into your custom audience campaigns, the better your results and your return on ad spend (ROAS). - **Don’t put all your eggs in one walled garden basket.** It’s true that campaigns in the walled gardens can give you great match rates and help you attract lookalike audiences. But U.S. internet users spend 66 percent of their time online out on the open web, outside of those walled gardens. Make sure your media mix is balanced so you’re not ignoring potential customers, and work with partners that can help you reach custom audiences across the web. It sure felt like a bombshell announcement, but when you consider all the factors, what we’re left with is a future that honestly, for the time being at least, looks a lot like today. We’re not rewinding the clock and going back to the heyday of behavioral targeting using cookies. Keep moving forward with your first-party data plan, and you’ll be well-prepared to adjust to future industry changes. **Blog categories:** Omnichannel Marketing --- ### [Retention Marketing for the Digital-First Customer: Make an Impression with a Golden Record](https://www.redpointglobal.com/blog/retention-marketing-for-the-digital-first-customer-make-an-impression-with-a-golden-record/) **Published:** February 26, 2021 **Author:** Steve Zisk **Content:** The adage that it is more expensive to acquire a new customer than it is to retain an existing one (some studies suggest about [5X more](https://www.invespcro.com/blog/customer-acquisition-retention/)) takes on greater significance amid changing consumer behaviors, particularly a shift toward a digital-first mindset marked by expanding consumer choice and fleeting brand attachments. The competition for loyal customers in this new environment is fierce, and highlights the inefficacy of old school retention strategies such as universal pricing discounts. Price and product, already largely commoditized, wane even further with consumers placing greater value on their experiences with a brand. The flight to digital appears here to stay. According to new [research from McKinsey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-global-view-of-how-consumer-behavior-is-changing-amid-covid-19), increases in online commerce of 50 percent or more are expected in several categories of consumer goods, including household items apparel and food/grocery. Further, 73 percent of US consumers admit to having tried a new shopping behavior in the past year – with 80 percent claiming they will continue the new behavior moving forward. ## **Retention Marketing and a Personal Understanding** In this new environment, retention is critical. And it will rest in large part on providing a differentiated, personalized experience. A customer who chooses a new behavior such as curbside pickup will continue the behavior – and become a repeat customer – if presented with a seamless experience. A brand that devotes a bulk of its resources to acquiring a new customer by promoting a flashy new curbside pickup service, for example, will have a poor ROI if those customers leave after experiencing friction in the customer journey. According to [research from Bain](https://hbswk.hbs.edu/archive/the-economics-of-e-loyalty), in conjunction with Harvard Business Review, a 5 percent increase in retention produces anywhere from between 25 and 95 percent increase in profits, depending on industry, service or product. Yet at the same time, up to 80 percent of companies spend over [70 percent of their marketing budgets](https://www.insightly.com/blog/2020/05/lead-gen-vs-customer-retention/) on lead gen, versus just 30 percent on retention. The key to flipping this script is a digital-first retention marketing strategy based on a detailed understanding of an individual customer’s journey with a brand through all digital and physical touchpoints. At a macro level, churn is easy to understand – and predict. Poor quality, failed promises, a data or privacy breach, a poor UX, or hidden fees will all make customers flee for the exits. At a micro level, the key to retention for the digital-first customer is demonstrating a deep, personal understanding of likes, dislikes, preferences and behaviors and using this insight to minimize churn factors at the individual level. A routine website visit, for instance, that displays images and products that are hyper-relevant to a customer’s intent will be more effective than the alternative – a static homepage the same for every visitor. Website personalization could fall under retention or acquisition depending on whether you’re targeting a known or an anonymous record, but the point is that this is the type of personalized experience that customers expect. In a recent Dynata survey, [70 percent of consumers](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/) said that they will *only* shop with brands that personally understand them. (In addition, 82 percent said they expect retailers to accommodate preferences and expectations.) Personal understanding extends to more than just analyzing behaviors, likes and preferences for the purpose of presenting relevant content at the time of engagements. Possible churn indicators could also relate to changes in a customer’s buying patterns (slowing or stopping), less frequent log-ons after a consistent pattern has been identified (such as with a banking website), negative feedback to a call center rep, not opening or clicking on an email, etc. Essentially, any and all customer data may provide important clues that a customer is about to leave, and empower a brand to proactively respond with a next-best action relevant for the customer at a slice in time of the individual customer journey. ## **Retention Marketing with a Golden Record** A customer retention strategy that rests on a [Golden Record](https://www.redpointglobal.com/single-customer-view/) offers the accuracy, precision and flexibility needed to engage today’s digital-first customer with personalized experiences to counter individual churn signals. A Golden Record is a single customer view that aggregates customer data from every conceivable source. A Golden Record pulls together data from all sources and all types – structured, unstructured, semi-structured and from known and unknown customer records – to create a holistic view of the customer. Combined with advanced identity resolution capabilities, it provides brands and marketers with a customer identity graph that encapsulates a persistently updated view of a unique customer’s behaviors, preferences, transactions, devices and IDs. Updated in real time and together with a real-time decisioning engine, a Golden Record is the key to providing each customer with a hyper-relevant, personalized experience that is always in cadence with the customer journey. For retention purposes, use of a Golden Record ensures a brand is never caught off guard. If notes from a call center interaction indicate a customer is dissatisfied with the contents of a shipped order, it might be worthwhile to dynamically switch out email content – sending an apology and a discount offer rather than boilerplate content asking them to rate the online purchase experience. A Golden Record makes this interaction possible because a single customer view eliminates the data siloes that – along with process, people and channel siloes – are largely responsible for introducing friction by being a step (or more) behind the customer. ## **Retention Marketing and Automated Machine Learning** Customer retention strategies that depend on producing an individualized next-best action at scale for hundreds of thousands or millions of customers, in real time, requires [automated machine learning](https://www.redpointglobal.com/machine-learning) (AML). Code-free, self-learning, in-line analytic models take data scientists and human judgment out of the equation; instead, with AML brands and marketers can deploy hundreds of models simultaneously, each running through optimization scenarios for whatever the desired metric relating to churn reduction. [Evolutionary programming](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/) strips away anything that is not laser-focused on delivering the chosen metric, ensuring the optimal decision – or next-best action – for the desired business outcome. With AML models tuned to retention and set to produce a next-best action based on a real-time Golden Record, brands and marketers can be confident that they’re reading churn indicators properly at an individual customer level. More importantly, they can be confident that their response is likewise perfectly tailored to how the customer is moving through a customer journey. Customers moving to digital-first engagements is a certainty, and brands need to rethink retention strategies in line with the flight to digital. Just because customers are in-store less frequently does not give brands the luxury of adopting an “out of sight, out of mind” mentality. That’s a misguided approach that fails to match today’s reality. With customers having more choices and more of an expectation for personalized experiences, retention is in many ways more challenging than ever. A Golden Record takes out the guesswork, giving ambitious marketers a next-level tool to delight a customer with every interaction and stopping any churn indicators before it has a chance to percolate. **Blog categories:** Retail, Single Customer View --- ### [The Role of a Golden Record in Providing a Consistently Relevant, Personalized CX](https://www.redpointglobal.com/blog/the-role-of-a-golden-record-in-providing-a-consistently-relevant-personalized-cx/) **Published:** June 16, 2021 **Author:** Vin DelGuercio **Content:** The term “Golden Record,” as a descriptor for a single source of truth for a customer profile, implies a certain infallibility, which is not to be confused with being immutable. Meaning that while a Golden Record will contain certain characteristics – namely identity and transactional components that, together, are a perfect representation of a customer for any given moment of a customer journey – those characteristics are fluid. Not only does the type of data collected differ for each business or industry, but the data are also constantly changing. The power of a Golden Record as the foundation and launching point for a hyper-relevant, personalized customer experience is that even with a continual influx of new data, it is always in the context and cadence of an individual customer journey. With a Golden Record, marketers can be confident that minute by minute, second by second, they will always have the most recent and complete picture of a customer at every stage of a customer journey. ## **A Unified “Profile” vs. a True Golden Record** Marketers need to be wary of vendors who often overpromise and underdeliver on providing a [single customer view](https://www.redpointglobal.com/single-customer-view/), or another term they may use in place of Golden Record to mean a unified customer profile. What many really offer may be more accurately called “single view light,” which often stops at integrating customer data from multiple sources, and letting marketers decide what they need. Many vendors ingest data from multiple sources and present it as a single view, but they require the data to have relevant keys to compile this “integrated view”. There is an assumption, in other words, that data quality processes are already complete and that it is ready to be made available across the enterprise. There are several reasons why this approach is inadequate to keep up with a dynamic customer journey. First, customer data integration is not a synonym for real time. Integrating data from sources where the data being compiled are out-of-date (meaning, in some cases, seconds or milliseconds old) compromises accuracy. A complete profile becomes incomplete the instant data latency enters the picture, for the simple reason that a marketer will be unable to trust that the profile is an accurate representation of the customer. Because customer interactions with brands occur during dynamic, [omnichannel customer journeys](https://www.redpointglobal.com/omnichannel-personalization/) across both physical and digital channels, seconds matter. A profile is only as accurate – and thus trustworthy – as the recency of the oldest data. Second, left to their own devices, marketers tend to default to the use of every available piece of data. The problem here is that data veracity and data quality are key components of a Golden Record. When data is cleansed, matched and merged at point of ingest (i.e., in-line), marketers are assured that the resulting profile accurately represents the customer they’re intending to interact with. A marketer, for example, should not have to deduce whether a “John R. Doe” is the same record as a “Jon Robert Doe” or if “123 Main St.” is in fact the same physical address as “123 Main Street”. Depending on the business, or the purpose for the match, there could be a combination of deterministic and probabilistic matching at play, and often both are important to ensure a marketer or business user is communicating with the right person, household or business. Assigning persistent keys help ensure consistency. A retailer, for example, may determine with a high degree of confidence that a device used to browse a brand’s website belongs to one individual, but the credit card and shipping address used during a subsequent transaction belong to another individual in the household. Through a combination of deterministic and probabilistic matching, the retailer ensures a seamless customer experience for the right individual. Another reason why data integration alone is not the same as creating an unassailable Golden Record is that combining data sources is not the same as making the resulting unified customer profile accessible to any person or system that needs it – at the moment it is needed. In addition to being in real time, with advanced identity resolution and data quality steps taken in-line, to be considered a true Golden Record a unified profile must be [made available in real time](https://www.redpointglobal.com/orchestration/real-time-optimization) to enable a marketer to engage in the cadence of a customer journey. ## **Constructing a Golden Record** A Golden Record in one industry may look vastly different from one in another industry, even though both will contain identity and transactional facets. A retailer or another transaction-based business will very likely use 30 days spend, lifetime spend, average transaction value, customer lifetime value and other similar metrics in a Golden Record, whereas a subscription-based business may be concerned with only the length of the contract and the billing cycle. An organization that sells high-end products or big-ticket items – luxury cars, say – will create a Golden Record that looks very different from a company selling perishables or fast-moving products like laundry detergent. A Golden Record worthy of the name will take a broader view of transactions to also include how a customer engages with content. Did they click on a link on the website, for instance? How long did they hover? Which pages did they visit, for how long and in which order? Did they open an email? A careful analysis of a complete transactional record will help ensure that a marketer engages with the right type of content – on the right channel. Identifiers, too, are not restricted to what we usually associate with a customer – a physical address, email, name, etc. Rather, an identifier will include anything possibly associated with a customer – all devices and IP addresses, household information, etc. As with the transactional trail, important identifiers may vary by industry. A Golden Record for a bank, for instance, will include all account numbers associated with a customer. The importance of various identifiers will also vary. An ecommerce company may have little use for a physical address, which will be very important for a company shipping a physical product. ## **Beyond the Golden Record** In addition to a Golden Record, there are other tools a marketer needs to consistently deliver a hyper-personalized, omnichannel customer experience. Automated machine learning is a requirement for drawing the best insights from the golden record in real-time and delivering a personalized customer experience at scale, while a real-time decisioning engine is vital for calculating a next-best action for an individual customer at the moment of interaction. But it starts with a Golden Record as the foundation that supports everything else. How it’s created will vary by industry and by company, but each will also be unique in that it not only represents a specific customer, but a specific customer at a precise moment in time. With cleansing, matching and merging done at the moment of data ingest, no data latency, consistent updates and instantly accessible, a Golden Record provides marketers with confidence that they will always engage a customer with consistent relevance throughout a dynamic, omnichannel customer journey. **Blog categories:** 1:1 Personalization, Identity Resolution, Real-Time Personalization --- ### [Retailers: Generate Revenue Through a Golden Record and a Personalized CX](https://www.redpointglobal.com/blog/retailers-generate-revenue-through-a-golden-record-and-a-personalized-cx/) **Published:** March 6, 2023 **Author:** John Nash **Content:** As a retail consumer, your proximity to a store and the price of a product used to be the primary value drivers that determined your loyalty to a brand. As we explored in an [earlier blog post](https://www.redpointglobal.com/blog/the-retailer-roadmap-for-personalizing-cx-while-reducing-costs/), those long-standing value drivers have given way to a desire for a unique connection with a retailer reflected by a personalized customer experience that demonstrates a deep understanding of you as a consumer. In a [2022 Dynata survey](https://www.redpointglobal.com/press-releases/74-percent-of-consumers-believe-brand-loyalty-is-about-feeling-understood-and-valued-not-discounts-and-loyalty-perks/) on brand loyalty, 74 percent of consumers said that feeling valued and understood by a brand was the key component in brand loyalty. Furthermore, 64 percent of consumers said they would rather purchase a product from a brand that knows them, with 34 percent claiming they would spend more money on the product to do so. The previous blog framed the emerging value drivers in terms of how producing that value (i.e., one-to-one personalization) leads to cost-efficiency benefits for digitally mature retailers, with their maturity level distinguished from laggards according to the [Accenture retailer maturity curve](https://www.accenture.com/us-en/insights/retail/store-tomorrow-future). ## **Generate Revenue Via Advanced Personalization** The focus in this follow-up blog will be on how the delivery of a personalized customer experience (CX) generates new revenue. Accenture defines digitally mature retailers as those who have aligned people, processes and technology around a customer-centric strategy as the core business priority. They have paired analytics and augmented third-party data with advanced technologies to intelligently orchestrate customer journeys for individual customers. > 74 percent of consumers said that feeling valued and understood by a brand was the key component in brand loyalty. There are four primary ways to generate revenue through one-to-one personalization: - Find the right customers - Increase purchase size/frequency - Create meaningful interactions - Keep customers loyal ### *Find the Right Customers* One characteristic of digitally mature retailers is that they understand the tastes, wants and behaviors of individual customers – not just demographic and behavioral cohorts – and they leverage this knowledge by taking predictive actions at the individual customer level at exactly the right time during digital engagements. A personalized website experience for a first-time visitor is one way digitally mature retailers drive revenue through increased affinity and conversions. When every behavior, preference and action is included in a Customer 360 or [Golden Record](https://www.redpointglobal.com/cdp/) that is updated in real time, retailers have the foundation for understanding every customer on an anonymous to known journey. [Intelligently orchestrating](https://www.redpointglobal.com/orchestration) that journey based on the actionable insights of a Golden Record is the key to first-touch personalization that demonstrates the personal understanding that aligns with customer expectations. And because a Golden Record is created using persistent keys, the unified profile helps build a deeper understanding of a customer over time. By leveraging existing profiles to build lookalike models for ad segmentation and personalization, digitally mature retailers discover audiences that capture what’s important and meaningful to an individual customer. ### *Increase Purchase Size/Frequency* Demonstrating a deep understanding of a customer increases purchase size and/or frequency because a brand that leverages a Golden Record is always prepared to deliver a [next-best action](https://www.redpointglobal.com/next-best-action/) across an omnichannel journey, in real time. Hyper-relevant offers maximize cross-sell and upsell opportunities. Brands personalize content across channels based on expressed and perceived customer preferences; across every touchpoint, brands engage with content that matters to the customer *in the moment*. In addition to providing personalized next-best actions in real time across an omnichannel journey, brands utilizing a Golden Record also reduce negative behaviors (e.g., cart abandonment) by using context and cadence as cues for [triggered events](https://www.redpointglobal.com/blog/triggered-actions-and-an-enhanced-cx-relevance-throughout-a-customer-journey/). There are many reasons for a cart abandonment, in other words, and knowing everything there is to know about a customer through a Golden Record allows a brand to respond to each one with the most relevant action. ### *Create Meaningful Interactions* An important distinction between a personalized experience on one channel vs. true omnichannel personalization in real time is that the latter drives revenue opportunities by creating an end-to-end frictionless experience. A seamless buy online, pick-up in-store (BOPIS) experience, or curbside pickup, are perfect examples of meaningful, omnichannel interactions. Value is created by serving customers relevant content in real time in the cadence of the journey. In a BOPIS or curbside pickup experience, real-time relevance might manifest itself with a personalized recommendation in the context of the journey, such as an SMS when the customer checks in with an offer on a product complementary to their recent purchase. ### *Keep Customers Loyal* There is a direct link between customer lifetime value (CLV) and revenue. One study, for example, found that personalization lifts revenue by [10 percent or more](https://www.redpointglobal.com/blog/to-optimize-revenue-growth-tune-in-to-customer-lifetime-value/) and delivers up to eight time the return on marketing spend. Contextually personalized offers such as open-time email, location-based offers and targeted advertising drive revenue growth by incentivizing return visits and purchases through hyper-relevant interactions at the moment of engagement. Customers at the receiving end of omnichannel personalization understand that a brand values them as an individual customer, not solely on a transactional basis. Mirroring the results of the Dynata survey that link brand loyalty to feeling understood and valued; [Gartner estimates](https://www.cmswire.com/customer-experience/how-personalized-customer-experience-delivers-brand-loyalty/) that a personalized CX drives 66 percent of customer loyalty, more than price and brand *combined*. The financial rewards for digitally mature retailers that meet or exceed customer expectations for an omnichannel, personalized CX are undisputable. Cost avoidance and revenue growth are both achievable for retailers that recognize there are new ways to progress on the maturity curve while delivering the type of personalization that consumers demand. Enterprise alignment around a customer-centric strategy as the core priority for the business delivers long-term loyalty and customer value over time. **Blog categories:** Retail --- ### [Customer Data Management: CDP vs. Data Readiness – What Brands Get Wrong](https://www.redpointglobal.com/blog/customer-data-management-cdp-vs-data-readiness-what-brands-get-wrong/) **Published:** January 13, 2026 **Author:** Steve Zisk **Content:** “Customer data management” is one of the most overloaded terms in digital transformation. For some, it means creating a Customer 360. For others, it refers to identity resolution, data pipelines, or campaign activation. And because the term is so broad, brands often equate it with the capabilities of their customer data platform (CDP). But in the age of AI, that narrow definition no longer holds. Customer data management must expand beyond what a CDP can do if enterprises want data that is accurate, complete, governed, and [fit for purpose across every use case](https://www.redpointglobal.com/data-the-defining-difference/), not just marketing. This is where data readiness comes in as a modern, enterprise-level interpretation of customer data management that addresses the realities of today’s data landscape. ## **What Customer Data Management Means Inside a CDP** For the last decade, CDPs have shaped the industry’s understanding of customer data management. In the CDP model, customer data management typically includes unifying data from multiple channels to create a single view of the customer, known as a [golden record or Customer 360](https://www.redpointglobal.com/profile-unification/). In this somewhat limited framing, customer data management followed the traditional linear “data, insights, action” process. As in, compile the data, gather some insights and take action based on those insights. But the activities of getting data, building insights, and taking action – as embodied in a CDP – don’t reveal much about the underlying purpose of customer data management. Getting the data and getting the data *right* are not the same thing. Building insights, too, doesn’t encompass making sure the data will fit for its intended purpose. The same holds true for taking action, which for a CDP generally means to activate data out to all CX channels. But this mindset generally limits “action” to marketing use cases, ignoring the need for clean, accurate, and timely customer data across all parts of the business. In short, while a CDP-driven view is useful, it is also limited in that it narrows customer data management to a fixed set of endpoints (typically marketing), a static approach to insights, and use cases that are tied almost exclusively to engagement and acquisition. But most importantly, this view assumes that data is already correct, complete, timely, and actionable – that it is ready for use. The reality is different. ## **Why the CDP View of Customer Data Management Falls Short Today** CDPs were not built to solve the rising complexity of customer data. First, they don’t address AI-specific data requirements. AI requires continuously updated, high-quality data that is both detailed (accurate and complete down to the individual attribute level) and contextually grounded (accurate and explicit metadata for meaning, relevance, and compliance). CDPs typically prepare data for *activation*, not for machine learning, modeling, or real-time decisioning. Second, it is not just marketing teams that need trustworthy customer data. Finance, service, product, operations – all require data readiness to make data right and fit for purpose for their unique needs. Third, the practice of building insights is not the same as preparing data to be ready for its intended purpose. Dashboards, ML models, and AI pipelines all demand different forms of preparation, cleansing, and standardization that CDPs do not provide. In short, limiting customer data management to a CDP frames it as a technology feature, not as an enterprise discipline. ## **Data Readiness: The Modern Interpretation of Customer Data Management** Data readiness takes a much broader view of customer data. Instead of a narrow scope with fixed endpoints, data readiness as a subset of customer data management looks at data more as a product. As such, it has to be [right and fit for purpose](https://www.redpointglobal.com/data-the-defining-difference/) for any possible use case. Certainly marketing and CX, but also AI, customer support and service, product management, lifetime value modeling, and preference management just to name a few. By opening the perspective, data readiness treats customer data as a valuable enterprise asset. It reframes the conversation around the idea of data as a product, engineered intentionally for reliable consumption across the enterprise. Data readiness focuses on making sure that customer data is complete, accurate, timely, actionable, trusted, and compliant. Data becomes accessible to any system and fit for every possible enterprise use case. ## **Expand the Scope of Customer Data Management** Data readiness is an ongoing process that recognizes the need to make sure that data reflects a current understanding of a customer. As such, every consumer (household, business, entity, model, decision engine, etc.) gets data tailored to its specific needs. Furthermore, preference, consent, identity and data lineage are managed at the foundational layer, never downstream. Data readiness better aligns with the original idea behind “customer data management” before it was reduced to a CDP feature set. With customer expectations rising, AI workloads expanding, and data volumes exploding, enterprises need customer data that is reliable, consistent, and immediately usable wherever it’s consumed. It is no longer tenable to treat customer data management as a CDP function. Data readiness recognizes customer data as a strategic asset, providing every system and team with data it can trust. **Blog categories:** Customer Data Platform, Data Quality, Data Readiness **Blog tags:** customer data platform, Data quality, Data readiness --- ### [Gift Wrap a Personalized CX for Consumers this Holiday Shopping Season](https://www.redpointglobal.com/blog/gift-wrap-a-personalized-cx-for-consumers-this-holiday-shopping-season/) **Published:** September 14, 2021 **Author:** Redpoint Global **Content:** While there is still about a week to go until the official start of fall, it is never too early to start thinking about holiday shopping. With the approaching change of season a tipping point for many to start thinking about the holidays, Redpoint set out to find out what is top of mind for consumers. In a [survey of more than 1,000 U.S. consumers conducted by Dynata](https://www.redpointglobal.com/press-releases/80-of-consumers-more-likely-to-shop-with-brands-that-show-they-understand-them/), new research found that a personalized omnichannel customer experience is high on consumers’ wish list. The overwhelming majority of consumers (80 percent) agree that they are more likely to shop with brands that show them they understand their needs by sending relevant, personalized offers this holiday season. Because dynamic customer journeys are now the norm, with consumers interacting with brands across multiple channels and engagement touchpoints, relevant personalized offers must be understood to mean that a brand possess a deep understanding of individual customers. A deep understanding, in turn, means knowing everything there is to know about a customer – preferences, behaviors, devices, household status, social footprint, transactions, etc. Importantly, customers expect this deep, personal understanding to be consistent across channels. In the survey, 78 percent of respondents reported being frustrated when a retailer’s communications and marketing messages are inconsistent depending on the channel they visit (in-store, online, social, call center, app, etc.). Survey findings also validated that dynamic customer journeys are indeed in fashion this holiday shopping season. Only small percentages of consumers said they will shop exclusively online or in-store this season (8 percent and 16 percent, respectively). Conversely, 27 percent of consumers plan to shop equally in-store and online, and 29 percent said they will conduct online research followed by in-store evaluation and purchase. ## **Unwrapping the True Meaning of Personalization** What does the research portend for brands aiming to deliver consistent messaging in line with consumer expectations? Importantly, there can be no data siloes between physical and digital channels. Consider, for example, the fallout if a team responsible for website personalization makes a personalized product recommendation for a known customer – based on an online session – when there is no integration with the physical store. If the consumer is one of the nearly one-third of consumers who said they will conduct online research followed by an in-store purchase, they may be frustrated if the recommended product isn’t available at any store in their vicinity. If the brand knows where a customer lives, and knows the customer frequently starts a journey online but completes it in-store, recommending a product the consumer cannot easily purchase with a quick trip to the nearest store will create friction in the customer journey. (Consumers ranked out-of-stock items as their No. 1 frustration when holiday shopping.) Alternatively, let’s look at the type of personalized experience (using a product recommendation engine as an example) that is possible with the deep, personal understanding that is reflected by consistent messaging across channels. Perhaps an online session starts from an unknown device. Yet through [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) capabilities that combine probabilistic and deterministic matching, a brand knows almost instantly the household dynamics from where the device is being used. Based on the session activity that is consistent with a member of the household, the same matching capabilities let the brand know who is using the device. Now it is a known customer journey. If, instead of data siloes, the website personalization team has access to an instantaneously updated, unified customer profile, it will know the customer recently called the contact center to inquire about a recently discontinued product. With automated machine learning, [intelligent orchestration](https://www.redpointglobal.com/orchestration) and a real-time decisioning engine running behind the scenes, the recommendation engine generates products that are hyper-personalized for the individual consumer. This is a key distinction between token personalization and the deep, personal understanding that customers expect – and respond to. In the former, a product recommendation engine may recommend a product that customers who bought the discontinued product also bought. Or, because the customer is a woman between 35-49, the recommendation engine offers products that women in that broad segment generally favor. Advanced personalization through [automated machine learning](https://www.redpointglobal.com/machine-learning) eliminates the need to rely on look-alike audiences, assumptions or other arbitrary associations. Instead of static rules, next-best actions – in this case real-time product recommendations – are based only on what the data say is important. A hyper-personalized product recommendation might be scarves that match color choices and styles expressed in a loyalty survey, or a selection of limited-edition kicks for someone identified as a sneakerhead. The living, breathing, real-time unified customer profile that is unique to each individual – [a golden record](https://www.redpointglobal.com/single-customer-view/) – lets marketers or any business user know why a specific customer is associated with a certain audience cluster. When rules are derived from the data – and not hard-coded – marketers are able to orchestrate next-best actions to customers with minimal presumptions. Also in line with customer expectations is the fact that a recommendation – or any personalized experience – is delivered in the cadence of the customer, which means that it is in sync with the dynamic nature of a customer journey. Real time – whether seconds, minutes, hours or days – is defined by how and when a customer chooses to engage with a brand, which makes every action appear perfectly timed with an individual customer journey. ## **Paying More for a Personalized Experience** Additional survey questions polled consumers about privacy concerns, the impact of global supply chain challenges and shopping preferences. As for preferences, 60 percent of consumers said that they will use a buy online, pick-up in-store model (BOPIS) this holiday season, showing again that combining the digital and physical shopping experiences is becoming more a rule than an exception. One interesting finding from the survey gets to the heart of what the possession of a deep, personal understanding of the individual customer really means to retailers from a monetary standpoint. Two-thirds of customers (66 percent) said they expect to pay more for gifts this holiday season, and 37 percent of those consumers have budgeted for an expected increase. It stands to reason, then, that if consumers are ready and willing to pay more they will be even more inclined to divert that business to brands that show they understand their needs. A bountiful holiday for retailers, then, depends on delivering the level of personalization that customers have come to expect. An omnichannel customer experience starts with knowing everything there is to know about a customer, and an understanding that from the customer’s perspective a holistic experience feels as if they’re being communicated to with one voice – irrespective of channel or engagement touchpoint. That is holiday magic. **Blog categories:** Data Quality, Identity Resolution, Real-Time Personalization, Segmentation & Activation --- ### [Future-Proof Customer Experience with an Omnichannel CX Platform](https://www.redpointglobal.com/blog/future-proof-customer-experience-with-an-omnichannel-cx-platform/) **Published:** October 20, 2021 **Author:** Redpoint Global **Content:** When businesses first started taking note of and prioritizing “customer experience,” it was primarily viewed as a marketing initiative. In this sense, experience was largely attached to a channel or touchpoint; experience mattered, as it were, mainly so far as it advanced marketers’ goals to drive revenue though a channel. Just think about the terms “marketing campaign” or “email campaign.” What comes to mind? The customer? Or a brand trying to entice a customer to take a certain action? The problem with attaching customer experience to marketing is that customers do not view a relationship with a brand through the same prism. Rather, to the customer an overall experience spans all stages of doing business with a company, organization or brand. A customer’s perception of a consistent brand value is what defines the relationship. A superlative experience, for the customer, trusts that the experience consistently reflects the brand value in a way that is relevant and meaningful to the customer in the moment. By accepting that customer experience must be on the customer’s terms, it should become obvious that the logic, rules and personalization aspects for delivering that experience must be centralized around a single view of the customer, and not locked into channels or departments. Think of a traditional car-buying experience. When a customer walks onto a lot after doing extensive online research, how often does the dealership even know about the online activity, let alone use it to create a relevant, personalized experience? Yet even that concept of using a combination of physical and digital channels barely touches a true omnichannel customer experience, because it ignores the fact that the world and customers are changing. For example, think of what a car driving experience might look like in five or 10 years with more advances in driverless technology. Will we be buying or leasing cars every few years, parking them in garages and maintaining them on our own dime? Or will the experience consist of a manufacturer sending a vehicle to our home every morning to drive us to work, with the entire interior personalized to our unique specifications? Our favorite news station comes over the speakers, our seat is in the right position and a coffee is fixed to our taste – hot during cold weather, iced in the summer, and with pumpkin spice added at the first sign of autumn. A true omnichannel customer experience is irrespective of channel or context. And it’s not future state, it is all possible now. In any industry where there is a consumer-brand relationship – retail, banking, healthcare, travel – there is an opportunity to engineer a customer experience that is defined, managed and triggered at any point in the enterprise. In a true omnichannel customer experience, every conceivable way a customer interacts with a brand must be consistent with a brand’s values at all times. ## **A Single View, A Single Experience** By definition, a consistent omnichannel customer experience must be engineered, managed and delivered via a single platform. There are a number of roles required to make this happen – which I will refer to as OCX Imagineers – from marketing to data science to operations and anyone that essentially creates new experiences or has to make them work. If this work is based on anything less than a full platform it will result in a fractured experience pockmarked with inconsistencies. An omnichannel customer experience platform (OCX) is where OCX Imagineers bring data, insight and action together to design and execute a seamless experience that brooks no data, department or process siloes. Organized around a single view of the customer, the platform is the brain of the central nervous system that triggers a hyper-relevant communication sequence that is always personalized against the single version of truth of the person. The automotive example clearly illustrates that an OCX is not limited to marketing use cases, although marketing use cases – onboarding, retention, next-best offer, etc. – abound. It also shows that brand interactions transcend product. Products, channels and people change constantly. An omnichannel customer experience platform, because it is controlled by a single brain acting on behalf of a single view of the customer, is positioned to adapt to any change based on real-time signals. That ability to adapt based on any update to the single view of the customer – and the context surrounding the customer – is the key difference between organizing an experience around a product, a channel or a department vs. organizing it around the customer. ## **A Consistent Experience & An Omnichannel CX Platform** Redpoint is architected as an omnichannel customer experience platform-as-a-service. Just as a typical cloud PaaS environment is for developers to build custom applications, Redpoint offers CX Imagineers a single platform with all the tools and components that are needed to design, build and execute a brand experience, personalized around a real-time single view of each customer. It is for enterprise brands to deliver customer experiences that bridge into any quickly changing, unpredictable future where brand experience transcends a channel-specific context. This omnichannel approach advances the concept of a [customer data platform](https://www.redpointglobal.com/customer-data-platform) (CDP) which has traditionally been defined as a unified customer database accessible to other systems. Accessible for what purpose and in what form, are the next logical questions? An honest accounting of the underlying purpose, and an examination of the capabilities of such a system, will expose the vast difference between the solutions and vendors that play in the CDP space, and an omnichannel experience platform. How, for example, does the so-called “unified database” handle identity resolution? Is it outsourced using someone else’s reference files (essentially matching to an external key)? If so, that alone renders it useless for meeting the expectation for a consistent and continual experience that transcends channels or context. The reason, of course, is that anyone using the database to design an experience is basing decisions on static data that does not accurately reflect a single view of the customer or data. Worse, there is little confidence in an identity match when there is a financial incentive for whoever is providing the reference file to over-match. Reference files, pre-existing identity graphs or data spines do not provide the depth of data essential for creating a consistent experience that spans every stage of doing business with a company, in real time. They provide a point-in-time match where keys change with every episodic match, and there is absolutely no longitudinal view of the customer from inception to the complete lifecycle across the brand. Reference files were never intended to provide the enterprise with the essential historical view of how customers change, or how the relationship with the brand changes. And, as we’ve seen, adapting to constant change is perhaps the key element for ensuring consistency of experience that accurately reflects brand value over time. ## **Advanced Identity Resolution & Value Exchange** Redpoint’s approach to [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) is fundamentally different, and the key to fostering a consumer-brand relationship built on trust. At the heart of the relationship is an exchange of value – the consumer receives a relevant experience across every stage of doing business with an organization. In turn, the business, brand or organization receives first-party data which is then used to enhance the virtuous cycle of data, insight and action that furthers the relationship. The gold standard of identity resolution is to have a customer willingly provide personal data for this exchange of value. If the underlying purpose of having a unified customer database accessible to other systems is to create a one-off experience for marketers to sell a product via push marketing, then maybe a point-in-time match is acceptable. But that will never create lasting value. This is the reason why the Redpoint approach to identity resolution uses persistent keys and in-built probabilistic algorithms using first-party data only to resolve individual identities from multiple sources, correct and standardize the data, accurately define households and use probabilistic methods to transitively improve data quality, define extended relationship groups (grandparent, parents, kids, cousins, etc.) and release all of the trapped value in the data. With first-party data processing at the point of ingestion, applied immediately to a customer record, the result is supreme confidence that a record consists of the freshest, most accurate and up-to-date data. Users know the record is up-to-date because customers themselves are handing over the data. When customers trust that their data is used in accordance with their stated preferences and to create an omnichannel customer experience, the virtuous cycle continues. Customer validation of their own data is THE Gold Standard for identity resolution, not reference files. Follow this space to learn more about Redpoint’s core capabilities surrounding data, insight and action that make it the only cloud-native, customer experience platform-as-a-service that supports a consistently personalized experience irrespective of channel or context, and one that is perpetually consistent with a brand’s values. **Blog categories:** Data Quality, Identity Resolution, Master Data Management, Segmentation & Activation --- ### [First-Party Customer Data: The Healthy Alternative to Cookies](https://www.redpointglobal.com/blog/first-party-customer-data-the-healthy-alternative-to-cookies/) **Published:** May 27, 2021 **Author:** Mike Ferguson **Content:** The value and importance of first-party customer data has always been key to smart marketers, and recently the rest of the pack has been catching on, in large part because of the phasing out of the third-party cookie. Without the tracking device that follows prospects and their devices around the internet – which brands use to serve up targeted advertisements – companies that rely on it to infer knowledge about a customer are left wondering how to replace it. Google has said Chrome will end support for third-party cookies in 2022, joining Firefox and Safari, which started blocking them by default in 2019. The erosion of consumer trust and the risks of data collection were cited in a recent [Google blog](https://blog.google/products/ads-commerce/a-more-privacy-first-web/) as two primary reasons for the change. Consumers, quite simply, are annoyed by the privacy intrusion, the ‘Big Brother’ aspect of being served an impression only because a device you may have used visited a certain website. The “creep factor” aside, consumers are turned off by the often irrelevant content, which happens for a variety of reasons. It is easy for the badly targeted behavioral segmentation to associate cookie profiles with inappropriate products. An advertisement or impression may be served for a product a customer has already purchased. Or a customer may mistakenly visit a website, which is then compounded by a seemingly endless string of irrelevant ads. The shortcomings of the third-party cookie accentuate the contrast with using first-party customer data to build a long-lasting (read: loyal) relationship with a customer based on trust and a personalized, relevant experience at the precise moment of every interaction with a brand, across every channel. But because so many brands are invested in using tracking cookies as a staple of digital advertising campaigns, they’ve lost sight of the power of using first-party customer data to shape a personalized customer experience. They question how relying on first-party data can make up for an inability to track an online experience spanning multiple websites. ## **First Party vs. Third Party** To highlight the difference between using a third-party cookie and first-party customer data to craft a personalized customer experience, I like to think of a customer in a neighborhood hardware store. A third-party cookie equivalent would be the shopkeeper perhaps a week or two earlier having surreptitiously followed the customer into a competitor’s store and watched as they browsed the aisles. Based on that stealth encounter, the shopkeeper may think they have an idea of what the customer might be looking for – even though it’s really little more than a shot in the dark. But even if the shopkeeper does offer the right product, the customer is a little off-put wondering how the shopkeeper acquired the information. However, an interaction that is derived from first-party customer data would be the shopkeeper welcoming the customer, and intuitively knowing why the customer came in because there’s a history – and not just transactional. The shopkeeper knows what projects the customer is working on, what tools might need to be replaced, and which projects are upcoming. The unfolding interaction feels natural and unforced, flowing from a personal understanding developed over time. In exchange for receiving such a personalized experience, the customer willingly offers more information – here’s what I might be starting on next, I’m curious about wiring lights in a drop ceiling, etc. – because he or she trusts the shopkeeper always has the right answer. ## **What is Zero-Party (Declared) Data?** First-party data isn’t to be confused with what some refer to as zero party data, a distinction that can be understood as data that is inferred (first-party) vs. declared (zero-party). In our hardware store, inferred data is collected based on a customer’s behaviors, preferences or transactions. Perhaps prior to coming into the store, the customer browsed the website and searched for a particular product, leading the shopkeeper to infer customer intent. Or the shopkeeper sees that once a year the customer purchases deck stain, and infers that they have a natural wood deck that might need to be treated for termites. Maybe the customer would be interested in a discount on a pest control solution. Declared data, by contrast, would be if the customer actively volunteers information – they sign up for a class on electrical wiring, or they download a pamphlet on wood rot. If they’re loyal to the neighborhood store, the customer offers their contact information so the shopkeeper can text or call when a certain product is in stock (rather than buy it from a competitor). ## **The Give and the Get** Zero-party data or declared data can be thought of as the culmination of what a customer may see as a value exchange with a trusted brand. The customer is willing to sign up for that newsletter, or provide contact information, because they know that in return they will receive relevant information that will contribute to an exceptional customer experience. The experience, not necessarily the product itself, keeps the customer coming back for more. If price was the lone consideration, the customer may tolerate being bombarded with endless irrelevant digital advertisements to find the cheapest product. The internet tends to commoditize all consumer goods, as there’s seemingly always someone, somewhere, willing to manufacture and sell a product for less. But that’s not what customers want, which brings us back to why third-party cookies are being phased out to begin with. In a recent Dynata survey, commissioned by Redpoint, [70 percent of customers](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/) said that they will only shop with a brand that understands them personally. The third-party cookie was never intended to achieve a deep personal understanding, which is why using it as a poor approximation for first-party customer data often creates a fractured, inconsistent and irrelevant customer experience. There is very little value, if any, to the customer. The calculation brands must make is whether to find alternatives to the third-party cookie to drive an advert impression (and some of the vendors of those impressions are already launching platforms) – versus the value of forming a relationship with a customer derived from a personal understanding. Only one of the two will drive customer loyalty, lifetime value and a differentiated customer experience that is proven to drive value for the brand and the customer. **Blog categories:** 1:1 Personalization, Anonymous to Known, Journey Orchestration --- ### [First-Party Customer Data Delivers Value - and a Personalized Customer Experience (CX)](https://www.redpointglobal.com/blog/first-party-customer-data-delivers-value-and-a-personalized-customer-experience-cx/) **Published:** January 29, 2021 **Author:** Steve Zisk **Content:** One unexpected result of the demise of the third-party cookie, which we [covered in an earlier blog](https://www.redpointglobal.com/blog/crocodile-tears-do-not-lament-the-extinction-of-the-third-party-cookie/), is a seemingly greater appreciation for the power of first-party customer data. After searching far and wide for a magic bullet to power a personalized customer experience, marketers have found that a solution has been close at hand all along. In recognizing first-party customer data as powerful currency to create personalized customer experiences, all that’s left is for marketers to understand how to optimize its use. First, what is first-party customer data and how to acquire it? There are a few types. There is first-party, cookie-based [customer data](https://www.redpointglobal.com/blog/first-party-customer-data-delivers-value-and-a-personalized-cx/), which in contrast to third-party data is data collected from online session on a company’s own website – pages and content viewed, time on page, clicks, items placed in a shopping cart, etc. This can be known (a person has logged in or otherwise identified themselves) or anonymous (affiliated with the device). Either way, this data help marketers personalize a website experience by populating forms, saving credit card information and search history, etc. Detailed, behavioral first-party cookie-based data tells marketers a great deal about what a customer is doing and what they’re thinking about, which are important clues for capturing customer intent in the context of a greater customer journey. Those clues solve more of the puzzle when session data is correlated with other forms of first-party data. Every point-of-sale system, e-commerce site, social site, CRM system or mobile app also collects first-party customer data, as do low-tech sources such as forms, letters and physical mail. Every interaction with a customer from a product evaluation phase through a buying phase is rife with potential sources of first-party data. Finally, first-party customer data includes data that a customer volunteers, otherwise known as “zero-party” data, which includes filling out and returning a product registration form, completing a survey, commenting on Facebook or another social site, participating in a loyalty program, etc. ## **First-Party Data and Contextual Interactions** Before using first-party customer data to optimize a personalized customer experience, it’s important to understand that real insight only comes from aggregating all the various sources of information to create a unified customer profile. A unified customer profile, also known as a golden record, will include a customer’s likes, dislikes, preferences and behaviors – on multiple devices and for any ID associated with the customer – over time. A [golden record](https://www.redpointglobal.com/single-customer-view/) is the key for marketers to use the information to present a relevant, personalized experience every time the customer interacts with a brand. Exactly how personalization is achieved through first-party customer data depends on possessing a keen understanding of the intent and current engagement of the customer. If a customer is operating anonymously on your website, for instance, first-party customer data will be used in a contextual manner – responding to the signals a customer (or the device associated with a customer) is providing in the moment to turn around and provide the customer with a better experience. An example of this is responding appropriately to a search, or otherwise recognizing affinities and choices a customer makes throughout an online session. Conversely, a marketer may have a deeper and more detailed picture of who the customer is – they may be in the mobile app, they’re logged in, they’re a repeat customer on the e-commerce site, etc. In that case, the context shifts to proactively enhancing or shaping the experience rather than reacting to real-time signals, showing products the customer has expressed an interest in, or letting the customer know how many loyalty points they’ve accrued and displaying items in that pricing tier. ## **Why Balance Matters with First-Party Data** Striking the proper balance is an important consideration for using first-party customer data. An appropriate engagement that delights a customer does not necessarily mean bombarding the customer with personalized offers just because first-party customer data reveals a customer’s preferences. Perhaps the customer is not in the buying phase of a journey. Or maybe you’ve sent two offers in the past five days and are reluctant to push your luck with a third. First-party customer data must also be mined to gauge customer intent, which is where rules for frequency, recency and customer fatigue come into play. [Automated machine learning](https://www.redpointglobal.com/machine-learning) is important to help marketers define and follow certain rules for delivering personalized customer experiences at scale rather than rely on educated guesses or intuition for how a customer – or thousands of customers – may respond to an offer, content or any action derived from use of a golden record. ## **First-Party Customer Data: Not Just for Marketing** A final consideration for how to use first-party customer data is its use beyond traditional marketing channels. A customer may appear on a support site, for example, looking for help setting up a product. Or perhaps they’re looking to join a community discussion about your company’s products or services. Customers consider every interaction with a brand as part of one cohesive experience; they do not care about the distinction between, for example, shopping online or talking to a call center agent. To meet this expectation of consistent experiences after a sale, marketers must think of first-party data beyond its importance in customer acquisition and other “pure marketing” uses. Rather, first-party data or [zero party data](https://www.redpointglobal.com/learn/zero-party-data) is often part of a value exchange with a customer; a customer who provides first-party data generally does so more than simply to receive offers, they’re sharing information about themselves in return for a better experience with the brand – on any channel and for any interaction. By treating first-party data as the basis for a relationship with a customer that’s built on trust, marketers reinforce the value of a personalized experience. ## **First-Party Customer Data and a Value Exchange** From the customer’s perspective, the value extends to a deepening trust that the brand will safeguard their personal information, protect their privacy, and honor their preferences. Marketers must be transparent about how their organization uses and shares [customer data](https://www.redpointglobal.com/blog/first-party-customer-data-delivers-value-and-a-personalized-cx/). In this sense, a personalized customer experience that reduces friction throughout the customer journey is just one part of the bargain. With a deepening trust, of course, customers are then willing to share even more first-party data. Used right, a wealth of first-party customer data allows a brand to provide a relevant experience on any channel, in real time, that is always in the cadence of the customer journey. Every interaction with a customer is an opportunity for a brand to demonstrate that it values the relationship, as demonstrated by the experience it delivers in the moment of interaction. The value extends beyond the personalization itself. It represents the brand, its products and services and the brand’s own values in terms of how it thinks about its customers. A personalized customer experience shows that a brand cares about managing a customer’s expectations through the collection of first-party customer data. The brand’s bargain, in return, is improved [customer lifetime value (CLV)](https://www.redpointglobal.com/blog/to-optimize-revenue-growth-tune-in-to-customer-lifetime-value/) from customers who remain loyal to a known, trusted partner who seems to know exactly what they want, when they want it, and exhibit respect for them as an individual with unique traits. ## **Related Content** [Combat Message Fatigue with Personalization](https://www.redpointglobal.com/blog/combat-message-fatigue-with-personalization/) [The Demise of the Third-Party Cookie Opens New Means of Engagement](https://www.redpointglobal.com/blog/the-demise-of-the-third-party-cookie-opens-new-methods-of-engagement/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Anonymous to Known, Journey Orchestration, Real-Time Personalization --- ### [Data Privacy, Data Quality, and Data Governance: Experts Weigh In](https://www.redpointglobal.com/blog/data-privacy-data-quality-and-data-governance-experts-weigh-in/) **Published:** January 24, 2020 **Author:** Redpoint Global **Content:** *The increasing importance of data privacy for customer-focused companies is also putting a spotlight on data quality and data governance, raising new questions about the value of data initiatives and who, ultimately, is responsible for their success.* *Redpoint’s Steve Zisk, Senior Product Marketing Manger, recently discussed the issue with George Firican, a leading expert on data governance and business intelligence and founder of* [lightsondata.com](https://www.lightsondata.com)*. Their wide-ranging discussion touched on many topics, including the impact of privacy laws, the relationship between marketing and IT, and how to think about data quality as part of comprehensive data governance. Below is a lightly edited transcript of the conversation.* **Steve Zisk:** George, thank you so much for joining me today. **George Firican:** Hi Steve, thank you for having me. **SZ:** As you know, GDPR, CCPA, and other data privacy regulations are making compliance a top priority, and that is certainly true for Redpoint clients. My point of view is that you cannot ensure privacy and compliance without a strong data governance program, with policies and access levels in place to identify data siloes and other potential problem spots. **GF:** You’re certainly right about data privacy being top of mind. To expand on your point, data management knowledge areas extend beyond privacy, security, and data quality to include master data management, metadata management, business intelligence, data warehousing, and so on. Data governance is essential to connect all the dots, if you will. As you mentioned, putting policies and procedures in place, establishing ownership, and defining roles and responsibilities is paramount. They all play important roles in each area of data management practices. **SZ:** Where do you see data quality fitting into that, or does it? Are they two completely different things in an organization? **GF:** First, you can’t do proper and stable [data quality](https://www.redpointglobal.com/automated-data-quality/) without a data governance program and I can get into details why that is. Second, when you start data governance, data quality becomes a byproduct – if you’re chasing privacy and security, you’re automatically going to tackle data quality aspects even if that’s not your core intention. You can’t ensure privacy and security and compliance with CCPA or GDPR if you don’t know, for example, whether a customer resides in Europe or North America. Even if data quality was not a main driver for starting a data governance program, regulatory compliance would be with data quality as an unintended consequence. **SZ:** That’s an interesting way to put it; that data quality really is a component or as you said a byproduct of governance. **GF:** Yes, they go hand-in-hand because data governance – the data governance functions and mandate – will help first to define data quality dimensions, to include identifying roles and responsibilities, assigning stewards to different data elements, and then obviously working with the business and IT to create solutions to resolve various data quality issues. I liken it to an HR department; they provide guidelines and guidance around hiring, firing, compensation, promotions and the like, but then it’s the employee’s manager who relies on the guidelines as well as their own experience and personal interactions with the employee to make informed decisions and implement change. **SZ:** In a data quality initiative, who in your opinion defines what data quality means? If I think about an organization that has 15 different data siloes and multiple disconnected departments, is there someone at the C-level who helps define what data quality really means beyond the basics like address standardization, phone number standardization, etc.? Where do you think that definition has to come from, considering that need for data consolidation? **GF:** Is it a copout to say “It depends on the organization?” That’s the normal consultant answer, but the truth is that it does depend on the data governance program in place, especially if it’s at the organizational level. If a data governance committee is tackling something like customer engagement that would affect the entire enterprise, they’re likely making a collective decision – with different stakeholders from different sides of the business all with a seat at the table. If the program was instead at a department level, specific to small ‘m’ marketing for example, it could be just the marketing business stakeholders making that decision. It’s always the business that ultimately makes the decision, with the IT team then acting on it. **SZ:** It’s interesting you say IT would be acting on it. Do you see [data quality](https://www.redpointglobal.com/automated-data-quality/) as strictly a function of IT? Not the ‘definition’ per se but the actual process. **GF:** You’re right. IT does really come up with those technical solutions; the business mentions “this is why we need to adopt this address standard” for example, and there’s quite a bit of collaboration with IT there as well because IT usually supplies even the business analysis skills. You might have business analysts working with the business to extract these requirements, but in the end it’s the business voicing the need to come up with whatever standard they want, and then it’s IT that develops the unique tools to get that data cleaned and audited and to make sure that no bad data is occurring again and so forth. Ideally there will then be someone from the business who takes the ownership, stewardship aspect of it and sometimes can run those reports or whenever they’re getting exception reports built by IT they’re acting on it. **SZ:** We work with a lot of marketing departments, and see a constant struggle with IT feeling like they don’t have that seat at the table, and that they’re not as vested in an engagement as they deserve. My opinion is that marketing needs to do a better job of selling their vision to the organization as a whole. **GF:** What I’ve noticed is that IT is seen more as a service provider. I think that’s sort of the classical way of thinking about it, but if they’re trying to be seen more as a collaborator in whatever the company is trying to achieve I think they should be engaged earlier on, be more of a sounding board, and be in a position to raise those flags and risks for different scenarios when it comes to technology and the data management aspect. **SZ:** It seems that IT and the data governance program is critical, because while different departments have siloes of data, IT has a lot of institutional knowledge about where data sits, who accesses it and why. I see that as a critical component, but on data quality – outside of the data quality basics such as address standardization, matching, and things like that, do you have any other thoughts on how you define data quality? **GF:** A sustainable data quality management program will not just deal with defining what needs to be the standard, but also – and there’s a bit of an overlap with other data management areas, IT, data governance, data stewardship, etc. – one needs to look at it really first from that whole ecosystem, so identify all those data sources that you mentioned, systems, and so forth – identify the data owners, the business rules, figure out the prevention methods, risks, audits, all of it. Ideally you do that whole analysis to find out who your stakeholders are so you can keep them in front of what you’re planning to do, what you’re doing, and what you’ve done. It’s about getting that continuity because a lot of data quality initiatives or projects have an end date. Fixing a problem, or fixing the data, is not the same as fixing the cause of the problem. **SZ:** So there’s the tactical aspect of certain things, but also process and procedures to help you identify how you got in that state to begin with. George, this has been a very interesting conversation. Thank you again for joining me to discuss this important topic. **GF:** It’s been my pleasure, Steve. Thank you for having me. *As Senior Product Marketing Manager for Redpoint Global, Steve Zisk leverages more than 35 years of expertise in software engineering and product marketing. At Redpoint, Steve develops messaging and marketplace positioning for Redpoint’s customer engagement platforms. Connect with Steve on [LinkedIn](https://www.linkedin.com/in/stephen-zisk-a210941/) and [Twitter](https://www.twitter.com/szisk).* *George Firican is a Data Governance and Data Management practitioner currently working with The University of British Columbia. As a passionate advocate for the importance of data, he is a frequent conference speaker and YouTuber and has been ranked among Top 35 Global Thought Leaders and Influencers on Digital Disruption and Top 15 on Innovation and Big Data. He advises organizations on how to treat data as an asset, and shares practical takeaways on social media, different industry sites and publications, and is the founder of .* **Blog categories:** Customer Data Platform, Data Governance & Security, Data Management, Data Quality, Master Data Management --- ### [Evolutionary Programming: The Survival of the "Fittest" Data Models](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/) **Published:** August 9, 2019 **Author:** Redpoint Global **Content:** Darwinism, the universally understood “survival of the fittest” evolutionary theory, holds that reproductive success for a species depends on adapting to environmental changes over time. Natural selection weeds out the ill-prepared. Human beings developed opposable thumbs, for instance, just so we’d be able to scroll through an Instagram feed. OK, so I’m not a biologist. But natural selection is a fitting analogy for customer engagement machine learning models that essentially determine winners and losers in the quest for personalized customer experiences that drive revenue. Evolutionary programming – also known as Evolutionary AI – is a Redpoint Automated Machine Learning proprietary process that basically works like natural selection; models that succeed in the environment they’re built for – hyper-personalization of a customer experience – survive to live another day. But whereas natural selection plays out over millions of years, evolutionary programming condenses this to real time. Automated continuous optimization ensures that an unsuccessful model becomes “fit”, if you will, to compete in the battle for driving revenue with personalization at scale. ## **Evolutionary Programming Feeding the Customer Data Beast** Evolutionary programming is fueled by the continual ingestion of customer data from every source – first-party, second-party, and third-party, as well as structured, unstructured, and semi-structured. Simulations perform the “eat or be eaten” function of the jungle. Tuned to deliver a specific metric, a simulation anchors a closed-loop feedback cycle to let marketers know if the metric they’ve chosen – a KPI, ROI, customer lifetime value, etc. – is being met. Like an animal stalking its prey, the simulator strips away anything that is not laser-focused on delivering the chosen metric. It is pure survival instinct at work. Importantly, what’s stripped away in this case is the need to write code for a data processing engine to help gauge a machine learning model’s performance. Building configurable models may be a common core competency, but in-line analytics that provides automated continuous optimization is what differentiates Evolutionary AI from anything else in the market today. It puts the power of AI into the hands of marketers, not data scientists. Evolutionary modeling tactics train, optimize, and automatically update fleets of models tuned to any business objective, such as acquisition, cross-sell, or retention. The modeling environment has capabilities to alter model type and parameters, fitness functions to assess models, and an efficient search mechanism to automatically select the best model – all without human intervention. Automation ensures that marketers are free to align business objectives with the advanced analytic models, leveraging the predictive analytics to deliver dynamic customer journeys in the context and cadence of an individual customer. ## **Eyes are Always on the Prize of Personalization: Evolutionary Modeling** A common misconception of machine learning models is that there is usually no more than a handful of models involved. This misconception took root largely because it takes vast resources to build and re-program models by hand. Evolutionary modeling completely changes this dynamic; Redpoint Automated Machine Learning customers are encouraged to have hundreds of models out in the field. Evolutionary modeling is so powerful that rending a next-best action for a customer in real time at the moment of interaction through a standard channel such as a website or a mobile app is the ground floor of its considerable reach. Complex next-best actions or decisions are more than just possible, they are in fact quite common. For quite a few customers of Redpoint Automated Machine Learning that do have a considerable number of models in the field, a next-best action is returned only after the platform interfaces with several other models to capture, package, and deliver information back to the customer in milliseconds. The platform provides marketers with the tools they need to intuitively access and manage models with a five-step wizard. The system takes marketers through the step-by-step process of re-training and moving models into production. Once set-up for a refresh, for instance, a model will re-train itself on new data on the hour, automatically generating a next-best action if any difference is detected – however insignificant. This is true lights-out modeling that, like the animal stalking prey, never stops doing what it is programmed for – in this case, unearthing any opportunity in the data to enhance a personalized customer experience. This capability has traditionally been beyond the reach of any hands-on approach, which might produce mediocre results but only after considerable expense, resources, and time. Earlier in this space, I wrote a two-part series on AI as a revenue driver. If you have yet to do so, I encourage you to read [Part I](https://www.redpointglobal.com/blog/harness-the-power-of-digital-technology-as-a-revenue-driving-engine/) and [Part II](https://www.redpointglobal.com/blog/the-sky-is-the-limit-with-ai-and-machine-learning-capitalize-on-the-moment-of-interaction/) to understand how evolutionary modeling is a revenue-generating engine and why many data-driven organizations now consider marketing as a mission-critical line of business as the frontline for creating personalized customer experiences. **Blog categories:** AI & Machine Learning, Data Management, Real-Time Personalization --- ### [What's Relevant in Evaluating a CDP: How The Relevancy Group Helps Cut Through the Noise](https://www.redpointglobal.com/blog/whats-relevant-in-evaluating-a-cdp-how-the-relevancy-group-helps-cut-through-the-noise/) **Published:** September 17, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/09/5x2-TheRelevencyGroup_logo-300x129.png)Over the past several years the Customer Data Platform (CDP) marketplace has quickly evolved and CDPs are now carving out a critical space in the marketing technology stack as a repository for the complete, accurate, and up-to-date customer profile, managed by the marketer for quantum improvements in customer engagement. Marketers’ needs have driven market hype, and many companies have started or have migrated into the CDP space in the last 24 months. The rush of [cloud vendors](https://www.redpointglobal.com/blog/what-marketing-clouds-dont-get-about-native-cdp-functionality/) to this space has further muddled the marketplace, with Adobe, Oracle, and Salesforce all announcing product plans to build a CDP or CDP-like capabilities. The August 2019 survey from The Relevancy Group also shows that market maturity is increasing. Some 44 percent of marketers surveyed currently have a CDP and an additional 32 percent are looking to implement one before Q3 2020. This reflects both market maturity (with many marketers recognizing a need and working to meet it) and confusion (with some marketers identifying their CRM, data warehouse, marketing cloud, or other systems as “the CDP”). **Defining the CDP Marketplace** While marketers have started to embrace the need for a CDP, consumers are changing even faster. Today’s continuously connected, online-savvy consumer –trained online, with a mobile phone always in hand – treats as table stakes a level of personalization and consistency that many brands considered impossible only a few years ago. As the sophistication of CDP offerings has increased, the marketer’s ability to deliver on consumer expectations is increasing. However, tangible capabilities and features are often drowned out by the noise of vendor claims that are far beyond their delivered products. To help clarify this market, The Relevancy Group lays out a few key technologies that are vital to the success of a CDP. A sophisticated CDP should be able to join and combine data from every source, create and manage customer profiles, analyze and enrich the customer data, and syndicate and activate audiences. The single view of the customer must also be available in a holistic, integrated, and persistent manner. **Core Features to Evaluate** In this report, The Relevancy Group recognizes the top CDP vendors who can deliver real-time targeting, personalize both anonymous and known user experiences, and deliver content, actions, and offers at the cadence of the consumer and with the preferences of the individual customer journey. To meet these demanding requirements, many different types of first-, second-, and third-party data need to be integrated and incorporated into a holistic customer record. These include traditional sources like CRM data, website behavior, channel response data, offline and online spending, mobile behaviors and a variety of new sources of unstructured data like social sites, sensor data, IoT technologies and more. **Redpoint Global Customer Data Platform** Redpoint Global initiated work in this marketplace in 2006, some six years before the CDP term was coined and a decade before the influx of new players. With the team of architects and business visionaries responsible for building Accenture’s CRM practice, Redpoint had the experience and foresight to build a solution that solved the challenges of siloed customer data, out-of-date decisions, and poorly coordinated customer engagement. Redpoint ranked highly in all categories in last year’s 2018 CDP Guide from The Relevancy Group, and the group’s analysts were impressed by improvements in 2019. A big differentiator for Redpoint was its ability to deploy in any configuration (on-premise, cloud, hybrid) with any type of data (structured and unstructured data, in traditional databases, and files or Big Data and Hadoop sources), a fact also noted by customers. Redpoint’s identity resolution and real-time, inline analytics were highlighted as strong points as well. Technically, Redpoint shines compared to peers when it comes to technical services and customer support as well as product innovation and usability. For 2019, Redpoint Global’s CDP was awarded Gold for Client Satisfaction, Technical Services, and Customer Support and Silver for Product Innovation and Usability. The CDP was also rated best in class for Integrated Customer View Completeness, Integrated Customer View Quality, Implementation Completeness, and Implementation Quality. The Relevancy Group noted, “Many of the customers we spoke with presided over large, sophisticated marketing programs and placed significant reliance in the Redpoint product and services teams to help them drive their core business objectives, both for marketing and, more broadly, customer engagement. The people at Redpoint ‘separate them from the pack’ remarked one customer, and another lauded the technology by pointing out that ‘it is absolutely the only solution in the marketplace that could meet our needs.’ ” *Leaders from The Relevancy Group and Redpoint Global will present a webinar on September 25 that explores the current CDP landscape. [Register today](https://attendee.gotowebinar.com/register/6697061954572780034?source=rpsocial) to discover key use cases being enabled by connected, actionable data, and to learn why the distinct value proposition for a CDP is gaining widespread momentum.* **RELATED ARTICLES** [Building vs. Buying a CDP? Why That’s Not the Only Question You Should be Asking](https://www.redpointglobal.com/blog/building-vs-buying-a-cdp-why-thats-not-the-only-question-you-should-be-asking/) [CDP Myths Debunked: More Than Just Another Data Platform](https://www.redpointglobal.com/blog/myths-debunked/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Data Quality --- ### [Redpoint Global Leaps forward with Usability and Machine Learning Enhancements](https://www.redpointglobal.com/blog/redpoint-global-leaps-forward-with-usability-and-machine-learning-enhancements/) **Published:** August 27, 2020 **Author:** Steve Zisk **Content:** Redpoint Global is pleased to announce several updates to the [rg1](https://www.redpointglobal.com/one-platform/) platform. The updates include enhancements to the Intelligent Orchestration (RPI) and Automated Machine Learning (AML) modules. With an updated user interface, enhanced real-time capabilities and improved workflows for building dynamic customer journeys, the latest release not only makes rg1 easier to use but also more powerful for building and executing relevant cross-channel customer experiences. **Real Time in Omnichannel** Updates to rg1’s real-time capabilities include support for open-time email personalization, dynamic link redirects and improved geo-targeting (along with weather and drive-time targeting). With open-time personalization, businesses can use rg1 to deliver experiences to email recipients when messages are opened, instead of when they are sent. Leveraging the latest available information about a specific person (or other situation data), rg1’s open-time email personalization ensures that businesses deliver contextually relevant email experiences to each and every customer. The ability to target customers based on their location provides marketers with another means to engage customers. Using rg1, a marketer can build a campaign and make real-time decisions based on an individual’s geographic location, the local weather conditions (or future forecast) or the expected drive time from one point to another. A hardware store chain, for example, may use a forecasted snowstorm to promote snowblowers, salt and shovels to a group of nearby customers. ![](https://www.redpointglobal.com/wp-content/uploads/2020/08/6.1-Geo.png) The latest rg1 update also includes a number of behind-the-scenes changes to real-time caching, supporting more client control over configuration and performance. **Smart Assets for Dynamic Customer Journeys** With a new capability called “Smart Assets,” marketers can define and control dynamic elements for both simple offers and complex multi-channel, multi-touch campaigns. Smart Assets let organizations personalize customer inbound and outbound experiences with dynamic offers and messages based on rules and machine learning. Smart Assets offer marketers three core capabilities: 1. Easily select and control all kinds of dynamic assets – images, HTML, links, SMS, and other channel-specific data that make up dynamic offers. 2. Build unified cross-channel, cross-treatment assets, with all variants and rules for using them in one place. 3. Optimize the use of assets with consistent and detailed views of usage and response over time and across campaigns. Smart Assets make it easy to update, publish and control content based on rules and metadata, so messages, offers, and actions can be consistent across channels and treatments while meeting specific design, content, and channel format requirements. **Clustered Audiences and Automated Machine Learning Enhancements** The latest update to rg1 make it easier for marketers to use the machine learning capabilities of the platform, by streamlining model building for segmenting audiences and predictive models. Clustered Audiences is a new feature in RPI that quickly segments any audience (using AML clustering) and allows the marketer to discover hidden similarities and differences within a defined audience. This brings the power of AI to campaign design and extends this power with Insights Dashboards to track audience and cluster changes and responses over time. ![](https://www.redpointglobal.com/wp-content/uploads/2020/08/6.1-AudienceInsightsCluster-1024x619.png) The latest version of AML includes Business Templates allowing marketers to easily build machine-learning models for specific business-goal-based use cases like “Predict Best Message Content” (or channel), “Predict Customer Retention/Attrition”, and Segment Customer Behaviors” (or Demographics). Business Templates simplify model training setup and deployment for marketing use cases while preserving the power of evolutionary model building using an organization’s own customer data. The Redpoint Business Templates use an organization’s business goals and data – customer behaviors and transactions as well as product and other brand-specific details – to build models that are specific to the business. They automate the process all the way to model test and deployment, shortening model building timeframes and reducing workloads for data scientists and data engineers. To simplify management and usage by the average marketer, rg1 offers a new “curated view,” providing all the details needed to define a machine learning (ML) model training project. Marketing analysts and “citizen scientists” get a checklist and summary, providing a faster and easier to use model building process, while always being one click away from the details needed to train an ML model. These exciting updates are now available in rg1’s Intelligent Orchestration and Automated Machine Learning as part of the latest release, 6.1. **Usability for Today to Solve Tomorrows Problems** The rg1 platform was purpose-built to support real-time interactions across every customer touchpoint throughout an organization – web, email, mobile app, in call centers, stores, and branches, IoT, etc. Across industries, ambitious marketers and business professionals use rg1 to solve complex customer use cases. **RELATED CONTENT** [rg1 is How Ambitious Marketers Lead Markets](https://www.redpointglobal.com/blog/rgone-is-how-ambitious-marketers-lead-markets/) [Algorithmic Optimization and the Magic of AML](https://www.redpointglobal.com/blog/algorithmic-optimization-and-the-magic-of-aml/) [Building a Blueprint for Real-Time Customer Engagement](https://www.redpointglobal.com/building-a-blueprint-for-real-time-customer-engagement/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Management, Omnichannel Marketing, Single Customer View --- ### [Reduce Gaps in Care and Drive Better Health Outcomes with Personalized Engagement](https://www.redpointglobal.com/blog/reduce-gaps-in-care-and-drive-better-health-outcomes-with-personalized-engagement/) **Published:** April 2, 2020 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/04/4-2-blog-healthcare-300x200.jpg)While much of the healthcare industry has been turned upside down because of COVID-19, the global pandemic is a reminder that innovation in healthcare consumer engagement, education, testing, and treatment is at the core of improving outcomes for all Americans. Once we get through the emergent health crisis, it will be important to leverage innovations in consumer engagement to acquire, retain, and deliver better outcomes at lower costs for the healthcare consumer. The Department of Health and Human Services publishes and tracks [leading health indicators (LHI)](https://www.healthypeople.gov/2020/Leading-Health-Indicators), a group of high-priority health issues that demonstrate and draw attention to factors that affect individual health and contribute to health disparities. They include access to health services, social determinants of health, clinical preventive services, mental health, and nutrition/physical activity/obesity. Guiding an individual health consumer to best influence the controllable factors is an important way for healthcare professionals – payers and providers – to achieve better outcomes and lower costs. Reducing gaps in care and driving health risk assessment completion are two key focus areas. Others are a focus on value-based care (VBC) engagement, the acquisition of Medicare and Affordable Care Act (ACA) healthcare consumers, and provider onboarding and navigation. Acquiring Medicare and ACA healthcare consumers are important segments not only for revenue growth for both payers and providers, but also because preventive care measures directed to those consumers achieve the greatest health outcomes. Revenue opportunities include financial incentives to reduce gaps in care as well as take on more risk for VBC arrangements. A focus on reducing gaps in care for many clinical preventive services is another big opportunity. For example, according to the Centers for Disease Control and Prevention (CDC), colorectal cancer screenings beginning at age 50 are the most effective way to reduce a person’s risk of getting the disease. However, only 25 percent of adults age 50 to 64 in the US – and fewer than 40 percent over age 65 – are up to date on this screening. Some consumers may simply be unaware that there are now noninvasive screening options pioneered by [Mayo Clinic](https://www.mayoclinic.org/diseases-conditions/colon-cancer/in-depth/colon-cancer-screening/art-20046825) and now widely available. In addition to seasonal and age-related screenings, other focus areas include prescription adherence, chronic condition management, telemedicine innovations, and IoT connected devices. What these examples of value-added opportunities share is a single point of control over healthcare consumer data, decisions, and interactions to drive down costs and produce better outcomes. To determine how to best achieve value from a single point of control, a worthwhile exercise for a healthcare organization is to analyze a population under care and develop applicable proofs of concept projects. A focus on a specific LHI or issues such as prescription adherence or management of a chronic condition present relatively low-hanging fruit that, while perhaps small in scope, provide far-reaching value with potential for measurable results in a short period of time. **Show Me: Trailblazers Carve out a Path** Redpoint customers in the healthcare industry have paved the way for other data-driven healthcare organizations to follow. One [healthcare center](https://www.redpointglobal.com/wp-content/uploads/2019/11/RedPoint_CaseStudy_Lucerna.pdf) tested the use of personalized communications to increase the percentage of consumers scheduling appointments. One group of healthcare consumers received personalized messaging, such as emails and SMS messages from a specific provider instead of a generic message from the medical center. Other personalized touches included listing a provider’s specialties relevant to the consumer’s condition, and including a picture of the provider in an email. Among some demographics, the population that received personalized touches scheduled 50 percent more appointments. A New England-based hospital turned to Redpoint as a technology partner for an innovative telemedicine research program that targeted a population of healthcare consumers under care management for congestive heart failure. With help from a third-party vendor that managed IoT sensor data, the Redpoint platform analyzed live clinical data and generated real-time decisioning that dictated an optimal response – an at-home nurse visit, a message on a wristband, a video teleconference, or other next-best action intended to avoid the need for hospitalization re-admittance. **Your Data, Your Project, Your Accomplished Goals** Devising a proof of concept starts with an organization determining where it can find a quick win. For instance, addressing a glaring inefficiency or gap in care that it can tackle using data from a population under care that it already possesses. Redpoint will help set up a proof of concept project using your own organization’s data, to test out specific use cases to determine ROI potential. An important differentiator of the Redpoint platform is that it provides organizations with a single view of the healthcare consumer – meaning that the data isn’t just a list of non-compliant consumers. Rather, advanced identity resolution capabilities narrow a healthcare consumer to a single identity across all devices and interactions, including behavioral, clinical, and claims data. A single view greatly enhances test and learn capabilities during a proof of concept, because it’s the basis for determining a next-best action for an individual healthcare consumer. With a single view, the organization knows the consumer’s channel preference, for example, and will optimize communications accordingly. As we saw in the congestive heart failure case study, the single view becomes actionable with automated machine learning that analyzes live data. A real-time decisioning layer and intelligent orchestration provide the healthcare organization with a single point of control that produces the next-best action. Testing various approaches – whether it’s a focus on personalization, frequency of contact, channels, etc. – will let the organization know which approach positively impacts individual behaviors. Once an organization proves what’s possible through a proof of concept, putting a model in production will allow for even more refinements as more data is collected and analyzed, thus allowing for even more finely tuned, relevant messages that will continually improve outcomes, drive down costs, or otherwise accomplish the desired goal. **RELATED CONTENT** [Survey Says: Healthcare Personalization Has Room to Grow](https://www.redpointglobal.com/blog/survey-says-healthcare-personalization-has-room-to-grow/) [Take a Data-Driven Approach to a Consumer’s Healthcare Journey](https://www.redpointglobal.com/blog/take-a-data-driven-approach-to-a-consumers-healthcare-journey/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2020/04/4-2-blog-healthcare-image-2-239x300.jpg)](https://www.redpointglobal.com/wp-content/uploads/2017/10/SB-HCUS0917-04-Healthcare_lo-res.pdf) **Blog categories:** Customer Data Platform, Data Management, Healthcare, Omnichannel Marketing, Real-Time Personalization --- ### [Embrace Complexity In Marketing, That’s Where the Magic Happens](https://www.redpointglobal.com/blog/embrace-complexity-in-marketing-thats-where-the-magic-happens/) **Published:** January 7, 2021 **Author:** Redpoint Global **Content:** In a recent [Forbes 2021 predictions column](https://www.forbes.com/sites/shephyken/2020/11/22/ten-business-predictions-for-2021/?sh=a40c2382d875), the No. 1 prediction – for the fifth consecutive year – was that customers will continue to get smarter. They tolerate fewer customer service failures and demand better overall experiences because they know it’s possible. Following close behind as the No. 2 prediction is that customers are becoming increasingly impatient. They want superior experiences *now*. This impatience is reflected in a Harris Poll survey sponsored Redpoint, where [37 percent of customers](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/) said they will stop doing business with any company that fails to provide a personalized customer experience. Despite a growing consensus that customer expectations for highly personalized experiences are solidifying, many brands are unable or unwilling to deliver on these demands due to the perceived complexity. The costs of complexity, the thinking goes, [outweigh the potential lifts](https://www.linkedin.com/pulse/segment-of-one-complexity-out-control-david-edelman/) from delivering personalized experiences through segment-of-one marketing. But if we accept that both the complexity and the demands for personalized experiences are here to stay, I would instead argue the opposite. Rather than shy away from [complexity in marketing](https://www.redpointglobal.com/blog/embrace-complexity-thats-where-the-magic-happens/), companies should embrace it because that is where the magic happens. ## **Customer-Centric Organizations Deliver Results** Even with complexity growing by the day – more devices, more data, more channels, more customer data sources – technology exists that fulfills the promised lift of segment-of-one marketing. The right technology, combined with strategy, process and change management overhauls, make it possible to embrace complexity and realize the benefits of becoming a customer-centric organization. According to [research from Gartner](https://www.gartner.com/smarterwithgartner/is-your-organization-customer-centric/), customer-centric organizations enjoy revenue lifts of 20 percent or more by using transactional, preference and historical customer data, as well as analyzing customer behavior across all devices, analyzing device usage, IoT and sentiment analysis, and first-party, second-party and third-party data across a complete anonymous to known customer record. This is in contrast with “persona-centric” brands that may see a 10 percent revenue lift with a limited personalized engagement strategy that stops at transactional and historical data analysis. The additional 10 percent lift from becoming a true customer-centric organization far outweigh the temporary costs of facing complexity head-on. This is high-intensity, high-expectation, high-quality interacting with customers, and whether we like it or not it’s the current competitive landscape. Some of the lingering doubt about the merits of squaring away customer data and eliminating siloes (data/process/channels) stems from many vendors jumping into the customer data platform (CDP) arena with empty promises to solve for data complexity, but for the most part over-promise and under-deliver – leading to more siloes and, yes, more complexity. ## **Solve for Customer Data Complexity with a Single Platform** Satisfying customer expectations for a holistic customer experience precludes data, process and channel siloes because the expectation isn’t just for a personalized experience on every channel, it’s for a seamless experience that spans every interaction with a company. The delivery of a core brand experience [transcends marketing](https://www.redpointglobal.com/blog/a-superior-customer-experience-cx-transcends-marketing/). This is why platforms that integrate customer data might claim the CDP mantle, but solving for customer data complexity demands far more. As the customer-centric approach outlined by Gartner suggests, meeting the modern customer expectations head-on requires engaging with customers with a relevant, personalized experience in the context of a customer’s individual buying journey across all touchpoints. This in turn requires the integration of customer data from any source and of every type, immediately structuring data at ingest and applying advanced identity resolution capabilities to create a true single customer view that is updated in real time. A [digital customer experience platform](https://www.redpointglobal.com/one-platform/) that also embeds [automated machine learning](https://www.redpointglobal.com/machine-learning), a [real-time decisioning engine](https://www.redpointglobal.com/orchestration/real-time-decisions) and [intelligent orchestration](https://www.redpointglobal.com/orchestration/real-time-decisions) capabilities tackles the inherent complexity, giving marketers a single tool with which to meet customers’ lofty expectations for a seamless, personalized customer experience. This is the enterprise-grade technology that is needed to drive new revenue with innovative customer experiences. ## **CX in Action: Where the Magic Happens** With the right tools and technology in place, facing complexity does not have to be an uphill battle. With the right approach, a simpler higher-fidelity interaction can be achieved by defining a customer-centric strategy around the messages they must receive in a specific order and then delivering those via a real-time engine anywhere the customer appears. Think of having a magic capability to land just the right message whenever a person lands on a website, a mobile app or even email at the moment of open. If you have the right analytics it is actually easy to develop the sequence and the various alternatives that may be relevant based on a customer’s behavior. Then imagine an orchestration tool that lands exactly the right message anywhere the customer engages with a brand. That idea is much simpler than how we think of marketing today – and it is possible today with the right preparation of data, analytics and real-time delivery. An acceptance that marketing is a mission-critical department with potential to be the top revenue driver for the enterprise will go a long way toward eliminating any doubt that the effort to achieve true segment-of-one marketing with [hyper-personalized experiences](https://www.redpointglobal.com/blog/data-matching-and-identity-resolution-keys-to-hyper-personalization/) is worth the time and effort to get there. Technology, strategy, process and change management overhauls may seem daunting, but in the long run they’re a small price to pay to satisfying customer demands for a superior customer experience. The alternative is to have your customers leave for a brand that makes the effort. ## **Related Content** [Is Your Marketing Platform Ready for Prime Time?](https://www.redpointglobal.com/blog/is-your-marketing-platform-ready-for-prime-time/) [Customer Journeys are Dynamic: Your Engagement Technology Should Be as Well](https://www.redpointglobal.com/blog/customer-journeys-are-dynamic-your-engagement-technology-should-be-as-well/) [The Sky is the Limit with AI and Machine Learning: Capitalize on the Moment of Interaction](https://www.redpointglobal.com/blog/the-sky-is-the-limit-with-ai-and-machine-learning-capitalize-on-the-moment-of-interaction/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [What Data Scientist Shortage? Empower Marketers to Use Machine Learning](https://www.redpointglobal.com/blog/what-data-scientist-shortage-empower-marketers-to-use-machine-learning/) **Published:** August 22, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/08/blog-image-telephone-switchboard-300x201.jpg)There is general acceptance of a data scientist shortage. There is less of a consensus, however, for what this shortage means for organizations in their digital transformation efforts. The 2018 LinkedIn Workforce Report estimated that more than [151,000 data scientist jobs](https://economicgraph.linkedin.com/resources/linkedin-workforce-report-august-2018?trk=lilblog_08-20-18_data-scientists-America-great_tl&cid=70132000001AyziAAC) went unfilled across the United States, with “acute” shortages in New York, San Francisco, and Los Angeles. Similarly, an IBM study estimates that there will be [700,000 openings next year](https://www.ibm.com/analytics/data-science) for data scientists, developers, and engineers – roughly more than half of the available positions for those roles today. The trend recalls the early days of telephony; as call volumes exploded in the early 20th century, there was an enormous demand for switchboard operators who manually connected calls until technology caught up with the innovation of the dial tone, direct dialing, and long distance networking. At its peak in the late 1940s, there were more than [350,000 telephone operators](https://ethw.org/Telephone_Operators) working for AT&T. Changing a process – in this case automating the call exchange – curtailed an entire profession. While we are far from data scientists becoming obsolete, there is similar pressure on technology innovation to alleviate the demand-supply imbalance. Automation with machine learning models lessens the reliance on data scientists to build, train, and deploy analytic models. Self-training models that are built to personalize the customer experience across an omnichannel buying journey put marketers closer to the customer data. Like the direct dial call, automated models eliminate a layer of disassociation that abstracts marketers from the objective of being as close to the customer as possible. **Humanize Analytics for Marketers** Some marketers might harbor the view that the switchboard analogy applies to their own jobs; if machine learning models can replace data scientists, why not the marketer? The simple answer is that a combination of creativity and subjective judgment will enable marketers to elevate their roles – marketing can spend the time designing customer strategies, journeys, and objectives while also managing the intelligent, self-training models that continuously strive to attain the KPIs. Marketing is empowered to deliver even more value to the business; they can test, tune and optimize models much more rapidly, leading to higher and more profitable revenue growth. This is accomplished by automation of both the model building and model deployment phases. Model building enables direct oversight and monitoring of models without having to query data scientists, leaving the data scientists to do higher-value work such as exploring new data, establishing new connections, and setting up effective guideposts for marketers to operate within. Model deployment is the other key area requiring automation, and marketers can now directly embed machine learning into their dynamic customer journeys, audience selection, and campaigns. This seamless connection to marketing operations is critical, as according to the International Institute for Analytics (IIA), “adoption of analytics at the enterprise level is still very low, with 87 percent of the data science models never making it to production.” Traditionally, the time and effort it took to access data served as an obstacle to marketing creativity. Eliminating this barrier frees marketers to pursue creative avenues that might otherwise be missed opportunities. The reason marketing is now able to do this is because [evolutionary programming](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/) has the power and capability to operationalize fleets of machine learning models, putting upwards of hundreds of models into production, all with the single-minded focus of creating a hyper-personalized customer experience for a segment of one. To borrow the switchboard analogy, it’s as if a group of operators was able to route hundreds of thousands of calls in the blink of an eye; enormous complexity still exists, it’s just hidden from direct view. Redpoint Automated Machine Learning puts the marketer in control of the switchboard with an intuitive dashboard that presents performance levels and other key metrics such as model type, date, and KPIs. For example, fitness reports detail model degradation which allows the marketer to make adjustments according to current business objectives. The modeling environment humanizes access to advanced analytics for marketers, and accomplishes in real time what it would take data scientists weeks or months to build and deploy. **The Customer at the End of the Data Model** Intelligent, self-training data models do not exist in a vacuum, of course, which is another way of saying that we can’t forget there is a customer at the other end of any business objectives. The fact that the customer is in charge of guiding their own omnichannel buying journey underlines the importance of evolutionary programming; marketers must be ready to pivot in the context and cadence of the customer, which could mean a new model or fleet of models that are in synch with the customer and continuously ready with a next-best action or offer. A reliance on data scientists introduces the possibility that a model – or models – that require adjustments will not be operational in the time needed, which is to say in line with the customer journey. This time delay also means that these previous models may have been built on old data, lessening their potential to deliver highly relevant messages to individual consumers. A customer engagement hub (CEH) provides marketers with the single point of control needed to orchestrate a journey in the context and cadence of the customer. A single point of control over data, decisions, and interactions ensures that models are deployed in the operational setting of a customer journey. A CEH takes clean, accurate, and up-to-date customer data from any source, applies in-line analytics with evolutionary programming and includes an orchestration layer that operationalizes the models by recommending and deploying a next-best action for a consumer across any channel or touchpoint. A CEH is equivalent to the modern telephonic switchboard with the marketer at the controls, abstracting the complexity to leave the marketer in charge of steering a hyper-personalized customer experience. Data scientists, like telephone operators, will always have a role to fill, but technological advancements always lead to process change. In this case, delivering personalized customer experiences that generate revenue necessitates an elevated role for marketers, giving them access and control over the data and analytics that traditionally have been solely within the purview of a data scientist. **RELATED ARTICLES** [Harness the Power of Digital Technology as a Revenue-Driving Engine](https://www.redpointglobal.com/blog/harness-the-power-of-digital-technology-as-a-revenue-driving-engine/) [Humanizing a Customer Experience without Humans: Let Machine Learning Take Control](https://www.redpointglobal.com/blog/humanizing-a-customer-experience-without-humans-let-machine-learning-take-control/) [The Role of Collaboration in a Human Approach to AI](https://www.redpointglobal.com/blog/the-role-of-collaboration-in-a-human-approach-to-ai/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, AI & Machine Learning, Customer Data Platform --- ### [Container Orchestration Platforms Heat Up: Eliminate Common Customer Pain Points](https://www.redpointglobal.com/blog/container-orchestration-platforms-heat-up-eliminate-common-customer-pain-points/) **Published:** November 11, 2021 **Author:** Steve Zisk **Content:** I finally decided to install smart thermostats in my house when I received a heating bill last winter that cost more than my first used car. I don’t usually look forward to the cold of winter, but I am excited for January to come around just to see the year-over-year savings. One big deep freeze and the system might even pay for itself this year. In a lot of ways, the on-demand control and flexibility of a smart thermostat system mirrors the advantages of containerized software. Just as it makes no sense to heat the downstairs family room when everyone’s upstairs asleep, it isn’t very economical to pay for clusters, racks or servers for that matter if they’re only used during peak hours, days, months or even seasons. In a [recent blog](https://www.redpointglobal.com/blog/kubernetes-vs-hadoop-as-a-processing-powerhouse-why-containerization-gets-the-nod/), we explored why container management software – Kubernetes in particular, with Docker Swarm also in play as a go-to container orchestration platform – is increasingly favored by enterprise companies, as compared with Hadoop, for big data processing. Flexibility, performance and cost were cited as the primary reasons why data-driven enterprises coping with enormous data sets are moving toward containerization. ## **Don’t Risk Frustrating the Customer** Those are significant benefits for the enterprise, of course. But what about the end consumer? Left unsaid in the previous blog on the topic was the impact of container management software for the customer the enterprise is trying to serve. Do they notice a difference between a pre-packaged application that runs in a container vs. a distributed cluster? The hard truth, of course, is that the customer will generally only notice in the event of a poor experience. Which is why, to avoid that probability, enterprises that employ distributed parallel processing of large datasets with Hadoop or another big data processor often pay for what they don’t need. In a cloud environment, that fixed monthly expense of hundreds of nodes and dozens or more of core CPUs may, at periods of high demand, provide the necessary processing power and raw speed to run an application for hundreds of thousands or even millions of users. The risk, though, is substantial. Recent studies show that a page that takes more than three seconds to load loses around [53 percent](https://www.business.com/articles/website-page-speed-affects-behavior/) of mobile users. That’s a lot of unhappy holiday shoppers leaving to spend their holiday budgets with another brand. Imagine a shopper with a partially filled shopping cart who clicks on one last item. Frustrated by the lag time, she abandons the cart and skips out on the entire transaction. A superior, omnichannel customer experience is often regarded as one that delivers a next-best action for a customer in the context of an individual customer journey, regardless of channel. The shopping cart example shows that a next-best action is only as good as the delivery mechanism. A real-time decisioning engine and intelligent orchestration capabilities that produce the perfect offer at the perfect time and on the perfect channel are all for naught if a business’s notion of real-time outpaces that of an overloaded application. ## **A Hands-Off, Stress-Free Environment, a Satisfied Customer** A container orchestration platform significantly minimizes this very real possibility by automatically spinning up (or down) a new container as resources dictate – holiday season, peak hours, etc. The other important aspect is this flexibility is not dependent on IT, as it is with a distributed parallel processing framework. Pre-packaged containerized applications are, as the name suggest, self-contained, with elements such as security, single sign-on, network configuration and interoperability controlled by the container orchestration platform. In a parallel processing framework, setting up a new cluster to meet high demand involves significant code changes to match queries on distributed files – often with programming in MapReduce, Pig and Spark which require specific skillsets. In addition to being time and resource intensive, the problem is that data-driven enterprises work with dynamic data that changes constantly. Adding clusters of nodes in an attempt to keep up with a constantly changing customer base or suddenly changing business objectives is an exercise in futility, a developer’s version of a dog chasing its tail. With a container orchestration platform, the enterprise dispenses with every manual shortcoming and inefficiency. By fully automating complex deployments on multiple hosts, an enterprise starts instead with an objective and avoids all the intermediate steps such as allocating resources or balancing loads. The platform ensures the objective is met, and from the consumer standpoint it unknowingly eliminates a lot of potential friction touchpoints. Another containerization benefit encompasses software selection and installation. Returning to the smart thermostat analogy, it would be as if the thermostat installation entailed little more than clicking a button in Amazon. No electrician needed, no drilling holes, site prep, etc. And, if you have a thermostat in every room, you would then be able to control everything via one interface, dialing up or down the heat in each room with one remote. That is what container orchestration platforms enable, with micro-services for sharing libraries, services, interfaces, and consoles across multiple containers. A container orchestration platform ensures that poor customer experiences that occur because of lags will never happen. Constant monitoring, even distribution and easy scalability eliminate the traditional pressure points. And even if disaster strikes and the figurative furnace does blow – a hack, outage, natural disaster – container orchestration is like having an on-demand repairman ready at a moment’s notice to get you up-and-running before the cold sets in. ## **Related Content** [Data Lake vs. Data Swamp: Why Enterprise-Class Businesses Can’t Afford Bad Data](https://www.redpointglobal.com/blog/enterprise-class-businesses-cant-afford-bad-data/) [Wave a Magic Wand: What Can You Accomplish with Perfect Data?](https://www.redpointglobal.com/blog/wave-a-magic-wand-what-can-you-accomplish-with-perfect-data/) [Customer Journeys are Dynamic: Your Engagement Technology Should be as Well](https://www.redpointglobal.com/blog/customer-journeys-are-dynamic-your-engagement-technology-should-be-as-well/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* --- ### [Ease Distrust in Advertising with Data Clean Rooms & PII Vaults](https://www.redpointglobal.com/blog/ease-distrust-in-advertising-with-data-clean-rooms-pii-vaults/) **Published:** April 8, 2022 **Author:** Steve Zisk **Content:** Advertising has been driven by customer data since the birth of advertising. Selecting an audience, bidding and paying for an ad, the medium, response measurement – every aspect of enticing a group of people to buy a product or service is dependent on customer data. In a data-driven economy, it’s perfectly natural for customer data to play a starring role. But as advertising moved into the digital space, the context attached to customer data has largely eroded. What is often overlooked is how – or whether – an advertisement enhances a customer’s overall experience as the customer engages with the ad. From the customer’s perspective, advertisers and publishers often seem more interested in clicks, opens, and views than how an ad aligns with their customer journey. Irrelevant ads, ads that seem to violate a customer’s privacy, and false advertising/phishing attempts all conspire to foment a general distrust in advertising. Infighting between advertisers and publishers, each of whom accuses the other (and third parties) of misrepresenting data, doesn’t help matters. The lack of trust becomes endemic; advertisers don’t trust publishers, publishers don’t trust advertisers, neither trusts results, and the customer presented with an ad is skeptical that the process looks after their best interests. With the state of digital advertising approximating the Wild West in terms of how customer data is collected and used, customers are circling the wagons to keep their precious data away from bad actors. Interested in having more control over their data, customers increasingly demand permissions, opt-outs, data contracts and other mechanisms to better manage how their data is used. ## **Honor Customer Expectations – or Face Consequences** Before examining a possible way out of the morass, let’s look at the extent of the problem. Consider a [Harris Poll survey commissioned by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/), where 73 percent of consumers surveyed said it is either very important or essential that a brand reveal how the information being collected about them is being used – and 71 percent said that it is up to the customer to provide explicit authorization for how the data will be used. Customers are leery, in other words, that brands have their best interest in mind vs. an interest in selling their data to a third party for marketing or advertising purposes. Furthermore, 88 percent of customers said they are likely to switch brands if the brand sells their data to another company for marketing/advertising purposes without their authorization. Data privacy regulations such as GDPR and CCPR are good faith attempts to protect customer rights, but advertisers and publishers seem to be trying to stay one step ahead of the posse, if you will, for continuing to monetize customer data. Knowing that customers are staking a bigger claim on protecting their data, they present confusing opt-in/opt-out forms or options, cookie-less identifiers, bait-and-switch tactics and other methods that all seem, to customers, as attempts to breach the perimeter, probing weak spots for further illicit use of their data. ## **First-Party Data to the Rescue** How, then, is trust restored so that everyone – the customer most of all – feels better about the process? Instead of continually probing for weak spots, feints, or loopholes, a better option is to simply take the bull by the horns. Rather than dance around the problem, address it head on by re-thinking and re-working what a customer interaction should look like. Bring context back to advertising, in other words, with a renewed focus on first-party customer data. This tactic works easily for a brand-owned site, of course. In this case, the value exchange is at play where customers provide personal data and in return the advertiser provides a more relevant, personal ad. Improving the first-party advertising experience with ads in the context of a customer journey can help alleviate the animosity between parties with goals that may be at odds. For a third-party advertising site, however, the way to become more relevant is to collect more contextual information on visitors to the site and use that context to target the right audience. This means using content affinity – pages read, links followed, searches performed – to gauge product and brand affinity. This requires a different kind of partnership between brand and publisher: “Show my ad to the people who are most likely to receive it well” rather than “Use my data (and everyone else’s) to target my customers and look-alikes.” For the brand-owned site where data is provided in exchange for more relevant advertising, [data clean rooms](https://www.redpointglobal.com/blog/ease-distrust-in-advertising-with-data-clean-rooms-pii-vaults/) and PII vaults help ensure that customer permissions and other data privacy issues are handled both to the letter and in spirit of matching customer expectations for the use of their data. ## **Compliance with Context – Data Clean Rooms** Both approaches separate PII and other information that is not explicitly authorized by a customer for marketing/advertising purposes, ensuring that consent is followed. Data sharing clean rooms utilize a type of data encryption that allows a company to analyze, match, and build models off anonymized data without ever accessing or decrypting personally identifiable information. Companies can interact through a clean room, becoming in a sense digital advertisers for their customer base using only fully anonymized data that is compliant with all regulations. For the big box retailers and multi-billion dollar brands – the Walmarts, Targets and Home Depots of the world – a data clean room demonstrates to a customer that the brand will use shared non-personal information to improve the customer’s overall experience. It’s collateral for honoring the value exchange. Smaller companies can also benefit from data clean rooms by banding together to approximate similar data volumes and thus deepening their understanding of customers. Multiple media outlets, for example, might share donor data to better understand how to interact and advertise to a wider audience. ## **Honor Permissions to the Letter – PII Vaults** Conceptually, PII vaults also allow access to data in a way that meets customer expectations, with a slightly different set-up in an advertising context. Whereas a data clean room anonymizes and aggregates data, a PII vault relates to an owned site and the use of first-party data, where the site owner/advertiser uses data according to the data permissions for each type of experience; a customer wishes to receive or not receive product ads or affiliate ads, they set a desired frequency, etc. PII Vaults also ensure that valuable customer data is not viewed, shared, or misused by a brand, limiting use to personalization of CX permitted (“contracted”) by the customer. The use of data clean rooms and PII vaults help restore trust in advertising by restoring context. Customers are reassured that advertisers and publishers care about protecting their [data](https://www.redpointglobal.com/blog/no-data-left-behind-analytics-orchestration-and-making-data-work-for-you/) – and care about delivering relevant advertising. Advertisers and publishers no longer have to play the delicate dance that breeds distrust and undermines the customer. In a data-driven economy, it stands to reason that advertising will be dependent on customer data for as long as advertising exists, perhaps even unreasonably so. With data clean rooms and PII vaults, the focus can revert to being dependent on the *right* data. That will improve the experience for everyone, particularly the customer. ## **Related Content** [There are no Third-Party Shortcuts to Understanding Customers](https://www.redpointglobal.com/blog/there-are-no-third-party-shortcuts-to-understanding-customers/) [The Demise of the Third-Party Cookie Opens New Methods of Engagement](https://www.redpointglobal.com/blog/the-demise-of-the-third-party-cookie-opens-new-methods-of-engagement/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Segmentation & Activation --- ### [Drive Revenue Growth with a Personalized Healthcare Experience](https://www.redpointglobal.com/blog/drive-revenue-growth-with-a-personalized-healthcare-experience/) **Published:** April 20, 2023 **Author:** John Nash **Content:** Significant revenue growth and increased conversions through a personalized patient experience were key takeaways at the “Personalization at Scale – Rising to the Challenge” session at the HIMSS (Healthcare Information and Management Systems Society) Conference in Chicago on Thursday morning. John Wagner, Vice President, Value-Based Care Growth, Lucerna Health, presented how driving a deep, personalized patient experience through rich, unified data delivers significant monetary benefits. “Healthcare personalization drives relevance and lift,” said Wagner, who attributed the delivery of an omnichannel patient experience with engagement rates and conversions that blow away industry benchmarks to the Redpoint CDP and journey orchestration capabilities. Wagner said that an omnichannel campaign using the single patient view with Redpoint produced a 33 percent conversion rate of 400,000+ patients who scheduled needed appointments, compared with a 6 percent industry standard, equating to roughly $7 million in revenue. The conversion rate for the Hispanic population, Wagner said, was over 50 percent. Patient surveys resulted in an addition $7 million in bonus payments. > “We drove 77,000 patient leads to a practice at $5 a lead, less than a quarter of the industry cost average.” > > – John Wagner Personalization techniques, said Wagner, included sending 1,000s of personalized SMS or email messages, each tailored specifically to a patient’s preferences and behaviors, as granular as changing a hero image to reflect their personal physician, having a personalized call to action or targeted messaging. Redpoint improves operating efficiency by matching the right patient to the right care based on health condition as well as financial arrangements with payers, particularly the value-based care (VBC) population – which is most of Medicare. “We maximize value to the patient through an omnichannel engagement campaign that is based on personalization,” Wagner said. “We drove 77,000 patient leads to a practice at $5 a lead, less than a quarter of the industry cost average. You do not generally find this type of focus with very many healthcare system.” The omnichannel approach, builds off its own success. Attributing over 500,000 patient visits to an outreach campaign since using Redpoint’s Journey Orchestration, the results feed back into the system, with the Redpoint orchestrating even deeper value-based engagements attuned to an individual patient’s healthcare journey. “The platform takes in patient data as an experience occurs and plays it back. It populates a provider team’s dashboard, and that insight panel optimizes patient engagement on a just-in-time basis,” said Wagner. For more on how the Redpoint CDP and Redpoint Journey Orchestration help deliver a personalized healthcare experience, click *[here](https://www.redpointglobal.com/healthcare/).* **Blog tags:** CDP --- ### [The Redpoint CDP Empowers Ambitious Business Leaders to Drive Real-Time Customer Engagement](https://www.redpointglobal.com/blog/the-redpoint-cdp-empowers-ambitious-business-leaders-to-drive-real-time-customer-engagement/) **Published:** June 3, 2020 **Author:** Redpoint Global **Content:** Redpoint Global today introduced its [customer engagement platform](https://www.redpointglobal.com/customer-engagement-hub), rg1. Bringing together real-time data, analytics and orchestration, rg1 offers the winning formula brands need to rapidly transform customer experiences and drive tangible ROI. The need to consistently deliver perfect customer experiences across all channels has never been greater. With sudden disruption and rapidly changing consumer behaviors, digital transformation is accelerating at an unprecedented pace. The ability to deliver innovative customer experiences is now essential for a brand’s survival. “The world is forever changed and a brand’s ability to consistently deliver perfect customer experiences is essential to surviving and thriving in the new reality,” said Dale Renner, CEO and co-founder of Redpoint Global. “Customer experience has become the most important battleground for competitive differentiation and now brands must be able to deliver that experience digitally. Those who are ambitious, act with urgency and commit to radical change are the likely winners – and speed and agility are essential. We have long helped our clients deliver innovative customer experiences and rg1 is the right solution to accelerate the transformation companies must now undertake as they rethink customer journeys.” Flexible and built for scale, rg1 offers an enterprise-class platform with an open garden approach to leverage existing investments in data and last-mile touchpoints. The solution provides brands with a continually updated “Golden Record” of each consumer – providing the best context for engaging each customer across any channel at any given moment. Enterprises can take advantage of fully integrated automated machine learning to deliver highly personalized next-best actions to customers at unmatched speed and scale. With the rg1 platform, organizations can: - Establish a contextual understanding of each customer, - Adapt faster to customers’ ever-changing needs in the proper context, - Personalize messages and offers in real time, across all touchpoints and stages of the customer journey, and - Maintain oversight and security of customer data by using a private or hybrid deployment option - Deliver helpful, relevant, timely and personal experiences “Our offerings have long helped brands adapt to shifting consumer behaviors. We believe in the power of harnessing consumer data to understand customer context in the moment and deliver experiences that are helpful, relevant, timely and personal,” said George Corugedo, CTO and co-founder, Redpoint Global. “Now, with rg1, businesses across industries have the solution they need to meet shifting consumer expectations – whether they must completely transform their operations to meet consumer needs or are working to keep pace with a surge in demand. We are focused on creating technology that makes an impact, and customers tell us that Redpoint is their #1 revenue-driving application.” Redpoint customers have driven transformative results with rg1, including: - A leading CPG company optimized customer engagement for real-time product recommendations, resulting in 79% lift in conversions. - Several healthcare organizations are using Redpoint to help manage consumer engagement, with one provider increasing scheduling of preventative care appointments by 50%. - A large international DIY retailer achieved a 99% compression in data-to-insight-to-action, supporting high growth in Buy Online Pickup in Store (BOPIS) customer journeys. - A travel & hospitality organization drove triple-digit percentage increases in revenue across numerous properties while also reducing customer interaction costs by 40%. Recently positioned by Gartner in the Challenger quadrant of the “2020 Magic Quadrant for Multichannel Marketing Hubs,” rg1 brings together Redpoint’s market-leading capabilities for customer data management, automated machine learning and intelligent orchestration. With Redpoint’s software platform, innovative companies are transforming their customer experiences across the enterprise and driving higher revenue. Redpoint’s solutions provide a remarkably unified, single point of control where all customer data is connected and every customer touchpoint intelligently orchestrated. Delivering more engaging customer experiences, highly personalized moments, relevant next-best actions, and tangible ROI—this is how leading marketers lead markets. **RELATED CONTENT** [Digital Transformation Just Became Accelerated](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) [Now is the Time to Set Ambitious Goals ](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Management, Data Quality, Omnichannel Marketing --- ### [Driving Patient Engagement Through Data Readiness: A Prescription for Improved Health Outcomes](https://www.redpointglobal.com/blog/driving-patient-engagement-through-data-readiness-a-prescription-for-improved-health-outcomes/) **Published:** May 6, 2025 **Author:** Steve Zisk **Content:** Roughly half of adult population in the United States has [at least one chronic illness](https://www.cdc.gov/pcd/issues/2024/23_0267.htm#:~:text=throughout%20the%20US.-,Introduction,at%20least%205%20(5).), with a little more than 40 percent having two or more. Managing heart disease, cancer, diabetes, obesity, hypertension and other chronic conditions account for roughly 90 percent of the more than $4 trillion of the U.S. healthcare expenditure each year. Helping patients manage chronic conditions through better **patient engagement** plays a key role in improving outcomes and minimizing the negative cost and health implications associated with chronic disease. But what does it take to improve patient engagement? One way is through creating a **personalized patient experience**. For a patient coping with a chronic condition, a personalized experience could mean designing a treatment plan that answers the patient’s questions, helps manage stress, provides education, and empowers the patient as an active participant in their care and overall well-being across the full patient journey. ## **Personalization and Patient Engagement** Guiding the patient on a personalized care journey drives higher engagement because the experience delivered is relevant to the patient. A health system which leverages a personal understanding of a patient to deliver a consistently relevant experience provides value. For example, in a [National Library of Medicine](https://pmc.ncbi.nlm.nih.gov/articles/PMC10272027/) comparison of personalized vs. generic texts to encourage diabetics to complete routine HbA1c tests, the overall HbA1c completion rate for those receiving a personalized text was double that of those receiving a generic text (22.5 percent to 11.2 percent). Personalization is vital. In a [Dynata survey](https://www.redpointglobal.com/press-releases/81-of-consumers-say-a-good-patient-experience-is-very-important-when-interacting-with-healthcare-providers/), 38 percent of respondents said they would switch healthcare providers due to a lack of personalization stemming from a lack of a patient understanding. One challenge for healthcare providers is that the complexity of the patient journey intensifies with the growing trend of the defragmentation of care. With outpatient care, virtual visits, and home health care all on the rise, health systems must be able to connect touchpoints consistently across a greater number of channels to create a holistic care experience. ## **Building Bridges Across the Care Continuum** The text experiment, even aside from the results, demonstrates that patients have more ways to engage with providers than ever before. The simple act of receiving a text vs. an in-person interaction is a sign of the times. In an [Sg2 Analytics](https://www.sg2.com/press-releases/sg2-forecasts-continued-hospital-capacity-challenges-as-patient-acuity-rises) 10-year forecast of evaluation and management (E&M) care, in-person visits by existing patients are projected to drop 2 percent, while virtual visits are projected to increase by 26 percent. In addition to telehealth, patients are also increasingly referred to urgent care facilities, ambulatory centers, and outpatient physical therapy locations. Successful long-term care management requires that healthcare systems engage with patients across a disparate healthcare journey. If a provider recommends physical therapy for knee pain instead of an MRI, for instance, an improved outcome may depend on the provider’s ability to coordinate care and recommend an individualized follow-up treatment plan. > With data readiness in place, health systems can deliver personalized experiences that empower patients, improve quality outcomes, and unify the care journey across every site of care and channel. In addition to the benefit to the patient, successful long-term care management is also essential in the transition to value-based care (VBC) reimbursement models, which reward providers on outcomes vs. a fee-for-service model. Effective value-based care also relies greatly on creating a personalized patient experience. Healthier outcomes have a lot to do with a patient’s lifestyle, and factors such as diet, exercise, proximity to a gym, access to reliable transportation and other social determinants of health (SDoH) all play roles in promoting long-term health. In a VBC model, providers are incentivized to consider these factors in how to personalize the patient journey. ## **One Patient, One Story: Enabling Connected Care Through Data** To create sustained, positive behavior change through personalized experiences, a provider needs to have a deep patient understanding. An individualized health care plan that accounts for a patient’s behaviors, digital, medical and claims history, SDoH and other factors across all the ways a patient can access care requires a single patient view. To generate a unified patient view, health systems must have a single source of truth for patient data. When data is siloed across various sources, health systems are challenged with providing a relevant experience that aligns with the patient journey. If a record from an urgent care visit is not included in a unified patient profile, for instance, that lack of a complete understanding may limit the provider’s ability to recommend the optimal care path. Having a complete view of the patient through combining all sources of data is essential for guiding a personalized journey, but just as important is the quality of the data. A consistently relevant patient experience requires being able to accurately differentiate one patient from another, and to do so in a way that aligns with the patient journey. When a patient with a chronic condition accesses the patient portal, for instance, a positive experience may depend on instantly showing the patient the right content, e.g., here is information on how to conduct a home HbA1c test, or here is a link to a video that provides instruction on how to use your new insulin pump. ## **Advancing Patient Engagement Through Data Readiness** To provide a personalized experience that increases patient engagement, data must be **complete**, **accurate**, and **timely**. All relevant data must be collected in the building of a unified patient profile that provides a broad and deep understanding of an individual patient. Furthermore, automated processes need to deal with the inherent messiness of patient data – duplicate identities, misspellings, inaccurate contact information, data entry errors, etc. Timeliness requires that a unified patient profile is updated consistently, as new data is ingested. Continuous, real-time updates are essential for being able to interact with a patient with relevance as the patient journey progresses. Patient data should also be **actionable**, **trusted**, and **compliant**, which are elements associated with patient data being fit-for-purpose. Making patient data actionable means that it is accessible and marketing-ready in the needed timeframe, e.g., it is structured in a way that every endpoint technology can access it and activate it. A patient portal, for example, is updated in real time. Ensuring that patient data is trusted means that provider leaders can understand the quality of the data they’re working with – as they are using it – and have the ability to make desired changes that fit with their intended use case. A health system might want tight matching rules when sharing PII, for example, with a looser standard for operational use cases. Data that is made compliant conforms with not only regulatory requirements such as HIPAA, but also patient preferences for how their data is stored and used. The process of making sure that patient data is right (complete, accurate, and timely) and fit-for-purpose (actionable, trusted, and compliant) represents the concept of data readiness. [Data readiness turns patient data into a valuable asset](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/) that enables personalized patient engagement. Data readiness in action is when a provider marketer, for instance, sends educational materials specific to a patient’s condition or treatment plan in the patient’s preferred channel. It is offering an individualized care management program that accounts for a patient’s social determinants, such as offering a virtual consultation for a patient having difficulty accessing in-person care. It is guiding a holistic patient journey that delivers improved outcomes through a consistently relevant experience and better patient engagement. With data readiness in place, health systems can deliver personalized experiences that empower patients, improve quality outcomes, and unify the care journey across every site of care and channel. Discover how Redpoint helps leading health systems orchestrate more connected, patient-centered experiences. -> [https://www.redpointglobal.com/healthcare-providers/](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.redpointglobal.com%2Fhealthcare-providers%2F&data=05%7C02%7CKenneth.Murphy%40redpointglobal.com%7Cb979e64750af4dcb0f6508dd87567427%7C16a3d2644987408aa6aa69dd136253fc%7C0%7C0%7C638815527574940595%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=rcTg1lpPPiXGiTdhitj9MfzMiT5Zs5qrE2GWEICBNHs%3D&reserved=0) **Blog categories:** Healthcare --- ### [Crocodile Tears: Do Not Lament the “Extinction” of the Third-Party Cookie](https://www.redpointglobal.com/blog/crocodile-tears-do-not-lament-the-extinction-of-the-third-party-cookie/) **Published:** January 21, 2021 **Author:** Steve Zisk **Content:** In the famous “Jurassic Park” lunch scene, Jeff Goldblum’s character Dr. Malcolm lectures Dr. Hammond about the ethics – and lurking dangers – of bringing an extinct species back to life. His speech draws a parallel to the lifespan of third-party cookies, a technology that initially intrigued advertisers who gave little serious thought to consequences. Decades in, those consequences help explain why third-party cookies are on the way out, with Google planning to [end support on Chrome](https://www.theverge.com/2020/1/14/21064698/google-third-party-cookies-chrome-two-years-privacy-safari-firefox) by January, 2022, joining Firefox and Safari in blocking them by default. “It didn’t require any discipline to attain it. You didn’t earn the knowledge for yourselves, so you don’t take any responsibility for it,” Dr. Malcolm says about using DNA breakthroughs to clone dinosaurs. “Your scientists were so preoccupied with whether or not they *could* that they never stopped to think if they *should*.” Marketers in the nascent days of online advertising play the role of Dr. Hammond. Amazed that they could put a bit of code on a device and track it across the internet, they did not think beyond the immediate benefits. Yes, they found a seemingly innocuous way to advertise to prospects and customers when not on their own website, but the mistake was thinking that third-party cookies were a window into a customer journey. The proliferation of ad blockers and privacy browsers is a solid clue that marketers were too clever by half. Tracking a device’s web history, it turned out, was incongruous with personalizing a customer experience with relevant content. Dr. Malcom’s lesson about discipline, earned knowledge and responsibility hold true here as well. The ease of tracking a device’s web history made it easy to overlook a few inconvenient truths about using the technology as a substitute for gathering first-party data to truly understand a customer’s behaviors, preferences and intent. ## **Third-Party Cookie Failings** First, device browsing history is a poor substitute for a contextual understanding of an individual customer for a host of reasons, including the fact that customers have multiple devices, each of which can be used by multiple people. Without [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/), tracking a device across the internet can easily lead to misguided assumptions about a customer journey. Second, third-party cookies cannot identify or self-correct for mistakes made by a device’s user. A website visited in error or an inconclusive search term can throw a marketer off track of the real customer journey, leading to false assumptions about customer intent and blowing up contextual understanding. A lack of context is a death knell for a personalized customer experience, which is the glaring deficiency of third-party cookies as a tool for learning about a customer. Even stipulating that a device is used by the customer you’re marketing to, and the customer is visiting the intended web page or entered the accurate search term in a browser, dropping a third-party cookie loses context with every passing moment. This is familiar to anyone who has visited a product web page, purchased the product on a different channel, yet is still bombarded with product advertising. Also likely is a scenario where a third-party cookie is never contextually relevant. If a device’s IP address shows a New England location, a third-party cookie on a visit to a sporting goods website may generate an ad for Patriots gear, using a segmentation rule that a New England resident interested in sporting goods is likely a Patriots fan. False assumptions and hedged bets result from the inability of third-party cookies to accurately capture basic customer attributes, much less a customer journey. Another problem is attribution. Third-party cookies may be useful for some marketers and advertisers for buying look-alike audiences and impressions even though they don’t really know who they’re advertising to, but for marketers trying to personalize the customer experience it’s difficult to attribute spend to an ad generated by a third-party cookie. Unless clicking on the ad takes the customer directly to the website where a purchase is made, it’s difficult to develop a useful attribution model on the backs of third-party cookies. ## **An Advertising Alternative: Retail Media Networks** Despite its significant shortcomings, the impending demise of the third-party cookie is still causing some consternation from marketers and advertisers accustomed to using third-party cookies as a crutch, or a substitute, for earning first-party customer data that is far more reflective of a customer journey, or customer intent. One alternative has been for several large brands, including Home Depot, Walgreens, Target and CVS, to create their own [retail media networks](https://www.searchenginejournal.com/retailer-media-networks/367118/#close) to sell advertising space on their own channels and partner channels to address ‘known’ customers. This model seeks to capitalize on the shift toward walled garden advertising, which dwarfs advertising on the open web for its power to reach a “captive” known audience. ## **A Closer Look at First-Party Data** Beyond the need to fill an advertising gap, the phasing out of third-party cookies serves as a reminder that there really is no substitute for first-party data, or what Forrester refers to as “zero-party data” – data a customer volunteers in exchange for a more personalized experience – for providing a contextually relevant, personalized web experience. The undisputed value of first-party data explains the rise of retail media networks, and why marketers are better off ending the reliance on third-party cookies as an imperfect barometer of a customer journey. Where relying on third-party cookies meant basing a decision or action on something a customer did at a previous point in time that may not have any bearing on the current situation, using first-party and zero-party data as key components of a [single customer view](https://www.redpointglobal.com/single-customer-view/) allows marketers to instead deliver the best possible message, action or content at the most opportune time. An example could be offering an ad for a discount on an accessory product to an item a customer just viewed during a session. You’re analyzing real-time behavior and advertising based on that contextual understanding, backed by a single customer view that will also derive that context based on every prior interaction – on any channel. In “Jurassic Park,” Dr. Hammond learned the hard way that the profitability of a course of action does not necessarily foretell its efficacy. Third-party cookies may not have unleashed the same chaos as the park man-eaters did when used for unintended purposes, but like the dinosaurs they too had their time and place. With their rumored extinction, marketers may be pleasantly surprised by what they can accomplish when their attention is more focused on generating insights from first-party customer data. ## **Related Content** [The Year in Marketing 2020: Lessons Learned](https://www.redpointglobal.com/blog/the-year-in-marketing-2020-lessons-learned/) [The Demise of the Third-Party Cookie Opens New Methods of Engagement](https://www.redpointglobal.com/blog/the-demise-of-the-third-party-cookie-opens-new-methods-of-engagement/) [Advanced Personalization: Complex Yes, Complicated No](https://www.redpointglobal.com/blog/advanced-personalization-complex-yes-complicated-no/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Identity Resolution, Real-Time Personalization --- ### [Retailers: Does Your CDP Support a Multi-Region Footprint?](https://www.redpointglobal.com/blog/does-your-cdp-support-multiple-regions/) **Published:** April 29, 2024 **Author:** Thomas Kaczmarek **Content:** Multi-region, global retailers have unique data and marketing complexities that not every [customer data platform](https://www.redpointglobal.com/customer-data-platform/) (CDP) can handle. Factors such as having to cater to multiple languages and cultures, multiple regulatory requirements and complex MarTech stacks complicate the chances for a successful enterprise CDP. Here, then, are the Top 5 questions a multi-region retailer should ask when contemplating an Enterprise Customer Data Platform to ignite their customer data and deliver a transformative customer experience (CX). ### 1. Does the CDP support data unification across regions? Another way to phrase this question is to ask if a CDP is enterprise-ready? A multi-region retailer may have customer data across multiple databases, marketing clouds and other marketing technology. An enterprise-ready CDP will integrate data from every source into a unified profile, or [Golden Record](https://www.redpointglobal.com/single-customer-view/), that includes all customer attributes and IDs combined with transactions, behaviors, preferences, permissions and data aggregates that provide an accurate, real-time representation of a customer. As a result, marketing teams truly can deploy data best practices such as evaluating customer lifetime value (CLV) of a given customer who may shop across multiple regions. This is a key factor for a multi-region retailer to deliver cross-sell or upsell experiences at scale, often a key objective for a conglomerate with customers overlapping across multiple regions. Some CDP’s are restricted in that they either do not integrate with various components of a MarTech stack, such as multiple marketing clouds for instance, or what they call a unified profile is little more than a basic match of customer data. Advanced [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) and robust [data quality](https://www.redpointglobal.com/customer-data-management/data-quality/) are required to form an accurate unified profile that will unlock cross-sell opportunities by letting a brand know everything there is to know about a customer, across multiple regions and multiple channels. ### 2. Does the CDP support a strategic vision across regions? One of the big advantages of an enterprise-ready CDP that unifies all disparate customer data is that when used with [Orchestration](https://www.redpointglobal.com/orchestration/) and [Real-Time Interactions](https://www.redpointglobal.com/real-time-interactions/) it provides organizations with a single point of operational control over all customer data. That is, with real-time decisioning and the intelligent orchestration of next-best actions that are unbound by channel, every application and user works with the same unified profile. For a multi-region retailer, a single point of control is invaluable because it helps align various regions that may have competing interests for how a customer proceeds through a customer journey. Different regions may have different campaigns for how to reduce churn, for example, for a segment of customers at a certain level of annual spend. A single point of control that is based on knowing everything there is to know about a customer will support what may be conflicting strategies across regions. ### 3. How does the CDP handle data privacy and permission-based marketing? Customers expect the brands they interact with to be transparent about how they collect and use the data a customer provides. The expectation includes using data to enhance the overall customer experience (CX), and not in a way that violates trust, i.e. selling it to a third-party without consent. Different regulations governing data privacy in the U.S. and Europe, for instance, increase complexity for a global company. Honoring customer preferences to the letter, both for how their data is used and their permissions dictating opt-ins and other preferences, requires that a CDP incorporate [preference management](https://www.redpointglobal.com/for-a-superior-cx-elevate-preference-consent-management/) into a Golden Record. By integrating a preference center into a CDP and incorporating preferences and consent into a Golden Record, a multi-region retailer is able to deliver a personalized CX for each customer, regardless of a customer’s location. ### 4. How does the CDP handle segmentation? The simple calculation is that more regions and countries translate to more customer data, and hence more testing. If your Golden Record contains potentially upward of 1,500 customer attributes for example (as one Redpoint retail customer’s does), discovering commonalities across your customer base spanning multiple regions necessarily becomes the job of machine learning. One of the key benefits of the [Redpoint CDP](https://www.redpointglobal.com/cdp/) is rules-based, dynamic [audience segmentation](https://www.redpointglobal.com/segmentation-activation/) that tests models on the fly, using real-time customer data that accounts for changing customer behaviors. This is an important feature for global retailers with an incentive to discover the potential synergies between customers who transact across multiple regions. What do customers in one region have in common with customers from a different region, and how might a next-best action for customers who have shown an indication to churn, as an example, differ from one region to the next? ### 5. What is the CDP’s approach to data quality and identity resolution? We mentioned identity resolution and data quality at the outset as important steps in data unification, but it’s worth expanding on the Redpoint approach, where data quality steps are completed immediately upon data ingestion. This is a continuous process, where cleansing, matching, validation and data governance are taken care of in real time, eliminating the latency that too often derails attempts to deliver a relevant CX in the cadence of a customer journey. Conversely, many CDPs outsource data quality to a third party, bouncing customer data off a reference file that may be days, weeks or months old and where a new key is created for every match. The Redpoint CDP solves for this problem with the use of [persistent keys](https://www.redpointglobal.com/customer-data-management/key-management/). As data quality processes are completed, persistent keys enable matched data to be assigned to a unique ID, providing a multi-region organization with a longitudinal view of a customer over time. This, in turn, provides a Golden Record with depth and context, of particular importance when a multi-region company wants to analyze a customer’s behaviors and interactions over time. To summarize, not every CDP offers the same features and capabilities. For a multi-region, global enterprise, some capabilities outweigh others in terms of being able to manage customer data on a global scale and intelligently orchestrate personalized next-best actions across different regions. For more on how Redpoint helps global retailers meet every customer with highly personalized experiences, [click here](https://www.redpointglobal.com/retail/). **Blog categories:** Retail **Blog tags:** CDP --- ### [Context is King: Deliver a More Personalized Customer Experience (CX) Through a Contextual Understanding](https://www.redpointglobal.com/blog/context-is-king-deliver-a-more-personalized-customer-experience-cx-through-a-contextual-understanding/) **Published:** June 28, 2023 **Author:** Ian Clayton **Content:** Context, according to Merriam-Webster, is the interrelated conditions in which something exists or occurs. It is an understanding of where that *something* belongs in its immediate surroundings, whether a person, an object, the written or spoken word. It is why “that’s taken out of context!” is so dispiriting; without a mutual understanding, there’s chaos. In the world of customer experience, context refers to an accurate understanding of a consumer within the setting of a broader customer journey. It is having all the information that’s needed to truly understand the customer at a given time and place, and to then use that information to deliver a hyper-relevant experience that reflects a current understanding. Context is crucial to providing a customer with experiences that drive retention, loyalty and lifetime value. In a recent [Dynata survey](https://www.redpointglobal.com/press-releases/80-of-consumers-more-likely-to-shop-with-brands-that-show-they-understand-them/) commissioned by Redpoint, 80 percent of consumers surveyed said that they are more likely to purchase from brands that demonstrate a personal understanding by sending relevant, personalized offers. Context is a large part of what makes that possible. ## **Context and Real Time** Relating “interrelated conditions” to a superior customer experience, what’s relevant is that those conditions can be as numerous and varied as a businesses’ customer engagement technology can gather and support. It stands to reason that the more information about a customer a system can gather, the more relevant the resulting CX. Increasingly, [real time](https://www.redpointglobal.com/real-time-interactions/) is becoming an “interrelated condition” important for layering the needed context for a complete understanding of a customer across an omnichannel journey, and demonstrating that understanding through a personalized CX. Consider a product recommendation engine that generates recommendations based on a customer’s purchase history. If transactional data is fed into the system on a weekly batch load, a recommendation is not as likely to resonate with a customer as one that makes a recommendation based on a real-time, unified profile of a customer that includes all behavioral, demographic and psychographic data, and maybe even includes factors such as time of day and the weather. It’s not just the volume of data that provides context, it’s when that data is made available to marketers and business users; the longer the lag, the less context for how that data relates to an ongoing customer journey. ## **A Proactive Connection** Providing a real-time understanding of a customer across an omnichannel journey is what separates Redpoint from customer engagement technology with a somewhat different understanding of “real time.” By bringing all possible information and relevant data about a customer, household or entity in one place, Redpoint provides the context needed to make the most informed decision and create the most compelling experience for the customer at the precise moment of engagement. Redpoint handles not only the data orchestration side of things, but also does real-time data capture for context as well as real-time decisions to deliver a response, i.e. content for an app, open-time email or website. Conversely, when an understanding of a customer is formed through multiple systems, a real-time concept becomes more nebulous, dependent as it were on coordination. A website personalization tool, for example, will gather session information from a particular device and call out to a CDP to attach the information to a unified profile for the purpose of delivering personalization on that channel. Often, though, there is an expectation that a CDP will feed the tool information on a need-to-know basis, i.e. when a source system is updated. But is that a nightly feed? Weekly? Even a hyper-personalized website experience based on the most up-to-date customer data, however, is still siloed in that personalization is limited to one channel. The importance of context is easily understood if a customer who just received an online offer then contacts the call center – and the call center either unaware of the new offer or, worse, makes an offer at odds with the previous one. Redpoint’s approach is to continually gather everything to create a persistently updated [Golden Record](https://www.redpointglobal.com/single-customer-view/) that is perpetually accessible and available to all users and channels, ensuring context by proactively connecting to all systems. In a dynamic customer journey where a customer moves from one channel to the next at will, marketers can’t always be certain what information about a customer will be relevant until the moment of interaction. With Redpoint, though, information is always updated. Always available. And always ready for use. ## **Redpoint’s Approach to Real Time** Redpoint’s approach to real time includes pre-decision and post-decision pipelines, which is where additional contextual information is gathered the moment preceding and following a decision. Immediately preceding a decision, a call can be made through an API to gather additional context, whether it’s a model, a weather update, a credit card check or another piece of information that is then loaded into the customer profile to layer in real-time context. The post-decision pipeline is where a decision can be manipulated, such as being sent to multiple systems or channels. Importantly, the pipelines are stackable, meaning they enable more than one decision. The pipelines enhance contextually relevant experiences because they’re built around dynamic rules that permit content to be switched out up to the moment of interaction. For a hospitality company or an airline, perhaps a guest digitally checks in. Based on the context in that exact moment – is their room ready, has their flight been delayed, is there a storm on the way, is there construction at the drop-off area – the pre-decision pipeline could call a machine learning model (Redpoint’s or a client’s) that returns real-time recommendations optimized against the decisions built into the platform. A resulting decision, then, is based on everything there is to know about the customer – does the customer prefer an SMS or email, what offers have they redeemed in the past, what is their lifetime value, are they a candidate for churn – as well as the immediate context. This capability prevents mistakes that, lacking real-time context, are sometimes made even with the best intentions. Consider a hotel that triggers an offer for a city walking tour when a new visitor to the destination checks in. Sounds great. But if you make the offer when the guest checks in during a three-day monsoon, you will introduce friction into the customer journey. With a real-time contextual understanding, the offer might be switched out right up to the moment of check-in, perhaps an offer for a museum tour instead. A post-decision pipeline works in the same fashion, providing marketers and business users with the opportunity to nuance/repurpose a decision before it is presented to a device, app, website, tablet or even in person. This might entail using generative AI to change the tone of a welcome greeting, as an example, or otherwise massaging a decision to optimize it for the intended recipient. The goal is to simply provide as meaningful a contextual interaction as possible, making it appear from the customer’s view as if they’re engaging with a person – even across digital channels. Redpoint also offers greater flexibility by virtue of being agnostic to the consumer of the decision result, i.e., there is no restriction to keep the message “as-is.” The content delivered, in other words, does not have to be the content shown. There is flexibility to use the content optimized to enhance the experience where the message is being delivered, such as using thumbnail images on a mobile app vs. a gallery display on a landing page. The decision call and result are the same, but a marketer or business user doesn’t have to worry about fixing the result to one channel. An interesting dynamic about contextual experiences is that the more they’re successful, the more context can be built into subsequent decisions. The result of the decision – did the customer buy the product, sign up for the tour, download the app, etc. – can be used to inform a subsequent decision the next time an offer is presented, either to the same customer profile or to alter the pre-decision or post-decision content. ## **Consistent, Contextual Relevance** With the Dynata survey, we’ve seen that relevant, personalized offers delivered to a customer in the right context drive repeat business. The opposite is also true, i.e. negative experiences drive customers away. In 451 Research’s *Voice of the Connected User Landscape, Quantifying the Customer Experience* (2022), nearly [9 in 10 consumers](https://www.spglobal.com/marketintelligence/en/news-insights/podcasts/451-research-episode-54) said that having a negative experience (in-store or online) makes them less likely to interact with a brand/retailer again in the future. Asked what constitutes a positive experience, 43% defined it as consistency across a brand’s website, retail store, mobile app and call center. Layering real-time context across the experiences that span an omnichannel customer journey delivers the consistency – and relevance – that delight customers and result in greater loyalty and lifetime value. **Blog tags:** CDP --- ### [Is Your Data Really Complete? Understanding the First Step in Data Readiness](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/) **Published:** June 9, 2025 **Author:** John Nash **Content:** Despite massive investments in data infrastructure, nearly half (46 percent) of organizations report not using data to gain insights or make decisions – and 72 percent say that they do not view their data as a strategic asset, according to an [NTT Data Innovation Index](https://www.nttdata.com/global/en/news/press-release/2023/november/only-21percent-of-organizations-achieve-their-innovation-goals-as-many-challenges-stand-in-the-way) survey. This disconnect is often rooted in the data itself. Incomplete, fragmented and siloed data makes it impossible to see the full picture. That’s why the first pillar of data readiness is **completeness**. To be ready for personalization, real-time engagement, analytics, AI, or even compliance, brands must start with a unified and comprehensive customer profile. Without it, everything downstream is compromised. As discussed in an [earlier blog,](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/) the term “data readiness” describes data that is both right and fit for purpose, with the six core characteristics being that customer data is **complete**, **accurate**, **timely**, **actionable**, **trusted** and **compliant**. This blog will focus on what is meant by “complete” when referring to a complete unified customer profile, also known as a Golden Record, that is the foundational asset for personalized engagement, analytics, operational efficiency and other business and CX use cases which may include AI. ## **Complete: A Full Checklist** A customer profile (or a ‘profile’ of any entity) accurately deemed to be complete will, for starters, include all relevant data types, with the unified record aggregating data across multiple domains: - **Identity Data**: Name, email, phone number, device IDs, social handles - **Demographic Data**: Age, gender, income, location, household - **Transactional Data**: Purchase history, returns, billing, subscriptions - **Behavioral Data**: Web visits, mobile app usage, email clicks, search terms - **Engagement Data**: Customer service interactions, campaign responses - **Channel Preferences**: Preferred communication and purchase channels - **Consent & Permissions**: Marketing opt-ins, privacy preferences, compliance data Complete also requires that data be unified across all systems – any possible source of data that will provide a brand with a full understanding of the customer, household or business that the use case demands. A brand cannot fully engage a customer unless it has a deep understanding of them at the individual level – and that understanding comes from a broad yet detailed set of data. A partial list will include data from a CRM, marketing automation platforms, a CDP, an eCommerce system, a POS system, customer service platforms, GenAI applications and offline sources such as in-store interactions and call centers. ![Data Readiness Complete Graphic](https://www.redpointglobal.com/wp-content/uploads/2025/06/Data-Readiness-Complete-graphic-2-800x376.jpeg)The Six Pillars of Data Readiness: Complete Collecting and unifying all relevant data types will ideally provide the enterprise with all necessary and relevant data about a customer. There will not be any important gaps. This combination provides a “complete” view because it represents cross-channel interactions and lifecycle history, and it contains first-party data, second-party data and even third-party data. The completeness of a record is essential to powering effective segmentation, analysis and customer engagement. Complete data isn’t necessarily accurate or trusted. Completeness refers to the presence of all relevant data, not its correctness or reliability – those qualities are addressed by the separate pillars of accuracy and trust. ## **Complete: A Contextual Understanding** To this point, “complete” has been described to mean collecting all types and sources of data. A complete view, however, also refers to the contextual view of a customer (household, etc.) afforded a brand by ensuring that there are no gaps. That is, when all types and sources of data are included in the building of a Golden Record, a brand has a real-time and historical view of a customer. Cross-channel interactions and lifecycle history tell a story about the customer. All new data adds to the breadth and depth of the customer view across time; having data that is both up-to-date (real-time) and historical add to the completeness of a profile that provides the full context of the customer journey. The need to have this longitudinal view is why persistent key management is part of any robust data readiness platform. Persistent key management enables a brand to maintain a stable identity over time across multiple systems and touchpoints. This allows the brand to recognize that *jane\_doe@gmail.com* and *jane.doe@gmail.com* refer to the same individual – even as identifiers change or vary across interactions. ## **Why “Complete” Matters** For training AI models, for powering LLMs and other GenAI applications – really any business or CX use case, it’s easy to see why having a complete, contextual understanding of a customer, household or business matters. Enterprise truly should know all that is knowable about a customer, within the bounds of privacy and consent. For instance, say a brand has a customer’s name and email address, knows their past online and in-store purchases as well as the customer’s browsing behavior and mobile app usage. That understanding may, in many cases, be enough to deliver a relevant interaction – certainly if the data is intended solely to send an email, or even personalize a webpage. But this collection of data may not be accurately described as “complete” if it is missing, say, a customer’s most recent interaction with a call center, or a chatbot, or a Facebook post that refers to the brand, in either a positive or negative light. The same is true if the customer is using a new email that has not been matched to the customer. A lack of completeness introduces friction into the customer experience because the brand is simply unable to interact with consistent relevance. Missing a new email or physical address, it sends information that the customer never gets. A call center agent, unaware a returns process has been initiated, asks a customer to complete a satisfaction survey. Whatever the issue – large or small, a known problem or unknown – a customer is likely to perceive a lack of completeness in a negative light. The brand, for whatever reason, fails to recognize the customer as an individual. Conversely, unifying all data types from all sources and having a full contextual understanding of a customer unlocks hyper-personalized offers and messages, more effective segmentation and targeting, increased retention, and a better overall customer experience. In addition, a complete profile is essential for a brand to ensure it is in compliance with privacy laws, providing accurate tracking of consent and communication preferences. Achieving a complete view of the customer is the foundation for any data-driven initiative – a from personalized engagement to AI-powered decisioning. But completeness alone isn’t enough. In our next post, we’ll explore the second pillar of data readiness: **accuracy** – ensuring that the data you rely on is not just comprehensive, but correct. **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [Four Hurdles Impeding Your Omnichannel Success](https://www.redpointglobal.com/blog/four-hurdles-impeding-your-omnichannel-success/) **Published:** August 30, 2018 **Author:** Steve Zisk **Content:** ![customer-engagement-data-girl](https://www.redpointglobal.com/wp-content/uploads/2017/10/customer-engagement-data-girl-e1507827213885.jpg)Every marketer strives to provide customers with a seamless experience across each retail touchpoint. Consumers expect that from brands, but what they experience often falls short. We call this the customer engagement gap, where experience and expectations clash. To bridge the gap, you need to look beyond your omnichannel goals and strategy and get to the heart of the matter: customer data. You probably have multiple data sources storing information on your customers: marketing automation software, social and web analytics, and more. But the amount of data you possess is useless if you can’t leverage it to inform your next best message or offer. Siloed, fragmented data is what is actually hindering your omnichannel efforts. Overcoming the obstacles customer data presents isn’t impossible. To execute truly effective omnichannel campaigns, and to improve overall customer experience, start by addressing the four biggest challenges marketers face when it comes to customer data. **1. Achieving One Version of the Truth Through Identity Resolution** Without the ability to capture, integrate, analyze, and act on data, enhancing omnichannel journeys is impossible. It’s essential to have a single point of control over all your customer data to maximize the impact of hyper-personalized campaigns. [Identity resolution](https://www.redpointglobal.com/challenges/identity-resolution/) enables an organization to analyze an individual’s identity based on his or her available data records and attributes. When an individual is searched for and analyzed through an identity resolution solution, a series of algorithms, probability, and scoring is applied to find and determine any associated records. This process gives companies the ability to connect customer data from third-party providers, both digital and traditional channels, as well as the customer’s devices to establish a [golden record](https://www.redpointglobal.com/blog/what-is-a-golden-record/). Ultimately, the golden record will be the catalyst of your marketing success. **2. Integrating Data Between Digital and Traditional Channels and Devices** You can’t manage what you can’t see. If your organization is blind to customers’ behavior, then you can’t effectively engage with customers in relevant and personalized ways. For instance, if a customer receives an offer via direct mail, but she has already signed up for the same offer via her mobile device, that’s a disconnect in her experience that could be avoided with the right insights. Data is responsible for much of an organization’s broken view into customers’ cross-channel and cross-device whereabouts and behaviors. A survey by Periscope, a unit of McKinsey Solutions, revealed that *45 percent of respondents said poor data quality contributed to their lack of 360-degree views of customers and omnichannel struggles*. Making sense of the overwhelming amount of unstructured data and deriving insights that will drive strategy forward is a major challenge to overcome. Marketers are finding that [customer data platforms](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/) (CDPs) solve this problem. The right CDP can access [all data sources](https://www.redpointglobal.com/challenges/data-integration-quality/) and types to get a complete and precise view of every customer. **3. Mending Silos of Specialty Systems to Create an End-to-End Platform** Technology fragmentation impedes true cross-channel success and leads to an inconsistent customer experience. Marketers are investing significantly more money in data-driven tools to avoid that. But managing and coordinating the growing set of disparate platforms, solutions, and interfaces in your technology stacks is a huge challenge. As you explore different options, identify platform that use an [open garden](https://www.redpointglobal.com/challenges/open-vs-walled-garden) approach. That way, you can leverage existing platforms and data sources without replacing technology. This will make creating a clear roadmap for integrating your technologies that will allow for the frictionless flow of data across your stack much easier. Getting your technologies to work better together will move marketers a giant step closer to your goal of delivering a cohesive and engaging customer experience across multiple channels. **4. Enabling Real-Time Data Capture, Processing, and Activation Capabilities** Leveraging [real-time data](https://www.redpointglobal.com/challenges/real-time-interaction-management/) brings tremendous value to today’s shifting omnichannel and digital landscape. Companies need to respond quickly and with relevance and value, or risk missing a key engagement or sale, or even experience customer attrition. Effective real-time marketing can no longer consist of sporadic actions. Instead, marketers must carefully design strategies and allocate resources to uncover the right audiences so they may target them with the right content at the right time. Having the ability to respond in real time will deliver such measured interaction, but this requires having the capability to capture data across all devices and channels, including those enabled by the IoT, and then respond in milliseconds. However, there is zero value in capturing real-time data, structuring it, and processing it if we don’t have the analytics engines in place to respond in real time. That’s why it’s essential to set up real-time data feeds. To ensure that data is acted upon quickly, companies must also set up corporate data-handling policies and procedures across the enterprise to ensure all employees know their roles and specific actions to take. **It Comes Down to This** Marketers need to become empowered by data, rather than overwhelmed by it. And while you can have a thorough omnichannel strategy, it won’t yield results unless you have a platform you can trust to give you real-time insights to inform your next best action. When marketing is supported by reliable customer data, it’s easy to bridge the customer engagement gap and provide hyper-personalized, relevant omnichannel experiences to every customer. **RELATED ARTICLE(S)** [Getting Personalization R](https://www.redpointglobal.com/blog/getting-personalization-right-in-omnichannel-retail)[ight in Omnichannel Retail](https://www.redpointglobal.com/blog/getting-personalization-right-in-omnichannel-retail) ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2017/01/RP_eBook_Path_Omnichannel_Marketing.jpg)](https://www2.redpointglobal.com/ebook-path-to-omnichannel-marketing-ebook-pr) **Blog categories:** Journey Orchestration, Omnichannel Marketing, Real-Time Personalization --- ### [The Power of Customer Journey Design](https://www.redpointglobal.com/blog/the-power-of-customer-journey-design/) **Published:** October 11, 2018 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2018/10/Customer-Journey-Image-e1539194082775.jpg)The modern customer journey has evolved. No longer do consumers travel a linear pathway from awareness of a brand to making a purchase. Today’s customer moves along a dynamic path to purchase that includes multiple channels and multiple interactions over a period of several days, weeks, months, or even years. The average marketer remains unaccustomed and often resistant to this change, with many brands still providing messaging separated out by touchpoint instead of unified into a coherent journey. The ability to design an efficient customer journey that allows consumers choice in which channel to use while still moving them toward purchase and then ultimately repurchase is extremely powerful. Marketers with this technology at their command can more readily provide the right messaging to the right customer at the right time on any platform. This enables them to more accurately deliver an engaging customer experience over the long term, while still moving consumers along a defined journey. It’s possible to build a customer journey in many ways, but one of the most efficient is doing so graphically. This is one of the key capabilities of the Redpoint Customer Engagement Hub™. The platform’s graphical approach to customer journey construction allows marketers to easily connect phases of the customer journey together in a coherent line. By doing this, the Redpoint platform empowers marketers with the ability to simply manipulate journey phases and bring consumers along the pathway from awareness to sale quickly and efficiently. This is a fundamental shift in delivering the customer experience and can be transformational for marketing results over the long term. In the video below, I demonstrate the customer journey functionality of the Redpoint Customer Engagement Hub, along with its ability to graphically edit emails, import digital assets from multiple management solutions, and construct email campaigns with an easy-to-use wizard. Take a look and let me know what you think in the comments below. ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2018/01/Open_Garden_eBook_thumbnail.png)](https://www2.redpointglobal.com/ebook-open-garden-approach-pr) **Blog categories:** Journey Orchestration, Real-Time Personalization, Segmentation & Activation --- ### [Data Management and the Modern Data Architecture](https://www.redpointglobal.com/blog/data-management-and-the-modern-data-architecture/) **Published:** November 22, 2016 **Author:** Redpoint Global **Content:** Investing in a powerful [data management solution](https://www.redpointglobal.com/solutions/) is a crucial component of long-term marketing success, especially given how difficult it is to engage customers in the modern multichannel environment. Because of this marketplace reality, it’s vital that organizations of all sizes deploy a modern data architecture that can adapt and grow to meet changing business needs as well as drive improved customer engagement over the long term. Given the plethora of options, it’s incredibly easy to choose a [data management](https://www.redpointglobal.com/customer-data-management) solution that doesn’t possess long-term viability. To help companies avoid this pitfall, I recently outlined five critical components of a modern data architecture: - **Flexibility at scale** - **Support for parallel and distributed processing** - **Democratized data access** - **Easy to use without specialized training** - **Ability to handle all data types** These five components, taken together, will ensure that your data management solution can evolve along with changing business needs and adapt to shifting customer behaviors. It’s incumbent on all organizations to find the right solution to address the problem, which is why we designed [Redpoint Data Management™](https://www.redpointglobal.com/solutions/redpoint-data-management/) expressly with the intent of making it adaptable for a customer engagement landscape that’s perpetually in flux. ## **Flexibility at Scale** In my previous post, I wrote that a modern data architecture needs to adapt to the growing volume, variety, and velocity of customer data without an increased failover rate. This is a consistent problem with [data management](https://www.redpointglobal.com/customer-data-management) technologies that leverage in-memory processing. As the number of records increases and memory requirements grow over time, failover rates also increase. Higher failover rates mean data processing takes too long, which lengthens data preparation time and shrinks the time spent on data analysis. This presents a problem for the viability of data-driven decision making, especially in the current fast-paced business world. [Redpoint Data Management avoids this problem](https://insidebigdata.com/2016/06/29/a-hive-free-approach-to-hadoop-data-management/) by conducting 100% of its processing directly in Hadoop through a native YARN application that “evaporates” from the cluster when processing is completed. This results in what we call a “zero footprint install,” which eliminates any significant memory requirements. Because of our processing method, it’s not surprising that MCG Global Services recently found that Redpoint Data Management conducts data-processing tasks [550% faster than Apache Spark and 1,900% faster than MapReduce.](https://www2.redpointglobal.com/report-hadoop-benchmark?_ga=2.179604514.762313034.1516823030-1457147060.1516823030) This is a substantial difference in processing speeds, and places our solution in a solid position over our competitors to handle the changes in data volume and type over time. ## **Support for Parallel and Distributed Processing** Parallel and distributed processing reduces time to insight dramatically through allowing organizations to process data queries much more quickly than linear methods. This becomes even more vital as data increases in volume and variety, which makes reducing time to insight a key priority. Redpoint Data Management was designed with time to insight in mind, and as such conducts its parallel and distributed processing directly in Hadoop via YARN. The solution’s fast processing speeds even occur in database environments that don’t run on Hadoop, which is still a significant portion of the companies leveraging data in their business decisions. This means that companies don’t have to upgrade to Hadoop in order to enjoy fast data processing capabilities. ## **Modern Data Management** IT has traditionally held the keys, so to speak, for business data. This made sense when there was less data generated in fewer channels, and the business cycle moved slower, but nowadays is a recipe for poor long-term results. In Redpoint Data Management, we created a graphical user interface (GUI) that allows DBAs and data analysts to perform their data quality and data integration tasks without specialized Hadoop coding experience. This WYSIWYG interface can allow business users to disintermediate IT and gain insight faster than ever before. ## **Easy to Use Without Specialized Training** In my previous post, I wrote that a modern data architecture should allow you to query the data and derive insight without having to learn a coding language or take a lengthy training course on the solution’s functionality. Marketers are increasingly performing their own data prep and data analysis, and they often don’t possess the specialized knowledge of DBAs and can’t hire one because of the expense. This makes it important for the solution to be usable by the line of business, no matter their knowledge base. Redpoint Data Management is designed to be used for advanced data analysis without specialized coding experience. We’ve built it with the business user, and the time-crunched data scientist, in mind. As a result, our no-code approach eliminates the skills gap inherent in the delayed adoption of Hadoop for data processing. Redpoint Data Management allows people without coding experience to benefit from leveraging Hadoop for powerful business analytics, driving insight with existing resources and incurring a lower total cost of ownership. ## **Ability to Handle All Data Types** As the volume, variety, and velocity of data increases over time, it’ll become even more vital that data management solutions can handle all different kinds of data. A modern data management architecture should ensure that data is processed effectively, regardless of its source. It’s for this reason that Redpoint Data Management has a wide range of data quality and integration capabilities, including ETL / ELT, cleansing, matching, de-duping, parsing, and master key creation. This wide range of functions ensures that Redpoint Data Management users can integrate high-quality data no matter the source or format and leverage it in their analytics efforts. A modern data architecture is vital for future organizational success, largely because the volume, velocity, and variety of data is only set to increase over the next few years. Redpoint Data Management was designed for this future, and I think it’s one of the most adaptable solutions on the market today. Regardless of whether you agree or not, the fact remains that your enterprise data management solution must be flexible, scalable, and able to handle the tidal wave of data heading your way. **Blog categories:** Data Management, Data Quality --- ### [Building vs. Buying a CDP? Why That’s Not the Only Question You Should be Asking](https://www.redpointglobal.com/blog/building-vs-buying-a-cdp-why-thats-not-the-only-question-you-should-be-asking/) **Published:** February 12, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/02/shutterstock_678483271-e1549908570789.jpg)In January, I wrote a blog that addressed many of the common misconceptions about the customer data platform (CDP). One misconception people have is that a CDP can deliver dynamic customer journeys out-of-the-box, without the hard work of the seemingly endless business user and technical configurations that must be completed for a CDP to truly differentiate the business. A CDP is an application that manages a persistent, unified customer database for a single view of the customer. Its purpose is to support real-time decisioning and omnichannel orchestration with an accurate, always up-to-date golden record. Understanding the truth about what a CDP is – and what it isn’t – is important knowledge to have before a business can answer the next logical question, which is whether to buy or build a CDP. For a marketing organization to arrive at that question, they’ve likely come to realization that, buy or build, the CDP is a worthwhile investment that will provide a much-needed unified view of the customer to optimize the [omnichannel customer journey](https://www.redpointglobal.com/challenges/omnichannel-marketing/). That’s a good start. **Differentiation vs. Risks?** There are several obvious questions an organization must answer before deciding on the buy vs. build path – cost, use case, ongoing maintenance, and performance among them. An IT department will understandably want to take the reins on any mission-critical system to maintain core control, but in the case of a CDP, that traditional way of thinking comes with a caveat. But the question that trumps all others is around differentiation: Is the CDP (and the personalized real-time engagement it supports) part of your organization’s market differentiation? Many marketing organizations and IT departments may believe that a CDP built in-house will deliver better performance at less cost, but ultimately if the platform doesn’t provide differentiation then it doesn’t really make much sense to go the build route. Differentiation with a CDP often means your organization expects to personalize engagement with your customers at the customers’ cadence, in their chosen touchpoints, with relevant messages that will drive loyalty, customer lifetime value, and revenue. Anything less makes the CDP an efficiency play. Differentiation is the game-changer that makes it worthwhile for a company to take on the risks of delivering the CDP on-time, under budget, and able to handle users’ performance expectations. To build the solution in-house, you are betting that the long-term value of a custom CDP will mitigate all those risks. **Technical Obstacles to Overcome?** One of the first to-dos in a build vs. buy decision is for the marketing organization to map out exactly how it believes a CDP will differentiate the organization in the market. Without a clear idea of what differentiation means for your business, you may be taking on an unnecessary endeavor. Agility – both for marketers and IT – is a primary differentiator for a CDP. If the organization can quickly add new sources of information, handle new or changing customer touchpoints, and incorporate new components of the customer engagement stack, then marketers will be able to provide personalized, relevant interactions, and customers will see the brand as valuable and meaningful. This kind of differentiation also relies on consistent performance to make interactions timely, even during high demand periods like retail holidays and insurance enrollment periods. Another area of distinction is [identity resolution](https://www.redpointglobal.com/challenges/identity-resolution/), which requires linking records across an anonymous-to-known customer journey. Building a robust CDP requires a solution to offer both deterministic and probabilistic data matching to match a wide range of data consistency and quality found across any first, second, or third-party data source. Simplicity is also a common CDP misconception, and it can creep into the build vs. buy decision. Assembling a data lake, for example, with the thought that it will deliver 75 percent of what a CDP provides misrepresents the potential of a CDP as a powerful digital transformation engine. For a truly differentiated CDP, a businesses would need to develop a [persistent golden record](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/) that accounts and plans for all underlying data requirements and structures, and is flexible enough to connect with existing and future customer data sources. **Is Your CDP a Revenue Engine?** If the technical challenges of creating a golden record system that eliminates silos can be achieved, building a custom CDP may be feasible. However, if the decision is made to build a CDP largely to have more control over what is intended as a mission-critical, revenue-generating solution, marketing should be aware that this traditional way of thinking doesn’t really apply to a robust CDP. The reason for this is that at its core a CDP itself is not the revenue generating system. A CDP will not magically deliver optimal continuous engagements. Rather, it enables the personalized, dynamic customer engagements that are the actual revenue drivers. In this sense, a CDP differs from something like a highly customized financial trading application where the solution itself generates revenue. A CDP’s value is defined in the context of how it is configured, and how that configuration supports the delivery of a personalized omnichannel customer experience. Maximizing a CDP’s value, performance, and functionality to deliver those dynamic customer engagements requires a combination of IT technical capabilities and marketing engagement “chops.” And it’s not just a question of time and resources. Rather, a CDP as a mission-critical revenue generating solution requires a deep understanding of every aspect of a golden record and what a marketing department can do with it. The best CDP solution may be neither a simple off-the-shelf tool to gather customer details from online sources nor a custom-built IT solution to handle connectivity and identity resolution. Instead, a flexibly configurable platform that offers deep connectivity, tunable identity resolution, and agile reconfiguration will meet both IT and marketing needs for a highly differentiated solution. Solutions like the Redpoint Customer Engagement Hub™ offer a way to apply AI and machine learning insights to the CDP-generated golden record and to deliver intelligent orchestration for the next-best-action to drive the customer journey. While it’s true that the buy vs. build discussion isn’t a binary decision – companies fall on a spectrum based on needs, resources, and the existing martech stack – it’s a mistake to think you can cut corners. Will the customer record that it or another cobbled together system delivers really going to be a difference maker for the marketing department? **Getting Started** The CDP build vs. buy decision is one of the more important decisions a business will make, with far-reaching implications for how well a marketing organization will be able to deliver on the promise of personalized customer experiences. It’s a serious investment that requires serious thought. Start with figuring out marketing’s goals for differentiating the customer experience, both now and down the road. With those goals in place, the actual decision may be easier than you think. ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2018/08/SB_CDP_Transforming_Customer_med.jpg)](https://www.redpointglobal.com/wp-content/uploads/2018/06/Solution-Brief-CDP-Transforming-Customer-Exp-0418-01.pdf) **Blog categories:** Customer Data Platform, Data Quality, Omnichannel Marketing, Real-Time Personalization --- ### [Art of the Possible – The Role Data Can Play in Unlocking Marketing Creativity](https://www.redpointglobal.com/blog/art-of-the-possible-the-role-data-can-play-in-unlocking-marketing-creativity/) **Published:** October 29, 2018 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2018/10/Art-of-the-Possible-e1540493361483.jpg)According to [Astro Teller](https://www.youtube.com/watch?v=cA_8IO3vbFs), head of moonshots for Google X, “It is a lot easier to hit a 10x number improvement than a 10% improvement.” The reason for this is when you set your sights on small improvements you tend to look for incremental ways to improve rather than big ideas to reach toward the stars. The concept can easily apply to marketing teams that adopt a 10x strategy to reach major growth objectives, and the rise of NoSQL and document databases helps marketers to deliver on that strategy. In traditional marketing campaigns the time and effort required to access data and easily query it has been a major obstacle to success and has limited marketing creativity. The need for a data scientist or an IT manager to assist has also added to the burden. With NoSQL databases, marketers have a new way to easily access data and unlock marketing creativity to get a multiplicative level of improvement instead of incremental improvement. NoSQL document databases offer more flexibility in accessing all data types, cost savings by reducing the overhead and expense of data wrangling, and allow marketers to scale and grow their efforts more quickly and easily. ![](https://www.redpointglobal.com/wp-content/uploads/2018/10/NoSQL-Database-image-1024x518.png) Following are a few ideas of how marketers can now utilize customer data to uncover the art of the possible: • **A picture says a thousand words**: We all know that messaging isn’t the only reason people click or engage with marketing campaigns. Often there are beautiful images or videos along with the campaigns, but it is hard to measure the impact of images on customer engagement. With a NoSQL document database, image content becomes searchable by engagement: who bought what, when, where and with that you can pull together a timeline of highly effective customer images. This allows for smarter and more contextual retargeting strategies in digital and traditional channels. With this capability you always can bring forward each customer’s favorite image to help boost conversions and drive revenue. • **Turn your frown upside down**: A bad customer experience doesn’t have to be the end of the customer journey. What if it was possible for you to run a marketing retention campaign more quickly and efficiently for people who returned items and had a bad experience? The ability to access product information in a timely manner from a NoSQL document database means that your marketing team can easily find and target all the people that have faced certain challenges with a product or didn’t connect with the messaging. You could also re-engage with customers who have return items or stopped services. • **Data defines you**: Data is one of the most powerful assets for today’s business and the ability to derive insights from customer data is what differentiates market offerings. Metadata has long been important for indexing and ranking, but there is marketing potential in metadata as well. With NoSQL document databases, you can simplify and reduce the time it takes to create audiences based on buying trends and metadata tags associated with them versus segmentation based on traditional age, gender, and affiliation demographics. Campaign development on similar metatags would be a simple process and could have potential to reach segments with much higher engagement and conversion rates. These are just a few examples of what is possible with the right tools. By enabling marketers with a new level of connectivity and access to data you unlock the potential of the data. Furthermore, with a NoSQL document database such as [MongoDB](https://www.mongodb.com/what-is-mongodb) or [Cosmos DB](https://docs.microsoft.com/en-us/azure/cosmos-db/introduction), all this information lives in one place. Then the question becomes more about how you encourage your team to embrace the art of the possible and think more creatively around the data and the customer journey. ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2018/10/SB-ArtIntelUS1018-01-COVER-768x995-2.jpg)](https://www.redpointglobal.com/wp-content/uploads/2018/10/SB-ArtIntelUS1018-01-Artificial-Intelligence-hi-res.pdf) **Blog categories:** Anonymous to Known, Data Management --- ### [What is Behavioral Marketing?](https://www.redpointglobal.com/blog/what-is-behavioral-marketing/) **Published:** October 16, 2020 **Author:** Steve Zisk **Content:** What is behavioral marketing? Most often referred to in the context of digital advertising, behavioral marketing targets customers and prospects based on an entirety of interactions, website visits, cookies, search history, transactions, call center, etc. Behavioral marketing is the process of analyzing patterns with the intention of serving more targeted content that will ultimately improve a customer’s overall experience and drive higher profits. Like a forensic detective investigating a crime scene for physical and digital and clues that will piece together a narrative, [behavioral marketing](https://www.redpointglobal.com/blog/what-is-behavioral-marketing/) combs physical and digital footprints across all channels. Instead of capturing a thief, behavioral marketing captures an audience’s attention with more relevant content personalized to a unique customer journey, leading to higher conversions. According to Accenture, [91 percent of consumers](https://www.accenture.com/_acnmedia/PDF-77/Accenture-Pulse-Survey.pdf) are more likely to shop with brands that recognize, remember and provide relevant offers and recommendations. ## **Behavioral Marketing: Beyond the Web** A big misconception about behavioral marketing is that it’s limited to analyzing a customer or prospect’s behavior on a website: click-throughs, time on page, etc. for the purpose of serving up first-party or third-party digital advertisements based on behaviors on a single channel. An example of this type of behavioral marketing would be a user on the real estate section of a newspaper’s website then being shown a realtor’s listings in her area. As a contrast with direct marketing, where that realtor might send a listings brochure to everyone in a ZIP code, marketing to a segment based on one behavior will likely have the desired effect of reaching a more targeted audience with a higher level of interest. ## **Enhance Behavioral Marketing with a Multi-Channel Approach** A more nuanced form of behavioral marketing is analyzing a customer or prospect’s behavior across a wide range of channels. To borrow the forensic detective analogy, a search of the basement may yield an evidence windfall, but a thorough detective will still look in the attic and pull cell phone records. An accurate prediction of intent depends on analyzing a full range of behaviors. An individual behavior may have significance as to a customer’s intent, but pieced together over time behaviors tell a fuller, more complete narrative. In a [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/wp-content/uploads/2020/07/Addressing-the-Gaps-in-Customer-Experience.pdf), 52 percent of consumers surveyed defined a personalized customer experience as receiving special offers only available to them, with 43 percent of consumers defining it as a brand knowing that they’re the same customer across all touchpoints. This level of recognition is possible only by analyzing how a customer engages across all channels, which means detailed analysis of customer behaviors at every interaction. Behavioral marketing becomes more effective when marketers account for behaviors across a multitude of channels. Behaviors do not exist in a vacuum, but rather each is an influencing factor in a customer’s omnichannel journey. ## **Behavioral Marketing and Identity** Behavioral marketing becomes even more powerful when behaviors are attached to a customer’s identity. A realtor who segments an audience based on a prospect’s visit to a landing page showing availability in specific neighborhood may enjoy a better ROI than a bulk mailing, but a realtor who goes farther will likely produce better results. For example, by knowing the visitor’s household status, age, income, demographics, ID’s, etc. a realtor may send even more relevant listings, such as only those with four bedrooms, a two-car garage, an in-law apartment or within walking distance to a grammar school. Compiling a more complete set of behaviors would entail more stringent analysis far beyond a single website visit. Perhaps the prospect is active on home improvement sites, or they’re inquiring about certain neighborhoods on social media. Have they commented on open houses they’ve seen? Compared mortgage rates? ## **How Does Identity Resolution Fit in With Behavioral Marketing?** Advanced [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) is a powerful tool for amassing a complete picture of a prospect or customer. Identity resolution goes beyond using a cookie match or a device identification as the basis for providing a customer with a specific offer – limited to a match at a moment in time such as realtor [audience segmentation](https://www.redpointglobal.com/orchestration/segmentation) based on one website visit. Identity resolution empowers a more successful approach to behavioral marketing because it accounts for the fact that consumers have different identities in different channels, identities in digital and physical locations, households and other variables such as different name spellings. Without a full accounting of each and every variable of an individual identity, behavioral marketing will by definition have an incomplete view of a customer. An incomplete view introduces the potential of incorrectly analyzing a consumer’s behavior. This may include attaching a misplaced importance to a behavior on one channel because you do not have a view into a potential competing behavior on another channel. ## **Types of Behaviors for Behavioral Marketing** Now that we’ve seen that behavioral marketing as a process extends beyond mining digital and physical clues across multiple channels, it’s also important to understand that the clues themselves are highly granular. For instance, surface-level website behavior such as content viewed, pages visited or shopping cart activity might provide a foundation for behavioral analysis, but other digital clues might tell a competing story. A retail customer might click on a landing page that shows blue down parkas, but how will the customer immediately clicking to a different page affect an intent calculation? Similarly, what does the time of day of the activity, the customer’s geolocation or another potential variable tell us about the story behind the content viewed? Additionally, the action a consumer takes while on a page forms an even more complete set of behaviors. Did the consumer click on an image, download content, submit a product review? There are countless behaviors a customer might take. Each website visit, each in-store visit, call center interaction, service request or any other engagement on any channel provides a gold mine of intent signals that astute marketers are able to mine for behavioral signals to better predict how a consumer will respond to specific content, whether an ad, an offer, or action that is most relevant to the consumer’s unique journey at a precise moment in time. ## **Behavioral Marketing Uncovers Patterns** Detailed analysis of every possible variable unveils not just customer behaviors across channels, but finer behavior patterns that inform marketers how or whether one behavior influences another. If, for example, a consumer clicks off a page showing blue down parkas after 2.5 seconds but had recently bought a pair of winter gloves at a physical store and had also searched for flights to Banff, analysis might discount the brief page visit as an outlier not representative of a totality of behaviors in a customer’s journey. ## **Behavioral Marketing and a Single Customer View** Behavioral marketing, as you may have guessed, is only possible with customer data. Data that inform how a customer interacts and engages with a brand exist in any source and type of customer data, whether first-party, second-party or third-party; structured, semi-structured or unstructured. Compiling data from every conceivable source provides marketers with a single customer view. A unified customer profile that is updated in real time, combined with advanced identity resolution capabilities, forms a [golden record](https://www.redpointglobal.com/single-customer-view/) – an accurate, complete and holistic picture of an individual customer. Knowing everything there is to know about a customer with a golden record includes an individual customer’s preferences, transactions, devices, ID’s – and behaviors – across every channel. It is the foundation for providing a real time omnichannel customer journey that is optimized with personalization and relevance. ## **Behavioral Marketing and Automated Machine Learning** Optimizing customer journeys with personalization and relevance at scale requires automated machine learning. [Automated machine learning](https://www.redpointglobal.com/machine-learning) extracts meaning from customer data; self-training, code-free models that are tuned to drive a business metric determine how one customer behavior relates to another, which behaviors are more indicative of intent, and discerns patterns that are far beyond the ability of human comprehension. If customer data provide marketers with random sets of clues, automated machine learning pieces the clues together to solve the mystery of what a totality of customer behaviors mean within the context of an omnichannel journey. With an accurate indication of a [customer’s intent](https://www.redpointglobal.com/blog/capturing-intent-in-an-omnichannel-customer-journey-is-next-level-marketing/), marketers are empowered to react at the exact moment an intent signal appears, infusing each interaction with a relevant, personalized experience that is in the context of the customer’s journey. ## **Behavioral Marketing and Sales Growth** According to a McKinsey article “Capturing Value From Your Customer Data,” companies that leverage customer behavioral insights outperform peers by [85 percent in sales growth](https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/capturing-value-from-your-customer-data) and more than 25 percent in growth margin. The article lists examples of insights such as time spent lingering on a page, frequency of customer service calls and information on a customer’s purchases. While most companies collect this information, it says, few piece together the disparate data points to inform a narrative about a customer journey. Yet according to the article’s research, tailoring outreach with personalization based on behavioral analysis delivers five to eight times the return on marketing expenditure. Behavioral marketing works. Each time a customer engages with a brand, they leave behind digital and physical clues about what matters to them. By finding patterns in behaviors, data-driven marketers are able to deliver personalized, relevant customer experiences across all channels that are proven to drive new revenue. ## **Related Content** [Don’t Surrender to Customer Journey Complexity](https://redpointglobal-my.sharepoint.com/Users/tjp/Dropbox/My%20Mac%20(TJs-MacBook-Pro.local)/Downloads/redpointglobal.com/blog/dont-surrender-to-customer-journey-complexity) [Evolution of the Customer Experience and the Role of Data](https://www.redpointglobal.com/blog/evolution-of-the-customer-experience-and-the-role-of-data/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Omnichannel Marketing, Real-Time Personalization, Segmentation & Activation, Single Customer View --- ### [Achieving True 1:1 Customer Personalization & Engagement: 1 Prerequisite, 5 Priorities](https://www.redpointglobal.com/blog/achieving-true-11-customer-personalization-engagement-1-prerequisite-5-priorities/) **Published:** July 2, 2018 **Author:** Redpoint Global **Content:** ![Customer Engagement Personalization](https://www.redpointglobal.com/wp-content/uploads/2018/07/customer-engagement-personalization.jpg)The bar has been raised on customer engagement, and it’s incredibly easy to fall short. Consumers have shown with their dollars that they will flock to those brands that deliver personalized experiences. Driving true customer engagement through personalization is a complex challenge for all organizations. And it’s not just a matter of resources, brands big and small are struggling to overcome this challenge. In order to reap the rewards of true customer engagement, marketers need to get their proverbial house in order by focusing on the prerequisite for success. Once marketers address that requirement, there are five priorities they need to focus on to truly achieve their goals. ### **First, Get Your Prerequisites Right** Everything starts here: *You must be capable of accurately identifying customers and their behaviors in real or near-real time*. If you don’t know what your customers and prospects are doing right now – in all channels – you’ll struggle with [customer personalization](https://www.redpointglobal.com/orchestration/real-time-personalization/). And that means you’ll struggle with engagement. People are constantly interacting with brands across a variety of channels, and if your engagement system isn’t aware of their latest actions, your messaging will likely be irrelevant. (We’ve all received recommendations for products we’ve just bought. Someone obviously didn’t notice and in doing so likely missed a potential cross-sell opportunity.) Today, the wrong message stands out like a sore thumb: hammering people with wrong messages is the surest way to disqualify yourself from a meaningful conversation. To ensure this doesn’t happen, you need to unify all your data in a consistent and up-to-the-second golden record. These days, a [customer data platform](https://www.redpointglobal.com/blog/what-should-you-look-for-in-a-customer-data-platform/) (CDP) is often the best way to achieve this goal and operationalize your customer view. But whatever you call it, you need infrastructure to maintain accurate, complete, and current customer data in a cost-effective and scalable way. ### **Then, Focus on These Five Priorities** With the right infrastructure in place, many new options open to you. But you must set priorities. While every company is different, these recommendations may help you focus your efforts for maximum value: 1. **Abandon segments***.* For years, companies have used [segmentation strategies](https://www.redpointglobal.com/blog/augment-customer-segmentation-with-a-personalized-cx/) as a proxy for personalization, because they didn’t have the technology to do better. Now that the technology exists, companies are moving to true 1:1 personalization based on each customer’s behavior. Consumers have noticed, and if you’re still treating them as part of a cohort, there’s a good chance they’ll recognize that your messages aren’t up-to-date or aren’t really for them. Segments emerged from the catalog world. They were based on point-in-time snapshots for inclusion in custom offers that were delivered at a far slower cadence. At one time, they provided great lift and a basis for performance measurement that did not exist with other targeting methodologies. Because of this early success, segmentation naturally extended into email. As channels continue to proliferate and the speed of customer interaction ever increases, segmentation grows ever more obsolete, in part because it is *impossible* to execute a segmentation strategy at the current speed of the customer. Once you have a unified, up-to-date, and actionable customer platform, it’s time to consider letting segments go. 2. **Identify and respect channel preferences**. Encourage customers to tell you how they prefer to engage. But even if they don’t, when you have complete customer information, you can typically infer this accurately. As you do so, focus your engagement through the channels they’ve shown they like. 3. **Add more channels***.* Your customers may have flocked to channels where you don’t engage at all. Look for opportunities to add channels, even if you need to gradually expand your channel presence over time. For example, if you’ve typically engaged through direct mail and email, consider addressing the customer as an individual at point-of-sale, through social media, custom mobile applications, or on your website. 4. **Unify messaging across channels**. Companies often treat channels independently when it comes to cadence and content. This compromises the consistency and value of your messages, and as a result, the effectiveness. Sometimes the problem arises from point technologies that are specific to a single channel (such as email) and haven’t been fully integrated in real time with other systems. It can also arise from an organizational structure where siloed teams are responsible for messaging in each channel. As you integrate your infrastructure to make omnichannel marketing technically possible, also build connections between or even consolidate channel-centric teams. You must address *both* technology and people because modern automated systems are needed to execute omnichannel marketing at scale at today’s customer velocity. 5. **Bring more sophistication to attribution***.* Many companies still measure marketing performance by attempting to identify the specific communication that caused a customer to buy. The problem with this approach is that customer experience and engagement is holistic. Attributing a sale to a specific email or direct mail, or even a simple combination thereof, is misleading and potentially counterproductive. Given this, how *do* you measure? Use machine learning technology to identify subtle combinations of cadence and message that deliver the best results. As you discover these patterns, use champion-challenger testing to see where they meaningfully outproduce previous methods and then switch to them. Or, better yet, let machine learning determine what combination of channels, cadence, and messaging perform best based on an established goal – for example, more visits or more loyalty registrations. It’s easy to see how all five of these steps rely on a unified and up-to-date view of the customer. It’s also easy to see why they’re essential for driving customer engagement in an era where customers demand to be known and respected as individuals. Brands that achieve this goal stand to benefit dramatically from improvements in customer engagement and retention, which will ultimately driver higher ROI for the brand. *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![Learn How Customer Data Platforms Transform Customer Engagement](https://www.redpointglobal.com/wp-content/uploads/2018/06/CDP-Transform-Engagement.jpg)](https://www2.redpointglobal.com/ebook-transforming-customer-engagement-pr) **Blog categories:** 1:1 Personalization, Real-Time Personalization --- ### [Data-Centric Abandoned Shopping Cart Email Strategies](https://www.redpointglobal.com/blog/data-centric-abandoned-shopping-cart-email-strategies/) **Published:** February 16, 2021 **Author:** Redpoint Global **Content:** There’s nothing as rewarding after a marketing campaign than seeing a full online shopping cart “move” through to a purchase. All too often, however, shoppers abandon their cart before completing a purchase. In fact, nearly 70 percent of shoppers leave goods in their carts, according to research from [Baymard Institute](https://baymard.com/lists/cart-abandonment-rate). Having an effective abandoned cart email strategy can make all difference between money left on the table—or, rather, in the cart—and revenue. An effective abandoned cart email strategy begins with using data to understand why website’s visitors left items in their cart at checkout and exited. Even if you don’t know the exact reason for each specific customer, you can use a combination of insights for why shoppers abandon their carts in general (see chart below) and behavior data specific to your customers and other site visitors to craft relevant messaging, reach out in the ideal time frame, and turn a lost sale into a purchase. Connected data is essential for this. When you’re able to link disparate data from first-party, second-party, and third-party sources to create a holistic view of your customers, you can use that unique insight to transform common abandoned cart email strategies into data-driven ones that increase performance. ![](https://www.redpointglobal.com/wp-content/uploads/2021/02/Abandoned-shopping-cart-graphic-300x215.jpg) As with personalization in general, any personalization that you can apply to abandoned cart emails will help to improve their effectiveness. Abandoned cart emails that have a generic subject line, such as “Looks like you forgot something,” won’t be as effective as those that are directly relevant to each shopper. Let’s look at how connected data can help improve abandoned cart emails. ## **Abandoned Cart Email Strategy: Subject Lines** One of the most common reasons that shoppers abandon their cart is extra costs, such as shipping, taxes or other fees. If that’s a concern for your customers, consider using abandoned cart emails that highlight benefits that counter this issue. For example, citing top ratings or reviews that emphasize the value of your products. In this case, the subject line could say something like, “You’ve left a 5-start widget in your cart.” If a shopper abandoned several items, you could use past purchase data to select which product to highlight in the subject line. If people leave multiple products, focus on the one that’s likely to be the most compelling to a specific customer so the subject line is especially relevant, and does not dilute the messaging. ## **Ratings and Reviews** When you have connected data, it’s easier to see which customers have similar attributes or preferences. You can use this insight for abandoned cart emails to include reviews from customers who share similar attributes to the shopper who has abandoned their cart. Using this approach also can help build trust and credibility among newer customers. They’ll see that not only are your company’s products highly rated, but also that customers who are similar to them in some way have purchased and are enjoying those products. ## **Fear of Missing Out (aka FOMO)** Similarly, abandoned cart emails can create FOMO with “don’t be the one to miss out” type messaging. You can use connected data to see which customer types or segments exhibit similar behaviors and preferences and use that information. For example, “Don’t be the only one who’s bought product X not to complement it with product Y” or “Environmentalists swear by this widget. Don’t miss your opportunity to get one before they sell out.” Then you can personalize a call-to-action by directing people back to a specific item in their cart based on that FOMO messaging. ## **Loyalty Over Discounts** In most cases, it’s unwise to offer discounts in abandoned cart emails. You’ll train customers to abandon their purchase to wait for the coupon you’re certain to send. Additionally, not only will you sacrifice margin, but you also might erode the trust of high-value customers who see that all it takes to get a discount is to abandon a cart. Instead, use the data from a customer loyalty program in abandoned cart emails. For instance, you can use loyalty data to see how many points a customer has accrued to inform relevant messaging. If the data shows that they have X points accrued, messaging can remind them that they can use those points toward their purchase. If they don’t have points accrued, you can note how many points they’ll need before earning a reward. Even better, you can cite how many points their abandoned purchase would be worth if they complete it. ## **Abandoned Cart Email Strategy Best Practice: Timing and Frequency** When should you reach out and how many follow-up emails should be in the series? There are many “standard formulas” for calculating this. Instead, base an abandoned cart email strategy on overall past performance and data specific to your customers. One customer may respond to a first abandoned cart email sent within the hour, while another shopper might find that intrusive. Use connected data to move at the cadence of each customer. You can also use connected [customer data](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/) to see whether asking for a longer-term relationship is appropriate for a specific abandoned cart email series. For instance, if you have an email newsletter and know that a customer who has left items in a cart at checkout is not a subscriber, you can use an abandoned cart email series to encourage them to subscribe. This will help you stay top-of-mind for shoppers who do want to make the purchase but aren’t ready for some reason. ## **Stay Connected** It’s easy to set and forget a generic abandoned cart email series. Even nonspecific abandoned cart emails — “Looks like you forgot something” — will generate conversion. Using connected data to inform and support abandoned cart emails will allow for the type of personalization customers expect today. As a result, the [customer experience](https://www.redpointglobal.com/blog/commercial-excellence-begins-with-customer-experience/) improves, customer satisfaction rises, and the performance of abandoned cart emails increases. ## **Related Content** [Dynamic Offer Management Can Be Customer-Centric: Here’s How](https://www.redpointglobal.com/dynamic-offer-management-can-be-customer-centric-heres-how/) [What is Marketing Automation?](https://www.redpointglobal.com/blog/what-is-marketing-automation/) [Augment Customer Segmentation with a Personalized CX](https://www.redpointglobal.com/blog/augment-customer-segmentation-with-a-personalized-cx/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* --- ### [The Top Customer Data Platforms for Data Readiness](https://www.redpointglobal.com/blog/the-top-customer-data-platforms-for-data-readiness/) **Published:** April 24, 2026 **Author:** Redpoint Global **Content:** ## Why Do Most CDP Implementations Fail? Most CDP projects do not fail at activation. They fail upstream, before any campaign or model is ever run. The root causes are consistent: customer records that cannot be reliably matched across systems, data that arrives incomplete or in inconsistent formats, profiles that are hours or even days old by the time they reach a downstream tool, and no automated mechanism to catch or correct these problems at ingestion. The result is a downstream trust problem. Marketing teams override CDP-generated segments with manual lists because the data feels unreliable. Data science teams build workarounds rather than consuming unified profiles. AI models trained on dirty data produce recommendations that erode rather than build customer relationships. [Data readiness](https://www.redpointglobal.com/resources/what-is-data-readiness/) is the discipline of making data accurate, unified, and usable before it is activated. It is what separates CDPs that deliver enterprise value from those that become expensive data routing layers. ## What Is Data Readiness in a CDP? [Data readiness](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/) refers to a [CDP’s](https://www.redpointglobal.com/customer-data-platform/) ability to make customer data accurate, unified, and usable before it reaches downstream systems such as marketing automation, analytics platforms, or AI models. Most CDPs are evaluated on activation features, channel integrations, or interface design. However, most CDP failures trace back to upstream data problems: data that arrives late, incomplete, unresolved, or untrusted. A CDP that cannot fix data before it is used forces every downstream team to compensate for problems that should have been solved at the source. **A data-ready CDP must deliver:** - **Automated data quality:** cleansing, validation, and standardization applied inline at ingestion, not as a manual post-processing step - **Persistent identity resolution:** matching records across sources in real time to maintain an accurate, continuously updated single profile per customer - **Unified customer profiles:** assembled and maintained on an ongoing basis, not reconstructed at query time or only at export - **Enterprise-wide reusability:** a single trusted data layer that all teams — marketing, analytics, AI, operations — can consume without rework ## What Are the Different Types of CDPs? Not all CDPs are built for the same job. Understanding their architectural intent explains why some platforms consistently underperform for certain use cases, and why choosing the wrong type of CDP creates compounding problems downstream. ### 1. Data Readiness CDPs **Purpose:** Make data right before it is used **Primary users:** Data engineering, IT, marketing operations, analytics, and AI teams **Strength:** Upstream data quality, identity resolution, real-time unification Data Readiness CDPs treat data quality as core infrastructure, not a downstream clean-up step. Data is cleansed, standardized, and identity-resolved at the point of ingestion. Profiles are maintained continuously rather than reconstructed on demand. These platforms are built for organizations that need to trust their data before they use it, such as enterprises with complex multi-source environments, regulated industries where data accuracy carries compliance implications, or organizations pursuing AI-driven personalization where model quality depends entirely on input data quality. ### 2. Traditional / Packaged CDPs **Purpose:** Collect, unify, and activate customer data **Primary users:** Marketing and digital teams **Strength:** Event ingestion, segmentation, and activation Traditional CDPs are typically SaaS-based and optimized for marketing use cases such as audience segmentation, campaign triggering, and channel activation. They are strong at collecting and routing data quickly. However, most traditional CDPs are designed with an implicit assumption: that data arrives in a reasonably clean and identifiable state. When it does not (which is the norm in enterprise environments) data quality and identity resolution are either unavailable natively, require significant manual configuration, or are deferred to downstream systems that were never designed for the job. ### 3. Composable / Reverse ETL CDPs **Purpose:** Activate data that already lives in a cloud data warehouse or lakehouse **Primary users:** Data engineering teams, analytics engineers, marketing operations teams in warehouse-mature organizations **Strength:** Warehouse-native activation, vendor neutrality, minimal data duplication [Composable CDPs](https://www.redpointglobal.com/blog/composable-cdp/) are built on the premise that the data warehouse (Snowflake, BigQuery, Databricks, or similar) is already the system of record, and that CDP capabilities should be assembled around it rather than replacing it. The primary mechanism is reverse ETL: syncing modeled, clean data from the warehouse into downstream operational tools such as marketing platforms, CRMs, and ad networks. Composability as an architectural principle is broader than this category alone. Many other types of CDPs also support composable, data-in-place architectures, integrating with existing infrastructure rather than requiring data to move into a proprietary store. What distinguishes this vendor category is that warehouse activation is not just supported but is the main value proposition. These tools often do not ingest raw data, resolve identity, or perform data quality work. They assume the warehouse is already clean and modeled, and their job is to make that data available to operational tools that cannot query the warehouse directly. This makes composable CDPs highly effective for data-mature organizations that have already invested in building a clean data foundation. For organizations that have not, composable tools surface and accelerate upstream problems rather than solving them. ### 4. Marketing Cloud CDPs **Purpose:** Extend an existing vendor ecosystem **Primary users:** Enterprises standardized on a single platform stack **Strength:** Tight integration and reduced operational overhead within a specific vendor suite [Marketing Cloud CDPs](https://www.redpointglobal.com/blog/take-your-marketing-cloud-to-the-next-level-with-enterprise-data-readiness-2/) are purpose-built to operate within a specific vendor ecosystem, the most well-known being Adobe and Salesforce. Their primary value is integration convenience: they reduce the friction of connecting CDP capabilities to other tools in the same suite. The tradeoff is meaningful. Data models, identity logic, and readiness capabilities are defined and constrained by the broader platform. Organizations that need to operate across multiple ecosystems, or that require flexible identity resolution and data quality capabilities, often find marketing cloud CDPs limiting. ## Which Customer Data Platforms are Best in 2026? With those categories in mind, here is how the leading platforms in each group perform when evaluated based on their ability to deliver accurate, unified, and trustworthy customer data to activate to downstream systems. ### Data Readiness CDPs **Redpoint Global** **Category:** Data Readiness CDP **Best for:** Organizations prioritizing AI-ready, trusted customer data #### What does Redpoint Global do? Redpoint Global is built on the principle that data must be fixed before it is used. The platform is engineered so that data quality, identity resolution, and profile unification are not optional configuration steps; they are the foundation. Customer data is cleansed, standardized, and identity-resolved automatically as it is ingested, and profiles are maintained continuously in real time. By the time data reaches a downstream system, such as a marketing tool, analytics platform or AI model, it is already accurate and unified. This architecture is particularly significant for organizations pursuing AI-driven personalization or operating in regulated industries. AI models are only as reliable as the data they consume. Redpoint’s upstream approach means that the data foundation that AI systems depend on is accurate and current, rather than requiring separate data preparation pipelines or accepting that model inputs will contain errors. ##### Key strengths: - **[Automated Data Quality:](https://www.redpointglobal.com/automated-data-quality/)** Cleansing, standardization, validation, and monitoring occur inline as data is ingested. Rules can be configured for specific data types, sources, or business requirements. Data quality is not a batch process but is applied continuously. - **[Advanced Identity Resolution:](https://www.redpointglobal.com/identity-resolution/)** Uses both deterministic matching (exact matches on known identifiers such as email, phone, or customer ID) and probabilistic matching (statistical inference across behavioral signals and partial identifiers). Rules are tunable across individuals, households, and accounts, including anonymous-to-known resolution as customers move from unknown to identified states. - **[Real-Time Processing:](https://www.redpointglobal.com/blog/data-readiness-real-time-relevance-delivering-context-in-every-moment/)** Data is made ready in real time from ingestion through activation. Profile updates are reflected immediately, supporting time-sensitive decisioning, real-time personalization, and next-best-action recommendations. - **[Data-In-Place & Composability:](https://www.redpointglobal.com/blog/why-you-need-data-readiness-in-a-modern-data-cloud/)** Supports a data-in-place model that minimizes unnecessary data movement and reduces replication risk. Integrates with existing data infrastructure rather than requiring organizations to replace it. Enables composable architectures for teams that want to combine Redpoint’s data readiness capabilities with other specialized tools. ##### Who should choose Redpoint Global? Redpoint is best suited for organizations with complex, multi-source customer data environments; organizations in regulated industries such as financial services, healthcare, or insurance; and businesses building AI-driven customer experiences where the quality of model inputs directly determines business outcomes. It is not optimized for teams primarily seeking a marketing campaign orchestration tool. ##### Tradeoffs: - UI is functional and prioritizes data accuracy over visual design polish - Best value realized by organizations with genuine data complexity, not simple use cases --- **Amperity** **Category:** Data Readiness / Analytics-Led CDP **Best for:** Retail and consumer brands focused on customer analytics and loyalty #### What does Amperity do? Amperity was built primarily to solve identity resolution for consumer brands, particularly in retail, where customer data is fragmented across point-of-sale systems, e-commerce platforms, loyalty programs, and digital channels. Its AI-driven identity modeling is designed to stitch together consumer records even when shared identifiers are absent or inconsistent — a common challenge in high-transaction retail environments. Amperity’s analytics layer is a strength. It provides robust tools for customer segmentation, lifetime value modeling, and behavioral analysis, making it a strong fit for teams whose primary output is customer insights and audience building rather than real-time operational activation. However, Amperity’s data readiness work is largely post-ingestion rather than inline. Data is ingested first, and quality and identity work happens in subsequent processing stages. This means the platform does not provide the same guarantee of data readiness at the point of ingestion that purpose-built data readiness CDPs deliver. ##### Key strengths: - **Identity Modeling:** AI-driven identity resolution designed for high-volume consumer datasets with fragmented or inconsistent identifiers across touchpoints. - **Customer Analytics & Insights:** Robust segmentation, lifetime value analysis, and customer intelligence tools suited to retail marketing and loyalty strategy. - **Retail-Specific Data Model:** Pre-built connectors and data structures aligned to retail data sources and use cases. ##### Who should choose Amperity? Amperity is a strong choice for mid-market to enterprise retail and consumer brands that need to unify fragmented customer records and build a strong analytics foundation. It is less suited to organizations that require automated data quality at ingestion, real-time operational activation, or support for non-retail data models. ##### Tradeoffs: - Data quality automation at ingestion is limited compared to purpose-built data readiness CDPs. - Platform is strongest for retail and consumer brand use cases. - Recently introduced real-time capabilities, although analytics remains the primary use case. ### Traditional / Packaged CDPs **Tealium** **Category:** Traditional CDP **Best for:** Event streaming, consent management, and data routing across a large tech stack #### What does Tealium do? Tealium grew out of tag management and its architecture reflects that origin. The platform is capable collecting event data from digital touchpoints, such as websites, mobile apps, connected devices, and routing it quickly to downstream tools. Its connectivity is broad, its event collection is fast, and its consent and privacy management capabilities are mature, making it well-suited to organizations with complex compliance requirements across jurisdictions. Where Tealium falls short is in what happens to data before it is routed. Native data quality and capabilities are minimal. The platform moves data efficiently, but the assumption is that data quality work will happen either upstream (before Tealium receives the data) or downstream (in the systems that receive it). For organizations that need trusted, unified customer profiles as the foundation of their activation, Tealium may require complementary investment. ##### Strengths: - Real-time event ingestion from web, mobile, and connected device touchpoints - Pre-built integrations and routing capabilities - Mature consent management and data privacy compliance tooling ##### Who should choose Tealium? Tealium is well-suited to organizations that need a robust data collection and routing layer and already have, or are building, separate infrastructure for data quality and identity resolution. It is not the right choice for organizations that expect the CDP itself to deliver unified, trusted profiles. ##### Tradeoffs: - Minimal native data quality automation - Identity is capable for digital touchpoints but less suited to complex cross-system enterprise identity challenges. - Data often moves downstream before being made ready, creating quality problems that are difficult to remedy after the fact. - Implementations tend to be services-heavy, with significant ongoing configuration and maintenance requirements. --- **Treasure Data** **Category:** Traditional CDP **Best for:** Technically mature organizations managing large, complex datasets that have the engineering resources to build custom data readiness pipelines #### What does Treasure Data do? Treasure Data provides a flexible data platform capable of handling very large data volumes across diverse source types. It has strong roots in enterprise data management and offers flexibility in how data is modeled, queried, and analyzed. For organizations with mature data engineering teams, it can serve as a capable foundation for customer data work. Recently, Treasure Data introduced some data readiness capabilities including identity resolution and automated data quality. However, organizations with complex data environments may require custom configuration. ##### Strengths: - Handles large, diverse data volumes with good performance - Flexible data modeling and analytics capabilities - Well-suited to technically mature teams comfortable with SQL and custom development ##### Who should choose Treasure Data? Treasure Data is best for organizations with experienced data engineering teams who want maximum flexibility and are willing to invest in building custom data readiness capabilities. It is a poor fit for teams expecting out-of-the-box data quality, identity resolution, or unified profiles. ##### Tradeoffs: - Data quality and identity resolution have been added although depth varies compared to purpose-built data readiness CDPs. - Advanced use cases require SQL and scripting expertise. - Real-time readiness depends on bespoke pipeline architecture, not native platform capability. --- **Rokt mParticle** **Category:** Traditional CDP **Origins:** Mobile and digital product analytics **Best for:** Product-led organizations focused on event-level data collection and visualization #### What does mParticle do? mParticle was built for product and engineering teams that need visibility into how users interact with digital products (mobile apps, web applications, and connected experiences). It is strong at capturing high-fidelity event streams, visualizing user journeys, and forwarding that event data to downstream analytics and marketing tools. For organizations where instrumentation quality and data volume are the primary concerns, mParticle delivers well. ##### Who should choose mParticle? mParticle is well-suited to product and engineering teams in digital-native companies where mobile and web event instrumentation is the primary use case. It is not the right choice for organizations that need unified customer profiles, strong identity resolution, or automated data quality. ##### Tradeoffs: - Identity resolution is available but less sophisticated than dedicated data readiness CDPs for complex cross-channel and longitudinal matching. - Data quality provides schema validation at ingestion, although comprehensive data readiness may require complementary tooling. - Not designed as a system of record for unified customer profiles. ### Composable / Reverse ETL CDPs **Hightouch** **Category:** Composable CDP (Reverse ETL) **Best for:** Organizations with strong warehouse discipline and already-clean, already-modeled data #### What does Hightouch do? Hightouch sits at the activation end of the data pipeline. It takes data that has already been modeled and made ready in a cloud data warehouse, such as Snowflake, BigQuery, or Databricks, and syncs it into downstream operational tools: marketing automation platforms, CRMs, ad platforms, support tools, and others. The core premise is that the warehouse is already the source of truth, and Hightouch’s job is to make that data available to the tools that need it. Although Hightouch has recently introduced some limited data readiness capabilities, it remains most useful for organizations that have already invested in building a clean, modeled data foundation in their warehouse. For those organizations, Hightouch removes a significant operational friction: data that exists in the warehouse but was previously inaccessible to marketing or operations tools can be activated without building and maintaining custom integrations. ##### Strengths - Good for activating warehouse-curated, already-clean data into operational tools - Composable and vendor-neutral: works with any warehouse and any destination - Minimal data movement and replication overhead - Connectivity to a wide range of downstream destinations - Portfolio of AI agents to support building marketing campaigns ##### Who should choose Hightouch? Hightouch is the right choice for data-mature organizations that have already built clean, unified customer models in a cloud data warehouse and need a reliable, low-friction way to activate that data in operational tools. It is not suitable as a standalone CDP or for organizations that have not yet solved upstream data quality and identity resolution. ##### Tradeoffs: - Data quality and identity resolution are newer additions and less proven at enterprise scale. - Upstream data ingestion is available but less comprehensive than dedicated CDP ingestion layers. - Not a system of record for customer profiles. **How Hightouch fits:** For organizations with complex upstream data challenges, Hightouch complements data readiness CDPs but does not replace them. An effective architecture pairs a data readiness CDP, which handles ingestion, quality, and identity resolution, with Hightouch as the activation layer for warehouse-native workflows. ### Marketing Cloud CDPs **Adobe Experience Platform** **Category:** Marketing Cloud CDP **Best for:** Large enterprises already standardized on the Adobe ecosystem across Experience Manager, Analytics, and Target #### What does Adobe Experience Platform do? Adobe Experience Platform (AEP) is the data foundation for the Adobe Experience Cloud suite. It ingests data from digital and offline sources, builds customer profiles, and feeds those profiles into Adobe’s activation and personalization tools (Target, Journey Optimizer, and Campaign). Its primary value is integration depth within the Adobe ecosystem: for enterprises already committed to Adobe tooling, AEP connects the customer data layer to those products with relatively low friction. ##### Strengths: - Deep native integration across the Adobe Experience Cloud suite - Strong support for digital experience and content-driven activation use cases - Established enterprise deployment footprint with broad partner ecosystem ##### Who should choose Adobe Experience Platform? AEP is most appropriate for large enterprises deeply committed to the Adobe ecosystem whose primary activation use cases live within Adobe’s suite. Organizations with multi-vendor environments, complex data quality requirements, or the need for flexible identity resolution should evaluate alternatives carefully. ##### Tradeoffs: - XDM data model may require more schema management overhead than alternatives. - Limited native data quality automation. Ingestion-time cleansing and validation require custom configuration. - Identity resolution capabilities are less flexible than dedicated data readiness CDPs. - High total cost of ownership when factoring in implementation, licensing, and ongoing schema management. - Full value requires broad Adobe suite adoption. --- **Salesforce Data 360** **Category:** Marketing Cloud CDP **Best for:** Organizations deeply standardized on Salesforce CRM and Marketing Cloud where operational convenience outweighs data flexibility #### What does Salesforce Data 360 do? Salesforce Data 360 (formerly Salesforce CDP and Data Cloud) extends Salesforce’s CRM with a unified customer data layer, ingesting data from Sales Cloud, Service Cloud, Marketing Cloud, and external sources to create profiles that trigger Salesforce-native workflows and automations. Salesforce’s acquisition of Informatica brings enterprise data quality, master data management (MDM), data governance, metadata management, and data catalog capabilities into the platform. The stated intent is to establish a trusted data foundation for Agentforce, Salesforce’s autonomous AI agent platform, addressing the data readiness gaps that have historically been a limitation of the platform. Integration is ongoing, and the degree to which data readiness capabilities will be embedded continues to evolve. ##### Strengths: - Deep integration with Sales Cloud, Service Cloud, and Marketing Cloud - Unified customer profiles accessible within familiar Salesforce interfaces - Informatica acquisition adds enterprise data quality, MDM, governance, and metadata management to the platform roadmap - Strong strategic alignment with Agentforce for AI-driven enterprise workflows ##### Who should choose Salesforce Data 360? Salesforce Data Cloud is most appropriate for organizations running customer-facing operations primarily within Salesforce whose primary activation use cases live within the Salesforce ecosystem. The Informatica acquisition makes it a more credible option for organizations with serious data readiness requirements, but enterprises with immediate needs should evaluate how much of Informatica’s capability is natively integrated today versus roadmap. ##### Tradeoffs: - Identity resolution is less mature than dedicated data readiness CDPs for cross-system identity scenarios. - Data quality and MDM are not yet fully embedded in Data Cloud natively. Need to assess current capabilities and promised roadmap. - Implementation may require more heavy resource lift than dedicated out-of-the-box data readiness options. - High vendor lock-in: - Zero-copy federation capabilities reduce lock-in for organizations with existing warehouse capabilities, though full value still requires deep Salesforce adoption. - Data model is rooted in Salesforce’s architecture, limiting flexibility for non-Salesforce data sources. ## How Do I Choose the Right Customer Data Platform? Most CDPs can move customer data. Far fewer can be trusted to make it right. The organizations seeing real returns from personalization and AI are not the ones with the most sophisticated activation tools; they are the ones that solved the data foundation first. Before evaluating features, the more useful question is: where does data quality and identity resolution break down today, and which platform fixes that problem at the source rather than passing it downstream? For more on choosing the best CDP, download the eBook,[10 Questions to Ask Your CDP Vendor](https://www.redpointglobal.com/resources/10-questions-to-ask-a-cdp-vendor/). **Blog categories:** Customer Data Platform, Data Readiness **Blog tags:** CDP, Data readiness --- ### [Data Readiness Architecture: The Foundation for AI-Ready Customer Data](https://www.redpointglobal.com/blog/data-readiness-architecture-the-foundation-for-ai-ready-customer-data/) **Published:** February 6, 2026 **Author:** Steve Zisk **Content:** In April 2025 a global survey found 60 percent of business leaders lacked confidence in their organization’s data readiness to unlock value from generative AI, even though 79 percent expect GenAI to deliver competitive advantage (InsideHPC, Apr 2025). That gap between high expectations and low readiness helps explain why enterprises struggle with relevance, consistency, and real-time decisioning across channels. A data readiness architecture addresses this by giving organizations a single, operational source of [trusted customer data](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/) – cleaned, unified, governed, and ready for immediate use – so every CX and AI system makes decisions from the same reliable profile. The result: fewer conflicting customer views, faster model deployment, and defensible lineage for privacy and compliance. A data readiness architecture is like running every power tool in your garage – the leaf blower, the trimmer, the mower and the chainsaw – on the same battery pack, a concept Ryobi unveiled in the mid ‘90s. Predictable performance, no duplication, and a clear accountability for every decision that depends on data. ## What is a Data Readiness Architecture? In a fundamental shift from a fragmented, channel-centric system, a data readiness architecture embodies a data readiness hub to bridge the gap between raw data ingestion and business-ready activation. A data readiness architecture treats customer data as a key enterprise asset. There are three core architectural components: ### A Customer Data Refinery – - The foundational layer of data readiness [handles the “messy” work of data quality](https://www.redpointglobal.com/data-the-defining-difference/), including automated cleansing, normalization, data enrichment, and advanced identity resolution. The output of this layer is a trusted, accurate, and timely unified profile. ### *Customer Data Management with Built-in AI* – - Embedding[ integrated AI and analytics throughout the architecture](https://www.redpointglobal.com/blog/ai-inside-how-redpoint-embeds-intelligence-to-drive-smarter-cx/) unlocks deep insights from first-party data, powering predictive modeling. ### *Customer Data Activation* – - More than generating insights, a data readiness architecture turns refined data – the unified profile – into tangible outcomes. [Dynamic segmentation](https://www.redpointglobal.com/blog/dynamic-rules-based-audience-segmentation/), journey orchestration, and [real-time interactions](https://www.redpointglobal.com/blog/data-readiness-real-time-relevance-delivering-context-in-every-moment/) extract the optimal value from data across the enterprise. ## Composability in a Data Readiness Architecture: Why Modular Design Matters Central to a data readiness architecture is the concept of [composability](https://www.redpointglobal.com/blog/driving-composable-cdp-success-with-data-readiness/), with a modular approach where the enterprise can select “best-of-breed” components that complement existing investments in AI and customer engagement rather than being forced into a monolithic system. This modularity allows users to build their own custom data pipelines for specific business use cases (AI agents, [retail media networks](https://www.redpointglobal.com/blog/you-cant-have-a-data-clean-room-without-data-quality/), etc.). With the data readiness hub as a source providing the single source of truth for the customer/household/product/etc., each application shares a common understanding of the entity that is backed by cleansed, accurate, and timely data. This solves for the common problem of different applications having different methods – even different concepts – for data quality. In addition, a **data readiness hub** that utilizes **no-code software** with **pre-built functions** allows business users to **manage complex data tasks** without deep technical expertise or constant IT assistance. Custom data pipelines, a [data readiness hub that cleanses data at ingestion](https://www.redpointglobal.com/data-readiness-hub/), and no-code software with pre-built functions are essential elements of a data readiness architecture that make sure data is ready and fit for purpose the moment it’s needed, not a fraction of a second later. ## Data-in-Place Processing: A Core Principle of Data Readiness Architecture Another critical aspect of a data readiness architecture is how it handles [data movement and storage](https://www.redpointglobal.com/blog/a-composable-cdp-is-not-just-data-in-place/). The demands of AI require that operations like inference and reasoning occur at the “edge” where data resides, rather than moving everything to a centralized database. Edge processing in a distributed system minimizes latency and reduces the risks associated with large-scale data migrations. A data readiness architecture must provide a way to assemble a golden record where the data resides without the cost of data migration. ## What are the Benefits of Edge Processing? Edge processing is important not just because of AI, but because of the growing expectations for real-time experiences. Providing real-time access to data in a database without real-time updates is not sufficient to meet today’s decisioning requirements. And, as a key tenet of data readiness, making the data fit for purpose must happen as it is ingested, to avoid the “data debt” of delayed or repeated processing. An ancillary benefit of such a data-in-place environment with a data readiness architecture, particularly for regulated industries, is that companies maintain control of data within their own security perimeter. By bringing applications to the data in a data cloud like Snowflake, for example, the enterprise is guaranteed clean, accurate, and timely data with the added peace of mind of maintaining the highest levels of security. ## Governance in a Data Readiness Architecture A final hallmark of a data readiness architecture is allowing for **real-time visibility** and **control through tunable governance**. With distributed intelligence, particularly when it is mediated by AI, there must be guards in place to distinguish between legitimate and malicious AI activity. With LLMs and other forms of GenAI where information is in the form of words, it becomes very difficult – and very important – to identify bad actors. Built-in governance in a data readiness architecture ensures that all internal players have the right permissions, visibility, and privacy controls. The bottom line is that in a distributed world, trust is fragile. If you can’t explain the lineage of a customer record, you’ve failed the governance test. Because trust is a competitive advantage, it has to be baked into the data from the moment it’s ingested. ## Data Readiness Architecture vs. CDP - **Customer Data Platforms (CDPs):** Were designed to unify customer data for marketing and engagement use cases. They remain effective tools for basic segmentation, personalization, and campaign activation. - **Data Readiness Architecture**: Operates at a more foundational level. Rather than serving a single function or team, it ensures that ***all* enterprise systems** – AI models, analytics platforms, decisioning engines, and engagement tools – access the same trusted, governed, and continuously updated data. While CDPs often depend on upstream systems for data quality and identity resolution, a data readiness architecture embeds these capabilities directly into the architecture. It also emphasizes data-in-place processing, reducing latency, duplication, and the risks associated with large-scale data movement. In practice, a data readiness architecture does not force organizations to replace a CDP but instead makes it more effective by providing clean, consistent, real-time data as a shared foundation for activation. ## Maximize Value with a Data Readiness Architecture A data readiness architecture serves as the essential foundation for organizations aiming to leverage customer data for advanced AI and customer experience initiatives. By unifying data management, quality, and governance through modular, composable components, businesses can ensure their data is consistently accurate, timely, and secure regardless of where or how it’s accessed. This approach streamlines operational efficiency, supports real-time decision-making and strengthens trust through transparent governance and robust security practices. Ultimately, adopting a data readiness architecture empowers enterprises to make the most of their data, positioning them to deliver exceptional customer experiences and drive innovation in the AI era. For more on how Redpoint helps companies get their data ready to power innovative AI and CX initiatives across the enterprise, click [here](https://www.redpointglobal.com/data-readiness-hub/). **Blog categories:** Customer Data Platform, Data Readiness **Blog tags:** customer data platform, Data quality, Data readiness --- ### [Data Readiness for Providers: Is Your Patient Data Healthy and Ready for What’s Next?](https://www.redpointglobal.com/blog/data-readiness-for-providers-is-your-patient-data-healthy-and-ready-for-whats-next/) **Published:** August 14, 2025 **Author:** Beth Pfefferle **Content:** As the healthcare market advances in generative AI (GenAI), agentic AI, real-time interactions, and other data-driven innovations, system leaders should not overlook data quality, which is recognized as a crucial factor in expanding AI initiatives and achieving a frictionless patient experience. Today, when approximately 65 percent of U.S. hospitals report using AI-assisted predictive models, according to a study published in [Health Affairs](https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2024.00842), future-proofing data is no longer optional; it’s strategic. Health system leaders must ask: Is our data [complete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/), [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/), [timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/), [actionable](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/), [trusted](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/), and [compliant](https://www.redpointglobal.com/blog/ready-data-is-compliant-data-does-your-data-hold-up-to-scrutiny/) enough to meet tomorrow’s demands? Health systems collect vast quantities of healthcare consumer data yet fail to make it usable across the enterprise. Most systems place valuable information into data lakes or warehouses, which while well-intentioned, lack curation and context, leading to bottlenecks, misalignment, and dirty data. Data, Marketing, and Patient Experience teams are then left to operate with conflicting versions of truth due to disparate data systems. To operationalize data intelligence and drive engagement, health systems must prioritize a [data readiness](https://www.redpointglobal.com) foundation to yield individual profiles that are complete, accurate and up to date. ## **Why Patient Data is So Hard to Use** Systems and their cross-functional teams require data that is readily available to deliver high-value, human-centered experiences. Yet, going beyond usability to consider empathy, context, and the holistic journey of the user requires a wealth of data. Patient data is especially challenging because it is scattered across EHRs, claims systems, call centers, web interactions, third-party apps, and more. Each source may use different formats, identifiers, and update cycles – making it difficult to unify, interpret, and act on the data in real time. Dirty data leads to poor predictions, irrelevant messaging, operational inefficiencies, and missed revenue opportunities. The challenge of fragmented data becomes even more apparent when trying to follow a patient’s care journey across the continuum. System leaders have relayed it takes at least 12 months to convert a prospect to bariatric surgery. That means they need to be able to maintain a persistent view of that prospect as they navigate the process. Unlike fairly linear consumer retail journeys, other health events may present along the way, i.e., they might develop a diabetes diagnosis and begin that course of treatment. These parallel or shifting paths make it incredibly difficult to maintain a cohesive view of the patient, let alone understand which interactions influenced their decisions. This complexity also makes campaign attribution a major challenge. Health systems often struggle to determine which messages, channels, or touchpoints actually move the needle. Without persistent [identity resolution](https://www.redpointglobal.com/identity-resolution/) and feedback loops, it’s nearly impossible to connect long-term outcomes – like a completed surgery or improved A1C – to specific marketing or engagement efforts. ## **The Illusion of Data Readiness in Health Systems** Despite significant investments in data technology, most health systems still struggle with poor data quality, unresolved patient identities, and fragmented care journeys. These issues severely limit the effectiveness of patient engagement campaigns. A common approach is to tackle these problems within the IT department with solutions like Master Data Management (MDM). However, MDM systems typically focus on static data and often miss the dynamic aspects of patient behavior including transactions, interactions and predicted intent. These behavioral signals are critical for delivering personalized experiences but fall outside the scope of traditional MDM. Customer Data Platforms (CDPs) were originally designed to bridge this gap by connecting mastered data with behavioral and transactional insights. In practice, though, most CDPs simply aggregate and unify upstream data – flaws and all – and pass it along to marketing teams without addressing the underlying quality issues. As a result, the disconnect between master data and business-ready data persists. Similarly, storing customer data in a cloud environment is often mistaken for data readiness. But just like many CDPs, cloud storage alone doesn’t solve for identity or data quality. Without these foundational elements, organizations risk misinterpreting patient needs and delivering subpar outcomes. ## **The Data Readiness Solution** A focus on patient data readiness solves these complex challenges. When data is truly ready for use, it’s not just stored, it’s trusted, connected, and actionable. This means that patient identities are resolved across systems, behavioral and transactional signals are integrated, and predictive insights are [accessible in real time.](https://www.redpointglobal.com/real-time-interactions/) The result is a unified view of the patient that supports better decisions across the organization. A strong data readiness foundation includes the following critical components. - **Automated Data Quality** In healthcare, where patient data is constantly flowing from different systems, from digital interactions and appointments to lab results and billing, continuous data quality is a necessity. That’s why data standardization and error correction shouldn’t be one-time events, they must happen continuously and automatically from the moment data enters the system. This ensures that every new record, update, or signal is immediately validated, and cleaned. This approach prevents errors from compounding downstream, keeps analytics and engagement tools running smoothly, and ensures that every decision is based on reliable, up-to-date information. - **Advanced Identity Resolution** Effective identity resolution is foundational to data readiness. By combining deterministic (exact match) and probabilistic (likelihood-based match) techniques, organizations can tailor match rules to fit specific use cases—whether it’s looser match rules for basic prevention messaging or tighter match rules for communicating sensitive information. It also helps to understand data from anonymous-to-known journeys. But matching alone isn’t enough. Unlike basic, point-in-time matching, [persistent key management](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) enables a longitudinal view of each patient. This means identities are continuously tracked and updated over time, allowing for a richer, more contextual understanding of the patient journey. From initial engagement to ongoing care, this continuity is essential for delivering personalized, coordinated experiences. Householding — the ability to group individuals who share a household or financial relationship—adds another layer of insight. For example, understanding that two patients are part of the same household can inform outreach strategies. It also helps avoid redundant communications and enables more empathetic, context-aware engagement. Advanced identity resolution doesn’t just connect data, it connects people to their stories, enabling smarter decisions and better outcomes. - **Contextual Profile Unification** [Profile unification](https://www.redpointglobal.com/profile-unification/) allows you to curate customer data into clear and actionable profiles. Too often patient profiles get weighed down by excess detail. Effective unification filters out the noise and surfaces what matters most — validated contact information, meaningful clinical and behavioral signals, and key interactions across the care journey. In this step, profiles are automatically enriched with AI models, calculations and trusted third-party data. This provides deep insights that enables marketers and patient experience teams to communicate with speed, accuracy and empathy. - **Smart Activation** Smart activation is the final step in the data readiness journey, where clean, connected, and contextual patient data becomes a strategic asset. It’s not just about pushing data downstream; it’s about delivering precisely the right data to the right touchpoints, at the right cadence, and in the right context to drive meaningful engagement. What sets smart activation apart is its reliance on [dynamic, real-time segments](https://www.redpointglobal.com/segmentation-activation/) that evolve as new data flows in. These segments are continuously orchestrated across multiple (digital or physical) engagement channels, ensuring that every interaction is timely, relevant, and personalized. In healthcare, this means the right patients receive reminders, education, and support exactly when they need it. ## **Data That’s Fit for Purpose – Now and in the Future** The cost of poor data isn’t just inefficiency; it’s lost patients, missed revenue, and diminished trust. A data readiness solution helps fill the data fragmentation gaps that can arise from varying technology and workflow processes. Data that’s clean, accurate, and ready for business use the moment it’s needed allows health enterprises to understand a patient’s social drivers of health and for marketing and operational teams to act with precision and confidence. For example, following data readiness principles, one health system was able to personalize outreach across the entire care journey. This hyper-personalization helped to expand scheduled appointments by 50 percent and drive healthcare center expansion. With a strong data readiness foundation, health systems can finally future-proof their data, turning information into impact across the entire patient journey through data that is fit for purpose—complete, accurate, timely, actionable, trusted, and compliant. **Blog categories:** Healthcare **Blog tags:** Data quality, Data readiness --- ### [Liberated Data and the Art of the Possible: Data Readiness for AI](https://www.redpointglobal.com/blog/liberated-data-and-the-art-of-the-possible-data-readiness-for-ai/) **Published:** May 8, 2025 **Author:** John Nash **Content:** In a survey of hundreds of chief data officers (CDO) sponsored by Amazon Web Services and the MIT CDO Symposium, data quality and finding the right use cases were ranked as the two biggest challenges ([46 and 45 percent, respectively](https://hbr.org/2024/03/is-your-companys-data-ready-for-generative-ai)) for realizing the potential of generative AI (genAI). And while 93 percent of survey respondents agreed that data strategy is critical for getting value from genAI, only 37 percent agreed that their organization **has the right data foundation in place**. A Gartner survey on AI in general produced similar findings, with 40 percent of organizations claiming that a lack of AI-ready data is the *top barrier* to implementing AI – with the real-world consequence of more than half of AI projects expected to be abandoned by next year. ## **Solving AI Challenges with Data Readiness** The urgent need to support AI use cases **elevates data readiness as a top priority** for organizations looking to optimize use of their customer data. With AI rapidly becoming indispensable for broader customer experience (CX) use cases, ensuring that data is accurate, timely, complete and actionable is essential to guarantee that results meet business expectations. While some enterprises consider data management simply a necessary operational cost, those that think of it more strategically will best maximize the ROI of AI and CX initiatives. Robust data quality, semantic consistency, persistent identifiers and accurate matching set the foundation for successful AI model training and deployment, as well as streamlined operations and data-driven decision making. Data readiness directly addresses several key challenges in AI: - **Relevance:** A deeper understanding of customers is only possible through data. By having the most complete, accurate and timely customer profiles, AI applications are best able to make predictions and decisions that in turn create relevant offers, messages and content in the context of each customer’s journey. - **Enhanced Model Accuracy and Reliability:** High-quality, clean data minimizes noise and bias, leading to more accurate and reliable AI models. Inconsistent or erroneous data can result in suboptimal model performance and flawed predictions. - **Improved Feature Engineering:** Well-organized and semantically consistent data facilitates the extraction of meaningful features, which are critical for training effective AI algorithms. - **Reduced Training Time and Compute Costs:** Automated data prep streamlines the preprocessing stage of the AI lifecycle, significantly reducing model training times and the associated computational resources. - **Explainable AI (XAI):** Consistent and well-documented data, coupled with persistent identifiers for traceability, enhances the interpretability and explainability of AI model outputs, fostering trust and facilitating debugging. - **Scalability and Interoperability:** Data readiness ensures data assets can be efficiently accessed, integrated, and used across multiple AI applications and platforms, promoting consistency and reuse within the data ecosystem. Forrester says that “companies that prioritize data readiness for AI see a significant improvement in operational efficiency and customer satisfaction.” Investing in data readiness is therefore not merely a cost mitigation strategy, but a fundamental enabler for realizing the transformative potential of AI and achieving a significant competitive advantage. ## **Data Readiness for AI in Action** One organization that realized the potential of AI through investment in data readiness includes a CPG company that used Redpoint technology to achieve a 79 percent increase in conversion rates and a 144 percent increase in sales. One challenge that prevented the company from achieving its goal of increasing direct-to-consumer sales was having 20 different data sources – including batch and streaming – comprising more than 2,400 data elements. This made it all but impossible for the company to make relevant, real-time product recommendations for customers engaging online. In addition, the company was unable to provide personalized offers based on product registrations, which eliminated a powerful avenue for driving long-term customer loyalty. > With AI rapidly becoming indispensable for broader customer experience (CX) use cases, ensuring that data is accurate, timely, complete and actionable is essential to guarantee that results meet business expectations. Hosted on Microsoft Azure for virtually unlimited scalability, Redpoint delivered a production-level recommendation engine using out-of-the-box predictive models and built-in machine learning to deliver real-time product suggestions across all enterprise touchpoints. Previously, decisions had been siloed without taking into account an individual customer’s preferences, behaviors or previous transactions. This all changed with having a solid data foundation from reliably integrating all the different data elements – each with unique characteristics, latencies, and levels of quality into an accurate, real-time unified profile for each customer. Real-time, highly relevant product recommendations were also made possible because Redpoint technology enabled marketing teams to select their own campaigns, identify and segment audiences, and pull content for use across channels without the need for IT assistance – or the need for deep technical expertise. In addition to higher sales and an increase in conversions, the company used Redpoint to increase e-mail sign-ups, reduce cart abandonment, provide triggered coupons for product registrations, market gift subscriptions more effectively, and execute more effective win-back campaigns by targeting lapsed customers with relevant, personalized offers. ## **The Data Readiness Difference** A Redpoint customer in the travel and hospitality industry achieved an 80 percent reduction in model prep time by using better data. “By leveraging Redpoint’s data management strengths, we quickly got a 360-degree view of our customers. This saved us an enormous amount of time and capital expense,” said the company’s Director of CRM and Marketing. By using Redpoint, the company was able to keep its entire IT infrastructure intact without having to change a single reservation platform. Redpoint integrated more than 100 data sources from an extraordinarily diverse set of properties, transactional, and management systems into a common database, handling all cleaning, standardization, and enrichment with hundreds of third-party appended attributes. With a unified customer profile for each individual guest in place, the company used Redpoint’s machine learning capabilities to create dynamic segments using eight distinct personas. “Using Redpoint, we focused our campaign messaging on guests’ personal interests, experiences, and past interactions with our offers. This resulted in a 91 percent year-over-year revenue improvement and 103 percent year-over-year increase in transactions,” said the marketing director. ## **Explore the Data Readiness Difference** Because more than half of AI projects will be abandoned by next year due to a lack of AI-ready data, the costs of inaction are mounting. In contrast, Redpoint customers are seeing measurable returns by investing in data readiness, including steep revenue gains, improved conversion rates and faster time-to-value for AI models. By addressing data quality upfront, Redpoint eliminates inefficiencies, boosts model accuracy, and enables enterprise-scale AI success. The results speak for themselves: Organizations that prioritize data readiness are building a competitive advantage in today’s AI-driven economy. For more on Redpoint’s approach to data readiness and what it means to have your data ready for business use across the enterprise, click [here](https://www.redpointglobal.com/). **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [How Data Readiness Drives Effective Analysis, Segmentation, and Personalization](https://www.redpointglobal.com/blog/how-data-readiness-drives-effective-analysis-segmentation-and-personalization/) **Published:** January 20, 2026 **Author:** Beth Pfefferle **Content:** In today’s digital landscape, businesses gather more customer data than ever before. Yet, for many organizations, the sheer volume of information collected doesn’t automatically translate into better customer experiences or business outcomes. In fact, a recent survey revealed that [54 percent of marketers](https://www.invespcro.com/blog/data-driven-marketing/) see poor data quality and completeness as the biggest obstacle to achieving data-driven customer experience (CX) success. And 87 percent consider data their organization’s most under-utilized asset. Relying on siloed systems, manual processes, and other traditional methods for extracting value from data are falling short. This is where data readiness emerges as an essential strategy, ensuring data is not only available but also trustworthy, accessible, and actionable for analytics. ### **What is Data Readiness?** [Data readiness](https://www.redpointglobal.com/data-readiness-hub/) means preparing your data so it’s “right” and “fit-for-purpose” ([clean, accurate, complete, and timely](https://www.redpointglobal.com/data-the-defining-difference/)) before it’s ever used for analysis or decision-making. This involves automating processes such as data cleansing, matching, and creating contextual relationships (like householding), all of which address the [inherent messiness of customer data](https://www.redpointglobal.com/blog/the-heart-of-customer-data-technology-minding-the-data/). The result is a solid foundation for deriving actionable insights, driving personalized engagement, and building lasting customer relationships. ### **Why Data Readiness Matters for Customer Analytics** The ultimate goal of data readiness is to convert raw, fragmented data into insights that enable impactful customer experiences. Without robust data preparation, organizations may find themselves “data rich, but insight poor,” leaving them unable to trust or act on the information they’ve collected. When businesses invest in data readiness, they gain the confidence needed to conduct meaningful analysis and craft successful offers that truly resonate with their audience. Data readiness provides the much-needed [context around customer data](https://www.redpointglobal.com/blog/does-your-customer-data-have-enough-context-heres-how-to-tell/), a situational awareness essential for aligning experiences that are hyper-relevant for a customer at a precise moment in time. ### **Data Readiness and Data-Driven, Dynamic Segmentation** Data readiness is essential for building accurate customer segments that fuel effective AI. The better a company understands its customers, the better its models can capture and reflect the nuances of what motivates a customer. Traditional age-based or gender-based segments or any other artificial, surface-level slicing of an audience are not adequate to drive personalized interactions that respond to intent signals, or that drive real-time next-best actions that perfectly align with an omnichannel customer journey. Fueled by data readiness, models instead can slice and dice an audience in an unlimited fashion, wherever the data leads or according to a specific marketing or business objective. What we think of as “smart” AI is intelligence that is grounded in deep learning. Machine learning models find meaningful similarities and segment audiences accordingly, and the resulting dynamic segments are automatically updated to reflect a real-time customer journey. Intelligence is based on a deep customer understanding rooted in accurate, real time, contextual data. The expansion of AI and agentic AI use cases only enhance the need for high-quality data through data readiness. ### **Data Readiness and True Customer Understanding** Data readiness is more than a technical requirement; it’s a [strategic enabler for every organization seeking to unlock the full potential of customer data](https://www.redpointglobal.com/data-the-defining-difference/). By investing in robust data preparation processes, companies can overcome the pitfalls of poor data quality, support advanced analytical initiatives, and deliver more personalized, meaningful experiences for their customers. In the age of data-driven business, readiness is the foundation for gaining deeper insight into customer behaviors and preferences, which itself is foundational to a long-term personalization strategy for growth. To learn how the Redpoint Data Readiness Hub gets customer data ready to power any AI or CX initiative across the enterprise, click [here](https://www.redpointglobal.com/data-readiness-hub/). **Blog tags:** Data readiness --- ### [Data Readiness & Real-Time Relevance: Delivering Context in Every Moment](https://www.redpointglobal.com/blog/data-readiness-real-time-relevance-delivering-context-in-every-moment/) **Published:** October 15, 2025 **Author:** John Nash **Content:** Delivering a “right time” customer experience (CX) is essential to provide customers with the level of personalization that drives higher satisfaction, loyalty, and lifetime value. It is the key to relevance, reflected by a brand’s ability to recognize a customer’s needs and wants at the moment of engagement. With dynamic, omnichannel customer journeys now the norm, the “right time” to meet a customer with a next-best action is often in real time, or near real time. Many enterprises lack the type of situational awareness that consumers are expecting in real time. That is why real-time engagement must start with [data readiness](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/), a concept and methodology for delivering a unified customer profile enriched with the contextual understanding needed to adapt every experience in the moment. This is the foundation of the Redpoint approach to real time engagement. ## **The Real Time Imperative** Experiences that call for a real time engagement might include a health provider’s website displaying content, imagery, and information relevant to the health condition of the website visitor – whether the individual is known or unknown. Or a call center agent who has instant access to a complete, accurate, and updated unified customer profile resolving an issue to a customer’s satisfaction without having to ask probing questions. Or a brand optimizing an abandoned shopping cart strategy with real time (and right time) triggered actions based on the behaviors of an individual consumer. Seamless real time experiences are now integral to a brand’s ability to deliver a relevant, personalized CX, and consumers are holding brands accountable. In a [Dynata survey](https://www.redpointglobal.com/press-releases/80-of-consumers-more-likely-to-shop-with-brands-that-show-they-understand-them/), 80 percent of respondents said they are more likely to transact with brands that demonstrate a personal understanding. With real-time so critical to a frictionless CX, why is it still an obstacle for many brands? A major culprit is siloed data, channels, and processes that put real time engagement beyond reach. Consider a Gartner report that shows that roughly 50 percent of large organizations will have failed to unify engagement channels by the end of this year, resulting in a disjointed and siloed CX that lacks context. ## **Real Time Requires Complete, Accurate & Timely Data** Data readiness is the way forward because it provides brands with an important contextual understanding of customers, patients, and households. It is also invaluable in parsing relationships, such as identifying individual customers within a business account. Data readiness solves the traditional barriers that prevent brands from real-time engagement. One is not having a deep enough understanding of a customer to be able to engage with a relevant, personalized CX in real time across all channels. Even if a unified profile is [timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/), in other words, it may still be [incomplete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/) or [inaccurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/). When a marketer or business user has the wrong information, a real-time engagement likely just means a brand delivers the wrong message more quickly. Not having the data available in the needed time-frame is another obstacle, which might occur if the data is siloed by channel, or if there is a lag with the source data. The longer the lag, the less of a contextual understanding for how the data relates to an ongoing customer journey. From an operational standpoint, these barriers prevent consistently relevant real-time experiences in part because each channel or team has its own view of a customer, which is built through multiple systems. There is no uniformity. As for data lags, consider the ramifications if a web personalization tool calls out to a CDP to attach browsing session data to an existing profile. If the CDP provides the tool with data only when a source system is updated (nightly, weekly), it may be missing important information – a recent purchase, a call center interaction – that would materially alter the real time website experience. ## **A Unique Approach to Real Time Engagements** Real-time engagement isn’t achieved by simply adding a faster decision engine, it’s achieved by ensuring the data feeding those decisions is always ready, accurate, complete and contextual. A data readiness platform eliminates the problems that stem from incomplete and untimely data in several ways. The [Redpoint Data Readiness Hub](https://www.redpointglobal.com/data-readiness-hub/) separates itself from other customer engagement technology solutions because of its unique understanding and approach to real time, a large part of which is making sure that data is continuously [ready and fit for purpose](https://www.redpointglobal.com/data-the-defining-difference/) the moment it enters the system. Not only is all possible information and relevant data about a customer in one place, but Redpoint continuously applies full data quality processes as data is ingested. Real time data processing extends to source systems, which means that the resulting unified customer profile always reflects the most accurate and updated understanding of a customer. Identities are also resolved in real time, whether that means matching a website visit to an existing customer, matching a person to a household, etc. With a full contextual understanding, brands have the situational awareness to make informed decisions and create the most compelling experiences for the customer at the precise moment of engagement. ## **Augmented Real Time with Real Time Decisioning Pipelines** Beyond delivering a full contextual understanding through a complete, accurate and timely unified profile, Redpoint’s approach to real time includes pre-decision and post-decision pipelines, which is where additional contextual information is gathered the moment preceding and following a decision. Immediately preceding a decision, a call can be made through an API to gather additional context, whether it’s a model, a weather update, a credit card check or another piece of information that is then loaded into the customer profile to layer in real-time context. The post-decision pipeline is where a decision can be manipulated, such as being sent to multiple systems or channels. Importantly, the pipelines are stackable, meaning they enable more than one decision. The pipelines enhance contextually relevant experiences because they’re built around dynamic rules that permit messages, offers, and/or content to be switched out up to the moment of interaction. A sudden storm warning, for instance, might impact how a hotel desk clerk interacts with a guest who is checking in. A pre-decision pipeline calls a machine learning model – Redpoint’s or a client’s – that returns real-time recommendations optimized against the decisions built into the platform. The immediate context (inclement weather) becomes part of the updated profile. A post-decision pipeline works in the same fashion, providing an opportunity to update a decision before it is presented to a device, app, website, tablet or even in person – such as using generative AI to change the tone of an email. The practice of engaging customers with real-time decisions that are contextually relevant at the moment of engagement builds on its own success, enabling additional context for subsequent decisions. Results of any real time decision, such as the customer making a purchase, downloading the app, signing up for a loyalty program, etc., are fed back into the platform to update an individual customer profile and to alter the pre-decision or post-decision content. ## **Real Time with Redpoint** Marketers chase real-time engagement, but it’s context that drives value from it – revealing exactly who the customer is, when it matters most – in the cadence of the dynamic customer journey. For more on the Redpoint approach to real time engagement, or to see how Redpoint can help you achieve results such as a 79 percent increase in conversions through real-time product offers, click [here](https://www.redpointglobal.com/real-time-interactions/). **Blog categories:** Real-Time Personalization **Blog tags:** Data quality, Data readiness --- ### [Data Enrichment, Data Validation and a Personalized Customer Experience (CX)](https://www.redpointglobal.com/blog/data-enrichment-data-validation-and-a-personalized-customer-experience-cx/) **Published:** January 5, 2023 **Author:** Beth Scagnoli **Content:** Trust is an underrated concept as it applies to an organization’s use of business data, including customer data. Marketers and other business users of data often take for granted that the data they’re working with has been vetted, is accurate, up-to-date and fit-for-purpose for the business objective they’re trying to achieve. Data enrichment and data validation, in other words, are under-appreciated as important components in underpinning the accuracy of a resulting [Golden Record](https://www.redpointglobal.com/single-customer-view/): a pristine, unified customer profile that is the basis for delivering a personalized, omnichannel customer experience. There is some confusion about what the deprecation of the [third-party cookie](https://www.redpointglobal.com/blog/the-demise-of-the-third-party-cookie-opens-new-methods-of-engagement/) means for data enrichment, which by definition entails using third-party data to enrich an organization’s own first-party data. The loss of the third-party tracking cookie, however, does not impact the validity of *all* third-party data. What is true is that [data enrichment](https://www.redpointglobal.com/blog/what-is-data-enrichment/) and data validation are not an open invitation to collect and/or trust every additional source of data to bolster existing first-party data. ## **Data Enrichment vs. Data Validation** Before addressing best practices for incorporating data enrichment and data validation processes into the creation of a Golden Record, a quick primer on the terms. Data validation is commonly understood as the process of verifying the accuracy, structure and quality of data prior to processing. Data validation can be as simple as confirming that a U.S. state abbreviation value is two characters. . However, data validation can also involve leveraging a trusted third-party to confirm a given dataset. A good example is the National Change of Address (NCOA) database, of which there is only one in the United States. As such, it is accepted as the single source of truth for validating a customer’s current mailing address. (The NCOA logs and keeps all change of address records for four years). Data enrichment is not as clear cut. It can refer to leveraging first-party data to enhance existing data sets (common in more siloed organizations), or the use of one of many third-party vendors for processes such as demographic or geographic overlays, any of which may use different methods to find out information about a person. Or, for that matter, have different standards. For instance, many companies will offer age overlays, wealth overlays, or reverse email appends. Unlike the NCOA, however, which relies on a customer filling out a change of address form, there is no universal understanding of where the information about a customer’s age, income/wealth or email originates from, or how the vendor files it. Even if a vendor returns an age overlay with a specific birthdate, the company that buys the overlay is asked to trust its veracity. Other vendors might return ranges – here’s an overlay of people aged 25-34. One way to think of the difference between the two is that data validation should be used along with data enrichment. That is, when a given dataset is sent for enrichment, the return data should be run through the same data validation processes as first-party data where applicable. Data enrichment without validation may not be harmful on its own, but in order to maintain data integrity for the end goal of a Golden Record, the validation step is critical prior to leveraging any data in a production environment. ## **Consider Your Business Purpose** Other factors to consider when incorporating data enrichment and validation processes into the creation of a Golden Record is the underlying business purpose, which frequently relates to the type of industry a business is in. A non-profit media company that heavily relies on accurate, up-to-date donor lists – and with a target demographic typically skewing older – will be far more interested in age overlays (or even death overlays) than a retailer. Likewise, a financial services company will have more of an interest in a wealth overlay or credit scores than a healthcare organization. As for the business purpose itself, generally speaking the more personal a communication the more a business will have to trust in the method of enrichment. A healthcare organization that emails customers about a new provider joining the network may be comfortable with enriching its email files with a reverse email append, whereas it would not rely on a verified email to communicate with a customer about a diagnosis. ## **Privacy and Compliance** The above example also takes us into the area of privacy and compliance. Data enrichment and data validation do not give an organization free reign to use data any way the business sees fit. Rather, there are a host of regulations – industry-specific and otherwise – that businesses must comply with for how data is collected, stored and used. Most people are familiar with GDPR and CCPA as government regulations that protect consumers’ data privacy, but other regulations such as HIPAA in healthcare and GLBA in financial services spell out in more detail how those organizations must treat patient and consumer data, respectively. Government regulations aside, a business must also self-regulate in terms of its approach to using customer data. That is, customers themselves are the ultimate arbiter for what’s acceptable or not for how their data is used. It might be off-putting, for example, for a customer to open a Happy Birthday email from a brand it rarely patronizes. Following the tried-and-true “less is more” adage, often the best approach is to only reveal what you know about a customer when it will help guide a customer through a customer journey or give the customer something of value. The customer data value exchange, which we’ve [covered extensively](https://www.redpointglobal.com/blog/new-survey-findings-reveal-customers-grasp-the-value-of-their-personal-data/), refers to consumers willing to provide organizations with personal data in exchange for a more personalized – i.e. relevant – customer experience. In a recent [Dynata survey](https://www.redpointglobal.com/blog/new-survey-findings-reveal-customers-grasp-the-value-of-their-personal-data/), 73 percent of consumers surveyed said they either “rarely” or “never” provide personal data without knowing explicitly how it will be used. Meanwhile, 59 percent said receiving personalized offers or discounts is their main inducement for providing personal data. Data enrichment and data validation, then, are instrumental in ensuring that first-party data is fit-for-purpose to provide consumers with the type of personalized customer experience they’ve come to expect. Even so, brands must take heed that enrichment and validation are not an open invitation to use all data that comes into an organization, fit-for-purpose or not. ## **An Ongoing 24/7 Process** One best practice for data enrichment and data validation is to undertake those processes with the understanding that a businesses’ first-party data is the core of a Golden Record. Perfecting an organization’s own first-party data and making it fit-for-purpose is, after all, the entire purpose of data enrichment and data validation. To that end, [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) is the process of using probabilistic and deterministic matching to increase the veracity of a customer record, and is indispensable in creating a Golden Record. If an updated NCOA file shows a customer by the name of Jon Smith living at 123 Main St. in Boston, and your record shows a Jonathan Smith at 123 Main Street, identity resolution will make the determination if the two records should be matched. With that in mind, another important best practice is that data enrichment and data validation are continual processes. If a marketing campaign depends on having a customer’s correct mailing address, a yearly NCOA file might be insufficient for reaching campaign metrics. Likewise, an age append that sends a file of customers aged 25-34 might suffice for some campaigns, but people will age in and out of that demographic daily. A pristine Golden Record that drives real-time, omnichannel customer experiences must be continually updated with fit-for-purpose data which requires that data enrichment and validation steps are completed as soon as data is ingested. A [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/resources/harris-poll/) underscores why data enrichment and data validation are vital in providing customers with omnichannel personalization. Just 18 percent of consumers surveyed said they are very confident in the quality of data that brands have about them. They say it’s all-too common that brands engage using outdated, inaccurate or inconsistent data, with more than half (51 percent) claiming that the result is an impersonal or irrelevant experience. Furthermore, marketers themselves say that improving the accuracy of their customer data is their No. 1 data quality objective. Adopting a sound data enrichment and data validation processes is a good way to start. ## **Related Redpoint Orchard Blogs** [What We Mean When We Talk About Data Quality](https://www.redpointglobal.com/blog/what-we-mean-when-we-talk-about-data-quality/) [What is Augmented Data Quality, and Why Does it Matter?](https://www.redpointglobal.com/blog/what-is-augmented-data-quality-and-why-does-it-matter/) [The Role of a Golden Record in Providing a Consistently Relevant, Personalized CX](https://www.redpointglobal.com/blog/the-role-of-a-golden-record-in-providing-a-consistently-relevant-personalized-cx/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Identity Resolution --- ### [No Data Left Behind: Analytics, Orchestration, and Making Data Work for You](https://www.redpointglobal.com/blog/no-data-left-behind-analytics-orchestration-and-making-data-work-for-you/) **Published:** July 29, 2019 **Author:** Steve Zisk **Content:** *Editor’s Note: This is the first blog in a two-part series that will explore the importance of continual data infusion, orchestration, and a closed-loop data cycle over the complete customer data lifecycle.* Ask a dozen marketers what it means to be “data-driven” and you will most likely receive a dozen different answers. Most will ruminate on the importance of customer data for creating personalized experiences that today’s always-on, always-connected customer craves. Fewer, though, will demonstrate a true understanding for how vital data is for breathing life into analytics and orchestration that are foundational for creating innovative customer experiences. Continual data infusion ensures the long-term vitality of machine learning models, and also requires marketers to meticulously govern the underlying data orchestration to ensure that models are continually in sync with evolving business rules and outcomes. By grasping the true, full use case of customer data as the driving force for guiding and understanding a customer journey, marketers avoid common opportunity costs for underutilized data and avoid introducing friction into a customer journey. ## **Don’t Let Your Data Go Stale** A common misconception about AI in the customer engagement realm is that marketers can dump customer data into a machine learning model, sit back, and watch the magic happen. In reality, analytic models go stale over time. There is a cadence to developing models, deploying them, retraining them, continually testing them, and recognizing the precise moment when they have outlived their usefulness. Continual data infusion helps dictate the data model cadence, answering questions such as when to redeploy or retrain. Also, new customer data could mean that an existing model might not be answering the right questions; different data, in other words, leads to different questions. A re-examination of models leads to a re-examination of the data, and vice-versa. New customers, products, channels, or ideas could all be the impetus for re-examining data or models to ensure the right questions are being answered. Experimentation also plays a role; marketers want the freedom to test boundaries, and a broad range of questions requires a broad range of data to answer new questions. Multiple data models account for the innumerable intersections between a customer’s desire, marketers’ intent, and an organization’s constraints at any moment in time. Experimentation is about assigning certain data to a certain model to test, for example, the likelihood of a customer to buy based on a previous interaction. ## **What Is Data Orchestration?** Data orchestration is a process carried out by a piece of software that takes siloed data from multiple data storage locations, combines it, and makes it available to data analysis tools. A laser focus on data infusion as it relates to the vitality of data-driven models is half the battle for marketers for seizing the right moment of interaction with a customer. Data orchestration completes the picture, beyond orchestrating channel connectivity with a next-best action for a customer to include behind-the-scenes orchestration, with a system in place that ensures data flows where and when it’s needed. Having the appropriate connectivity with automatic cleansing, merging, matching, and data transport, as well as automatic feeding into tools for model building, training, and assessment and into every task associated with orchestration is vital for marketers to achieve their personalization ambitions. An analogy can be made to building a luxury new home but skimping on the plumbing; a gleaming new data model will quickly become a dilapidated shack without a continual data pipeline that guarantees a proper flow from the data source to every orchestration touchpoint. Manually “carrying” data around via spreadsheets is a big culprit for missed data opportunities, introducing the possibility of data becoming stale, being put into the wrong campaign, lost or otherwise misused. ## **Dance with the Data That Brought You** An orchestration dance, if you will, is a symbiotic relationship between the marketer and the customer that is in constant motion from always-changing inbound and outbound touchpoints. Choreographed movements can be determined by marketer’s preference (I want to sell more of item X,) customer preference (I’m going to abandon a shopping cart to shop in-store), or by business rules or machine learning rules that select audiences, determine cadence, and have the dance’s next step planned out depending on the last step. The heart of orchestration entails measuring and acting on each of these preferences as they relate to one another in real time – which requires having the right data at the right time and the right place. In recognizing that customer engagement – like the modern customer journey itself – is a continually ongoing process rather than a destination underscores the need to continually breathe new life into models or create new models in line with changing objectives, as well as to ensure that robust orchestration is in place to guarantee that customer data is not left behind. *In the next blog installment, I will dive deeper into data orchestration and focus on the importance of a closed-loop data cycle, including the continuous incorporation of feedback, retraining, and the customer’s voice*. **Blog categories:** Journey Orchestration --- ### [Addressing the Gaps in Customer Experience: Redpoint Global/Harris Poll Benchmark Survey](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) **Published:** March 27, 2019 **Author:** John Nash **Content:** ![Gaps in Customer Experience](https://www.redpointglobal.com/wp-content/uploads/2019/03/shutterstock_562044043-e1553632747385.jpg)There is a pressing strategic imperative for brands to compete on customer experience (CX) in nearly every industry. Marketers in retail, insurance, financial services, CPG, travel and hospitality, and health and wellness are pressured to meet growing expectations from the empowered consumer for an experience tailored to their preferences at every stage of the customer journey. To better understand and address the challenges of today’s marketers and consumers, Redpoint Global commissioned The Harris Poll to conduct quantitative research among these audiences for the first “Addressing the Gaps in Customer Experience” annual report. In looking at responses from over 450 marketers and 3,000 consumers across the U.S., Canada, and the U.K., we found several gaps between marketers’ customer experience strategy and consumers’ expectations. **Marketers More Optimistic about Gains in Personalization Than Consumers** In 2019, marketers’ confidence is consistently high for success across all four dimensions of the Customer Experience Index Score (measuring customer understanding, personalization, omnichannel, and privacy). This is a clear contrast with consumers who average a **15-point drop** from the marketers’ own view in each of the four CX Index dimensions. The study shows brands are not delivering the level of CX today’s customers want. The deep gap between a consumer’s expectations and the experience being delivered is magnified by the inability of brands to execute their strategies. Data fragmentation, system fragmentation, and organizational fragmentation all contribute to the frustration for consumers and marketers alike in the delivery of a seamless, omnichannel customer experience. In measuring several key dimensions for how well customer engagement systems deliver on CX goals, the study found an across-the-board gap between how marketers and consumer rate the effectiveness of these systems. In every area, marketers are roughly **2.5x more likely** than consumers to believe they achieve excellence in effective execution. The dimensions evaluated looked at how well brands hyper-personalized offers, anticipating customers’ needs and intentions, provided a comprehensive picture of the customer, among others. Having an effective customer experience is critical to the bottom line in a marketplace where products are largely commoditized. Brands that can look realistically at their own effectiveness and excel in delivering customer experiences will be winners that separate from the pack. To their credit, marketers appreciate that the customer experience needs to evolve, but many are unsure of where to start. Some 65 percent indicate the sheer number of systems they manage makes it difficult to provide a seamless customer experience. **Personalization is Key to a Modern CX** Marketers understand they need to focus on the end goal of creating a differentiated hyper-personalized experience. The survey sheds light on what hyper-personalization looks like from the consumer’s point of view. Importantly, 43 percent said it means that a brand recognizes them as the same customer across all touchpoints. This seems to indicate that for consumers, personalization goes far beyond, for example, addressing someone by name in an email. Consumers are telling us that they expect a brand to be able to consistently keep pace with them at every stage of a dynamic path-to-purchase that has individualized beginnings, middles, and ends. For the consumer, impersonal or irrelevant interactions are simply unacceptable. About one-third of consumers said it was “very frustrating” when a company sends an offer for a product they just bought or one that wasn’t relevant to them. Frustration will irrevocably shatter the consumer-brand relationship; 37 percent of consumers indicated that they will no longer do business with a company that fails to offer a personalized experience. ![CX Strategy Stat](https://www.redpointglobal.com/wp-content/uploads/2019/03/Image_6_Blog_800Pixels-1024x536.jpg)Brands must expertly manage each journey in the context of the complete customer lifecycle to drive the personalized consistency that consumers expect. Customer cadence – past, present, and predicted – must be included as part of a complete contextual analysis. A marketer that knows where a customer has been and where and when they’re going can begin to not only personalize interactions but do so with relevance to an individual customer – at the precise moment of the engagement. Real-time responsiveness also underpins the delivery of a personalized experience throughout every stage of a customer journey. Real-time data, real-time decisions, and real-time interactions are critical to keeping pace with the customer. Without these real-time dimensions, an offer, a recommendation, a notification, or any other interaction will miss the optimal moment of engagement. Based on the CX gap and the challenge for marketers in aligning strategy with execution, it’s not a surprise that the survey also reveals real-time engagement to be a major challenge for marketers in delivering a modern experience, with half (50 percent) identifying it as the primary hurdle. **Consumer and Brand Data Value Exchange** Real time is clearly critical to the delivery of a contextually relevant, omnichannel experience, and without it, everything grinds to a halt. Consumers expect that and more, as transparency is also a major element in establishing trust and a willingness to share information. When a company is transparent about how a customer’s data is being collected, stored, and used, the customer is more apt to share the personal data that is necessary to personalize the experience. Data shows two-thirds of customers expect transparency about what is being collected, how the information is being used and that the control over how information is being used remains in the hands of the consumer. While transparency is a top priority, most consumers also understand that personal data drives personalization in a modern, omnichannel world. Research reveals that more than half of consumers (54 percent) across all age demographics will willingly share personal data in exchange for a more personal experience, with a substantial percentage increase among younger generations. Transparency does not give a marketer carte blanche to use personal data however they see fit. Consumers want some level of control over their preferences in how data is used, and trust that it is primarily being used to personalize experiences. Consumers view this as table stakes, which is why they rated privacy as the most important across the four CX dimensions (privacy, customer understanding, personalization, omnichannel/consistency). Marketers are looking to move beyond table stakes to create differentiated sources of revenue, which is likely why they were split – citing privacy and customer understanding as equally important. **Building a Future-Proof Modern CX** Traditional methods of engaging with customers, where data is siloed by channel, by system, or by program are entirely insufficient to deliver the levels of transparency and personalization that customers demand. It is these types of challenges that cause so many marketers – 63% in this survey – to struggle with executing their strategies very well. Marketers also struggle in that they are looking for a transformative step-change to keep pace or even a step ahead of competitors. Marketers indicated in the survey that there were a number of challenges to optimizing strategy and execution across all channels. Siloed and inaccessible data, customer data lacking depth, and fragmented systems, all are factors in preventing the delivery of a unified customer view across touchpoints. Nearly 40% also said that the complexity of technology solutions was the top barrier to bridging the gap between strategy and execution. There is a way to resolve these challenges, through establishing a single point of control over all data, decisions, and interactions. This overcomes the typical fragmentations and puts marketers in a position to deliver against expectations of individual customers. It provides marketing with the foundation needed to deeply understand customers and provide an instantaneous response or a proactive, predictive action that will resonate with a customer in their context and cadence. **Single Point of Control** A single point of control that overcomes marketers’ biggest challenges begins with having a single view of the customer across all enterprise solutions and data sources. This golden customer record includes everything there is to know about a customer far beyond basic transactional history or personal information; behaviors, interests, and preferences are combined into an always-updating record that tracks a customer throughout the entire customer lifecycle. The ingestion of real-time data into a golden record, verified through robust identity resolution, is key to unlocking the full potential of personalization. Advanced analytics layered on top of a persistent golden record provide self-training models and continuous optimization, which gives marketers real-time decisioning that keep pace with customers. Intelligent orchestration of interactions then provides for the consistency that consumers expect in an omnichannel experience, across every stage of a customer lifecycle. According to our research, marketers are keenly aware of what’s at stake in the race to compete on customer experience, with nearly all (91 percent) agreeing that investing in MarTech solutions is a key initiative for their company. Open garden connectivity that can enable companies to leverage their existing technology investments as they create a single point of control helps overcome the complexity introduced by individual systems. This research should put to rest any lingering doubt that personalization is the key to a differentiated customer experience. Customers are clear about their expectations, and marketers are equally clear about the current barriers and challenges to deliver on those expectations. If marketers utilize the right technologies to implement a single point of control across all touchpoints in an open garden environment, there is little holding marketing back to keep pace with their customers and provide an experience worthy of gaining the modern consumer’s business. **Blog categories:** Customer Data Platform, Data Management, Data Quality, Omnichannel Marketing, Real-Time Personalization --- ### [The Secret to a Customer-Centric Approach is Hiding in Plain Sight: First-Party Customer Data](https://www.redpointglobal.com/blog/the-secret-to-a-customer-centric-approach-is-hiding-in-plain-sight-first-party-customer-data/) **Published:** June 30, 2021 **Author:** Brian Morris **Content:** *Editor’s Note: This is a contributed guest blog from PWC, a Redpoint partner* Old habits die hard. Often, though, external forces have a way of inducing reluctant change. In the financial industry, this is what’s happening today with the deprecation of the third-party cookie as the catalyst for organizations to – at long last – abandon retargeting and embrace the role of first-party data to truly understand customers, both from the perspective of an overall enterprise data strategy as well as for marketing and analytics. With change comes confusion. Many financial organizations are off on fits and starts, unsure of how to adopt a true customer-centric approach without the crutch of a tracking cookie and retargeted advertising to attract and market to customers. Even with widespread anxiety over the best way forward, there is also a general recognition that the best place to start is with something organizations all have – customers; there’s a burgeoning recognition that the secret to customer-centricity has been hiding in plain sight. The question, then, becomes how best to derive insight from the entirety of first-party customer data to go above and beyond the benefits of using a tracking cookie to instead deliver a personalized, relevant customer experience across channels and departments. ## **Beyond Basic Identity Stitching** The immediate concern for many institutions is that siloed businesses stand in the way of having a single view of the customer. While there has been some movement toward breaking down institutional siloes, particularly among the largest banks, independent divisions are a more common sight. Credit cards, retail banking, mortgage, wealth management, etc. traditionally have their own marketing and analytics teams, often with competing or divergent tactics for how to attract or retain what in many cases is the same customer across multiple businesses. There is a mindset out there – albeit misguided – that identity stitching will solve for the data silo problem and break down barriers. If every business simply places its customer data in a data lake, the thinking goes, everyone can go in there pull out what they need to create a more robust view of the customer and at least begin to experiment with different use cases for a personalized CX. The issue there is that democratic access to customer data does not magically produce a single source of truth. It’s tantamount to a construction zone crowded with pallets of equipment and raw material with a dozen or so foremen each with his own vision for the finished building. With a goal to truly understand customer lifetime value, or to segment an audience to drive an individualized marketing campaign, there needs to be synergy between core and ancillary systems. ## **Marketing and an Enterprise Data Strategy: One and the Same** The confusion over how to bridge this gap stems from a traditional viewpoint that a customer identifier as it relates to an enterprise strategy – an overall operational view – must be separate from an analytical view used for marketing purposes. There is uncertainty over who is responsible for what, and how a universal key for a customer that contains every transaction and anything related to risk, compliance, finance and accounting will mesh with the marketing side, which will contain household information, channel activities, campaigns run, lifetime value, and other data points historically used by and for marketing. A consequence of divergent strategies is uncertainty that an enterprise strategy is, in fact, based on an accurate view of the customer. A familiar example is the pricing of term deposits. Many institutions offer blanket or individual rates rather than pricing the fixed-term investments as loss leaders for their most valuable customers. For many, the reason is that they just don’t know who their most valuable customers are, and thus are unable to price any product as part of a customer’s overall portfolio. That’s just one small example of how knowing everything there is to know about a customer may help spur revenue growth, and it wouldn’t by conventional standards be considered a marketing use case. Everything stems from the single customer view, and in some instances driving revenue growth may not even be the overarching objective. Perhaps the institution wants to segment an audience to attract a more diversified customer base, or to attract customers that better align with a brand’s image as it pertains to social or environmental issues. The point is, a strategy centered on the customer must have a solid understanding of an individual customer, and that understanding depends on combining the traditionally separate operational and marketing views. ## **The Way Forward Starts with a Focus on the Customer** At [PwC,](https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/future-of-customer-experience.html) we spend a lot of time educating clients on either side of the divide, how to align an enterprise data strategy with the marketing/analytics side to drive growth, prevent churn, plan and orchestrate omnichannel campaigns. What’s attractive in working with Redpoint is that proofs of concept with the platform helps them see, using their own data, not only what a true single customer view looks like, but one that’s fitted for both marketing/analytics and the enterprise data strategy. Many other vendors in the [customer data platform](https://www.redpointglobal.com/customer-data-platform) space have a very difficult time playing both sides of the aisle, if you will, primarily because they stop at basic identity stitching – leaving it up to marketers to decide what data are pertinent. But that’s in keeping with what is increasingly an antiquated notion of institutional siloes. To trust that the customer data is providing not only a single view of the customer, but one that is in real time and relevant to the entirety of the customer’s journey with all departments, there must be a capability to do householding, to do advanced identity resolution with both probabilistic and deterministic matching, and to bring online and offline data together. Redpoint helps companies advance the vision, from knowing they should start with what they know – the customer – to actually envision what a unified, real time profile looks like and how to begin experimenting with using a single view to deliver a personalized, customer-centric experience. **Blog categories:** Anonymous to Known, Identity Resolution, Segmentation & Activation --- ### [Why Customer Permissions Must be Applied Dynamically in the Customer Lifecycle](https://www.redpointglobal.com/blog/why-customer-permissions-must-be-applied-dynamically-in-the-customer-lifecycle/) **Published:** September 30, 2019 **Author:** John Nash **Content:** In Gartner’s [2018 State of Personalization survey](https://www.gartner.com/en/documents/3892113/2018-state-of-personalization-report), 74 percent of organizations surveyed said they struggle to scale personalization efforts, finding it difficult to balance a desire to use data to deliver personal and relevant interactions with the need to manage consumer consent in an era of increasing privacy protection. Managing customer data privacy is top of mind for marketers. With General Data Protection Regulation (GDPR), California Consumer Protection Act (CCPA) and additional state privacy laws on the horizon, organizations recognize the urgent need to prepare for a permissions-based future. Further, safeguarding customer data is key to revenue growth, as the data is foundational to providing a more personalized customer experience that results in greater customer lifetime value. **Data Value Exchange** Customers are willing to provide personally identifiable information (PII) for a more personalized customer experience – but only if they trust that a company will protect their data and be transparent about its use. According to the [Harris Poll survey ](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/)commissioned by Redpoint, 54 percent of consumers said they will share personal data to achieve a more personalized experience, with the percentage trending higher for younger consumers (72 percent for Gen Z, and 70 percent for Millennials). There is a high expectation among customers that their data will be collected, shared, and used appropriately, with 74 percent of the consumers surveyed in the Harris Poll saying that it is “very important” or “absolutely essential” that a company reveal what information is collected, with 73 percent saying that it is at least very important that a company share how the information is being used. As for control, 68 percent said it was at least very important that they be able to set specific preferences. There is a straight line between data transparency and revenue. In the same survey, 37 percent of consumers said that a personalized customer experience that results from sharing personal data will make them more likely to purchase from the brand in the future. This is known as the data privacy value exchange, and it is a win-win for the marketer and consumer. A personal customer experience is rewarded with more personal data, which leads to greater personalization, more loyalty, and enhanced customer lifetime value. **Apply Preference and Permissions Dynamically** Even with a clear path to revenue, brands struggle to build a dynamic, permissions-based system. Most legacy technology was built to process outbound, batch or drip communications through audience lists, yet today’s consumers are increasing online and readily addressable for two-way engagement in real-time. Combined with new compliance requirements that require an auditable, closed-loop preference center, many brands simply cannot deliver the real-time, personalized, omnichannel customer journeys that consumers demand. A robust closed-loop preference center derives its value from the capability to integrate preferences and permissions within the actions and behaviors of a dynamic customer journey. Marketing campaigns must keep up with dynamic customer preferences. Marketing clouds can centralize some customer data and integrate a preference center, but their linear campaigns and inflexible audience definitions lack the real-time functionality to apply preferences dynamically in concert with a customer journey. **Rules-Based Orchestration vs. List-Based Execution** What is required for effective CX is a platform that can derive audiences, channels, actions, and preferences dynamically at the time that an action is going to be taken, rather than on a static basis derived from a computation that may have occurred hours, days, or weeks before. A campaign based on a static segment has the high likelihood to include out-of-date information. A typical example could be building a list-based campaign that triggers an SMS message to loyalty club members two days after they’ve been sent an email. Once the campaign runs, list-based systems may mistakenly send an SMS message even if a member has expressed a preference not to be messaged on that channel. Applying rules dynamically at the time of each interaction seamlessly integrates all segment and preference data into the process at each stage of the journey, making it dynamic and eliminating the potential of frustrating a customer by ignoring or mishandling their preferences. **Break Through Campaign Boundaries with a Golden Record** As the Harris Poll research indicates, the consequences for failing to honor a customer’s preferences can be significant, but many companies still lack the capabilities to dynamically apply customer preferences within campaign parameters. A persistently updated, unified customer profile that includes preferences and permissions is the foundation for complying with data privacy regulations while also keeping pace with a dynamic customer journey and delivering a personalized experience. The golden record, combined with automated machine learning and intelligent orchestration, provide marketers with a single point of control over data, decisions, and interactions. The single point of control is the basis for delivering a next-best action or recommendation for a customer that is always in the context and cadence of the customer journey. As it relates to data privacy, a single point of control additionally provides the dynamic framework that allows for real-time reaction to permissions. Because permissions and preferences are tracked and updated in real-time as data points in a golden record, marketers are free from the risks associated with static applications. The flexibility afforded by rules-based dynamic application of customer preferences is a prerequisite for a permissions-based future in accordance with privacy regulations. Customer preferences govern how and when a brand engages with a customer. As such, making sure that they are applied throughout the entire customer lifecycle is vital to providing a personalized customer experience. **Comply with Data Privacy Regulations, and Drive Revenue** The [Redpoint Customer Data Platform](https://www.redpointglobal.com/cdp/)™ provides marketers with the single point of control over data, decisions, and interactions that is needed to dynamically combine customer preferences with behaviors and other signals for a next-best action in a hyper-personalized customer journey. Marketing clouds are simply not purpose-built to dynamically apply customer preferences in an ongoing campaign. Customers notice the difference, because a seamless, frictionless customer experience means receiving the right message in the right channel while heeding opt-outs and opt-downs. IDC estimates that businesses that invest in providing a frictionless user experience will see a 20 percent decrease in customer attrition by 2021. Honoring a customer’s preferences dynamically throughout the customer lifecycle is an important part of maintaining a frictionless user experience. Yes, it satisfies data privacy regulations that will figure more prominently in the months and years to come, but it also drives revenue, as good a reason as any to move away from legacy systems that cannot keep pace with the customer. **Blog categories:** Customer Data Platform, Data Management, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Customer Loyalty and an Emotional Connection for the New Reality](https://www.redpointglobal.com/blog/customer-loyalty-and-an-emotional-connection-for-the-new-reality/) **Published:** June 30, 2020 **Author:** Steve Zisk **Content:** Fewer shoppers, airline passengers, car rentals and hotel stays throughout the first few months of coronavirus have upended many loyalty programs, with many taking drastic steps to retain loyal members who, through no fault of their own, have become more infrequent customers. Most airlines, for example, have [extended elite status](https://thepointsguy.com/guide/airline-elite-status-coronavirus/) regardless of miles flown. Many hotels are [waiving points expiration dates](https://thepointsguy.com/guide/hotel-elite-status-coronavirus/), reducing status thresholds and extending status levels through the end of 2021. Some credit card companies have shifted from travel-based points to [“everyday rewards”](https://www.traveldailynews.com/post/loyalty-programs-have-big-opportunity-pivoting-to-everyday-rewards-during-covid-19) that award points for food delivery, streaming services and other home-based goods and services. While the long-term ramifications for loyalty programs are still unclear, what is certain is that customers are drastically altering long-standing behaviors and buying patterns. There’s been a [documented transition](https://www.redpointglobal.com/blog/the-new-reality-brings-data-transformation-to-the-forefront/) toward a more digital customer journey, an increase in contactless purchases and a similar increase in home deliveries, particularly groceries. These changing behaviors are forcing brands to rethink the [customer experience](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty) through the lens of convenience. As customer values evolve, brands must adapt to new expectations for what a relationship with a brand built on trust looks like. **What Customers Want from a Loyalty Program** Shifting behaviors and a transition to a more digital customer journey will require brands to understand and measure the value of every customer interaction; as customers embark on new, highly unpredictable journeys, brands that deliver relevant experiences throughout an omnichannel engagement will establish trust, thereby strengthening existing customer relationships and building new ones. Loyalty programs in a post-pandemic economy may take a new shape to reflect the new reality, and their value to a brand will likely increase in parallel with the increasing value of customer interactions. Fortunately for brands, customers are already on record with what they want from a loyalty program, and it has less to do with price, product or points than one might think. In [Bond Brand Loyalty’s](https://www.globenewswire.com/news-release/2019/04/29/1811230/0/en/New-Bond-Report-Reveals-Loyalty-Program-Expectations-on-the-Rise-Could-Unlock-Billions-in-Spend-by-Bringing-Experience-up-to-Par-with-Expectations.html) ninth annual loyalty report (2019), research revealed that traditional rewards account for just 25 percent of what drives loyalty member satisfaction. Personalization done well, meanwhile, results in a 6.4x lift in member satisfaction with a program. Furthermore, eight in 10 consumers surveyed said that they wanted more of their data to be used to improve personalization. The report concludes that “top programs lead by prioritizing the program experience over the reward itself.” As the economy begins to re-open, personalization in lieu of a loyalty program’s traditional rewards may, for instance, take the form of a brand granting a status extension as a show of empathy for a customer’s new circumstances. Perhaps a brand provides specialized offers that are relevant to a customer’s unique situation. Or it tailors a loyal member’s experience in accordance with the member’s evolving preferences and behaviors, with an enhanced focus on convenience over a transactional approach. **A Deep Customer Understanding and an Emotional Connection** To meet the expectations of their loyalty program members for a personalized omnichannel experience, brands must develop a deep understanding of each customer at an individual level. Being able to deliver a relevant experience in real time at every touchpoint regardless of channel requires a single view of the customer. By knowing everything there is to know about a customer – all customer identifiers (addresses, emails, devices, social, etc.) as well as complete behavioral and transactional data – a brand has the ability to infuse context into every customer interaction. Consistent relevance is the foundation of a meaningful, contextual relationship with a customer at an individual level. This is what the [Redpoint Golden Record](https://www.redpointglobal.com/single-customer-view/) delivers. Built upon singular identity resolution capabilities, the golden record provides a brand with the deep customer understanding from which to build and extend a deeply personal and meaningful relationship throughout not only a unique customer journey, but the complete customer lifecycle. In a post-COVID-19 world, loyalty programs that establish a deeper customer understanding will be better positioned to cement an emotional connection, which has been shown to increase retention and, ultimately, drive new revenue. A [2018 study from Motista](https://www.prnewswire.com/news-releases/new-retail-study-shows-marketers-under-leverage-emotional-connection-300720049.html) found that consumers with an emotional connection to a brand have a 306 percent higher customer lifetime value (CLV) and are far more likely to recommend a brand vs. when there is no emotional bond (71 percent vs. 45 percent). While personalization has always been important for delivering a superior customer experience, especially for a brand’s most loyal members, sudden disruption and evolving consumer behaviors change the dynamic. When the dust settles, a personalized experience that is consistently relevant, forges a connection and shows empathy is more apt to deliver value to a customer and a brand than one rooted in a transactional approach. **Blog categories:** Data Management --- ### [Triggered Actions and an Enhanced CX: Relevance Throughout a Customer Journey](https://www.redpointglobal.com/blog/triggered-actions-and-an-enhanced-cx-relevance-throughout-a-customer-journey/) **Published:** October 13, 2022 **Author:** Steve Zisk **Content:** From a modest beginning as application logic executing on specific events in a relational database, such as a simple “if/then” decision when a record is added, modified or deleted, triggered actions have evolved as key components of a personalized customer experience (CX). Triggered actions – alternatively known as triggered events – are powerful and relevant to an individual customer journey because the consumer at the receiving end often expects or welcomes the communication as a natural extension of their overall experience. A notification that a subscription is about to lapse, an SMS that their yearly spend qualifies them for a gold-level loyalty tier, or even a phone call that a customer’s prescription is ready are all examples of data-driven triggered actions that enhance customer experience and demonstrate a personal understanding of a customer. ## **What is a Triggered Action?** A triggered action or actions that spur a set response can range from simple to complex, from single channel to omnichannel, and from a standalone trigger to a series of triggers spanning a customer journey. Trigger events share certain characteristics; a channel that services the trigger, a context for the trigger, and an attribute related to the trigger metrics. In the case of alerting a customer that it is time to re-order a product, for example, the attribute is a re-order date based on purchase history. The context for the trigger is a unified customer record or Customer 360 that will contain purchase history, and the channel could be an SMS push notification, an email, a call center interaction or any channel that ideally aligns with the customer’s preferred method of communication. Product availability or various business rules can also serve as contexts for a trigger. While triggers may have started as simple “if/then” decisions, what’s true for how they’re used in customer experience today is that the logic tree often has many branches. That is, companies need to be listening to signals from across the enterprise and making evaluations often based on a high volume of online and offline data. In the product availability context, for example, a customer might qualify for a certain communication having satisfied a half dozen or more criteria, but if the product the company wants to offer is not available when those conditions are met, that will automatically stop the next trigger, which could be the sending of the communication. ## **Tune Triggers to Business Goals** Whatever the business goal – retention, acquisition, CLV, etc. – trigger events are tuned to optimize the objective by listening for the appropriate enterprise signals, evaluating whether the criteria is met, and then activating the set response. A multi-trigger campaign will designate set timeframes and actions between triggers, such as triggering a second email (or SMS, direct mail, Facebook, etc.) if there wasn’t a click-through on the initial email. Listening queues can also be optimized to evaluate and qualify customers after a certain period of time has elapsed, depending on the business goals or whether a threshold number of qualified customers has been reached based on, say, available inventory. A welcome campaign a is common use case in an omnichannel trigger account. For instance, a previously anonymous customer might self-identify on a brand’s website by signing up for a newsletter and providing an email address. The known record is then incorporated into a contact graph as part of the updated Customer 360 – also known as a Golden Record. The activity on the website then triggers a welcome email, or a different communication depending on a customer’s opt-in preferences. ## **Triggered Actions and a Relevant CX** Trigger events have gained traction in the delivery of a hyper-relevant CX for the simple reason that they work. Shopping cart abandonment is perhaps the most well-known trigger example, applicable to retail certainly, but also travel, financial services and gaming. It’s a universal pain point, with about 7 in 10 customers who fill an online shopping cart failing to complete the transaction. Yet automated cart abandonment emails triggered off the event have a [45 percent open rate](https://moosend.com/blog/cart-abandonment-rate-infographic/), a 21 percent click-through rate and an 11 percent conversion rate. According to Omnisend, a trigger series of three emails results in [69 percent more orders](https://www.omnisend.com/blog/cart-abandonment-emails-the-best-practices-and-stats-infographic/) than a solitary email campaign. By using the Golden Record in an email trigger campaign, a brand can increase relevance by incorporating a next-best action with the triggered communication. A brand might, for instance, analyze the abandoned items and determine the perfect complementary product based not just on the shopping cart activity but on browsing sessions and other behaviors. The follow-up email could then include an offer for a specific product relevant to an individual customer. Brands can also qualify triggers to a select audience. A car rental agency, for example, could use geofencing capabilities to waive a gas refill for any premier customer within a certain radius of the airport, sending a “Enjoy your flight. This one’s on us” push notification to the mobile app. Conversely, the trigger could be set up for a customer deemed a high churn risk. ## **Triggered Actions and a Continuing Conversation** In a recent [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/), 82 percent of consumers surveyed said that they are loyal to brands that demonstrate a thorough understanding of them as a unique customer (up from 77 percent in 2019). In this context, triggered actions as one-off communications are effective tools in any nurture campaign to help move a customer journey forward. By setting up triggered actions, a brand shows that it maintains a vested interest in strengthening a relationship with a customer by providing value in a tactful way. Done well, triggered actions make customers feel valued beyond a transactional basis. That value, received in the form of a highly relevant customer experience, translates to loyalty, higher lifetime value, better brand appreciation and, ultimately, more revenue. ## **Related Redpoint Orchard Blogs** [Five Ways Retailers Can Drive Customer Loyalty](https://www.redpointglobal.com/blog/five-ways-retailers-can-drive-customer-loyalty/) [What is Customer Lifetime Value (CLV)?](https://www.redpointglobal.com/blog/what-is-customer-lifetime-value-clv/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Identity Resolution, Real-Time Personalization --- ### [Resolve Downstream Data Problems with Customer Data Technology](https://www.redpointglobal.com/blog/resolve-downstream-data-problems-with-customer-data-technology/) **Published:** March 13, 2025 **Author:** John Nash **Content:** Despite all of the investments in customer engagement technology, customers still believe brands are falling short of their expectations. At the heart of this paradox, brands simply do not understand customers enough to deliver relevant messages, offers, content and products at every stage of the customer journey. And the root of this lack of “understanding” is customer data that is simply in poor shape – it is inaccurate, incomplete, untimely and/or simply unavailable when needed. This is why 84 percent of marketers said that data is their organization’s most under-utilized asset. It is also why 54 percent of marketers said that the lack of data quality and completeness is their biggest challenge to data-driven CX. For most organizations data is simply not ready for its intended use to create value. Data is not ready to drive personalized CX. Data is not ready to drive AI. Data is not ready to drive new customer acquisition programs. Data is not ready to drive conversions via paid media. Data is not ready to drive real-time relevant engagement. This is because customer data is messy, data is hard, and data needs evolve as customer needs evolve. And technology to-date has largely failed to resolve these issues. In this series on data as the defining difference, we will outline customer data technology solutions for the future – and ones that are delivering results today. Because even with so many customer data technology misfires, Redpoint has managed to create value with industry leaders in Healthcare, Retail, Travel, Financial Services, Media and Entertainment, with select results illustrated below. (See **Figure 1**), ![The Impact V.02](https://www.redpointglobal.com/wp-content/uploads/2025/03/The-impact-v.02-800x446.png)**Figure 1**: Value with Redpoint customer data technology ## **From the Corner Store to Omnichannel Personalization** For some added perspective on why data is simply not ready to drive personalized CX, consider the quintessential corner store experience that is now mostly a relic of a bygone era. A friendly proprietor greets you by name, makes you feel welcome, and engages in some lighthearted banter about your family, the weather or local events. The conversation naturally flows to the status of your home project, the purpose of your visit. The shop owner knows what phase of the project you’re in, recommends the exact products you’ll need and even suggests a product you hadn’t considered. When you leave, you’re happy with your purchase and even more pleased with your overall experience. Your main takeaway is that you received something of value that far exceeded the transaction. While this type of experience remains the goal of major brands in almost every industry, the challenge is to now do this at scale in an era of highly dynamic customer journeys across an increasing number of digital and physical channels. This is why it is such a challenge to get your data right. Yet as the challenge intensifies, so too do the hardening customer expectations for a more seamless personalized experience. Even with multiple devices, different identifiers and continual change (life stages, family dynamics, etc.) customers hold brands accountable for knowing who they are across all channels, and for knowing with some degree of accuracy why they’re engaging. While retail may have pioneered personalizing a digital-first experience, the expectation for a personalized experience now extends to relationships with healthcare providers, health plans, travel companies, financial services and other industries that interact with consumers. For instance, a customer accustomed to relevant, real-time recommendations from a retailer anticipates that that a mortgage lender will be able to offer a real-time quote. Or have the expectation that a health plan administrator will have the same information that the customer has already provided on an online form. > More than merely applying data, the secret to solving the personalization paradox is applying the right data at the optimal time. Enterprise companies that compete on customer experience rely on customer data technology to turn customer data into insight. Customers become frustrated when the companies they interact with have trouble with these seemingly routine experiences, giving them pause to continue a relationship with a brand. Consider a Broadridge CX Survey in which [54 percent of consumers](https://www.retailcustomerexperience.com/news/more-consumers-want-better-customer-experience/) (63 percent of GenZ) said they will stop doing business with a company that delivers a poor customer experience. Or a McKinsey study in which [76 percent of consumers](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) said that receiving personalized communications is a key factor in prompting consideration of a brand, with 78 percent claiming that a personalized CX makes them more likely to repurchase. These and similar proof points validate the inescapable truth that companies now regularly compete on customer experience. Reducing churn, as the referenced statistics demonstrate, is just one of many benefits that result from refining data with the goal of delivering personalized customer experiences. Brands that prioritize personalization generate up to 25 percent in new revenue, also according to the McKinsey study, which found a direct link between revenue and an organization’s ability to execute personalization initiatives – the more skillful in applying data to grow customer knowledge and intimacy, the greater the returns. ## **Recognition, Relevance & Real Time: The Heart of Personalization** Applying data to grow customer knowledge and intimacy speaks to the ultimate goal of personalization; to mirror, as closely as possible, the corner store experience. But because today’s personalized CX must account for the dynamic nature of complex customer journeys, in order to meet customer expectations a brand must first recognize the customer at the moment of interaction, and be in the position to provide a relevant interaction that displays a recognition of the customer journey itself. More than merely applying data, the secret to solving the personalization paradox is applying the right data at the optimal time. Enterprise companies that compete on customer experience rely on customer data technology to turn customer data into insight. A modern marketing technology stack will have one or more systems tasked with collecting, cleaning and storing customer data for the purpose of generating insights that it will use to deliver personalized experiences. Singularly or in tandem, these systems will typically be integrated to generate a single customer view, a cohesive profile that provides the enterprise with a single source of truth for the customer. One system, which typically will be the centerpiece of the marketing stack, will house the unified profile and make it available across the enterprise, to marketers and business users. ## **Actionable Steps for Liberating Your Customer Data** As customer journeys become more complex, and as it simultaneously becomes more important and more difficult to deliver a personalized CX, organizations are seeking the most optimal, cost-effective way to organize their marketing technology around building the single customer view. There are a lot of questions and different opinions about the best approach. In this series, we’ll address key questions enterprise companies face when leveraging customer data to drive business value. Our focus will be on actionable strategies and insights, including: - Customer data as the foundation to pivot toward a customer-centric approach - How did we get to the point where customer data technology is failing to deliver results - How customer data technology enables a truly personalized customer experience - Why data readiness is critical to successful value creation - A practical guide to choosing data management technology - Real-world examples of customer data technology use cases that deliver measurable impact - Navigating the data-driven personalization capability maturity model to achieve superior outcomes in a stepwise manner These actionable steps will help you to turn customer data into business vale. By leveraging customer data effectively, building unified profiles, and applying the right insights at the right moment, brands can deliver the seamless, personalized experiences that drive loyalty, reduce churn, and unlock new revenue opportunities – all while reducing operational costs along the way. In the coming weeks, we’ll explore the strategies, technologies, and frameworks that enable organizations to overcome these challenges and master the art of using customer data to drive all kinds of potential uses. Stay tuned as we dive deeper into actionable steps for turning customer data into business value and elevating the customer experience to new heights. **Blog categories:** Data Readiness **Blog tags:** Data quality, Data readiness --- ### [New Customer Shockwaves Drive Need for Personalized Customer Experience (CX) in Banking](https://www.redpointglobal.com/blog/new-customer-shockwaves-drive-need-for-personalized-customer-experience-cx-in-banking/) **Published:** October 5, 2020 **Author:** John Nash **Content:** Banks have experienced several financial shockwaves over the last 10-15 years, among them the housing collapse and the Great Recession as well as regulatory compliance changes. Banks of late also must contend with customer shockwaves driven by remarkable increases in preferences for digital channels and expectations for a seamless personalized experience across physical and digital touchpoints. The introduction and popularity of digital-only banking products disrupted the industry for a time, but an intuitive mobile banking app is now table stakes for the digitally-savvy consumer who expects a holistic omnichannel journey across channels and products. Issues many small business owners faced when trying to secure Payment Protection Program (PPP) emergency funding through large banks is an example of the disconnect between the experience that consumers expect and what is being provided. Many small business owners, [like this owner of an ice cream shop](https://www.usatoday.com/story/money/usaandmain/2020/06/02/ppp-loans-community-banks-more-helpful-small-businesses/5300871002/), or [this owner of a small marketing firm](https://www.foxbusiness.com/financials/community-banks-shine-during-ppp-loan-rush-as-big-banks-fall-short), turned to small or midsize community banks after being ignored by a larger bank where in many cases they had been customers for years. According to the [Small Business Administration](https://home.treasury.gov/system/files/136/SBA-Paycheck-Protection-Program-Loan-Report-Round2.pdf), banks with less than $10 billion in assets accounted for 45 percent of loans granted under the PPP, while banks with assets of $50 billion or more accounted for 36 percent. A common complaint from many small business owners was that the larger banks did not show that they valued them as long-term customers. Instead of a personalized customer experience, they were given the cold shoulder. ## **Bank Customers Frustrated by a Poor Experience** Customer frustrations with large banks highlight the opportunity for small, community banks to differentiate by quickly responding to customers’ needs. And customers’ willingness to quickly abandon a large bank specifically because of one poor customer experience should also serve as a stark reminder to any bank of the overall value that customers place on experience. A [McKinsey report](https://www.mckinsey.com/industries/financial-services/our-insights/remaking-banking-customer-experience-in-response-to-coronavirus) on remaking the banking customer experience after coronavirus concludes that “doing the right thing” by the consumer is a competitive advantage. Investments in CX, it said, already an imperative, have become even more relevant during the pandemic. In an analysis of 23 publicly traded banks, the half with a high customer satisfaction score delivered 55 percent higher returns to shareholders between 2009 and 2019. ## **Blazing a Digital Banking CX Trail** The McKinsey report identified the improvement of the digital banking experience as an important way to close the gap between the experience a customer expects and the one delivered. The article points out that roughly half of US banking customers engage digitally infrequently or not at all, thus the incentive for banks to pivot to a digital-first mindset has trailed other industries. According to a report from the [Financial Brand](https://thefinancialbrand.com/102120/data-and-technology-are-transforming-digital-lending/), banks are failing to keep up with consumer expectations for a seamless digital experience (especially as it pertains to lending) for many reasons, particularly a resistance to change or reluctance to accept risks combined with the prospect of a digital transformation overhaul. However, digital trailblazers such as [Ant Group](https://edition.cnn.com/2020/09/25/tech/ant-group-alipay-intl-hnk/index.html), which revolutionized banking in China in just a few short years, show that customers are indeed primed for a digital-first experience. Ant Group, the largest fintech company in the world, provides digital financial services in payments, credit scoring, lending, insurance and wealth management – managing $560 billion of wealth. Ant succeeded in part by taking a sledgehammer to conventional banking norms, where it is not just data or channel siloes that make it difficult to provide a seamless end-to-end customer experience, but also product. US banking customers know that, with few exceptions, home loans are separate from auto loans, which is separate from lending, etc. Ant’s digital platform encompasses a single financial view, with an intuitive app that allows customers to easily manage wealth, invest in money markets, view and use perks related to their up-to-date credit score, and even order food. Often called a “financial supermarket”, Ant Group is preparing for an IPO this month. ## **Personalization Will Close the CX Gap** One banking executive, the head of [Consumer & Community Banking Technology at JPMorgan Chase](https://thefinancialbrand.com/80704/sonia-wedrychowicz-chase-digital-transformation-technology-interview/?internal-link), asked to define the biggest gap between consumer expectations and what banks delivered, said that it comes down to the personalized customer experience. “Personalization driven by an effective usage of analytics is therefore, in my opinion, the biggest gap that the banks need to continue to bridge quickly in order to be able to effectively compete and win in today’s world,” says Sonia Wedrychowicz. “The world that is dominated by non-banking players (Amazon, Google, Netflix, etc.) that started from the customer-centric mindset and have mastered over time.” What might such a personalized banking experience look like? For starters, like the increasingly popular buy online, pick-up in-store (or curbside) service in retail, it would combine digital and physical touchpoints. Again, with few exceptions, a visit to a physical location is usually its own process. If one had to deposit a large amount of cash, for instance, it’s accepted practice that filling out a deposit slip and completing the transaction would all have to be done at the bank, rather than simplified by starting the process on a mobile app. ## **Becoming More Ant-Like: Opportunity Abounds for Banking Personalization** Adopting a more consumer-driven experience applies to any customer-facing experience, such as applying for a home equity loan or a mortgage. Why can’t a bank unveil an Ant Group-like app that provides the customer with an easily accessible customized dashboard to carry they through the process? Or use the trove of transactional data to enhance the CX by, say, personalizing fee communications and/or actions. If I’m a direct deposit customer who exceeds minimum balance requirements, the bank could easily show that it values me as a customer by how they charge or communicate fees – instead of taking a one-size-fits-all approach and trying to hide the fee hike in the small print of my monthly statement. Or, for a more data-driven approach, sentiment analysis might indicate a likelihood to churn, in which case the institution could or offer some other benefit to increase retention. ## **A Single Customer View and the Personalized CX** Digital banking enhancements that become part of a holistic customer experience requires having a [single view of the customer](https://www.redpointglobal.com/single-customer-view/) – across all channels, touchpoints and products. A single customer view, also known as a golden record, includes customer data from every source and of every type (first-party, second-party, third-party, unstructured, semi-structured, structured), and is updated in real-time to provide marketers with an accurate, up-to-date unified customer profile. A single view eliminates data siloes that contribute to a fractured experience – such as not being able to start a mobile cash deposit at home and finish it inside a physical bank. With a single customer view, [automated machine learning](https://www.redpointglobal.com/machine-learning) and [intelligent orchestration](https://www.redpointglobal.com/orchestration), a banking institution could easily use sentiment analysis, as one example, to decide on a next-best action for an individual customer based on the totality of that customer’s interactions, behaviors and transactions across every device and ID. ## **Keep Up with the Banking Customer: Play the Real-Time Game** Real-time decisioning breathes life into the golden record by enabling marketers to keep pace with the customer throughout an omnichannel journey. Because the golden record is continuously updated with real-time data from any conceivable source, a banking institution can proactively engage with a customer wherever the customer appears. In the sentiment analysis example, one churn indicator (a negative social post) may trigger a different response than a different churn indicator (a reduction in deposit frequency) based on that specific customer’s identity graph; real-time decisioning permits the bank to respond at the exact cadence that will optimize the customer’s journey. That might mean sending an immediate email to one customer to alert them of a fee change, while another customer receives an SMS in three days that offers a better rate on an auto loan. ## **The One-Size-Fits-All Approach is Yesterday’s News** Rolling out a mobile banking app may have been a revolutionary banking CX a decade ago, but the playing field is far more equal today. The customer expectations now go well beyond easy, intuitive mobile banking (and to avoid having to go to a physical location except when necessary). The expectation is the same as it is in every industry – that a customer’s bank recognizes the customer as an individual regardless of digital or physical interactions and across products and channels. This is what Redpoint delivers with the [Redpoint CDP](https://www.redpointglobal.com), which provides the single customer view that is the basis for a personalized customer experience that customers value ahead of price and largely commoditized banking products that fail to provide competitive differentiation. Because it’s now possible to know everything there is to know about a bank customer and to tailor a journey according to an individual’s preferences and behaviors, an impersonal, cookie-cutter offer may be worse than simply ignored, it may drive a customer to a bank with a digital transformation already underway. **Blog categories:** Customer Data Platform, Data Management, Journey Orchestration, Real-Time Personalization, Single Customer View --- ### [Cross-Sell and Upsell Done Right: Why the Right CDP is Essential](https://www.redpointglobal.com/blog/cross-sell-and-upsell-done-right-why-the-right-cdp-is-essential/) **Published:** June 3, 2021 **Author:** Steve Zisk **Content:** Marketers involved in cross-sell or upsell activities have the proverbial ear to the ground, continuously monitoring customer behaviors to stay in sync with customer journeys. One common customer journey – a cross-sell or upsell campaign – may be triggered by a marketer or by customers themselves depending on a behavior, an activity, a signal, or a collection of signals. A [customer data platform](https://www.redpointglobal.com/one-platform/) (CDP) is instrumental to ensure that the right offer, content, or message is delivered at the opportune time and on the opportune channel for either scenario – a marketing-triggered campaign or customer-triggered journey. ## **Signals and Segments** While the two scenarios start quite differently from one another, the objective is ultimately the same: to retain an existing customer with a well-timed offer that feels unforced – a natural extension of an existing relationship with the brand. In the marketing-triggered campaign, a signal or collection of signals serve as the basis for an action taken by marketing. Typically, initiation will involve a broad cross-section of customers for whom the action is likely to generate the intended response. A time signal is a basic example. Because these 10,000 customers bought running shoes in June, one year later we might build a campaign on an email reminder that it is time to replace them – oh, and here’s a newer model that offers better performance. A set of signals may entail mining social media or call center interactions for either positive or negative feedback that will then trigger an appropriate response. Active promoters, for instance, may be chosen to receive a digital advertisement for a new product. Depending on the marketer’s underlying goals, which can be customer-related, product-related or brand-related, the objective is to create and activate the appropriate customer segments to be the target of a specific marketing-driven campaign. ## **Account for Dynamic Customer Journeys** When listening for signals from customer data, it is important to recognize that customer journeys are not static. The right CDP helps ensure that the campaign (offer, content, channel, timing) can change dynamically when new signals emerge. Perhaps an email is sent to the 10,000 customers due for a new pair of running shoes, but soon after the email is sent some of the customers purchase a new pair online. A [dynamic](https://www.redpointglobal.com/blog/dont-surrender-to-customer-journey-complexity/) capability such as open-time email personalization allows a change to email content up to the moment it’s opened. An email with a discount offer for a new pair of running shoes can be changed to a thank you message, or an offer for running attire. There are countless examples where an initial decision – send an email to this group – may be replaced with a new action. Using all the signals across channels, including understanding exact customer intent, is critical to choosing the right message. A classic example is marketing baby products to all customers who purchase a baby gate, when a customer may have bought the gate to keep a puppy at bay. In this instance and those like it, marketers either choose to ignore – or not look for – conflicting signals that may have revealed the true purpose. Flexibility results from marketers understanding the entire trajectory of what’s happening – having a complete collection of signals and the ability to analyze precisely what those signals say about a customer. If a marketer, for instance, knew that the baby gate purchase was made by a household consisting of retirees with grown children, that might have led to a different action. A dynamic marketing-driven campaign must be able to start with choosing which customer segment or segments will be the target while still adapting to the dynamic nature of a [customer journey](https://www.redpointglobal.com/resources/video-predict-adapt-how-machine-learning-can-optimize-the-entire-customer-journey/). ## **A Customer-Driven Journey** Conversely, a customer-driven journey starts with recognizing an action, behavior or signal from a specific customer, determining what the action or behavior says about the customer’s intent, and deciding what actions will guide the customer journey to its desired conclusion. Analyzing customer signals will inform a marketer of important details of the journey. A negative review on a social media site versus a customer indicating intent to purchase would result in very different courses of action to guide the customer journey. An abandoned shopping cart is a good example of a customer-driven journey. Because there are many reasons for an abandoned shopping cart, the act of abandonment will (or should) never have a hard-fast rule in place to steer everyone who has abandoned a cart down the same path. Rather, the unique nature of the action makes it important to understand the specific customer’s rationale in order to respond with the appropriate action. A complete, accurate, and up-to-date set of information about the customer is essential to executing a next-best action that is hyper-relevant to the customer’s situation, and one that is in the context of the journey itself. As with a marketing-driven campaign, a deep understanding of the entire trajectory of a customer journey is essential to delighting a customer with the right offer at the right time and on the right channel. ## **Overcoming Cross-Sell Challenges** There are typically three key reasons why many organizations fail to convert cross-sell and upsell opportunities. One is siloed data. If a cross-sell opportunity presents itself but requires a real-time response measured in milliseconds, having process or channel data siloes makes it all but impossible to present a customer with a next-best action that is relevant at the moment of interaction. Siloes include not just disparate data from touchpoints usually associated with marketing – such as a CRM system, an email campaign, a website, etc. – but also sources that are outside the usual realm of marketing touchpoints such as product registrations, point of sale information, service department surveys, call center notes and the like. Combined, these sources tell a story about a customer journey. They are a rich set of signals, and for a marketer to understand the detailed nuances of each journey there must be a seamless integration of all data sources, and integration must be in real time. The second challenge organizations have with converting cross-sell and upsell opportunities is identity resolution. Bringing together dozens of customer data sources is a start, but creating an accurate, up-to-date identity graph from often conflicting data – different names, addresses, devices, etc. – is essential to make sure the right offer is going to the right customer. Decisions must be made about what data to keep, how to prioritize it when to make a match, how to relate an individual to a household, and other important considerations. Marketers must also make or update calculations for each identity on metrics such as lifetime value, churn propensity and other factors that may influence the type of cross-sell or upsell offer presented. Assuming organizations solve for the first two challenges, the third and final challenge is real-time orchestration in digital channels, which entails a marketer making sure that they understand not who the customer was yesterday, but right this very second. Real-time intelligent orchestration provides a contextual understanding at the precise moment of interaction, an important capability to determine, for instance, which cohort a customer belongs to for a marketer-driven campaign or, for a customer-driven campaign, important data points as to how to follow the customer through the journey to know when, where and how to change course. Customers react differently when they feel a brand understands them and meets their needs. A CDP that integrates data from every possible source in real time, applies advanced identity resolution capabilities and intelligently orchestrates real-time decisions at scale can help a brand offer a value exchange to the customer: Attention, data, and loyalty are exchanged for relevant and delightful interactions. Presenting the wrong cross-sell or upsell offer – or letting the opportunity pass by altogether – is a marketing journey with significant downstream revenue implications. ## **Related Content** [3 Ways a Customer Data Platform Can Increase Average Revenue Per User (ARPU)](https://www.redpointglobal.com/blog/3-ways-a-customer-data-platform-can-increase-arpu/) [5 Ways to Tell if You Have a Customer Data Platform](https://www.redpointglobal.com/blog/5-ways-to-tell-if-you-have-a-customer-data-platform/) [What Should You Look for in a Customer Data Platform?](https://www.redpointglobal.com/blog/what-should-you-look-for-in-a-customer-data-platform/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Journey Orchestration, Real-Time Personalization --- ### [A Careful Diagnosis: a CRM and CDP Offer Different Use Cases in Healthcare](https://www.redpointglobal.com/blog/a-careful-diagnosis-a-crm-and-cdp-offer-different-use-cases-in-healthcare/) **Published:** December 13, 2022 **Author:** Sarah Lull **Content:** Parsing the difference between a customer data platform (CDP) and customer relationship management (CRM) solution for healthcare use cases is a bit like deciding between aspirin vs. ibuprofen. Some overlapping functionality creates a misconception that the two are somewhat interchangeable, when the reality is that each fills a specific need. Some healthcare organizations that harbor the misconception make the mistake of thinking that because they have a CRM, they have little use for a CDP. We will explore why that approach is misguided, particularly when the goal is to differentiate on patient experience, both within and outside of a clinical setting. ## **Managing a Relationship vs. Managing Data** A key difference is revealed in the name of the solutions, where a CRM is designed to manage customer relationships, while a CDP is designed to manage customer data. A simplistic explanation, yes, but it does capture the essence of each solution. For a robust, enterprise-grade CDP, managing data entails applying [data quality](https://www.redpointglobal.com/customer-data-management/data-quality/), [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) and governance processes at the point of data ingestion, ensuring a single view of the healthcare consumer throughout an entire healthcare journey. A CRM, conversely, is not concerned with any of that. A last-mile end point to the patient, a CRM is both a source and a destination for patient data that will ultimately be used for the purpose of engaging with a patient. ## **Patient Engagement vs. Patient Experience** Another reason for the confusion between a CRM and CDP stems from the fact that while patient engagement is a key CRM use case, the purpose of a CDP is to improve the overall patient experience. Yet unlike a CRM, an enterprise CDP improves the patient experience as a single source of truth for all patient data – clinical and non-clinical data – which is then activated to provide a patient with a personalized, omnichannel experience throughout an end-to-end healthcare journey. The key distinction is that an enterprise CDP perfects customer data, making it fit-for-purpose for any engagement channel, CRM included. A CDP will connect with every database, every EHR system, email, call center, website, mobile app, etc. By that assessment, what is also true is that a CRM is as reliable, accurate and trustworthy as the data that feeds it. Without a CDP, a CRM is likely integrated with a database built and managed by IT, which in turn means that it may or may not have the data that a care manager needs at the time they need it, depending on when updates have been made. If a pertinent update has not been made when a patient calls in for a question about a bill, to complete a post-op questionnaire, or to find out the next step to follow in a treatment plan, that may translate to a poor patient experience. By contrast, when integrated with a CDP that perfects data and makes it fit-for-purpose at the moment data is ingested from every source, a CRM will be guaranteed to have the most accurate and up-to-date patient information. In addition, it has the same updated patient [Golden Record](https://www.redpointglobal.com/single-customer-view/) as every other engagement system, which means a consistently relevant experience regardless of which channel a patient chooses to interact with. One way to think of this key difference is that where a CRM leverages perfected data, a CDP perfects the data for a CRM and every other patient engagement channel. ## **Omnichannel Personalization with a CDP** Perfecting patient data for use by a CRM system and other engagement channels is what extends a CDP from improving patient experience on one channel (as a CRM does) to delivering a personalized, omnichannel patient experience. In the example of a patient contacting the call center for help with the next phase of post-op treatment, let us examine some possibilities, first with a CRM maintained by IT, and then through the lens of a CDP. With the CRM, we’ve already noted one possibility that the call center agent must first hope that the patient record has been updated. With updated information, the agent is then able to help the patient with the post-op treatment and whatever other workflows are attached to the CRM. The patient receives a positive experience for that slice of the healthcare journey. It is important to note, however, that even with updated information, the CRM is still constrained by field limitations. It will not have data about a patient’s behavior on the website, for example. Conversely, when a CRM is a destination source for data that has been fit-for-purpose through an enterprise CDP, a call center agent is first assured that the data is accurate and up-to-date. When data quality and advanced identity resolution processes are completed at the point of data ingestion, the agent – and all users and applications – are now working with a single source of truth of patient data. If a patient calls for assistance with post-op care, a Golden Record built and maintained by the CDP will let the agent know everything there is to know about the patient, preparing a next-best action to perhaps advance the patient journey in addition to satisfying the reason for the call. The Golden Record will include the patient’s website behavior, including pages viewed, time on page, clicks, etc. A real-time analysis of that behavior might inform a next-best action that guides the patient through a different journey with the potential to deliver a better outcome. A CRM system and a CDP both deliver tremendous benefits to healthcare organizations intent on improving patient experience. But because the solutions are not mutually exclusive in terms of use cases, organizations should carefully consider their business purpose before deciding whether a CRM, a CDP – or both – will best meet their enterprise needs. **Blog categories:** Data Quality, Healthcare, Identity Resolution, Real-Time Personalization --- ### [Going Beyond Compliance to Infuse Loyalty into Customer Relationships](https://www.redpointglobal.com/blog/going-beyond-compliance-to-infuse-loyalty-into-customer-relationships/) **Published:** March 12, 2019 **Author:** John Nash **Content:** Compliance with new data privacy regulations is a top priority for companies worldwide. The California Consumer Privacy Act (CCPA) and General Data Protection Regulation (GDPR), combined with several high-profile data breaches and hefty fines levied against companies for violations have thrust [data privacy into the limelight](https://www.redpointglobal.com/blog/what-you-need-to-know-about-consumer-data-privacy-compliance/), giving marketers the daunting task of helping to ensure compliance without sacrificing program effectiveness. Marketers are being asked to personalize customer engagements on one hand, while adhering to tightening data privacy regulations on the other. It is a mistake, however, for marketers to think that their hands are tied, and that compliance and innovation are mutually exclusive. A smarter mindset is for marketing to view an onslaught of data regulations as an opportunity to deepen a continually evolving relationship with a customer. Taking this approach shows you’re complying not just because you must, but because doing so optimizes the customer journey in line with a customer’s communications preferences. Instead of compliance for compliance’s sake, viewing data privacy through a transactional lens as a current event, a more effective approach looks beyond compliance to view data privacy through the prism of enhancing customer lifetime value (CLV). The more a brand knows about a customer – to include intent, behaviors, and transactions – the more it can minimize customer friction and maximize share of wallet. **A Carefully Managed Customer Experience** Data privacy regulations give customers more control over how their data is collected and used, establishing guardrails for marketers in terms of managing ongoing permissions, preferences, response requests, and other controls guiding the use of personal data. By tying privacy preferences to CLV, a brand can proactively shape future marketing outreach strategy and escape the trap of constantly pushing boundaries and putting the onus on the customer to limit interactions. When customer data preferences become part of a single customer view, a marketer can score a customer’s preferences based on behavior over multiple events and devices, and thus carefully manage the customer’s experience in accordance with their data privacy preferences. The next time the customer conducts a search, opens an account, or breaks a geo-fence, a marketer will know which response, notification, or outreach – if any – has the highest likelihood to produce a desired result. From a practical standpoint, understanding permissions as an aggregate over time rather than on a transactional basis gives a marketer back some of the control that data privacy regulations put in the hands of the consumer. Say a customer tends to opt-in whenever they initiate an engagement, such as visiting a website or contacting a call center. The same customer also opts out whenever they’re targeted for an outbound marketing program. By tracking this behavior over time and across multiple devices and channels, a marketer can expertly manage the entire customer experience. Even with permission to send an email, for example, a better strategy might be to wait to bring the customer to an outbound program until such time as you build trust and loyalty by having shown deference and respect to preferences. In this scenario, the customer isn’t even presented with an option for an opt-out because marketing has unobtrusively managed the end-to-end experience. **Data Privacy, Less is More** Adopting a customer-centric approach to data privacy management governs contact frequency and intensity beyond simple adherence to a customer’s preferences. The ultimate goal for marketing is to be in the position to offer a next-best action recommendation for a customer at any point of a multi-channel, multi-event customer journey. This capability stems from applying advanced analytics to a single customer view to yield a single point of control over all data, decisions, and interactions. Incorporating data privacy preferences into a unified profile adds another layer of intelligence to the next-best action recommendation. Managing contact frequency as part of a next-best action provides a level of communication sophistication that can differentiate a brand. Marketers always strive for the precise moment of engagement – a “sweet spot” – that will most resonate with a customer, but the default modus operandi has been to test the boundaries, continually probing the lines to discover the exact point that creates friction for a customer and stopping there. Data privacy regulations force marketers to regroup with a new approach. Smart marketers realize that a holistic view of data preferences across channels and devices enables a “less is more” strategy that can all but eliminate customer frustration by recognizing each customer’s uniquely granular requirements. A brand that understands and responds to a customer’s preferences at an individual level becomes a far more relevant and desirable communications partner with a customer versus a brand with a transactional approach that is seemingly interested only in checking off the compliance box. **Blog categories:** Single Customer View --- ### [For a Superior CX, Elevate Preference & Consent Management](https://www.redpointglobal.com/blog/for-a-superior-cx-elevate-preference-consent-management/) **Published:** July 27, 2022 **Author:** Steve Zisk **Content:** Preference management and consent management in the realm of customer experience, while often used interchangeably, are really two sides of the same coin. Closely related, yes, but with the key distinction that consent is an unambiguous declaration from the customer (“You may/may not share my personal data with a third-party”). Preferences may involve consent, e.g., a customer consents to SMS notifications, but preferences may also be inferred, such as a brand analyzing transactions to know a customer prefers blue to green. Nuances aside, what’s most important for brands to understand about consent and preference management as they relate to delivering a relevant, personalized customer experience is that both are essential to develop and maintain trust. Honoring consents and preferences to the letter and in spirit increasingly represent brand equity, thus a failure to abide by a customer’s stated or unstated wishes erodes trust. Consider new Dynata research in a survey commissioned by Redpoint, where 48 percent of consumers surveyed said they would stop doing business with a company that gave away personal data without direct consent. As for preferences, a [2021 Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/) revealed that 82 percent of consumers said they are loyal to brands that demonstrate a thorough understanding of them as a unique customer – with a “thorough” understanding naturally to include understanding – and abiding by – a customer’s preferences. A closer look at the intricacies of consent and preference management will shed light on why both are key components for building a relationship with customers built on trust. **What is Consent Management?** Consent management refers most commonly to compliance with data protection regulations, GDPR and CCPA chief among them. The former requires that users must give clear and affirmative consent prior to having personal data collected, with the latter requiring businesses to make it possible for customers to opt-out of having their data disclosed or sold to third parties. From the customer’s perspective, merely a choice to opt-out is not the same as directly opting in – particularly if the opt-in option is buried in small print at the bottom of a web page. Part of managing consent includes the decisions a brand makes about whether to comply with the letter of the law, but not the spirit; i.e. does the brand believe it covers its bases by offering a difficult opt-out process? If 48 percent of consumers will leave the brand if it shares data without consent, an obscure opt-out option may not placate an angry customer. A brand’s interpretation of consent should include a risk/reward calculation. Is selling data to a third-party worth potentially alienating a customer who has provided soft “consent” (by not opting out)? Consider an [Accenture study](https://www.accenture.com/t20180503T034117Z__w__/us-en/_acnmedia/PDF-77/Accenture-Pulse-Survey.pdf#zoom=50) where 83 percent of consumers surveyed said they are willing to share data with a brand in exchange for a personalized experience – on the condition that the brand is transparent about how the data will be used, and as long as the customer has control over the data. And in the Dynata survey referenced above, 74 percent of consumers surveyed said they either “rarely” or “never” share data without knowing precisely how a brand will use it. Honoring the sharing of personal data in exchange for a personalized customer experience is consent-based relationship management; whether clear and affirmative consent as per GDPR, or a more subtle dynamic where customers trust their data is protected and used in good faith, a consent-based relationship adds value outside of a transactional basis. It builds trust. **What is Preference Management?** Preference management, as its name suggests, is not as ironclad as consent management. While technically optional for a brand, the risk in lost customers may be as significant, if not more so, than financial penalties for violating a data privacy regulation. Preference centers are one way brands can honor a customer’s stated preferences for choices such as channel, frequency, delivery location, styles, etc. For subscriptions and services, preference centers might include options for type of content to receive, renewals, payments, etc. Preference centers might also include customer preferences that a brand infers from interactions, such as size, color, best time of day to receive an email, etc. An analysis of online behavior might yield preferences that include types of images clicked on, content interacted with or any other signal that tells a brand how a customer likes to engage online. Preferences might both be directly stated as an option and inferred. Size, color, and email frequency are all examples. A brand might directly ask a customer how often the customer wants to receive an email, or it might send an email biweekly rather than weekly simply from knowing how often the customer opens an email. **Managing Preference & Consent Management** Managing preferences entail managing the entire scope of a customer’s preferences, declared and inferred. Both can live in the preference center, and demonstrating a personal understanding of a customer entails perpetually honoring a customer’s preferences throughout a customer journey. Preference management can be thought of in the context of a home having a main circuit breaker, with light switches scattered throughout the house. In the analogy, the main breaker is the preference center, with the switches representing how a customer engages throughout a journey. Managing preferences isn’t just flipping one main switch. With the breaker powered on and the juice flowing, the brand must see how a customer is moving through a journey and deliver against every preference – declared and inferred – at every touchpoint. Throughout a dynamic journey, there may be additional opportunities to collect consent or analyze preferences, but the overarching objective should be to use the information about a customer’s preferences responsibly in the service of a highly relevant and frictionless customer experience. Secure in the knowledge that their preferences and consent are managed to the spirit and letter of the law, exemplified by those preferences consistently being acted upon, a customer reciprocates that value by providing more information about themselves that in turn is used to further enhance the experience and deepen the trust between a customer and a brand. **Related Orchard Blogs** [Why Trust Unlocks a Superior CX & Builds Brand Equity](https://www.redpointglobal.com/blog/why-trust-unlocks-a-superior-customer-experience-cx-and-builds-brand-equity/) [The Elevation of Experience: Why a Deep Customer Understanding Matters Even More](https://www.redpointglobal.com/blog/the-elevation-of-experience-why-a-deep-customer-understanding-matters-even-more/) [Omnichannel Isn’t Digital First, or Physical First, it’s Experience First](https://www.redpointglobal.com/blog/omnichannel-isnt-digital-first-or-physical-first-its-experience-first/) **Blog categories:** 1:1 Personalization, Identity Resolution, Journey Orchestration --- ### [A New Vision: Contact Lens Retailer Focuses on a Personalized CX](https://www.redpointglobal.com/blog/a-new-vision-contact-lens-retailer-focuses-on-a-personalized-cx/) **Published:** August 4, 2020 **Author:** John Nash **Content:** Over the past few months, we’ve written a lot in this space about what a [new reality might look like](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) for consumers and businesses when the country emerges from the coronavirus pandemic. Which changing consumer behaviors will take hold, and which will recede? Will businesses make the necessary digital transformation to keep pace with a digital-first customer journey, or will they hope a stopgap effort will suffice, pinning their hopes on customers reverting to familiar engagement models? Sometimes when you get caught up in the macro trends, it’s easy to forget that there are real people at the other end of customer engagement scenarios, people for whom the economic and health tolls may have prompted unexpected changes. I was reminded of this during a recent conversation with Phil Bienert, the Chief Marketing Officer for [1-800 Contacts](https://www.1800contacts.com/), a new Redpoint customer and the largest contact lens retailer in the U.S. ## **Seeing a New Way Forward** Contact lenses do not immediately spring to mind as an online product. According to Bienert, before COVID-19 only 20 percent of contact lens wearers regularly shopped for lenses online. So even though many types of retail ecommerce sales [skyrocketed in the spring](https://edition.cnn.com/2020/05/15/economy/retail-sales-record-drop-april/index.html) with the onset of shelter-in-place directives and business shutdowns, many contact lens wearers remained confused about their options; as turning to an online purchase did not seem, to them, an obvious alternative. This challenge, Bienert said, drove the point home that the goal of delivering a personalized customer experience isn’t just to check off a marketing box, but to provide convenience and relevance as a means of helping to improve people’s lives. Because 1-800 Contacts was up to the challenge, they were able to help meet the needs of audiences the company hadn’t previously targeted. These include first-line responders unable to wear prescription eyeglasses because of PPE restrictions/challenges, and new customers who were unaware that they could procure contacts from anywhere other than their now-closed doctor’s offices. Telemedicine eye exams that were unfamiliar to consumers pre-COVID, were suddenly the norm in how medicine was being practiced. A recognition that every customer has a unique need helped 1-800 Contacts accelerate its digital transformation efforts, based on fundamental business changes which had started well before the pandemic. As Bienert explained, the company was not preparing for a pandemic, but once it arrived the company’s ability to engage customers with a personalized experience paid off. Bienert said, “We’re most proud of the fact that we’ve been able to help hundreds of thousands of people who didn’t think they had an option.” An increase in revenue is undoubtedly a nice ancillary benefit for the company, but for many of the employees delighting a customer with a [personalized experience](https://www.redpointglobal.com/blog/a-superior-customer-experience-cx-transcends-marketing/) unique to their customer journey is ultimately more rewarding. “Over the last two years, we have been evaluating what we could do to run more like a lean, ecommerce direct-to-consumer company by 2020. A lot of that was thinking about customer engagement marketing in a much more digitally-centric way,” he said. “A lot of thought also went into managing more personal communications. Most of our marketing was not sophisticated, rather a standard ‘order more contacts’ message. While often effective, we realized how we connect with the consumer could dramatically improve the experience on all levels. We’ve re-engineered the business to make those communications far more personal to each customer; to know what they’re wearing, their true pattern of wear, and to know the right thing to say at the right time.” ## **A Single View Comes into Focus** Using the Redpoint CDP, 1-800 Contacts can enable a one-to-one personal connection at scale. The company is also in the process of developing a real-time, [single customer view](https://www.redpointglobal.com/single-customer-view/); the Redpoint [Golden Record](https://www.redpointglobal.com/blog/what-is-a-golden-record/) provides marketers with an instantly accessible up-to-date record of every transaction and behavior. With advanced identity resolution and automated machine learning, marketers have a single control over data, decisions and interactions with which to intelligently orchestrate a next-best action for a customer that is always in the context of a unique customer journey. Bienert said that one of the key benefits that he is excited about in partnering with Redpoint is the simplification of the process. Because the personalization platform does all the heavy lifting of integrating the data and optimizing insights behind the scenes, it’s actually less work for the everyday marketer to provide a far more sophisticated level of personalization with a wider array of customers – all because the next-best action recommendation is always at their fingertips, in real time. The technology, he said, allows for scale but also maintains and improves upon the human connection that 1-800 Contacts is known for. “Redpoint will serve as the engine to orchestrate all those personalized experiences for our customers no matter where they touch us. When they have that human-to-human, personalized connection with our associate, our employee seems almost like a superhero,” Bienert said. “The technology informs the associate everything else the customer has done, so they can proactively offer help for that customer’s situation at the exact moment of need. To the consumer, it’s almost like they’re mind-readers; that’s their superpower. And that capability will be largely powered by Redpoint.” “We’re excited to see how the market continues to evolve,” Bienert continues. “I think there have been some fundamental changes in consumer behavior that will have lasting impacts. Together with Redpoint, we are working to meet the needs of those consumers now and into the future.” *To learn more about how Redpoint Global can put your personalization goals within reach regardless of which industry you’re in, you can* [*schedule a demo*](https://www.redpointglobal.com/blog/#demo-signup)*. Or* [*click here*](https://www.redpointglobal.com/one-platform/) *to learn more about how Redpoint helps ambitious marketers lead markets.* **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization, Retail, Segmentation & Activation --- ### [What You Need to Know about Consumer Data Privacy Compliance](https://www.redpointglobal.com/blog/what-you-need-to-know-about-consumer-data-privacy-compliance/) **Published:** February 4, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/02/shutterstock_1097989835.jpg)The security of personally identifiable information (PII) data is top of mind as individuals become more concerned about their data and how it’s being used. As industry and global privacy standards such as [General Data Protection Regulation (GDPR)](https://eugdpr.org/) and the [California Consumer Privacy Act (CCPA)](https://www.caprivacy.org/) continue to expand, organizations that aggregate, process, and store personally identifiable data must take ownership in protecting that data. This also creates an opportunity to deepen trust and relationships with individual data subjects by empowering them to state their preferences and consent, along with delivering transparent communications. GDPR, which regulates data protection in the European Union, went into effect in May, 2018. CCPA was enacted the following month and will go into effect on July 1, 2020. And on January 16, U.S. Senator Marco Rubio (R-FL) introduced the [American Data Dissemination Act (ADD)](https://www.rubio.senate.gov/public/_cache/files/afe71d4b-201e-4273-b136-eb0555623b98/2F5D3F8CBF7E2BF65DB6E0FCF99D2797.add-act-one-pager.pdf) which would, if it becomes law, regulate data privacy for consumers at the federal level. A CCPA provision grants that the law will not apply if it is preempted by or in conflict with federal law. Rubio, Chair of the US Senate Committee on Small Business and Entrepreneurship, said that the legislation “provides overdue transparency and accountability from the tech industry while ensuring that small businesses and start-ups are still able to innovate and compete in the digital marketplace.” **GDPR Unleashes Data Privacy Transparency** Google’s recent [$57 million fine](https://www.mediapost.com/publications/article/330825/google-fined-57-million-over-gdpr-violations.html) for GDPR violations, along with Facebook’s [data privacy scandal](https://www.techrepublic.com/article/facebook-data-privacy-scandal-a-cheat-sheet/) help to push the issue of data privacy to the forefront. Brands face tremendous pressure to ensure compliance but are often stymied by the breadth of the compliance requirements they need to follow. A look at any homepage of an EU-based company reveals the measures companies must take to ensure GDPR compliance. They must show, for example, every partner that may have access to your device, cookies, and segment characteristics and they must provide an opt-in for each of them. And as your device’s information is bought and sold, it has downstream GDPR implications for every EU company that touches your data. Even though the device and cookie information are on their own not personally identifiable information, Recital 26 for example is clear that this information can become personally identifiable when combined with other information that a server has. For example, a cookie/device combination associated with a person makes that data personally identifiable and therefore this information falls under GDPR via Recital 26. This is one reason why EU web sites contain very specific and detailed opt-out capability. The old “by using this site you accept our use of cookies” just doesn’t comply with GDPR. CCPA privacy laws are similar in many ways to GDPR. The four main consumer protections are the right to be informed – what data is being collected, from which device, the purpose, and how it’s being shared – the right to opt-out of data sharing, the right to be forgotten, and the right of fair treatment. Some US-based multinational companies sought to ensure GDPR compliance on a global level in anticipation of a U.S. national law such as the one Rubio introduced, taking steps to let every visitor to a website now how their device is being tracked, what data they’re collecting and what they’re doing with that data. Heightened awareness over data privacy is introducing a lot of complexity to preference centers, in other words, and companies must know what to do with the data they’re collecting, whether first-party, second-party, or third-party data. **Processing Privacy Data Points with a CDP** One of the values of a customer data platform (CDP) as it relates to data privacy is that a CDP ingests data from any source. The [Redpoint Customer Data Platform](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/) can provide an integrated [master data management (MDM)](https://www.redpointglobal.com/customer-data-management/master-data-management/) component that processes all salient privacy data points, such as source, date, and the type of opt-in or opt-out (single, double, etc.). With an MDM solution tracking changes to selections over time it becomes a GDPR-compliance vault for the data processor role. It provides heuristic and probabilistic matching, and if data is not matched to a persona for a certain time period, the data will just live in the data layer until it’s safely deleted. Once matched, it is pushed to the MDM for first-party handling. The process for third-party data is similar, with a key exception being that [data matching](https://www.redpointglobal.com/customer-data-management/data-matching/) usually consists of detailed, small records. When a device is not matched to a persona, device information is preserved to enable marketing to a device, or based on behavior or site activity until it becomes stale. Once the [data matching](https://www.redpointglobal.com/customer-data-management/data-matching/) and standard data handling cycles are complete, the beauty of MDM is that it dynamically updates the aggregates for a record. For the customer, GDPR and CCPA compliance entails subscribing to the updated record to use CDP as a marketing database. This fulfills the core requirements of data privacy regulations, which is the tracking of source date, device, person information, changes over time, frequencies, channels, and opt-out status. The persistent view is so thorough that preference centers are often adjusted with information preference centers know nothing about. The CDP with MDM also handles audit activities related to data controller and data processor compliance with GDPR, and audit activities related to CCPA requirements for up to one year. **Meeting the Rights of the Consumer** The right of inquiry is another transparency related regulation in both GDPR and CCPA, stipulating that a brand must satisfy a consumer’s inquiry about how their data is being collected, used, and shared – whether the inquiry is made by a phone call, a letter, on the website, or by email. An MDM solution satisfies these types of inquires because it tracks every opt-in and opt-out over time, at every interaction, and the data can easily be supplied back to the person who asks for it. This satisfies right of inquiry for a data processor. A data controller – the company or brand itself – must satisfy the other parts of the right of inquiry requests apart from an MDM capability, such as providing a customer with the back-up information policy and whether the customer’s data is part of profiling analytics. A key difference between right of inquiry under GDPR and CCPA provisions is that CCPA requires less detail in the information provided; a company can inform the consumer about the categories to which they’ve opted in or out, such as by “newsletter” or “website activities.” GDPR requires more specificity, where a consumer must be told how and when each data point was collected. A more difficult provision to satisfy is the right to erasure – commonly known as the right to be forgotten. Under GDPR, an erasure request compels a brand to anonymize and disassociate transactional data from a record. Under CCPA, the data must be deleted. However, the right to erasure is not an absolute right; there are varying rules and regulations for a host of scenarios. Most EU companies choose to anonymize data after a right to erasure request as per the GDPR regulations. Redpoint solutions support both GDPR and CCPA provisions. We wrote about the strategic importance of data in a recent [blog post](https://www.redpointglobal.com/blog/why-data-quality-matters-for-it-and-business-stakeholders/), with a focus on data quality and it importance for both marketers and IT to deliver new insights. The introduction of data privacy regulations underscores the importance of data quality for another reason, to satisfy requests from your customers and to satisfy current or future state and federal regulations. More information on how Redpoint supports data privacy rules can be found at our [solution center](https://www.redpointglobal.com/solutions/redpoint-data-management/) and in a data management [solution brief](https://www.redpointglobal.com/wp-content/uploads/2017/10/DS-DMUS0216-07-RPDM-lo-res.pdf) with more details on MDM. **RELATED ARTICLES** [Why Data Quality Matters for IT and Business Stakeholders](https://www.redpointglobal.com/blog/why-data-quality-matters-for-it-and-business-stakeholders/) [Art of the Possible – The Role Data Can Play in Unlocking Marketing Creativity](https://www.redpointglobal.com/blog/art-of-the-possible-the-role-data-can-play-in-unlocking-marketing-creativity/) ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Governance & Security, Data Management, Data Quality, Identity Resolution --- ### [Accurate Data, Confident Decisions: Building Trust Through Data Readiness](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/) **Published:** June 17, 2025 **Author:** John Nash **Content:** From errors in [travel](https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know), [retail](https://www.theguardian.com/world/2023/aug/10/pak-n-save-savey-meal-bot-ai-app-malfunction-recipes) and [banking](https://www.cnbc.com/2023/06/23/ai-has-a-discrimination-problem-in-banking-that-can-be-devastating.html), training AI on inaccurate data can produce disastrous results. In healthcare, one example of a particularly poor outcome is when a [model](https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/) trained on inaccurate patient data returned erroneous cancer treatment advice. Unsafe treatment advice may be an extreme example of the potential negative consequences of AI being trained on bad data, but it demonstrates the reality that biased outputs, inaccurate predictions, an inferior customer experience and reduced trust are all issues that may arise when starting out with inaccurate data. A previous post in this series on the pillars of data readiness focused on the importance for data to be complete. Here, the attention shifts to the importance of **accuracy** as the second of the six pillars of data readiness (complete, accurate, timely, actionable, trusted, compliant.) What is accurate data as it relates to data readiness? Accuracy means that data is free from input errors, matching errors, identity errors, miscalculations, duplicates, misclassifications, and a range of other data issues. In essence, accurate data correctly represents the individual, household or business that a business is trying to understand. ## **What it Means for Enterprise Data to be Accurate** Data being complete and accurate are often linked together in terms of the process of data readiness. That’s because both are foundational for making sure that data is right. It is of course possible to have one without the other, with a simple example being collecting data of every type and from every source but being unable to extract value due to poor data quality processes. Or, conversely, to have perfectly clean (read: accurate) data that is really narrow in scope and misses key data sources, transactional and/or behavioral information. While both can have a negative effect on business and CX use cases, accurate data is important to keep the data readiness process moving forward. Data accuracy is a critical upstream activity in making sure the data is right – it is cleansed, matched and householded with precision so that you can confidently build out your high value use cases. ## **Data Accuracy and Cleanliness** Accurate data reflects the truth about a customer or entity, having gone through standardization and normalization processes to remove errors, resolve conflicts, and ensure consistency. Ideally, the full scope of data quality processes take place at data ingestion when building unified profiles – ensuring that the matching process accounts for all pertinent data and data is continually correct from there going forward. Correcting data input and formatting errors, missing fields, duplications, incorrect values (pounds vs. ounces, etc.), and validations are among the common standardization and normalization tasks. Others include flagging or removing outliers and ensuring categorical consistency (Road vs. Rd.). ![Data Readiness Accurate Graphic](https://www.redpointglobal.com/wp-content/uploads/2025/06/Data-Readiness-Accurate-graphic--800x372.jpeg)The Six Pillars of Data Readiness: Accurate The data cleansing process is indispensable for precise matching. The reason why data quality needs to be taken care of at data ingestion is because that’s when the enterprise begins to develop a comprehensive, contextual understanding of a customer (household, etc.) with the building of a Golden Record. If data cleansing is put off until activation, or if it is left to various point solutions, what’s lost is a single view of the customer. If cleansed at activation, recency becomes another problem; the customer may have new interactions and behaviors that do not become part of a match. If left to point solutions, data cleansing essentially becomes a free-for-all exercise without standards or integration. ## **Data Accuracy and Precise Matching** Identity resolution is about accurately detecting patterns to determine whether different facets or signals constitute the same customer, person, household or organization. By using the right combination of deterministic and probabilistic matching, an organization considerably improves the likelihood of success of engaging with the right customer or household, and for training AI models with data representative of a company’s actual customers. The better the matching process, the better a brand can accomplish business, CX and AI use cases that depend on having a solid customer understanding. Creating a unified profile with basic deterministic matching –exact matches of records with hard identifiers – is an important, albeit partial, element of an accurate match and an indispensable component of advanced identity resolution. Because the goal of identity resolution is to gain a better understanding of a customer or household by reconciling different records for a customer across different engagement systems – different channels, different identifiers and/or with anonymous-to-known journeys – deterministic matching must be combined with probabilistic matching. ## **Probabilistic Matching** Probabilistic matching is a key step in resolving various identity proxies for a customer across different systems, multiple data sources and various data types. Probabilistic matching is also instrumental in providing context around a customer, i.e., the customer’s relationship within a household or a business. An example is tying two identity proxies to the same physical address. Another key benefit of probabilistic matching is to resolve conflicting or unclear signals to produce a higher degree of confidence in a match than using deterministic rules alone. One example in a householding context is to have two identities – say a wife and husband – tied to the same address. Probabilistic matching uses other signals to determine which of the two is connected to any one transaction – and then links that transaction to an individual profile. Advanced identity resolution using probabilistic matching also corrects for human errors. A call center agent, for example, enters “Jon Smith” on a form instead of “John Smith.” If other signals indicate the customer belongs to the same record, probabilistic matching will correct for the error where a basic deterministic rule will likely not. ## **The Role of Diversity in an Accurate Dataset** Accuracy also demands that data is diverse as well as representative, particularly as it relates to AI use cases. A brand may have data specific to customers belonging to a loyalty program that is uses to draw inferences about those customers. But while that data is representative of loyalty customers, it is not representative of *all* customers. Likewise, if a brand wants to introduce a new product designed for Millennials and GenZ but its data skews toward GenX and Boomers, inferences made from the dataset will not be accurate for the intended demographic. This is the classic polling problem – phoning only landlines and using the response set to make presumptions about the population at large. A lack of accurate data has the potential to derail almost any CX, business or AI use case that depends on a precise understanding of a customer, household or business. Skewed AI results, incorrect/incomplete matching put a brand at a disadvantage when it comes to extracting value from customer data. Prioritizing accuracy in a comprehensive approach to data quality will ensure that a brand maximizes one of its most valuable assets – customer data. While accuracy gets the ball rolling on the process of making data right and fit-for-purpose, the entire data readiness process consists of six distinct pillars. The next part of our series on data accuracy will focus on the importance of data being timely. **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [CMOs, Take the Reins: Why Data Quality Elevates to the C-Level](https://www.redpointglobal.com/blog/cmos-take-the-reins-why-data-quality-elevates-to-the-c-level/) **Published:** April 12, 2022 **Author:** John Nash **Content:** Henry Ford is often attributed with saying that if he had asked people what they wanted, they would have said a faster horse. The quote speaks to innovation being a lonely pursuit. Many are able to describe a problem, but few are able to find the right solution. The saying aptly captures why driving a customer experience strategy around data quality should be a C-level initiative. Operational marketers focused on incremental process improvements are after the faster horse; responsible for a channel, they narrowly focus on step changes that may offer nominal improvements for the metrics they care about. The problems with this approach are two-fold. First, process improvement does not stanch the flow of poor data quality across departments and functions. Nothing is gained when a slow process using inferior data becomes a fast process using inferior data. Second, even a ten-fold improvement – open rates in an email campaign, clicks on a product image – do not factor how a customer’s behavior on one channel influences the totality of a customer journey. With a top-down focus on data quality to drive a CX strategy, a chief marketing officer solves for both problems. Marketers presented with perfected data, that is a robust single customer view available and accessible to anywhere and anytime, can then shift their focus from a process to where it belongs – on the customer. And then from an alignment standpoint, making data quality a C-level initiative underscores the importance of the consistent use of data across channels. ## **Stop Bad Data in its Tracks** [Gartner research](https://www.gartner.com/smarterwithgartner/how-to-improve-your-data-quality) reveals the extent of the problem with inferior data and why it has reached the C level. Estimating that poor data quality costs organizations an average of $12.9 million each year, Gartner predicts that 70 percent of organizations will rigorously track data quality levels via metrics, with the result that data quality will improve by 60 percent, significantly reducing operational risks and costs. From a customer experience standpoint, poor data quality translates to irrelevant, untimely experiences that introduce friction into a customer journey. Organizations lose revenue when they fail to demonstrate a deep understanding of a customer at a personal level. Consider a [2021 Harris Poll](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) commissioned by Redpoint in which 39 percent of customers surveyed said they will not do business with any company that does not offer a personalized experience. Making data quality a top-down, enterprise initiative supports the delivery of a consistent experience across touchpoints, lessening a focus on a team’s contributions to a single channel or process. The through line that enables the shift is a single view of the customer, or golden customer record. A [golden record](https://www.redpointglobal.com/single-customer-view/) integrates data from every conceivable source and includes a long-tail transactional record along with a unique identifier encompassing all devices, names, addresses, social, etc. When it is updated in real time and made accessible across the enterprise, every part of the customer experience – call center agents, email content, web pages, etc. – works with the same unified customer profile. Enterprise use of a golden record empowers a CMO to rethink bonus and pay structures. Satisfaction surveys, open rates, time on page, and other channel-centric metrics give way to those that instead key in on a team’s contribution to the overall customer experience – lifetime value, retention, acquisition, conversions, revenue etc. ## **Perfected Data and a Deep Understanding** Supporting a consistent experience across all channels with perfected data that is accessible enterprise-wide has become an imperative in large part because dynamic customer journeys consist of multiple channels. In a joint survey between Dynata and Redpoint that explored consumer behaviors, [29 percent of consumers](https://www.redpointglobal.com/press-releases/80-of-consumers-more-likely-to-shop-with-brands-that-show-they-understand-them/) said they regularly conduct online research prior to in-store purchases. And 60 percent said they plan to use a buy online, pick-up in-store service. The survey also makes clear that no matter how often consumers move at will between channels, the expectation remains that brands will maintain a deep understanding and be able to move with them through a customer journey. Consider that 78 percent of customers said they find it frustrating when a brand’s communications and marketing messages are inconsistent depending on the channel they visit (in-store, online, social, call center, app). To meet this expectation, a golden record not only must be made accessible to a business user in the last mile to the consumer, but also accessible in the customer’s cadence to a brand’s operational systems. A call center agent or front desk clerk, for example, with a real-time view into a customer journey is then not only empowered to provide a relevant experience and move a customer journey forward, but they are also making real-time updates to the golden record during the interaction. With data quality pushed out to the edge and customer service reps and other front-line business users empowered to correct mistakes, having strong data governance systems and policies in place becomes important. To maintain a golden record’s integrity, controls must be in place to ensure accurate changes and record-keeping. Dynamically updated across functions and departments, a golden record never becomes stale, outdated, or inaccurate. The recognition that one function impacts another – such as a poor sales experience becoming a customer service problem – puts emphasis on the need for operational alignment and thus elevates data quality to the C-level. A Harvard Business Review study highlights the insidious nature of poor data quality and shows how easy it is to fall into the “faster horse” rut. According to its research, employees who work with data waste up to [50 percent of their time](https://hbr.org/2013/12/datas-credibility-problem) hunting for data, identifying and correcting errors, and seeking confirmatory sources for data they do not trust. With perfected data that persists across departments and functions, that wasted time could instead be used to deliver a consistently relevant customer experience that spans an entire dynamic customer journey. A focus on data quality as a top-down initiative helps ensure an entire organization is empowered with perfect customer data to deliver a perfect customer experience. **Blog categories:** Data Quality, Identity Resolution, Segmentation & Activation --- ### [To Be More Effective Closing Care Gaps, the Healthcare Industry Needs a Single View of the Consumer](https://www.redpointglobal.com/blog/to-be-more-effective-closing-care-gaps-the-healthcare-industry-needs-a-single-view-of-the-consumer/) **Published:** September 11, 2020 **Author:** John Nash **Content:** According to electronic health records company Epic, telehealth visits in the U.S. in July accounted for [21 percent of total encounters](https://www.statnews.com/2020/09/01/telehealth-visits-decline-covid19-hospitals/), down from 69 percent in mid-April during an early peak of the health crisis. Several factors contributed to the surge, including the Centers for Medicare & Medicaid Services (CMS) expanding the list of covered telehealth services, issuing temporary waivers and loosening restrictions; healthcare consumers reluctant to make in-person visits, reduced provider office hours, and an increase in electronic consultations in lieu of direct care. Despite the drop between April and July, the surge in telehealth visits that preceded it shows that telehealth helped fill an unprecedented void and was a viable short-term option for many healthcare consumers, with the trend expected to continue. One market research report forecasts a global [$55.6 billion telehealth market by 2025](https://www.prnewswire.com/news-releases/telehealth-market-worth-55-6-billion-by-2025--exclusive-report-by-marketsandmarkets-301030816.html), up from $25.4 billion in 2020. The recent decline in telehealth visits does indicate, however, that in many cases there is no substitute for in-person care. This is especially true for colorectal, cervical, and breast cancer screenings and other preventive measures that contribute to Health Effectiveness Data and Information Set (HEDIS) scores. ## Closing Care Gaps In Healthcare is Top of Mind The below graphic showcases the cost of reduced screening tests in stark terms of a delayed cancer diagnosis. As the first column shows, there was a 69 percent reduction in breast cancer screenings for a three-month period ending early June, representing 7.2 million fewer tests (of 42 million annually). At a standard 1:200 rate of positive cancer diagnosis per test, that equates to 36,000 patients delaying a cancer diagnosis as a direct result of a delayed screening because of the pandemic. Early breast cancer diagnosis, as with another cancer diagnosis, results in higher survival rates, more treatment options, and reduced costs. ![](/wp-content/uploads/2020/09/close-gaps-in-care-1-300x172.jpg) The pandemic also has had a significant impact on managing chronic conditions such as diabetes, COPD, and hypertension. According to a Wellfleet study from June of roughly 1,000 patients with a chronic condition, [57 percent said](https://www.businesswire.com/news/home/20200603005121/en/Survey-Finds-Coronavirus-Pandemic-Motivated-Patients-Control) they delayed receiving healthcare due to the pandemic, either at their own discretion or their doctor’s. With approximately 60 percent of all U.S. adults managing at least one chronic condition, that represents tens of millions of delayed visits. Not surprisingly, in a separate survey of healthcare providers from Definitive Healthcare, the impact of delayed care was cited by [24.4 percent of respondents](https://www.healthleadersmedia.com/finance/hospital-revenue-loss-most-important-healthcare-trend-going-forward) as one of the largest emerging trends, second only to losses sustained due to the cancellation of elective surgeries (36 percent). Interestingly, these trends overtake the two biggest trends of 2019 – industry consolidation and the growing influence of healthcare consumerism – as being top of mind for providers. ## Orchestrate a Personalized Healthcare CX Journey Providers generally follow a [three-pronged strategy](https://carenethealthcare.com/closing-the-care-gap-with-engagement-and-hedis-best-practices/#:~:text=Quality%20measures%20and%20scores%20from,care%20over%20the%20long%20term.) for closing care gaps: quality scorecards, population health management, and patient engagement. While many organizations have identified quality metrics and have population health initiatives underway to satisfy the first two pillars, most fall short of their full potential, as they are missing the third leg of the stool: effective patient engagement. Metrics and reporting are simply not enough. Effective patient engagement that helps reduce gaps in care requires more than just knowing a healthcare consumer’s medical history – or compiling patient data relevant for quality metrics reporting or managing broader population health. It also requires knowing – at an individual consumer level – a consumer’s risk tolerance for an in-person visit, their means for engaging with a telehealth option (if available), access to transportation and other social determinants of health, current health condition, and other risk factors. But knowing all this still does not bring the healthcare consumer into the clinic or the doctor’s office. There is also the matter of maximizing appointment scheduling by optimizing engagement with the healthcare consumer. Will an SMS directly from a provider that notifies a member of a gap in care result in a confirmed appointment better than an email or call? Can you offer a day and time that works best with the member’s schedule? Creating a relationship with a healthcare consumer to drive better outcomes requires having every conceivable piece of [consumer data](https://www.redpointglobal.com/blog/evolution-of-the-customer-experience-and-the-role-of-data/) in one place. We’ve covered the emergence of healthcare consumerism and the implications of increasingly dynamic healthcare consumer journeys amid industry uncertainty in previous blogs [here](https://www.redpointglobal.com/blog/the-data-driven-acceleration-of-the-consumer-healthcare-journey/) and [here](https://www.redpointglobal.com/blog/a-dynamic-healthcare-journey-deserves-a-coordinated-personalized-approach/). Alleviating pent-up demand for healthcare services throughout the pandemic poses yet another significant challenge for healthcare payers and providers who struggle to help consumers manage a frictionless healthcare experience. Demand for unmet services and care illustrates the crucial need for payers and providers to understand the needs of their patients. Without that full understanding, it becomes difficult if not impossible to address those needs by orchestrating a personalized experience that reduces a consumer’s fear and anxiety and increases the positive impact a payer/provider can have on individual health outcomes. The pandemic exacerbates the challenge to provide a holistic healthcare experience, exposing long-standing, built-in operational inefficiencies in the healthcare system caused by disparate data and an implicit tolerance for friction. Until Covid-19, an unintegrated network of payers and providers and a fractured, confusing, and often frustrating experience by the end consumer were chalked up as routine hurdles. A pent-up demand raises the stakes; with the real human and economic costs of delayed care, payers and providers no longer have the luxury of accepting the status quo. ## Improve the Healthcare CX with a Single View of the Consumer Improving health outcomes – in this case, closing higher-than-usual [care gaps](https://www.redpointglobal.com/blog/to-be-more-effective-closing-care-gaps-the-healthcare-industry-needs-a-single-view-of-the-consumer/) – requires that healthcare providers and payers have a [single view of the healthcare consumer](https://www.redpointglobal.com/single-customer-view/). To engage a consumer in the channel of his/her preference with a next-best action that recognizes, in real-time, the consumer’s current situation presupposes that there be no data siloes; claims and clinical data, for instance, must be integrated with everything there is to know about the healthcare consumer. A single view provides stakeholders with the full understanding of a consumer, ensuring that healthcare professionals always engage in the right channel, at the right time, and with the right content or message – at every point in the [consumer’s healthcare journey](https://www.redpointglobal.com/healthcare/). Doing this at scale for hundreds of thousands or millions of healthcare consumers requires automated machine learning (AML), with self-training, code-free models that do not have to be taken offline every time a desired outcome or metric changes. With AML, healthcare professionals can easily test the efficacy of every previous outreach, using the results to optimize all subsequent interactions without having to rely on data scientists, human judgment, or trial and error. ## One Platform for a Personalized Experience Customers in the healthcare industry are using the [Redpoint rg1](https://www.redpointglobal.com/one-platform/) digital experience platform to intelligently orchestrate personalized experiences, at scale, to drive better outcomes. One, for example, not only has integrated claims and clinical data but is also plugged into a provider’s scheduling system so an optimized interaction includes letting a consumer know – in real time – appointment options that fit with a consumer’s availability. If one provider’s office or clinic has long wait times, the healthcare consumer is notified of another nearby provider that may have more capacity. The notification even includes an estimated drive time from the consumer’s location. This type of personalized healthcare experience may seem simplistic, but many healthcare payers and providers lack this capability; consumer marketing that builds relationships with individual consumers is rarely prioritized, especially in fee-for-service payment models that incentivize volume. Establishing a meaningful relationship by personalizing a holistic experience throughout a healthcare journey removes many of the traditional barriers to improved health outcomes. Taking the view that devoting resources to building out this capability somehow interferes with operations or traditional marketing efforts is short-sighted. With so much now at stake with an unexpected and concerning widening of [care gaps](https://www.redpointglobal.com/blog/to-be-more-effective-closing-care-gaps-the-healthcare-industry-needs-a-single-view-of-the-consumer/), healthcare marketers must adopt a new approach that prioritizes the healthcare consumer’s experience. A single view is a starting point to improve outcomes, lower costs and improve overall satisfaction. ## Related Content [Survey Says: Healthcare Personalization Has Room to Grow](https://www.redpointglobal.com/blog/survey-says-healthcare-personalization-has-room-to-grow/) [Clear the Hurdles for a Personalized Customer Experience](https://www.redpointglobal.com/blog/clear-the-hurdles-for-a-personalized-customer-experience/) [A Personalized Customer Experience Delivers Loyalty](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Healthcare, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Real-Time Website Personalization: Client Side vs. Server Side](https://www.redpointglobal.com/blog/real-time-website-personalization-client-side-vs-server-side/) **Published:** March 29, 2021 **Author:** Redpoint Global **Content:** A slow-loading web page bleeds money. In a study from [Unbounce](https://www.searchenginejournal.com/nearly-70-of-consumers-say-page-speed-impacts-their-purchasing-decisions/290235/#close), 70 percent of consumers said that a website’s loading time affects their purchasing decision, with more than half claiming that they give a page three seconds to fully load – max – before moving on. This aligns with research from [Google](https://www.thinkwithgoogle.com/future-of-marketing/creativity/marketing-personalization-statistics/) finding that mobile sites loading in two seconds or less have a 15.3 percent higher conversion rate. Speed, though, is just half the battle. The other half is presenting a customer with personalized content in that very small window, which for at least one Redpoint customer is an [18-millisecond SLA](https://www.redpointglobal.com/resources/how-godaddy-personalizes-the-real-time-customer-experience-in-milliseconds/). As customers increasingly move to digital-first customer journeys, real-time website [personalization](https://www.redpointglobal.com/omnichannel-personalization/) is becoming an essential engagement tool. Brands that do it successfully have an edge; by delighting a customer or prospect with a hyper-relevant message, offer or content, a brand demonstrates that it values the customer beyond a transactional basis. In a recent Dynata study commissioned by Redpoint, [70 percent of customers](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/) said that they will *only* shop with brands that demonstrate a personal understanding. Real-time website personalization is a great way to start. ## **Limitations of A Real Time Client-Side Approach** A key question pertaining to website personalization is whether to adopt a client side or server-side approach. Until recently, [real time client](https://www.redpointglobal.com/blog/real-time-website-personalization-client-side-vs-server-side/)-side personalization has been more prevalent because of its relatively low entry cost and simple integrations with an array of personalization tools, each with its own niche functionality that, combined, take shape as what a website visitor will perceive as personalization. The trade-off for the adoption of basic personalization through a client-side approach, however, is that third-party tools often have a difficult time keeping up with the traffic, making it hard to scale particularly when there is a high volume – holidays, cyber-Monday, etc. A typical request workflow when a visitor lands on a site is for the browser to request content. As part of the loading process, a call goes to the personalization services that will then retrieve the content and send it back while the browser is rendering the content. It’s a lot of action – especially with millisecond response time SLAs. There is also an issue of the various [personalization](https://www.redpointglobal.com/blog/get-at-the-why-for-true-personalization-with-automated-machine-learning/) tools having to work together. A small change on a page may affect something else that happens on the site, thus impacting every tool. If it gets to be too unruly, JavaScript may timeout and render a blank or take too long and result in a dreaded or flash of original content. In both situations – the slowly loading page or a timeout issue – the bigger problem is a customer on the other end who has just been presented with a poor customer experience. In addition to the performance issue, security – or lack thereof – is the second strike against client-side website [personalization](https://www.redpointglobal.com/omnichannel-personalization/) compared with the alternative. Client-side website personalization requires customer data to be stored in the web browser, with the drawback that it’s not possible to completely mask the network traffic when a request is made for content. When you have JavaScript running on pages, in theory any bad actor with sufficient knowledge of website technology can view the traffic and make a substitute request, forge consent or use bots for the same purpose. With the phasing out of third-party cookies, adtech companies will grow more desperate to secure opt-in consent, and we will likely see more consent bots – or code thrown onto the site to force consent. The potential for malfeasance will rise if for some reason an organization decides to store PII in a visitor profile – which for obvious reasons should be a non-starter. With ample opportunity for lawlessness, brands may be exposed to legal fights over responsibility for inappropriate use of data. To mitigate this, the best practice is to rip out as much JavaScript as possible from the front of the site and use server-side personalization. ## **Server-Side Delivers the Goods** Server-side website personalization, by contrast, offers greater control and security. With API integrations directly into a content management system (CMS), marketers essentially have a unified platform to direct decisioning with a consistent definition of customers, rules and audiences. Because everything is managed from one place and there are no multiple network hops to retrieve content, the timeout/flash of original content issues are non-factors. As for the security issue, the traffic visibility drawback in client-side personalization is negated completely; the CMS makes a request to a server on the same subnet and retrieves and injects the content as part of the response to the browser request. Every action on the server is completely masked from a visitor. Additional performance gains can be had with a server-side implementation when data needs to be retrieved from an internal data store. Unlike traditional client-side implementations, which by nature must be publicly facing, a server-side approach does not require a proxy to communicate to the databases behind a firewall. Rather than make a network hop to a public server that may have to make a network hop to a proxy that will then query a database, return and inject the content, etc., Redpoint’s real-time web services can connect directly to the databases – without having to go through another service in a server-side deployment. ## **Website Personalization is Worth Doing Well** With greater security and better performance, more and more organizations are foregoing the low-hanging fruit of client-side website personalization for server-side implementations that deliver the real deal – real-time, hyper-relevant personalization that customers respond to with more conversions and repeat business. In a [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/) that delves into the customer experience gap, 33 percent of customers say they are “very frustrated” when a company sends irrelevant offers – and 37 percent say they will stop doing business with a company that fails to offer a personalized experience. Real-time website personalization meets customer demands for a personalized omnichannel experience, with a server-side approach mitigating performance and security issues that have a potential to introduce friction into a customer journey. ## **Related Content** [Get Real Time with AI Marketing](https://www.redpointglobal.com/blog/get-real-time-with-ai-marketing/) [Master Personalization Capabilities Across Anonymous Touchpoints](https://www.redpointglobal.com/blog/master-personalization-capabilities-across-anonymous-touchpoints/) [Getting Personalization Right in Omnichannel Retail](https://www.redpointglobal.com/blog/getting-personalization-right-in-omnichannel-retail) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Real-Time Personalization --- ### [Key Factors for Multinational Businesses to Consider When Implementing Enterprise Engagement Platforms](https://www.redpointglobal.com/blog/key-factors-for-multinational-businesses-to-consider-when-implementing-enterprise-engagement-platforms/) **Published:** August 25, 2021 **Author:** Mike Ferguson **Content:** “Think Global, Act Local” is a concept that has existed for over 100 years. Very few organisations excel at this challenge. Every couple of months I find myself discussing with a client: “As a marketing technologist, how do I create the perfect local customer experience in a global context?” You might be a global brand with data streaming in from all over the world, or you might be a strong single brand with a global customer reach, the problem is how do we make our customers feel like our message is crafted individually for that customer in their local context? This is the heart of customer centricity. For multinational organizations, enterprise customer engagement platforms need to meet the requirements of **multiple brands** and **multiple regions**. Consider, for example, the sheer scale and complexity of some multinational retail groups, automotive manufacturers and global pharmas that have operations, marketing, IT and data teams all over the world. While there are many benefits of an enterprise-wide approach, when it comes to implementation, the “devil is in the detail.” Some key areas for consideration are: - The balancing of global versus local needs (i.e. a “one size fits all” approach versus an approach that allows for more regional differences – and everything in between) - Privacy and regulatory factors - Cultural and data differences We will explore some of these further and look at some key areas to consider in the implementation of an enterprise-wide platform of this nature. Customer experience is a is key factor, because regardless of the complexity that multinational organizations face, customers will not tolerate customer experience failures. Consider a [PwC study](https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/future-of-customer-experience.html) on the future of customer engagement, where 32 percent of all global customers surveyed said that they will stop doing business with a company after one poor customer experience. In general, organizations with a multi-region, multi-brand scope tend to have quite complex solutions. This makes it especially important to identify the use cases that need to be satisfied at both global and regional levels. The scale and maturity of operations in various regions may also affect the focus in different geographies and necessitate a phased implementation approach. ## **Different Regions, Different Data** From a data standpoint, having to account for customers on a global scale presents its own set of challenges. Global differences to consider include not only different currencies, time zones, languages and dialects but many small and large cultural differences that can vary country to country even in the same geographic region. Date formats (8-15-21 vs. 15-8-21), name and/or address formats (Sr. vs. Señor, family name listed last or first) and channel preference (email in EU, mobile in Africa) are among the key identifiers that vary by region. These factors make it important to work with a provider that can cleanse and verify “multi-country” data and leverage available postal address data as needed for all geos. Other data considerations include: - **Matching**: Working with a provider that can build an accurate view of each customer even when the data is coming from many different sources that can vary enormously from brand to brand and region to region. - **Character Sets**: The need for a double byte Unicode capable solution stack that can cater for varying Western and non-Western character sets. - **Data Model**: Preferable to work with a provider that can hold all the data that is needed over time (rather than one with a fixed data model). Additional flexibility will stem from a common data model with an allowance for some regional variations. - **Types/Volume of Data**: Some regions may have large volumes of anonymous data vs. other regions that have far larger volumes of first-party data. ## **Privacy Regulations** Considering the platform itself and hosting location(s), it is vital to understand the needs of different regions in line with regulatory and privacy legislation. Global organizations must of course account for GDPR and CCPA, as well as other regulations on the horizon, such as Brazil’s Lei Geral de Protecão de Dados (LGPD) and China’s Personal Information Protection Law (PIPL). An organization with operations in neighboring countries, and with customers who transact in both must account for a wider range of options, including different rules governing consent such as the right to be forgotten, opt-in and opt-out preferences, right of access and others. ## **Communication Nuances** Turning our attention to communications, just as with the potential for vastly different formatting, there are cultural regional differences that must be accounted for when engaging with a diverse global audience. - **Channel Preference**: Understanding one channel’s importance vs. another based on region may help the business prioritise the phasing in of different channels. - **Marketing focus**: Marketing teams will likely prioritise their communications strategies differently in different geos depending on variables such as how established a customer base is. Acquisition may take precedence in one region vs. retention or driving a loyalty program in another. - **Language**: An obvious point to get right, but important to understand also that language and dialects may often differ within a single country. With a customer base in Canada, for instance, it would be important to know whether a Quebec resident speaks French or English. - **Time Zone Differences**: Timing of communication is critical – get it wrong and open rates will drop and your customer will miss your message - **Cultural Differences**: A deep understanding of regional cultural differences should be reflected in how an organization drives different copy variants such as tone of voice, imagery and addressing. ## **Data Quality: A Global Undertaking** Comprehensive data quality underpins all the issues that multinational organizations must contend with to “Think Global, Act Local.” Because not all customer engagement platforms treat data quality the same, it is important that global organizations or any large enterprise operating internationally consider only enterprise-grade customer data platforms (CDPs) that embrace the data challenge. This will entail providing data cleansing, matching, de-duplication and governance within milliseconds of data ingestion, preparing it for use and making it instantly accessible in the form of a golden record. A golden record includes everything there is to know about a customer, combining all proxy identities – known and unknown – with a robust long-tail of transactional information, preferences and behaviors. Because a golden record is updated in real time, as data is being ingested while a customer moves through a customer journey, a brand is able to provide contextually relevant personalised engagements in the cadence of the customer, on any channel, throughout an ongoing customer journey. ## **One Chance to do Right for the Customer** So what does it look like when you get “Think Global, Act Local” right? Relevance will account for all the regional differences mentioned here. Customers expect that any organization they do business with takes the time and effort to learn everything there is to know about them, which would of course include language, channel preference, culture and other variances. In a [Harris Poll survey commissioned by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/), 63 percent of customers surveyed said that a personalized experience is now a standard expectation, with 43 percent defining such an experience as a brand knowing they are the same customer across all touchpoints – email, social, online, mobile, in-store, call center, etc. While a poor customer experience may not be intentional, it reflects poorly on an organization that does not give data quality the attention it deserves to account for local or regional differences. Great customer experiences are where the customer feels like they are understood by the brand in that local moment of interaction – we all know that the best examples of this are underpinned by great data quality, and insightful personalization of that interaction. Global organisations that lead their markets deliver that local experience at a global scale. ## **Related Content** [Cadence of the Customer and the Corner Store Experience](https://www.redpointglobal.com/blog/cadence-of-the-customer-and-the-corner-store-experience/) [Perfect Data, Creative Marketing: A Cyclical Flow of Excellence](https://www.redpointglobal.com/blog/perfect-data-creative-marketing-a-cyclical-flow-of-excellence/) [Can Your CDP Deliver Perfect Data? How a Need for Perfection is Shaping the Market](https://www.redpointglobal.com/blog/can-your-cdp-deliver-perfect-data-how-a-need-for-perfection-is-shaping-the-market/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Identity Resolution, Master Data Management, Segmentation & Activation --- ### [Chart a Course for Success: A Customer Data Platform for a Cruise Line](https://www.redpointglobal.com/blog/chart-a-course-for-success-a-customer-data-platform-for-a-cruise-line/) **Published:** April 9, 2024 **Author:** Mike Ferguson **Content:** In the fast-paced world of the cruise line industry, understanding and meeting customer expectations is paramount to success. That is why in recent years the business of attracting guests has shifted from a focus on the destination to the entire cruise experience. Customers expect personalized experiences that span the entire customer journey – pre-trip, the cruise itself and post-trip. To meet this expectation, cruise lines are turning to a [customer data platform (CDP)](http://www.redpointglobal.com/cdp/) to enhance customer relationships, improve operational efficiency and drive growth . Let’s delve into the benefits of CDPs specifically tailored for the cruise line industry. ## **Making the most of First-Party Data** Personalization is key to engaging modern travellers, and CDPs empower cruise lines to deliver highly targeted marketing campaigns and communications. With the loss of third-party cookies for marketing use, it is imperative that cruise lines are making the most of first-party data that is available to them. By leveraging all customer data on past behaviours, demographics, and preferences, cruise lines can craft personalized offers, promotions, and recommendations that resonate with individual passengers. Whether it’s suggesting shore excursions based on past activities or sending targeted loyalty program perks, personalized communication enhances customer satisfaction and loyalty. ## **Unknown to Known Customers** For many cruise lines the guest booking journey starts from being an unknown prospect that visits your website. This unknown-to-known guest journey is a critical phase for cruise lines and the ability to tailor content on your website to the visitor’s interest is what will keep them engaged. Continually promoting Caribbean destinations to an individual who has shown an interest in a tour of Italy is a sure way to turn off your next prospective guest. Relevant content can be used to encourage your unknown visitor to engage further and provide you with the “all important” first-party data that can then be used to continue the customer journey in a more personalised way. Your CDP should be able to provide you with the ability to capture this “unknown” behaviour and append it to the customer record once the guest has made themselves known. At the point the guest has identified themselves the CDP will allow you to further personalise the journey based on information you know about them. As an example, using data insight from previous cruise trips, you can identify that the guest enjoys spa treatments and that they are vegan. This will allow you to tailor your communication and serve up relevant content and offers that are appealing. Generic marketing content can be swapped out based on this knowledge. “Sports activity offers” exchanged for “spa offers” and “steak restaurant” images exchanged for images of vegan dishes. Personalised campaigns will move your prospect toward booking their next cruise with your company vs. a competitor. ## **Excluding booked customers: A cost saving strategy** One of the most straightforward yet effective strategies for saving marketing costs is to exclude already booked customers from advertising campaigns. While it may seem obvious, without the right tools and data insights, cruise lines risk wasting valuable marketing dollars targeting individuals who have already made a booking. By integrating booking data into their CDP, cruise lines can ensure that advertising efforts are directed towards those who are most likely to convert – those who have not yet booked! ## **Optimising Onboard Operations** Beyond enhancing the guest experience, CDPs also play a vital role in optimizing onboard operations. By integrating with onboard systems such as point-of-sale terminals and guest services platforms, CDPs provide real-time insights into passenger activities and preferences. This data-driven approach allows cruise lines to streamline operations, anticipate passenger needs, and allocate resources more effectively. For example, crew members can receive personalized alerts about guest preferences, enabling them to deliver exceptional service and anticipate potential issues before they arise. ## **Enhancing Customer Service and Support** In today’s competitive landscape, exceptional customer service is non-negotiable. CDPs equip cruise lines with the tools to deliver proactive and personalized support throughout the passenger journey. By centralizing customer data and interactions, cruise lines can provide seamless omnichannel support, whether through chatbots, email or onboard staff. Moreover, CDPs enable cruise lines to anticipate customer inquiries and resolve issues swiftly, resulting in higher levels of satisfaction and loyalty. The information collected within the CDP can be utilised post journey to personalise any follow up activity and remind the customer of their great experience and help with customer satisfaction scores. ## **From Legacy Systems to Real-Time Streaming Data** One of the significant advantages of the Redpoint CDP is its ability to integrate data from legacy systems, such as reservation databases and onboard entertainment systems, with real-time interactions. By consolidating data from disparate sources with both fast and slow data, cruise lines can gain a comprehensive understanding of passenger preferences and behaviours across all stages of the journey. For example, data from legacy booking systems can inform onboard dining recommendations, while real-time interactions with onboard activities can be used to exclude customers from promotions of activities they have just booked. This seamless integration of historical and real-time data enables cruise lines to deliver hyper-personalized experiences that exceed passenger expectations. ## **Moving First-Time Cruiser to Repeat Booker** Moving the guest from single to multi-cruise booking is the goal of all cruise lines. By analysing data on guest preferences, behaviour patterns, and satisfaction levels, cruise lines can identify opportunities to enhance the guest experience and foster long-term relationships. For instance, insights may reveal that first-time travellers are particularly interested in certain onboard activities or dining experiences. Armed with this knowledge, cruise lines can tailor promotional offers or loyalty program incentives to encourage repeat bookings. Additionally, personalized follow-up communications post-cruise can reinforce positive experiences and encourage guests to book their next voyage with the same cruise line. Your CDP should also provide you the ability to understand details around group bookings, individuals and households. Understanding information relating to individuals is key for personalisation but marketers will also want to understand how to market most effectively to reach the booker and ensure a consistent message is relayed to all individuals relating to a single booking. Resolving segmentation at individual, house and group level is paramount for this and is something that the Redpoint CDP can provide your marketers. For more information on how Redpoint can assist you in plotting a course for great Customer Experiences visit our website at [www.redpointglobal.com](http://www.redpointglobal.com) or contact **Blog categories:** Travel & Hospitality **Blog tags:** CDP --- ### [Cadence, Scope, Flexibility: Understanding a CDP’s Data Architecture Needs](https://www.redpointglobal.com/blog/cadence-scope-flexibility-understanding-a-cdps-data-architecture-needs/) **Published:** December 14, 2020 **Author:** Redpoint Global **Content:** Albert Einstein once said that if had an hour to solve a problem, he’d spend 55 minutes thinking about the problem and five minutes thinking about the solution. If we hold that same standard to the research and implementation of a customer data platform (CDP), determining data architecture requirements will take up the lion’s share of our allotted time. Data architecture decisions stem from answering a business problem: the business needs customer data for normal business activities such as analytics, decisioning and marketing. In ruminating on the problem, data architecture needs crystallize, forming a clear picture about which solution will best satisfy business requirements. ## **Customer Data Cadence Architecture** The first architecture question to ask is what data cadence the CDP needs to support. Will data have to be updated daily, several times each day or in real-time with SLA response times measured in milliseconds? This question arrives at the fundamental business requirement, which is what purpose the data serve. Is the *fastest* cadence in support of a nightly batch feed for in-store transactions, for example, or in support of [real-time website personalization](https://www.redpointglobal.com/orchestration/real-time-personalization/)? Digging deeper, cadence is also a function of customer data source systems; how quickly can those source systems provide the data? A CDP should be architected to data constraints; if there is no source system returning data in real time, it makes little sense to deploy a CDP to process real-time updates. Unless, of course, additional source systems are brought to bear, which brings us to the third and final function of cadence – the service cost model, with the understanding that handling a nightly batch feed is less expensive than architecting for real-time capabilities. ## **Customer Data Scope** Once a determination is made about the functionality and cadence of customer data, the next consideration is the scope of the customer data. Will the CDP scope include data for known customers, unknown (anonymous website visits and/or prospects), or is it more commonly to support [unknown-to-known customer journeys](https://www.redpointglobal.com/orchestration/anonymous-known-data/) and the management of the entire customer lifecycle? Most organizations contemplating a CDP for the ultimate purpose of competing on customer experience would, I imagine, be interested in the latter, which would encompass managing the lifecycle from discovery (unknown to known) to managing an ongoing/existing customer and finally offboarding. Next, data architecture entails a host of considerations surrounding the [customer data integration](https://www.redpointglobal.com/integrations/) lifecycle. Data capture, data ingestion (supporting all required sources, cadences, and formats) and hygiene and cleansing capabilities must all be managed correctly according to business requirements. Data hygiene and cleansing is often an underappreciated part of the data integration lifecycle, but it is a core requirement for accurate customer data integration and formulating a valid customer [golden record](https://www.redpointglobal.com/single-customer-view/). These, in turn, are the foundation for delivering a hyper-personalized, omnichannel customer experience. Additionally, the CDP must be designed to manage the transformation of source system data resolution levels (typically the account, order or transaction level) to the customer level. When data hygiene, resolution level rectification, matching and keying processes are done in one coherent step rather than piecemeal, the unified customer profile and resulting golden record takes shape. These are essential if the CDPs business goal is to deliver personalized, relevant experiences at the cadence of the customer. ## **Customer Data Format & Stability** Organizations must also understand customer data format and stability, which will determine whether a SQL or NoSQL database is more suitable. Relatively stable and well-structured customer data sources may tilt the scale in favor of SQL, which will provide advantages in terms of skillset, cost and performance optimization. Conversely, if data are variable over time, a NoSQL database will provide advantages in terms of flexibility. The final data architecture consideration relates to technology choices for the database itself pertaining to size, complexity, performance of queries, cost constraints, etc., to determine whether a straightforward SQL database will suffice, or whether BigQuery, Snowflake or another modern cloud data warehouse platform is better suited for an organization’s data infrastructure. But as we learned from Einstein’s less-famous “theory,” that’s a decision that should take up far less time than the up-front decisions having to do with cadence, scope, flexibility and data integration. Because at the end of the day, a CDP is a technical solution to a business problem. A precise documentation of business requirements will shed light on the data architecture questions, and once those decisions are made everything else will fall into place. Arriving at a solution will certainly take longer than five minutes, but as we also learned from Einstein – everything is relative. ## **Related Content** [What is Data Lineage and Why is it Important?](https://www.redpointglobal.com/blog/what-is-data-lineage-and-why-is-it-important/) [Why Data Veracity is the Foundation for a Personalized Customer Experience](https://www.redpointglobal.com/blog/why-data-veracity-is-the-foundation-for-a-personalized-customer-experience/) [The Telltale Signs Your Business Needs a CDP](https://www.redpointglobal.com/blog/the-telltale-signs-your-business-needs-a-cdp/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Data Quality, Single Customer View --- ### [CDP Institute Explores Ascendence of Data Quality in New Whitepaper](https://www.redpointglobal.com/blog/cdp-institute-explores-ascendence-of-data-quality-in-new-whitepaper/) **Published:** January 11, 2022 **Author:** Redpoint Global **Content:** The goal of [data quality](https://www.redpointglobal.com/automated-data-quality/) is not perfect data: it’s data that’s fit for its purpose. Data, in other words, is always evaluated in the context of how it will be used, with no absolute standard. A new white paper from the CDP Institute and sponsored by Redpoint Global, “Data Quality for Marketers” explores the pertinent issues that are important for marketers and other business users to understand about data quality. Specifically, the white paper addresses reasons for the ascendence of data quality, concepts that are important for decision makers to understand, steps and processes for instituting a data quality program, technology’s role, and tips to get started solving common marketing data quality challenges. ## The Ascendence of Data Quality The paper comes on the heels of some startling new research that shows a deep-seated mistrust of data that prevents marketers and other business users from meeting customer expectations for personalized, omnichannel customer experiences. In the most recent Alation “State of Data Culture Report”, for instance, just 43 percent of marketers say they use data to guide their marketing strategies, and 46% of C-level executives choose gut instinct over data sometimes or always. Mistrust stems from several factors, as laid out in the white paper. Among them are an increasing volume of data, people, users and the impact from data being used more broadly across departments. With data being used more broadly, any error is multiplied by the number of processes that depend on data being correct. Forrester estimates that 21 cents of every media dollar is wasted due to poor data quality, to name one tangible financial impact. While technology is one path toward alleviating [data quality](https://www.redpointglobal.com/automated-data-quality/) challenges, the white paper details a roadmap for solving marketing data problems, which include defining data quality requirements, identifying gaps in existing resources, and setting a strategy that includes building a requirements checklist. Click [here](https://www.redpointglobal.com/resources/cdp-institute-report-data-quality-for-marketers/) to download the complete “Data Quality for Marketers” white paper. *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* --- ### [Beyond the Exam Room: Building a Unified View of the Healthcare Consumer](https://www.redpointglobal.com/blog/beyond-the-exam-room-building-a-unified-view-of-the-healthcare-consumer/) **Published:** September 29, 2025 **Author:** Redpoint Global **Content:** Healthcare providers are facing a data problem. They’ve never had more data at their fingertips, yet they still struggle to truly understand their patients. Clinical notes, claims records, and lab results capture the “what happened,” but they rarely provide “what’s next”. Without a real-time patient profile, one that integrates social, behavioral, and engagement signals, providers are left with blind spots that can make it harder to close care gaps, build loyalty, and deliver the kind of experiences patients now expect. Clinical data provides a look into a patient’s care history, but it doesn’t predict future actions or motivations. Factors that influence whether a patient truly engages in their care, including lifestyle, behavior, and communication preferences rarely appear in the EHR, yet they directly shape clinical outcomes. Healthcare organizations that will thrive in a consumer-driven market are those that can look beyond the exam room to bring meaningful consumer insights to create a unified, real-time profile. [![Infographic Beyond The Exam Room](https://www.redpointglobal.com/wp-content/uploads/2025/09/Infographic-beyond-the-exam-room-450x450.jpg)](https://www.redpointglobal.com/resources/beyond-the-exam-room-building-a-360-view-of-the-healthcare-consumer/) High-value patient engagement relies on health systems anticipating needs and guiding patients toward better health decisions, at the right time, through the right channel, and with the right message. When systems prioritize engagement with the correct information, they reap the benefits via improved satisfaction and quality scores, helping to strengthen their bottom line by reducing patient leakage and boosting retention. ## **Why This Matters Now** As AI, predictive analytics, and digital engagement tools accelerate, data readiness has become non-negotiable. Without it, even the most advanced technologies risk producing shallow insights or generic outreach that misses the mark. With it, providers can finally deliver personalized, consumer-grade experiences that patients reward with loyalty and trust. Our latest infographic, [**Beyond the Exam Room: Building a 360° View of the Healthcare Consumer**](https://www.redpointglobal.com/resources/beyond-the-exam-room-building-a-360-view-of-the-healthcare-consumer/), dives deeper into how healthcare organizations can connect the dots between clinical records and consumer realities. Download it today to see how a complete, real-time profile can transform engagement strategies into measurable ROI. You can view the infographic [here](https://www.redpointglobal.com/resources/beyond-the-exam-room-building-a-360-view-of-the-healthcare-consumer/). **Blog categories:** Healthcare --- ### [Build a Comprehensive Customer Understanding Through Perfect Data](https://www.redpointglobal.com/blog/build-a-comprehensive-customer-understanding-through-perfect-data/) **Published:** December 2, 2021 **Author:** Dale Renner **Content:** There is a considerable gap between the experience brands deliver to their customers, and the experience customers have come to expect. In new research from [Harris Poll](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236), in a survey commissioned by Redpoint, marketers were roughly twice as likely as consumers (51 percent vs. 26 percent) to say that brands consistently deliver an “excellent” customer experience. The report explores the various reasons for the customer experience gap, one of which is the fact that consumer expectations are solidifying around receiving a hyper-personalized customer experience in which a brand demonstrates a thorough understanding of a customer as an individual. This is a base-level expectation. In the survey, 39 percent of consumers said they will not do business with a brand that fails to offer a personalized experience. Roughly half of the consumers also said they feel undervalued by brands they interact with. The CX gap has not historically been as pronounced or noticeable primarily because of the predictable way many companies served their customers, combined with a complacency among many consumers. Experiences were largely driven by face-to-face or other single-channel interactions. As such, they were product-driven, and the relationship between a brand and a customer was very much transactional – going to a dealership to buy a car, or a retail store to buy a pair of jeans. The likelihood of an integrated post-sale service experience as part of the customer journey was slim, as was the likelihood of the brand using transactional data to generate a more complete customer understanding meant to improve the customer’s future experiences on the same or a different channel. ## **A Comprehensive Customer Understanding** But it is a new day. Digital transformation is unleashing a proliferation of touchpoints and creating an explosion of hugely valuable data as a result. Customer journeys are far more complex, dynamic and unpredictable than in years past, consisting of a combination of digital and physical touchpoints. Consider a [McKinsey report](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-global-view-of-how-consumer-behavior-is-changing-amid-covid-19) that probed changing consumer behaviors in the wake of the pandemic. Roughly 80 percent of US consumers said they had tried a new shopping behavior (post Covid), with 73 percent saying that they planned to continue the behavior indefinitely. Curbside pickup, buy online and pick-up in-store (BOPIS), and digital returns are among the trends gaining traction. The competitive landscape is rapidly reshaping through superior customer experiences where the customer is at the center of everything. Consumers expect seamless and frictionless experiences irrespective of how they choose to interact with a brand. To satisfy this expectation, brands must harness the wealth of customer data generated from both physical and digital channels, using it to deliver new and exciting customer experiences, and persist those experiences across the entire customer journey. Delivering superior customer experiences consistently starts with having the most comprehensive understanding of each customer and being able to effectively utilize that understanding across the entire enterprise regardless of when or how a customer chooses to engage. The fuel to drive this understanding — perfect customer data — is defined as the most complete, continually updated in real-time and always available data about each and every customer. Many brands fail to deliver superior experiences, in large part, because the quality of their customer data is simply not adequate — incomplete, siloed, latent, over-matched, under-matched, duplicated — making it near impossible to derive the best insights at the cadence of the customer. From the customer’s perspective, inadequate data translates to inconsistent, often frustrating experiences. Irrelevant offers, a lack of cross-channel awareness, or being out of synch with a customer journey are common points of friction that make it appear a brand does not care enough about a customer to communicate with one consistent voice. Unfortunately, these frustrations are all-too common. In the Harris Poll survey reference above, just 49 percent of marketers are “very confident” in the quality of their customer data. Accuracy is a top concern, cited by 34 percent of marketers as the area most in need of improvement to meet customer expectations. ## **An Enterprise-Wide Golden Record** One reason for the disconnects that produce less-than personalized experience delivery is that brands have historically relied heavily on third-party cookies to digitally track customers across the internet, with a misguided notion this approach was an acceptable stand-in for developing a deep customer understanding, and an opportunity to deliver relevant content. But privacy advocates have killed third-party cookies. Next to go are reference files, which are used by the biggest agencies to blend data about people, without their permission, from any number of their clients’ databases. The shifting landscape, far from generating panic, is a wake-up call for data-driven organizations that understand they already possess the best and highest-quality data: first-party customer data. Companies just need to bring it all together — transform, match, standardize, geo-code and build an enduring key — so the identity of each customer persists over time. The result is a pristine and perfected, always-on, always-updated “golden record” that contains everything that is knowable about each customer and can be used enterprise-wide. To achieve this, it’s crucial to have a single point of data control, meaning overcoming data silos and multiple versions of the truth. For the delivery of a seamless, omnichannel customer experience, the data about each customer must be the most complete, freshest, most accurate and timely required to deliver superior experiences and outcomes at the cadence of the customer. A golden record must include both a contact graph containing all of the identities of a person and all of the transactional and behavioral information about a that person. Soon, the overall data quality equation will soon include consent and contracts where value is exchanged between a brand and a customer for use of their data. From the perspective of a marketer, individual customer data, its purpose and the length of time the data can be used are all necessary components that comprise the golden record — whether this information sits in a customer database or in a PII Vault. Conversely, a brand that uses or sells customer data against a customer’s stated preferences will destroy the value exchange, erasing any built-up trust and goodwill. In the Harris Poll survey, 66 percent of consumers said they are willing to give brands more information about themselves if they use it to create a more valuable customer experience. Honoring consent preferences and being a good data steward builds trust and enhances the value exchange. ## **The Bottom Line** The good news for brands ramping up digital transformation initiatives to deliver an omnichannel customer experience is that they have the opportunity to capitalize on tremendous revenue growth and it starts with having perfect data. An Edelman report, [Trust Barometer: Brand Trust in 2020](https://www.edelman.com/research/brand-trust-2020), reveals that only 46 percent of consumers trust most of the brands they buy. If just 46 percent of consumers trust the brands they interact with, that means that more than half are open to becoming a loyal customer to a brand able to build and persist a trusting relationship through perfect data. As the Harris Poll research clearly shows, loyalty is earned by brands that demonstrate a thorough understanding of each customer. In the survey, 82 percent of consumers (up 5 percent since 2019) said that they are loyal to brands that demonstrate a meaningful understanding of them as a unique customer. For some measure on what this loyalty is worth, [McKinsey research](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) reveals that companies that excel at personalization generate 40 percent more revenue from those activities than those that don’t. The study suggests that shifting to top-quartile performance in personalization would generate over $1 trillion in value across US industries. To make the shift, brands need to recognize that the key to driving transformational change and a differentiated customer experience lies in having the most comprehensive understanding of each and every customer in each and every interaction. Customers expect and deserve nothing less. **Blog categories:** Data Quality, Identity Resolution, Master Data Management --- ### [Beyond Transactions: Do You (Really) Know Your Retail Bank Customers?](https://www.redpointglobal.com/blog/beyond-transactions-do-you-really-know-your-retail-bank-customers/) **Published:** November 5, 2025 **Author:** Renee Graff **Content:** A regular cadence of deposits, withdrawals, balances and statements make up a large part of a retail bank customer’s monthly activity and their relationship with the bank. But while the number of transactions, average monthly balance, credit scores and loan activity all contribute to understanding a customer and metrics such as lifetime value, transactions alone tell only part of the story. Because customers now attach value to the personalized experiences they receive, knowing a customer almost exclusively through transactional data is no longer enough. This limited view doesn’t translate into the seamless, consistent personalization customers expect across both physical and digital channels. Of course, none of this lessens the importance of maintaining clean, accurate transactional data. But to truly understand each customer, banks must also connect all available signals, including behavioral, demographic and contextual, into a unified, living customer profile. Only by blending transactional accuracy and a broader data foundation can banks meet customers where they are, with relevant and timely experiences. ## **The Dynamic Bank Customer** According to a recent ABA Banking Journal survey, only about half of retail banks utilize a CRM system, despite its foundational role in equipping bankers and marketers with a full picture of each customer across deposit, loan, wealth, and digital channels – which in turn leads to effective needs assessment and added revenue. It’s important to note that while powerful, a CRM serves best as the frontline for managing customer relationships and interactions, provided it’s fueled with the right data. Yet CRMs weren’t designed to aggregate large volumes of raw data from multiple systems, resolve identities across channels or devices, or continuously refresh profiles in real time. For that, banks need a data readiness engine working behind the scenes to power the CRM’s unified customer profiles. There’s also a [social revolution under way](https://sociallyin.com/resources/social-media-strategy-for-banks/). Banks now rely on Instagram, Twitter, LinkedIn, and Facebook to deliver the kind of personalization that used to be the exclusive domain of bank tellers, such as helping first-time homebuyers understand mortgage options through video content. AI-driven experiences are a newer but common way for customers to interact, carrying their own expectations for personalization via a chatbot or other natural language applications. Customers may start with account balance checks or other transaction questions, but pivot to product and services inquiries, or help with forms, tax returns, and other guidance. ## **A Complete Customer Understanding** For an AI interaction or social media interaction to be relevant in the moment (e.g., when the customer is scrolling Instagram, or starting an online chat) banks must treat these channels as not just engagement channels, but as important data sources that help enrich a deep, personal understanding of each customer. Take the video snippet for first-time homebuyers: the more signals the bank gathers about a customer or their household, the more relevant that video can be. Those signals can also reveal which products to recommend or what images to show to a first-time website visitor. Has a customer been searching for mortgage information? Are there multiple devices linked to the same household conducting similar searches? Is that second device linked to a different profile? Did a customer reach out to customer service asking for mortgage information to be mailed – and is the physical address linked to a unique profile? These insights create situational awareness – but only for the retail banks that have an ear to the ground. Being relevant in the moment requires a [complete understanding](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/) of the customer, which depends on data readiness. Data quality is central to that readiness. A unified customer profile must be [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/) – but accuracy on its own doesn’t reveal a customer’s journey, life stage or retirement goals. Context, such as household relationships and financial milestones, transforms clean data into actionable understanding. ## **The Benefits of (Really) Knowing Your Customers** A complete customer understanding that transcends transactions delivers measurable benefits. Reduced churn, better retention and stronger loyalty all stem from matching customer context with personalized offerings. Understanding needs and behaviors triggers the switch from personalization being a guessing game to a data-backed strategy with hyper-relevant product recommendations. Banks that maintain a real-time, consistently updated unified profile provide better customer service, both human and AI-driven alike, and align every interaction and product recommendation around the customer’s current situation. When a chatbot has the full picture, misguided recommendations and repetitive questions become a thing of the past. According to Gartner, a primary objective for bank CIOs this year is to grow revenue through customer experience excellence, driven in large part through advanced data capabilities. Achieving that requires data readiness – the ability to maintain a complete, accurate and contextual understanding of every customer. For more on understanding retail bank customers, please download the infographic, [Do you Really Know Your Retail Bank Customers? ](https://www.redpointglobal.com/resources/do-you-really-know-your-retail-bank-customers/) **Blog categories:** Financial Services **Blog tags:** Data readiness, identity resolution --- ### [Use Cases for Best-in-Class First-Party Identity Resolution](https://www.redpointglobal.com/blog/use-cases-for-best-in-class-first-party-identity-resolution/) **Published:** March 29, 2024 **Author:** Redpoint Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Blog categories:** Identity Resolution --- ### [Get at the “Why” for True Personalization with Automated Machine Learning](https://www.redpointglobal.com/blog/get-at-the-why-for-true-personalization-with-automated-machine-learning/) **Published:** March 23, 2021 **Author:** Redpoint Global **Content:** Pandora set the standard for music streaming when it debuted the Music Genome Project two decades ago. The algorithm famously organizes more than 450 attributes to determine a song or artist a listener may enjoy. Until recently, the app let a listener know *why* a certain song was selected, offering a peek behind the curtain. Level of electric guitar distortion, use of groove and type of background vocals are among the characteristics to let a listener who has “liked” dozens of Johnny Cash songs why Sturgill Simpson is now playing. The algorithm – and others like it, including the immensely popular Spotify – is far more sophisticated than other music streaming platforms, which may throw together seemingly disparate artists just because they belong to the same decade. As any music lover will attest it can be frustrating when the algorithm misfires and plays a song you despise – especially if you’re paying a premium subscription fee. Often, this occurs because as sophisticated as the algorithm may be, it usually includes a level of group think, especially for newer users. Guitar distortion aside, it recommends a song/artist because another listener who generally likes the same type of music also liked the new song/artist. What if, though, by knowing more about the *why,* an organization can then return that data back into a clustering model to perfect what song, what message, what content, what offer, a customer is exposed to? That, in a nutshell, is a true personalized approach as opposed to the group think approach that defines the bulk of [personalization machine learning](https://www.redpointglobal.com/blog/get-at-the-why-for-true-personalization-with-automated-machine-learning/) options available today. ## **Let the Data Speak for Itself** The automated machine learning (AML) component in Redpoint’s rg1 customer experience platform is unique in that with its integrated systems and systematic automation approach, it offers a level of stimulus/response pairing and a closed-loop feedback that allows for unmatched agility and flexibility. It gets to the why, in other words, eliminating the need to water down personalization by relying solely on look-alike audiences, assumptions or otherwise arbitrary associations. For a marketer to be able to determine why someone was put into a certain cluster is a powerful approach. Music aside, creating age buckets is a familiar example to most marketers. Often, those buckets are created using assumptions – women 18-35 will like X – and there is no deviation from the rule, or more precisely the static list. Conversely, a data-driven AML model that creates clusters based on data-driven information dials it up a notch, generating more trust in the associated outputs. But when a marketer can see *why* an individual was put in one bucket instead of another is the secret sauce, if you will, that allows marketers to deliver a next-best action – matching products or offers to an individual customer with a high degree of personalization while also optimizing business goals. The approach lets the data speak for itself; the data determines what’s important rather than make assumptions that may or may not be valid. It debunks the common misconception that data is the same as information, or that information must by definition, be pertinent to what matters to an individual customer. An appreciation that data is what provides insight, and that insight is the key to understanding informs the stimulus-response. Because the rules can be derived from the data and not hard-coded, marketers can easily extract, examine, and orchestrate messaging to their customers with minimal presumptions. This ability to use the response to a stimulus (the message/content/offer) in a closed-loop feedback cycle usually must rely on data scientists, who of course, typically have to take the models offline, analyze the data, and build new models. The problem, of course, is that customer journeys are dynamic. A hyper-personalized [experience](https://www.redpointglobal.com/blog/commercial-excellence-begins-with-customer-experience/) that matches the customer’s expectation for a frictionless engagement must be in the cadence of the customer. Cadence may be in real time, seconds, minutes, hours or days – but that’s up to an individual customer journey. This highly time-consuming, manual building (and refreshing) process of offline models is simply not cut out to keep up with today’s dynamic customer journeys. ## **Chain ML Techniques for Powerful Insights** Practical AML use cases broaden when clustering models are continually optimized based on data. Tying multiple machine learning techniques together can realize even deeper insights. For example, labels generated via clustering models can be used to generate fully data-driven rules. We can conceptualize this as a decision tree with as many branches as a marketer chooses to work with. Each branch point represents a decision point that provides insight into why a customer is in a certain cluster. It is essentially a window into a unique customer journey. The customer is here because he’s single, he’s 37-years-old, he lives in the Southwest and he has expressed preferences for jeans, cowboy boots and loud sports shirts. The information inside the cluster allows the classification models to determine the dynamic if/then/else rules that match an offer to the customer. Another powerful use case for tailored AML modeling is website product recommendation system that is truly personalized and not dependent on a ‘group-think’ approach (i.e., common collaborative-filtering). In a personalized approach, for example, fully-personalized AML recommendations can be used to expand a customer’s horizons, moving away from a simple recommendation system based on past transactions and behaviors of so-called ‘similar’ customers. In a basic recommendation system – akin to a lousy music streaming service – customers are often presented with a product almost identical to one they’ve already bought, a frustrating experience. Instead, recommendations are importantly based on individual characteristics, stated preferences, and historical behaviors, such as time on page, images viewed or clicked on, frequency, seasonality, weather, etc. The approach does not require transporting other customers’ intents, purposes or preferences onto someone else who may or may not be similar. Another common fault with many product recommendation systems is to make a false assumption that a purchase must always indicate a preference. Personalized AML technology allows for fine-tuned weighting of various attributes, such as discounting a purchase that is made around the holidays as reflective of the buyer’s preferences if it was bought as a gift. But again, the data lets us know the cluster make-up – not an assumption. ## **Not Sure? Start with a Single Use Case** One advantage of the integrated systems approach and the stimulus-response method with Redpoint is that it facilitates a crawl-walk-run approach that allows marketers to break free from previous misconceptions about the power of machine learning and quickly grasp the transformational power of keeping models current by continual re-learning, and utilizing feedback systems to adapt to changes in the data. By starting out with a website product recommendation engine, to name one use case, marketers can easily see for themselves how feeding response data back into a model – without having to rely on data scientists – determines the why with more precision the smaller the segment. With the ‘why’ marketers have the insight and the understanding that results in a next-best action for an individual customer journey and a superior, personalized [customer experience](https://www.redpointglobal.com/blog/commercial-excellence-begins-with-customer-experience/). ## **Related Content** [What is Automated Machine Learning](https://www.redpointglobal.com/blog/what-is-automated-machine-learning/) [Evolutionary Programming: The Survival of the “Fittest” Data Models](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/) [Beyond the Hype: Put the Power of AI into the Hands of Marketers](https://www.redpointglobal.com/blog/beyond-the-hype-put-the-power-of-ai-into-the-hands-of-marketers/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Real-Time Personalization, Segmentation & Activation --- ### [Drive Success with Customer Data in the Automotive Industry](https://www.redpointglobal.com/blog/drive-success-with-customer-data-in-the-automotive-industry/) **Published:** April 4, 2023 **Author:** Mike Ferguson **Content:** The automotive industry is a highly competitive market, with numerous brands vying for the attention of potential customers. With the proliferation of digital marketing channels, it has become increasingly important for automotive companies to leverage data to personalise their marketing campaigns and drive customer engagement. Customer data is siloed across manufacturers, dealers and partners and also between online and offline channels, making it very difficult to get a full picture as to where a customer is in their purchase journey. This is where a customer data platform (CDP) can play a vital role in optimizing marketing efforts. A robust, enterprise-grade CDP integrates customer data from all departments such as marketing, sales and service and various sources such as website, CRM, call centre, service desk, social media, etc. It [collects, cleanses and unifies this data](https://www.redpointglobal.com/data-quality-and-data-ingestion/) to create a single customer view or ‘Golden Record’. This view includes information such as purchase history, demographic information, behaviour on the company’s website, and interactions with the brand across various channels. As a result, the automotive marketer can use this data to: - Understand who are the high lifetime value customers - Identify which channels are most likely to drive conversion - Personalise web content in real-time to drive conversion - Suspend marketing to customers that currently have their car in for repair - Personalise offers that are most relevant to the audience – family vehicle vs sports car - Improve ROI from ad spend and social marketing: - Remove customers from targeted ads if they have recently purchased - Target lookalikes from your customer base with similar interests / demographics - Create upsell and cross-sell opportunities ## **Personalised – Inside and Out** Today’s car buyer has the ability to move along their purchase journey without ever stepping foot in a car showroom. They can configure their ideal car online, personalising it with every option available from paint colour through to the upholstery material and stitching. The data captured within these car configurators is a goldmine for sales and marketing. The customer is sharing with you their wants and desires. This information should be used to personalise their experience with your brand. Something as simple as recognising the car model they are interested in and ensuring that is the image they see when they return to your homepage shows the customer you understand them. The customer’s online experience should be as personal as a visit to the showroom. You wouldn’t expect a sales associate to approach someone viewing a 2-seater sports car and lead them back across the showroom to show them a family estate, but this is exactly what many automotive websites are doing. ## **Collaboration Improves the Customer’s Journey** The CDP should be your central point of control for all data and operations, not just something for the data scientists. Valuable customer insight can be shared by all departments – marketing, sales, finance, service – with each department benefiting from the data contributed as a whole. Imagine the scenario – A customer has booked their car in for service; a machine predictive model within the CDP has flagged the customer as prospect for a new car purchase. The sales department is notified. They gather information from marketing that the customer has recently used the online car configurator and has saved his selection. The salesperson checks availability of the same model within the demonstration carpool. The salesperson then contacts the customer and suggests booking a test drive while they wait for their car to be serviced. Marketing sends an email the day before the service with a promotion relating to the customer’s configured car. All departments working in unison to provide the customer with the best possible experience and the highest probability for a new sale. Data quality is also a key factor in ensuring you build an accurate view of your customers. Pulling all of your customer data into one place is only half the challenge. Once the data is there it needs to be cleaned, enriched, validated, matched and merged. The CDP should have the ability to use both deterministic matching and probabilistic matching to ensure the best possible match across online and offline data. ## **Accelerate the Customer Journey with Real-Time Actions** Many customers will be unknown to you as they visit your website, but this should not prevent you from beginning the personalisation process. Simple A/B testing of content on your home page can be utilised to see which content gets the best traction. Further to this as the unknown visitor browses your website the home page can be changed in real-time to reflect the content that they have shown an interest in – keeping the customer engaged and demonstrating you understand them. Personas can be identified through the car models and content they interact with, and this information can be used to begin to personalise their experiences. Content and images relating to their persona will keep the customer engaged and help them visualise the experiences they will have with your product. The CDP can persist this digital footprint until the customer identifies themselves. At this point the CDP unifies the unknown browsing behaviour with the customer Golden Record, which allows the marketer to go from Persona marketing to Hyper personalisation. The CDP should also incorporate automated machine learning which will allow you to utilise all that is known about the customer such as previous purchases, service history, marketing interactions, etc. and then build models such as lifetime value, propensity to purchase, car model that they are interested in to make sure the Nnext-best action is relevant and timely. Understanding your customers from a B2B perspective is also key for an automotive company when targeting individuals with the right type of content. A fleet manager may be more interested in fuel economy, service intervals and load space whereas the company car driver would be more drawn to the interior comfort, audio spec and car aesthetics. Being able to resolve customer identities at individual and business level is key to ensuring you are targeting individuals with the most relevant content. As our cars become smarter, the data we collect becomes richer. The IoT is allowing us to connect personal devices with our cars and our cars are beginning to know what we want to listen to, where we want to go and what temperature we want the interior to be. They are also feeding back information on engine performance, tyre wear and miles available until a charge top-up is required. The data captured can be used to improve the customer experience as well as provide valuable feedback to the manufacturer including: - Alerts for the customer of potential problems with the vehicle - Notifying service centres of worn parts that need replacing at next service - Feedback to the manufacturer for product design Redpoint is a leading CDP provider that will help you accelerate your plans to improving customer experience and ensure your brand is in pole position when it comes to your customers’ next car purchase. **Blog categories:** Real-Time Personalization --- ### [Aubuchon Hardware Embraces Cloud-Based Personalization in Next-Gen Marketing Push](https://www.redpointglobal.com/blog/aubuchon-hardware-embraces-cloud-based-personalization-in-next-gen-marketing-push/) **Published:** October 27, 2020 **Author:** Redpoint Global **Content:** Driving customer engagement in a traditional retail environment is a challenge facing retailers of all sizes, with a sustained push from customers to have personalized experiences across an omnichannel journey. A growing customer preference for online and other digital interactions has accelerated the challenge. One of the largest independent hardware store chains in the US, [Aubuchon Hardware](https://www.hardwarestore.com/), has been focused on delivering omnichannel personalization for some time. In late 2018, Aubuchon selected Redpoint Global to implement a robust customer engagement platform to drive innovation and growth. Aubuchon initially selected Redpoint to help modernize and scale its marketing efforts, as the independent chain looked to offer a differentiated, more personalized customer experience than big box stores – engaging with customers on a local, personal level. The company utilizes the [Redpoint CDP](https://www.redpointglobal.com/) solution to deliver a single customer view of data, driving expanded digital engagement and customer loyalty with an increasingly personalized customer experience. Founded by William E Aubuchon in 1908, Aubuchon Hardware is a fourth-generation family-owned chain of independent hardware stores. With more than 100 stores in New England and Upstate NY, the company is focused on not only sustaining, but modernizing the local hardware store shopping experience by providing the exact items local customers need, at affordable prices, with the convenient shopping options and friendly, knowledgeable service customers expect. ## **Single Point of Control** The old way of doing marketing has also been discarded. Aubuchon has virtually abandoned traditional item-priced marketing (such as flyers) and has discovered that automated campaigns tend to have significantly higher response rates than traditional marketing campaigns. A small player in a competitive marketplace, technology is key to building scale advantage. Redpoint aggregates all customer data in a way that makes it available for Aubuchon’s advanced marketing purposes. The customer record enables deep insights and becomes actionable to improve customer relationships, understand attribution of sales and drive expanded revenue growth. Today, Aubuchon can meet customers’ e-commerce needs, improve its loyalty program, and offer a more personalized buy online, pick-up in-store (BOPIS) experience. COVID-19 actually brought tremendous [growth](https://www.cnbc.com/2020/08/07/pandemic-home-remodeling-is-booming-what-your-neighbors-are-doing.html) to the hardware industry in 2020 with a significant uptick in home improvement projects, and Aubuchon outpaced the industry average. “Having all customer data available to us in one place, with the confidence that it is accurate, timely and comprehensive, has been the biggest asset of partnering with Redpoint,” said Will Aubuchon, CEO of W.E. Aubuchon Co. Inc. “Throughout the pandemic, we have been able to communicate with customers quickly and effectively about our curbside pickup service but also customize communications for their specific needs at a scale not possible before. With our data accessible from one place, we could easily react to any situation that evolved. We recognize that we’ve only just scratched the surface of what we can do with Redpoint and I’m excited about the additional growth we can drive — maintaining our local community feel, while becoming the modern marketing organization consumers expect.” ## **Loyalty Drives Growth** “It’s always easier to sell to an existing customer,” contends Aubuchon. “In a competitive marketplace we understand that creating real and lasting relationships with customers is more important to our long-term growth than just pushing to get new people through the doors. With Redpoint helping us gain control of customer data, we know better than ever before what people want and need and with intelligent orchestration capabilities of the platform, we can personalize offers and information at scale in a way never possible before.” Aubuchon’s 15-year-old [loyalty program](https://www.hardwarestore.com/membership) has been a key area of engagement — driving seven of every 10 transactions. With Redpoint, Aubuchon can take its loyalty program to the next level, capitalizing on this opportunity with more personalized customer communications that were previously not possible. The retailer now has a simplified way to review, analyze, and develop marketing programs to grow share of wallet and encourage loyalty to the brand. As a result, the average order size has grown in conjunction with the frequency of in-store visits. It was found that members who have signed up over the last 12 months and utilized BOPIS capabilities spend an average of 68 percent more than those who have not used online shopping. “Consumers today expect convenient, personalized, and real-time interactions with the brands and retailers they engage with,” continued Aubuchon. “Redpoint is helping us bring scale around something that is complex. Our vision is ’local matters more.’ People want local stores to succeed, but not at the expense of giving up modern experiences as a consumer. We want the trusted feel of a local store with the same high-tech omnichannel capabilities that have quickly become the norm. That’s where we’re going as a company, building that modern operating system to meet modern-day expectations, and Redpoint is critical to that.” ## **Looking Toward the Future** Deploying Redpoint’s rg1 solution in a cloud deployment model supports Aubuchon’s vision of building a modern operating system for local hardware stores. [W.E. Aubuchon Co. Inc](https://www.redpointglobal.com/blog/aubuchon-hardware-embraces-cloud-based-personalization-in-next-gen-marketing-push/). is the parent company for Aubuchon Hardware, Lyndonville Hardware, and others. As the retailer looks to expand, hosting applications in the cloud is pivotal to ensure a future-proof strategy that enables scale and minimizes complexity. Redpoint plays an instrumental role in Aubuchon’s expansion and growth for more sophisticated replenishment campaigns, machine learning modeling, and more. The company takes a holistic view of marketing, looking at the lifecycle of the customer and finding ways to connect and personalize for each customer (starting with loyalty members). Based on the data in the system, Aubuchon can push consumers the appropriate information based on any number of factors (time of year, sales, or specific customer preferences). Replenishment campaigns alone will have a tremendous ability to grow a customer’s lifetime customer value – possible with any type of product with a periodic reordering cadence. “There is simply so much we can do with [Redpoint](https://www.redpointglobal.com/blog/redpoint-global-and-lucerna-health-join-forces-to-advance-consumerism-in-healthcare-for-payers-and-providers/), but I view this partnership to just be in the early stages,” finishes Aubuchon. “We could leverage machine learning to expedite insights, deepen personalization capabilities with third-party data and a variety of other marketing campaigns. Having our data in order is the first and most significant step. As we expand with more brands and drive new campaigns in the next year, we will be sure to see added growth as a result. There is so much opportunity for us, and Redpoint is absolutely critical for this growth, now and in the coming months and years ahead.” **Blog categories:** Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization, Retail, Single Customer View --- ### [Balancing Goals and Capabilities for the Aspirational Marketer](https://www.redpointglobal.com/blog/balancing-goals-and-capabilities-for-the-aspirational-marketer/) **Published:** February 7, 2022 **Author:** Redpoint Global **Content:** The expression to be “over one’s skis” has a mostly negative connotation describing someone as hasty or rash, ahead of where one needs to be. The expression omits certain nuances, however. When on the slopes, to be ahead of one’s skis also entails a certain fearlessness. To attempt steep terrain that may be beyond one’s ability requires confidence, ambition and a willingness to fail. The analogy holds true for many marketers; supremely confident in their abilities, they’re not afraid to escape their comfort zone. Their aspirations make them willing to take on a certain amount of risk, even if the reality is that their ability and the technology on hand will not support what they’re trying to accomplish. They may accurately be described as being over their skis. Hasty, perhaps, but also with good intentions and not willing to be held back by organizational or architectural limitations. A recent [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) commissioned by Redpoint sheds light on the challenge for marketers intent on providing an omnichannel customer experience, yet faced with a complex technological landscape. In the survey of U.S. marketers, 67 percent said that technology has made it harder to effectively engage with customers, with 77 percent claiming that the number of customer engagement systems (an average of 20 deployed) make harder to provide a seamless customer experience (CX). The ambition and drive to provide customers with superior, personalized experiences is there; it’s technology that acts as a restraint. ## **Stay the Course** Before exploring a solution to the problem, it’s worth understanding potential consequences when ambition outpaces the current state of maturity of a martech stack, or the marketing programs that an organization’s technology and strategy is able to deliver. If the objective is, say, cross-channel awareness with real-time decisioning so that a customer will always receive hyper-relevant content with a consistent brand voice, marketing should be cautious about overpromising and underdelivering. Otherwise, campaigns, strategies and even architectures may be implemented against a reality that doesn’t exist, resulting in inconsistencies, complexity and frustration. Think of a skier careening downhill, flailing in vain to regain control. Marketers often take the plunge because they harbor a misguided notion that they’re behind their peers. They’re aware that customers increasingly demand personalized, omnichannel experiences, and they think every other company is regularly meeting this expectation. The truth lies somewhere in the middle. Consider a recent [S&P Global/451 Research report](https://www.spglobal.com/en/research-insights/featured/cx-leaders) on the state of digital transformation, particularly with a focus on CX as a “catalyst” for many digital transformation projects. In an analysis of digital transformation growth, even with a significant rise in “digitally driven organizations” from 2016 to 2020 (from 29 percent to 54 percent), 47 percent of companies remain “digitally delayed” (down from 71 percent). Furthermore, a full 28 percent of organizations still either have no digital transformation strategy or are considering it but are without concrete plans. ## **Balance Goals with Readiness** Clearly, not everyone is where they need to be – or want to be. But a better strategy for providing customers with consistent, relevant [omnichannel experiences](https://www.redpointglobal.com/omnichannel-personalization/) without flailing about is to make an honest assessment of existing capabilities. A true accounting of maturity levels will help close the gaps between capabilities and outcomes by forcing marketers to focus on attainable goals. Develop a keen understanding of the technology at hand and its contributions to customer engagement. Understand how systems work together. Test and learn. Don’t try to ski the whole mountain when you can you start with a small audience, perfect personalization for one campaign or one channel and move on from there. Focus on the data quality or shortening the gap between insight and orchestration to lay the foundation for the growth you aspire to. Smart marketers may sometimes go over their skis, but they also knew when to pull back and how to regain control by being honest about their capabilities. Doing so, they enjoy continual – if incremental – improvements that better prepare them for the next challenge ahead. Every company that has a brand-customer dynamic is interested in making CX gains. While it’s encouraging that there’s growing recognition of the importance of a personalized, omnichannel CX, objectives must be tethered to reality. If the goal is to employ data-driven insights to create meaningful, personalized experiences for customers, it’s okay to acknowledge that you’re not quite there. For many Redpoint prospects, conversations about how to kick off an omnichannel CX strategy begin with Redpoint’s [open garden architecture](https://www.redpointglobal.com/resources/innovating-customer-engagement-through-an-open-garden-approach/) as a starting point, as it enables organizations to begin thinking differently about how to drive CX improvements using their existing architecture without an expensive rip and replace. Arriving at a seamless, omnichannel CX with a consistent brand voice irrespective of channel does not happen overnight. Going from a “digitally delayed” to a “digitally driven” organization may take many months. Successful companies do a pretty good job of recognizing exactly where they’re at, and they take the time to formulate a plan for how to get there. In skiing, it’s called choosing your line. It rewards confidence, without the recklessness. And when accolades roll in for doing it right, with proof in the form of customers delighted by a consistently personalized CX, smart marketers will know that the glory was earned. ## **Related Content** [Show Me: The Importance of Proving Out a CDP Use Case](https://www.redpointglobal.com/blog/show-me-me-the-importance-of-proving-out-a-cdp-use-case/) [Marketing’s Star Turn: How to Secure Enterprise Buy-In For a Customer-Centric Approach](https://www.redpointglobal.com/blog/marketings-star-turn-how-to-secure-enterprise-buy-in-for-a-customer-centric-approach/) [Now is the Time to Set Ambitious Marketing Goals](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/) --- ### [The Role of AI in Adding Value a Human-to-Human Interaction](https://www.redpointglobal.com/blog/the-role-of-ai-in-adding-value-a-human-to-human-interaction/) **Published:** May 4, 2022 **Author:** Steve Zisk **Content:** We have all likely had a poor customer experience that in some way, shape, or form involved artificial intelligence (AI). One example familiar to many is an interactive voice response (IVR) system asking a caller a series of questions – often to resolve a customer service issue – followed by an interminable wait on hold, followed by a connection to a call center agent who invariably asks the same questions the IVR already captured responses for. It is no wonder that according to research from Vonage, [61 percent of consumers](https://www.prnewswire.com/news-releases/vonage-research-reveals-ivr-horror-costs-businesses-262-per-customer-each-year-300925404.html) say that IVR makes for a poor customer experience, with frustration, stress, and anger cited as commonly triggered emotions. If the purpose of AI in the customer experience realm is to facilitate or simplify a human-driven experience – to mediate the intersection of employee and consumer processes – then the IVR example above accomplishes the opposite. Yes, the IVR software captures important customer data, but the duplicative process ignores the customer at the heart of the process, and introduces friction into the customer journey. ## **AI at Your Service** Done right, the use of AI in a human-based customer interaction can reduce friction, but to make this happen, the right data need to be available, the right scripts or activities must be built into the process, and the AI should help move the process along to a favorable outcome, either through a next-best action or decision, or by learning (and then reusing) an outcome that improves the customer’s level of satisfaction. In most typical, real-world processes, other issues will crop up. For another example, an encounter with a hotel front desk associate is a common interaction – either face-to-face, over the phone, or even through a chatbot. In this case, a customer data record barely scratches the surface for the information an associate will need to resolve a customer issue. For a room change or to book a spa appointment or tee time, for example, an associate will need access to inventory, availability and pricing, be able to book and update the systems of record, and ensure everything is updated in the customer record to assist both the current customer and future customers who may make similar requests. In-store clienteling offers a similar data mining exercise that should similarly be frictionless as part of the process of satisfying the customer and offering the customer a seamless, personalized experience. Is a product available in the customer’s size or color preference? Is the associate able to immediately offer five related products that may be of interest to the customer, that are also in the right size and color and currently stocked? If a product is out of stock, is it available at another store or for direct shipment to the customer’s home? Depending on the use case, AI can enhance a customer experience in multiple ways that should all feel organic to the customer. For each use case, the baseline requirement for incorporating AI into a process is to ensure a tight connection between whatever part of the process AI facilitates, any other systems the process touches, and the humans involved in the process. This baseline requirement is also true for other interactions that may indirectly involve a customer, such as AI telling a company or brand something about sentiment, intent, or customer status when a customer sends an email, answers a question in a chatbot, or has posted a message on social media. ## **Add Value, Behind the Scenes** Beyond use cases for deriving intent, gathering data (such as a clienteling app finding similar product recommendations) or having an interaction with a customer (as in IVR), AI more broadly can help with the decision-making part of the process. If we think, for instance, of a front desk associate at an upscale hotel, AI can either make the decision for the associate or generate recommendations that an associate can choose from. Ultimately, the method should be whichever helps reach the desired outcome with the least amount of friction. A customer likely will have little patience for an associate having to bounce between four or five applications on multiple screens, the classic “armchair integration” situation. In addition to helping with decision-making, AI can also be used to analyze the decision for future human-to-human interactions. Did this specific decision move the customer along in their journey appropriately? Was the customer’s sentiment, stickiness, or loyalty improved by the human-to-human process? In this context, AI can look at every detail of the interaction and put it back into the customer record, to improve both the individual journey and to learn how to enhance future journeys. At its best, the use of AI in a human-driven interaction may not even be immediately apparent to the customer on the receiving end. A customer may simply see that their issue was expertly resolved, their needs perfectly met, and end up with a deeper appreciation for a brand that always seems to know what matters to them as a customer. ## **Related Content** [Get Real-Time with AI Marketing](https://www.redpointglobal.com/blog/get-real-time-with-ai-marketing/) [Beyond the Hype: Put the Power of AI into the Hands of Marketers](https://www.redpointglobal.com/blog/beyond-the-hype-put-the-power-of-ai-into-the-hands-of-marketers/) [The Role of Collaboration in a Human Approach to AI](https://www.redpointglobal.com/blog/the-role-of-collaboration-in-a-human-approach-to-ai/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Journey Orchestration --- ### [The Data Product Contract: Why Your Data Strategy Needs a New Roadmap](https://www.redpointglobal.com/blog/the-data-product-contract-why-your-data-strategy-needs-a-new-roadmap/) **Published:** February 12, 2026 **Author:** Steve Zisk **Content:** A few years ago, the Commonwealth of Massachusetts made a decision that sent thousands of commuters into a tailspin. To comply with federal mandates, they overhauled the highway exit numbering system. On 1-95, for example, Exit 14 became Exit 28. Drivers relying on their onboard GPS systems discovered that the digital reality didn’t match the physical signs, resulting in confusion and frustration, not to mention many lost drivers. In the tech world, this is referred to as schema drift. But in the real world, it’s a broken promise. A “contract” between the GPS provider and the driver that stipulated a real-time update for such as predictable change would have saved countless hours of grief. Today, enterprise data is facing its own “Mass Pike” moment. As companies rush to fuel AI engines, they’re realizing their data isn’t just disorganized, it’s unreliable. This is why the **Data Product Contract** has moved from a “nice-to-have” technical spec to a strategic imperative. ### **The High Cost of “Guesswork” Data** Treating data as a byproduct rather than a product is a recipe for failure. The statistics are sobering: - **The 80/20 Trap:** Data scientists still spend up to **80 percent of their time** simply cleaning and organizing data rather than analyzing it. - **The Trust Gap:** According to recent industry surveys, only **25 percent of executives** fully trust their organization’s data. - **The AI Failure Rate:** Gartner has previously estimated that nearly **80 percent of AI projects** fail to reach production, often due to poor data quality and a lack of clear requirements. For a company that views [data as a strategic asset](https://www.redpointglobal.com/data-the-defining-difference/), “close enough” is no longer enough. You need a contract that ensures **readiness, fitness, and timeliness.** ### **Core Principles: Building the “Smart GPS”** A data product contract isn’t just a document; it’s a formal Service Level Agreement (SLA) between the producer and the consumer/business user. It transforms data from a raw material into a precision tool through several key pillars: - **Schema Enforcement:** No more “Exit 14” surprises. This defines the exact structure and field names, ensuring that when the producer changes something, the consumer isn’t the last to know. - **Fitness Metrics:** This is the “Truth in Labeling” clause. It establishes measurable standards (e.g. “99.9 percent completeness”), confirming the data is actually fit for its intended purpose. - **Data Lineage:** Think of this as the provenance of your data. It tracks the entire history and transformation path, allowing for full audits in highly regulated environments. ### **It’s a Two-Way Street: Consumer Obligations** A contract isn’t just a list of demands for the data producer. In a healthy data ecosystem, the consumer (business user) has skin in the game, too. To get the most out of a data product, the consumer must commit to: - **Accurate Requirements:** You can’t complain about the destination if you gave the wrong coordinates. Consumers must define the *Intended Use* to help producers prioritize. - **Budgetary Alignment:** Data isn’t free. Contracts often include chargeback models based on volume and frequency, ensuring cost transparency across the enterprise. - **Proper Use (Governance):** This is the “Rules of the Road.” Consumers must agree to privacy and security restrictions, ensuring that sensitive data isn’t leaked into an external LLM training set. ### **AI and the “Neural Contract”: The Rise of MCP** As we move toward a world of autonomous AI agents, the stakes for these contracts have never been higher. We are seeing the emergence of the **Model Context Protocol (MCP)** – what many are calling “the new API for AI.” If the Data Product Contract sets the rules, the MCP is the officer on the beat. It provides a standardized interface for AI agents to access data securely. This creates a “synergistic enforcement” layer: - **Automated Enforcement:** AI agents can now read a technical contract and automatically run a validation check. If the data fails the quality test, the agent flags the violation before it can poison an AI model. - **Scoped Access:** Instead of giving an AI a “blank check” to your database, the contract and MCP work together to provide a scoped subset, ensuring the agent only sees what it needs to complete its specific task. ### **Moving Beyond the “Byproduct” Mindset** In the era of Generative AI, the bridge between data governance and machine learning is the Data Product Contract. It is the only way to ensure that your autonomous systems remain bound by human-governed policies. Without these contracts, you aren’t building an AI-driven enterprise; you’re just driving down a highway where the signs are changing and your GPS is five miles behind. It’s time to stop guessing and start contracting. Ready to get started? Get a sample data product contract template [here](https://www.redpointglobal.com/resources/data-contract-checklist-your-blueprint-for-trustworthy-data/). **Blog categories:** Agentic AI, Data Governance & Security, Data Management, Data Quality, Data Readiness **Blog tags:** Agentic AI, Data quality, Data readiness --- ### [When AI Goes Rogue: Lessons in Chatbot Disasters and Data Dependency](https://www.redpointglobal.com/blog/when-ai-goes-rogue-lessons-in-chatbot-disasters-and-data-dependency/) **Published:** January 8, 2025 **Author:** Steve Zisk **Content:** Chatbots: they’re supposed to make life easier, not inspire a CX *faceplant*. Yet, over the past few years, a handful of AI interactions have gone so spectacularly wrong they’ve become infamous case studies in “what *not* to do.” Let’s revisit two classics that left customers fuming and companies scrambling to explain how, exactly, they handed the reins to a digital loose cannon. In each instance, the company tried to blame the CX disasters on the chatbot. Their logic, in a nutshell: It is artificial “intelligence.” Ergo, since the chatbot is “intelligent” it is responsible for its own decisions. This is how an [airline](https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know) and a [home warranty company](https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know) tried to dodge honoring financial commitments a chatbot made to customers. The airline: The chatbot is “a separate legal entity responsible for its own actions.” (Translation: Our AI made the mess, not us.) The warranty company: Not responsible, because the chatbot was guilty of “miscommunicating.” (Translation: It’s not *our* fault our chatbot promised to send $3,000 and then ghosted you.) Here’s what happened. The airline’s chatbot assured a grieving customer that he could purchase a ticket at full price and then apply for an $800 bereavement fare refund afterward. Oops! Too bad the airline’s actual policy – explicitly stated on its website – requires pre-approval for such refunds. Cue a courtroom drama and a loss for the airline. The warranty company’s chatbot agreed with a customer’s request to be sent a check for $3,000 so he could replace a broken AC unit and install the new unit himself. Sorry, said the company when the check never materialized, that was just the chatbot going rogue. (The company changed its tune and made the payment once the local news picked up on the story). ## **AI Without Perfected Data is a Recipe for Chaos** At first glance, these stories seem like quirky AI mishaps. But the underlying issue is far from amusing: neither chatbot had a solid foundation of accurate, relevant data or a clear understanding of company policies. Instead, they were left to freelance their way into bad decisions. When chatbots are trained on incomplete or outdated data—or worse, given vague directives like “make the customer happy”—they’re bound to misfire. And while these rogue decisions might amuse social media, they cost companies money, trust, and goodwill. Let’s be clear: AI isn’t magic. It’s only as good as the data feeding it. For a chatbot to perform like a star employee instead of a liability, it needs: - **Clean, Accurate Data:** To understand customer history, preferences, and relevant policies. - **Training on the Right Data:** So it knows when to sympathize and when to escalate. - **Guardrails:** To stop it from approving hefty refunds or making promises it can’t keep. The bottom line? AI decisioning must be grounded in trustworthy data. Without it, companies risk repeating these horror stories – or creating new ones. Companies must start to think about AI trust and understand that AI-enriched information depends greatly on the underlying data. As AI-powered tools like chatbots and GenAI expand, the pressure is on for companies to perfect their data. A robust customer data platform (CDP) is no longer optional, but is essential to delivering the precise, real-time insights that AI needs to succeed. AI needs the best data for training purposes, and the fastest, most relevant data for predictions and calculations. That’s a good lesson to learn, which unfortunately may have come too late for the airline and the warrantor. **Blog categories:** AI & Machine Learning **Blog tags:** GenAI --- ### [Adapt to Rapid Consumer Change with a SaaS Delivery Option CDP](https://www.redpointglobal.com/blog/adapt-to-rapid-consumer-change-with-a-saas-delivery-option-cdp/) **Published:** April 10, 2020 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/04/4-10-20-blog-rapid-change-300x200.jpg)A recent Merkle report highlighted the urgent need for brands to provide a superior customer experience. According to the research, [66 percent of consumers](https://marketingland.com/consumers-indicate-experience-not-price-as-top-conversion-factor-263286) surveyed rated experience ahead of price when it came to making a purchase decision. Furthermore, 52 percent said that they left a website while shopping because of a poor experience. A 2019 Harris Poll commissioned by Redpoint produced similar findings, with [37 percent of consumers](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) saying that they will not do business with a brand that fails to deliver a personalized customer experience. While satisfying consumer expectations for a superior experience, brands must also hide complexity from the consumer, itself a significant challenge. In the Harris Poll survey, for instance, 63 percent of marketers said that they struggle to execute personalization strategies, with close to 40 percent attributing martech stack complexity as the top barrier to their ability to bridge the gap between strategy and execution. The consumer, however, rightly has no concern about the configuration of a martech stack, or how many systems must be integrated to provide a seamless experience. Siloed systems and data do not excuse a sub-optimal experience; introducing friction for any reason – a mis-identified person, a mis-timed offer, irrelevant content – will drive a customer to a competing brand. A challenge for many organizations amid an increased competition to deliver a superior customer experience, particularly small-to-medium sized enterprises, is that taming complexity and breaking down siloes often requires more resources than are at their disposal. Resource constraints often hamper their ability to access data, rapidly change customer journeys, and tune marketing programs to hit revenue targets – which in turn limit their capability to deliver contextually relevant offers and messages as part of a superior customer experience. **Adapt to Changing Customer Behaviors** For many organizations, the need to compete on customer experience precludes a months or years-long implementation of a customer data platform (CDP). Small to midsize enterprises are particularly susceptible to a lack of resources, and in many cases are even less prepared to withstand a thinned-out customer base than larger competitors. In addition, with consumers beginning to alter their purchasing habits in accordance with coronavirus stay-at-home directives – including more online buying or buy online, pick-up in-store (BOPIS) activity – organizations do not have the luxury of time to adapt to rapidly changing consumer behaviors. The Redpoint SaaS Delivery Option was designed to provide organizations with the capability of delivering a superior customer experience with a rapid deployment and quick time to value. An enterprise-ready CDP and best-in-class security precautions set the Redpoint SaaS Delivery Option apart, differentiating from channel-centric and list-based solutions offered by the majority of marketing cloud vendors. A “CDP” label from a marketing cloud vendor, in other words, is often just a combination of point solutions that does little to eliminate customer data siloes. **A Quick Time to Value** The Redpoint SaaS Delivery Option offers a standardized data model and pre-configured marketing channels to facilitate omnichannel engagement and a quick time to value for delivering a personalized customer experience at scale, with best-in-class security and availability SLAs. The pre-configured marketing channels include SMS, email, social, direct mail, and a preference center – facilitating omnichannel engagement. Additionally, real-time decisions and reporting provides the ability to deploy dynamic content on digital channels such as websites and landing pages, and campaign templates provide a series of preconfigured customer journeys – from welcome to cross sell and retention programs. Built on the Microsoft Azure cloud platform, the Redpoint solution helps marketers achieve higher value more rapidly with limited IT support required – with implementation measured in weeks rather than months or years. Security precautions include Redpoint leveraging its own enterprise grade cloud-based security capabilities for effective authentication, encryption, and monitoring, as well as using cloud-native intelligence to improve threat detection and response time. By sharing responsibilities with the cloud provider, Redpoint takes advantage of a more robust security coverage – remaining solely responsible for protecting the security of the data, accounts, and identities – not hosts, network, or datacenter. **A Single Customer View and the Next-Best Action** One Redpoint SaaS Delivery customer, a family-owned retail chain, implemented the solution to create a best-in-class BOPIS service and improve real-time product recommendations. It modeled its deployment after another Redpoint customer’s success building a BOPIS service with a 99 percent cycle time compression from gathering data to generating a next-best recommendation. Real-time personalization is enabled with the solution generating a single customer view – a unified and accurate Redpoint Golden Record that may be accessed in real time – on an automated basis by engagement systems or by in-store associates for use cases that include clienteling. An up-to-date customer profile is easily visualized from a web user interface that includes everything there is to know about a unique customer – from name, address, and email to social profiles and key metrics such as lifetime customer value, behavioral data, and detailed marketing and transaction history. This view empowers marketers to easily determine a next-best action that is in the context and cadence of a customer’s unique journey. In a BOPIS use case, for instance, immediate access to a customer profile that is updated in real-time ensures that a marketer will always be in synch with the customer. An online transaction could then generate an in-store offer for one accessory over another based on the customer’s behavior between purchase and the in-store pick-up. There are many reasons why a SaaS-enabled CDP remains the most viable option for an organization. The Redpoint SaaS Delivery Option covers all the bases with a pre-packaged solution for organizations to provide customers with a superior experience without a years-long implementation that is often at a slower pace than changing customer behaviors. **RELATED CONTENT** [If Your CX Strategy Doesn’t Include BOPIS, You’re Doing it Wrong](https://www.redpointglobal.com/blog/if-your-cx-strategy-doesnt-include-bopis-youre-doing-it-wrong/) [Clienteling and the Personalized In-Store Experience](https://www.redpointglobal.com/blog/clienteling-and-the-personalized-in-store-experience/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Actionable Insights and a Consistently Relevant Customer Experience (CX)](https://www.redpointglobal.com/blog/actionable-insights-and-a-consistently-relevant-customer-experience-cx/) **Published:** October 19, 2022 **Author:** Ian Clayton **Content:** In the customer experience realm, data, insight and action are the pillars for what it takes to deliver personalized interactions at scale across an omnichannel customer journey. The “insight” leg, however, is often misunderstood. One reason for the misunderstanding is that companies believe that once they’ve collected and perfected all the customer data they need, they can activate the data on behalf of the customer. In a sense, they’re not wrong in that insight may not technically be a requirement. But it does make all the difference in terms of delivering the depth of relevance that customers expect. Consider, for instance, the latest [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/) commissioned by Redpoint that explored the customer experience gap. More than three-quarters (82 percent) of customers surveyed said they are loyal to brands that demonstrate a thorough understanding of them as a unique customer. Further, 39 percent of customers said they will not do business with any brand that fails to offer a personalized experience. Think of a simple real-world consumer experience of walking into a showroom to buy a car. A salesperson, without knowing anything about the customer beyond maybe a name and address, may steer the customer toward one vehicle over another based solely on the customer’s approximate age, who they’re with, how they’re dressed or whether they’re wearing a wedding ring. This is, in a word, insight. The salesperson is augmenting some basic data with knowledge driven by experience to inform a next-best action: here is the perfect vehicle for you. ## **Deliver Consistent Relevance** In retail, healthcare, travel and hospitality or any industry with a customer-facing dynamic, gathering in-person insights, while valuable, is clearly not scalable for a host of reasons. Beyond volume, customers increasingly engage with brands across digital and physical touchpoints, often with no obviously discernible pattern. The research and evaluation that goes into buying a car, for example, likely consists of visits to a dozen or so websites on multiple devices. Within the construct of millions of customers moving freely through unpredictable customer journeys, skipping out on insight – going right from data collection to activation/orchestration – misses a key opportunity to layer data with context that is meaningful to an individual customer. In other words, using only demographic or diagnostic data – a customer’s name, address, ZIP, email address, etc. – will generally not reveal what makes a customer unique or provide any details about a customer’s intent, preferences or behaviors, all of which get at the heart of what customer experience (CX) really means. Because while on the one hand, CX refers to the sum of all the interactions with a brand over the life of the relationship the company, on the other it also entails the feelings, emotions and perceptions a customer has about those interactions. And to strike the right chord with a customer to generate a lasting, positive perception requires consistent relevance. ## **Dynamic Audience Segmentation** Consistent relevance is a byproduct of advanced analytics. The aim of machine learning, which is a subset of artificial intelligence (AI) is to augment human intelligence by using algorithms to enhance CX; humans determine a business objective and let predictive, clustering or next-best action models, such as those that recommend products, to find patterns in the data. Machine learning analyzes data at scale to provide the needed context that ultimately produces a next-best action. In the Redpoint CDP, machine learning is a key component of the actionable insights layer that bridges the gap between collecting/perfecting data and orchestrating a consistently relevant omnichannel experience. For a closer look at what we mean by actionable insights, we’ll start with [segmentation](https://www.redpointglobal.com/segmentation-activation/). One important feature of the Redpoint CDP is that segments are rules-based and dictated by the data. Instead of cutting a list of customers based on demographic data, rules-based segmentation is dynamic. Machine learning algorithms access updated data from an operational data store and train on the new information, with models creating new segments as needed to optimize the business objective. The two main types of models are supervised and unsupervised; the former uses historical data to train and predict future results, while the latter uses data to find underlying patterns and presents them to users. Another guarantee against introducing stale data is that the models themselves are automatically retrained and updated; a model will run against an algorithm only when it’s been called for batch or real-time use. Redpoint’s clustered audiences capability allows marketers to provide a large group of customers and let machine learning create a number of segments from it, effectively removing the work marketers have to do to create those segments manually. The clustering algorithm finds correlations in the data – deriving context beyond the ability of humans sorting through data. But because marketers ultimately run campaigns, it makes sense they should know why – or why not – a machine learning model has made a correlation. Another Redpoint CDP feature is an [audience insights dashboard](https://www.redpointglobal.com/machine-learning/data-visualization/) for users to view the segments that were created, which include a description taken from the decision tree used to generate the clusters showing users the decision tree model for why a set of customers is unique. ## **Machine** **Learning + Distributed Data** Audience insights provide another layer of trust for marketers to have confidence that the data will ultimately provide a hyper-relevant customer experience in the context of an individual customer journey. The need to have complete trust in data underscores the importance of establishing clear goals for machine learning models at the outset, and ensuring that a model has the right data – and enough data – to support a model. Because a model is only as good as the data it’s been given to learn on, it is sometimes the case that without the correct distribution of data that a predictive algorithm will produce incorrect results. The introduction of synthetic data helps ameliorate this problem. A model that is optimized to detect fraud, for example, will be biased against detecting fraud if the vast majority of transactions it analyzes are without fraud. By introducing fraud in the data as part of the training set, a model will eventually learn how to detect fraud. Alternatively, humans would either have to manually pick datasets correctly or assess what the algorithm is doing and bias the algorithm after the fact. ## **Machine Learning + Next-Best Action** Model outputs are dependent on the type of data being used, as well as how the data needs to be leveraged. A propensity score, for example, might be represented in an audience insights dashboard by a range of “least likely” to “most likely” in terms of a customer’s propensity to purchase, again showing a marketer or business user *why* the model arrived at a conclusion. Whatever the resulting propensity, the output highlights the purpose of machine learning: find insights that a human could not based on an objective set by a human, i.e., “find me X.”. These types of calculations bring us into how a [next-best action](https://www.redpointglobal.com/next-best-action/) is determined, recognizing that there are many decisions made by a customer before a purchase – what links did the customer visit, what did the customer click on, what did they view, how long was an online session, did they contact the call center, visit a store, post on social media, etc.? In essence, figuring out how each facet relates to another is what machine learning algorithms are tasked with. As an example, for machine learning to provide the next-best action at any point in the customer journey, it would need to be aware of everything that happened for a potential sale to occur, for instance, and then depending on the next inbound or outbound interaction will offer the next-best action that will lead to the sale. Advanced analytics driven by machine learning ultimately provide a customer with the type of digital-first personalized experience that they’ve come to expect, all without marketers showing their hand. Meaning the customer likely is unaware that machine learning is behind the perfectly relevant offer or message, only that it’s been delivered at precisely the right time, on the right channel and in the cadence of a unique customer journey. *Note: A follow-up blog on actionable insights will explore pre-built machine learning models, their role in next-best action calculations, and how machine learning prepares data for activation through intelligent orchestration.* **Blog categories:** Segmentation & Activation --- ### [Real-Time Ubiquity: How Cross-Channel Awareness and Sensitivity Supercharge A/B/n Testing & Personalization](https://www.redpointglobal.com/blog/real-time-ubiquity-how-cross-channel-awareness-and-sensitivity-supercharge-a-b-n-testing-personalization/) **Published:** October 8, 2020 **Author:** Redpoint Global **Content:** A real-time, [omnichannel personalized](https://www.redpointglobal.com/omnichannel-personalization/) customer experience (CX) is often thought of as the holy grail of marketing; a noble yet unattainable pursuit. Channel siloes, data latency, list-based campaigns and, ultimately, the need for a marketer to commit to an action – an email sent, web content updated, etc. – that may not accurately reflect a customer’s real-time journey all conspire to thwart the ambitious marketer from keeping pace with a customer independent of channel. Breaking down these barriers requires more than eliminating data siloes, data latency and replacing list-based campaigns with rules-based campaigns. Orchestrating a seamless, frictionless omnichannel CX requires real-time cross-channel awareness and sensitivity. Consider a customer, for example, who shows an affinity for a specific product or category on a brand’s website. If the customer has an unopened email offer in their inbox that becomes irrelevant based on the customer’s last-viewed content, is a brand prepared to change the email content up to and until the point of open? Real-time ubiquity, an open-time personalization capability based on the most recent information in a customer’s [Golden Record](https://www.redpointglobal.com/single-customer-view/), is one of the new smart assets in the latest release of the Redpoint [rg1](https://www.redpointglobal.com/one-platform/) digital experience platform. ## **Real Time, Friction-Free Relevance** The ability to delay committing to an action until the moment a customer opens an email saves precious time to consider additional influencing behavior that determines the true next-best action. Intervening behavior include a customer’s behavior on the website or another channel, the purchase of a product, signing up for a rewards programming, redeeming an existing offer or another interaction. By eliminating a lapse, a brand removes the possibility of sending a redundant, irrelevant email which – as most customers will attest – introduces friction into the overall CX, interfering with what may otherwise be a seamless omnichannel journey. A personalized CX marked by real-time relevance both delights a customer – and drives revenue. In one Marketing Insider Group study, [78 percent of consumers](https://marketinginsidergroup.com/content-marketing/content-marketing-personalization-imperative/) said that personally relevant content ups their purchase intent. And in a Harris Poll commissioned by Redpoint, asked to name their top consumer frustrations, [34 percent of respondents](https://www.redpointglobal.com/wp-content/uploads/2020/07/Addressing-the-Gaps-in-Customer-Experience.pdf) said it was receiving an offer for something they just bought, while 33 percent said it was receiving offers that are not relevant. ## **Improved A/B/n Testing** Open time email personalization materially improves A/B/n testing, providing marketers with the capability to test and report on real-time email content in much the same way as traditional A/B/n web testing – with the same consistency and specificity that are the usual hallmarks of the web’s random audience approach. The standard process for testing X amount of email offers is to keep a certain percentage as a control, and assign a small percentage of emails to each offer. With a statistically significant result from this segmented audience approach, the remaining percentage of emails are then sent to the “winning” offer. In a standard web testing process, rather than define and segment an audience, content is randomized for the first X amount of views or impressions and once that threshold is reached the “winning” content is released. With [real-time personalization](https://www.redpointglobal.com/real-time-interactions/) of content at the point of open in rg1, marketers can choose either method on either channel. On the web, for instance, marketers can assign an audience to see specific content and then – like the standard email testing process – wait for the desired amount of impressions, and then show the winning result to the rest of the population. The new innovation in rg1 allows marketers to treat an email as they would web real estate; randomize the first X amount that open the email, and with a statistically significant response the winning content is assigned to the remaining emails. But because all customer behaviors are considered up to the opening of an email, once there is a statistically significant response marketers can far more effectively attach relevance to the remaining emails. This type of randomized audience [A/B/n testing](https://www.redpointglobal.com/blog/real-time-ubiquity-how-cross-channel-awareness-and-sensitivity-supercharge-a-b-n-testing-personalization/) approach provides email testing with the same benefits of the random audience approach known on the web, namely that it’s more efficient and returns an answer much faster. Yet open-time email personalization retains the benefits of the segmented audience approach by attaching specificity to who sees what and understanding with intricate detail the behavior of the particular audiences that see the content – all without burning audience. If you’re testing an email offer for camping equipment, for example, but a recipient (before opening the email) goes to the web and lands on hunting gear, and that action aligns with another email, you can switch. In addition, functionality in some popular email systems allow you to pin emails of special interest to the top of your inbox so that they’re always there. Imagine the benefit of getting customers to pin these real time portal emails to the top and getting them accustomed to going back to those emails for new personalized content. In addition to reducing the number of emails sent, you could also establish a very private channel with a consumer that would always feel very personalized to their preferences, tastes and interests. Possibilities are almost endless; the trick is to start experimenting and see where the capability and user behaviors interact to evolve into a new experience. Thinking of an email marketing campaign as a stand-alone, siloed touchpoint or engagement channel is a missed opportunity to engage a customer with consistent relevance, particularly because the customer journey of today is dynamic, non-linear and highly unpredictable. Real-time cross-channel sensitivity and awareness far more accurately reflect the way an always-on, connected customer engages with a brand, and open-time email personalization reinforces why the delivery of a seamless, [omnichannel](https://www.redpointglobal.com/blog/retailers-need-omnichannel-approach-customer-engagement/) CX is now table stakes. Customers view a relationship with a brand as a holistic experience, not a collection of fragmented interactions. It is why brands that meet this expectation with a true customer-centric experience drive new revenue. It is why ambitious marketers choose Redpoint to move markets with a digital experience platform that delivers a uniquely superior CX. ## **Related Content** [Augment Customer Segmentation with a Personalized CX](https://www.redpointglobal.com/blog/augment-customer-segmentation-with-a-personalized-cx/) [Customer Journeys are Dynamic: Your Engagement Technology Should Be as Well](https://www.redpointglobal.com/blog/customer-journeys-are-dynamic-your-engagement-technology-should-be-as-well/) [Top 10 Benefits of Rules-Based Orchestration](https://www.redpointglobal.com/blog/top-10-benefits-of-rules-based-orchestration/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Journey Orchestration, Real-Time Personalization --- ### [A Ticket to Revenue: Personalized Travel & Hospitality Experiences](https://www.redpointglobal.com/blog/a-ticket-to-revenue-personalized-travel-hospitality-experiences/) **Published:** April 21, 2021 **Author:** John Nash **Content:** Americans are ready to travel. With the Centers for Disease Control (CDC) [lifting travel restrictions](https://www.bostonglobe.com/2021/04/02/nation/cdc-guidance-says-vaccinated-people-can-travel-safely-us/?p1=Article_Inline_Text_Link) for the more than 100 million (and counting) vaccinated adults, people are dreaming of summer getaways. Many hotel and rental property managers in Maine, Cape Cod and other New England resort areas report [2021 bookings](https://www.bostonglobe.com/2021/04/08/lifestyle/weve-never-seen-anything-like-it-rush-is-book-summer-rentals/?p1=StaffPage&p1=Article_Inline_Related_Link) far outpacing 2019 demand. From [campgrounds to beachfront hotels](https://www.cnbc.com/2021/03/24/is-it-safe-to-travel-this-summer-optimistic-travelers-booking-now-.html), it’s a similar situation across much of the country. According to a new [Dynata survey commissioned by Redpoint](https://www.redpointglobal.com/press-releases/67-of-consumers-frustrated-by-travel-industrys-messaging-throughout-the-pandemic/), 79 percent of people who have been vaccinated for COVID-19 say they will travel either about the same or more as they did before the pandemic. While everyone wants to enjoy a long, hot summer day at the beach, lake or mountains, the pandemic has altered expectations. Traditionally, many travelers primarily associate value with low-cost options. Post-pandemic, cost must be weighed against a raft of new considerations for determining value – does the destination state have a mask mandate? Is a proof of vaccine required? What are the social distancing protocols? ## **Re-write the Travel Script** With the change in how customers place value on travel & hospitality experiences, understanding an individual customer’s preferences, and communicating timely, relevant information becomes more important than ever for travel and hospitality companies that essentially must re-write the script for how to engage with consumers in a post-pandemic world. In the Dynata survey, 73 percent of respondents said that they expect personalized, real-time messaging from travel companies to satisfy their need to feel safe and comfortable – including clear information and real-time updates on mask mandates, crowd management and other pandemic-related issues. Furthermore, 69 percent said that they expect seamless, personalized communication across the entire travel experience (from pre- to post-trip) to last long after the pandemic. Yet just 24 percent said that travel companies meet this expectation today. ## **Be Consistently Relevant with a Single Customer View** What could a personalized approach look like? If a travel brand knows, for instance, that a customer is wary of crowds and has searched for a rental with a kitchenette and private bathroom, displaying a banner image of a crowded nightclub might be a turn-off. By the same token, a vaccinated customer who has posted on social media that they’re tired of seclusion and anxious to mingle with other families might be excited by an image of a busy water park. To consistently provide the most relevant experience for an individual customer or prospect on every channel, a travel brand needs a complete picture of each customer. A unified profile might include – as a start – preferences, behaviors, tolerance for risk, travel history, household status, location and search history. A single customer view that combines customer data from every source provides travel brands with a foundation for consistent relevance. Importantly, an ability to deliver a real-time, hyper-personalized omnichannel experience requires that this unified profile or [golden record](https://www.redpointglobal.com/single-customer-view/) also include customer data across unknown to known records and consist of data of every type – structured, semi-structured and unstructured. Applying [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) capabilities to forge a golden record helps ensure relevance by eliminating guesswork; a travel brand that displays the busy water park image within milliseconds of a website visitor landing on the page will be confident that the image is tied to a unique customer profile versus simply to a device. ## **Deliver Personalized Hospitality Experiences at Scale** Real-time updates are vital for ensuring persistent relevance across the entire travel experience. If a hotel guest posts negative feedback about a breakfast buffet on Facebook, it will be to the travel brand’s benefit to present a relevant offer that day – or at least before the guest checks out. An offer for a free dessert does little good if the guest has already left the premises. > “Working with Redpoint for years we’ve been able to create a hyper-personalized guest experience that is customized from end-to-end.” > > – Andrew Heltzel, corporate director, Marketing & CRM, Xanterra Travel Collection To present a personalized, next-best action at scale for hundreds of thousands or millions of customers, marketers need a platform with embedded automated machine learning and a real-time decisioning engine. Because each customer has a unique journey in terms of how and when they engage with a brand and the status of travel from pre- to post-trip, an ability to deliver precise relevance in the cadence of each journey is beyond the capability of human-driven segments. Evolutionary programming in the Redpoint CDP, by contrast, allows for hundreds of inline, code-free, self-training models optimized to deliver a next-best action for a segment-of-one. When a decision or next-best action is automatically rendered, [intelligent orchestration](https://www.redpointglobal.com/orchestration) capabilities ensure that it is always in the context of a customer journey and delivered at the right time and on the right channel. The Redpoint platform makes a next-best action available along with a single customer view that is instantly accessible for marketers and for customer-facing associates such as call center reps, front desk clerks, reservation agents and other front-line travel and hospitality workers. “Working with Redpoint for years we’ve been able to create a hyper-personalized guest experience that is customized from end-to-end,” said Andrew Heltzel, corporate director of Marketing & CRM for [Xanterra Travel Collection](https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fwww.xanterra.com%2f&c=E,1,kFmex9MP2HjkQEjb2YsXISPLvhMnCrVotcE09xCdgW4iWuqgHRF0MzDp3KD3Wgnh_yYFCQ1R0f9pZAxTnqyD-VEU_9N9QE8NWYJN0AAtbu4COpSFJ8w,&typo=1). “Because of this access to customer data, we were able to hear directly from our guests about the type of messaging they wanted and quickly deliver that content. We have prioritized health and safety messaging since the early days of the pandemic and highlighted travel options based on guest behavior and travel preferences across our portfolio.” With Americans eager to end a year-long exile, the travel and hospitality industry is primed for a comeback. Personalized, revenue-generating customer experiences are possible for travel brands that recognize that traditional methods of engagement – including a one-size-fits all approach with a focus on cost-savings – are as out of favor as time shares. Unique customers deserve unique experiences, and the technology is available to turn every adventure into a dream vacation. **Blog categories:** Anonymous to Known, Identity Resolution, Real-Time Personalization, Segmentation & Activation, Travel & Hospitality --- ### [Five Key Reasons You Should Embrace Cross-Channel Marketing](https://www.redpointglobal.com/blog/five-key-reasons-you-should-embrace-cross-channel-marketing/) **Published:** July 14, 2016 **Author:** John Nash **Content:** Classic linear [customer journeys are dead](https://www.cmswire.com/cms/digital-marketing/its-official-forrester-says-campaign-marketing-is-dead-024784.php) and it’s time to move on. We know that today’s customer, regardless of industry, is moving from device to device, moving from online to offline, and often times sharing the love with other members of their network or in their own household. It’s no longer valid to prescribe a one-size-fits all path to success. How do you define success anyway? It’s different to every consumer. As marketers how can we make sense of all these touchpoints? How can we automate and coordinate the experience? What about the data? Classic batch and blast direct marketing campaigns played the numbers game. Higher volume activity drove a certain portion of clicks, opens, and conversions. More mail in the trash with the hopes for an occasional purchase. What marketers often overlooked was the diminishing impact that activity had on the consumer in the long term. Gaining a short term win for the sake of marketing attribution is not worth the cost of fatiguing a customer and losing them for life. What’s the saying about the cost to acquire versus retain? Then we entered the multichannel marketing paradigm. We focused on adding email, direct mail, text messaging, mobile apps, Facebook – you name it. We blasted to all of these channels with reasonable amount of efficiency, but again what about the data? Often times these channels had separate databases, data structures, un-coordinated messages, and lots of content. Oh, did I mention lots of data? Further adding to the complexity, how can we address today’s consumer in a cross-channel marketing world? How can we ensure that all touch points are contextual and relevant, all while driving conversions and delighting the customer? How can we ensure that the data collected from any source is leveraged and orchestrated? Does the right hand know what the left hand is doing? Here are five quick reasons to consider moving your marketing mindset from mass marketing to cross-channel marketing: ### **1. Your customers expect it.** Whether or not they even realize it, today’s consumers are connected to [15 billon devices](https://www.redpointglobal.com/knowledge-center/whats-next-customer-engagement/) and growing, engage in as [many as five channels](https://www.redpointglobal.com/knowledge-center/whats-next-customer-engagement/), and often jump from consideration to purchase lifecycle phases and back again in perpetuating cycles. The experience you deliver to your customer must be orchestrated. It must be consistent. It must be cross-channel. Oh, and it can’t be annoyingly transparent. This comes back to the data. You need to understand customer history and provide them with the right experiences, but you need to do it in such a way that is subtle. Just because you know a lot about a consumer does not mean you should flaunt that insight. With great data insight comes great responsibility. ### **2. It drives relevance.** I always say content is king and context is queen. Without great data, your content has very little impact. Great data is not just saying you have their address, email address, name, and their preferences. This means you have data from all aspects of the consumer’s journey, whether its unstructured data such as social media and web data or semi-structured data like keywords and metadata. Relevance drives engagement and engagement is the key to generating long term customer loyalty and brand advocates. If you can automate this process with insight from the wisdom of the crowds, you are really ahead of the curve. ### **3. It further unifies your organization.** Multiple channels mean multiple silos. We all know that many organizations are set up around channels and somehow orchestrating them is an organizational and political hurdle as much as it is a technology challenge. This is a great opportunity to break down the silos, force the cross-company collaboration between website and email, or direct mail and social. This also bridges the divide between Marketing and IT. The role of the Chief Customer Experience Officer (CXO) has increasingly played a role in this paradigm. Of course the technology cannot solve all problems, but the strategy of being cross-channel will further usher your organization toward better alignment and customer centric focus. ### **4. It centralizes your view of the customer.** None of us wants to hear another “360-degree view of the customer” diatribe. However, there is real value in being able to combine big data, unstructured, semi-structure, and structured data, from any source, together in one place to be leveraged in any channel. This “golden record” does not magically appear out of the sky. This requires your data to be merged, cleansed, de-duped, joined, and keyed. This type of work, if done correctly, can be highly automated and save you and your database marketing brethren huge headaches. By enabling data to go through this process, you can ensure success with the most foundational elements of your customer engagement process, effective targeting and personalization. ### **5. Your business depends on it.** [Research shows](https://chiefmartec.com/2016/05/marketing-technology-budgets-surpassed-advertising/) that an increasing amount of organizations are [investing in cross-channel marketing technology](https://econsultancy.com/reports/cross-channel-marketing-report). The payoff can be significant if done well. Major brands like WGBH, Xanterra Parks & Resorts, and numerous others have seen significant lift by being able to orchestrate their campaigns with greater efficiency and effectiveness. By more precisely targeting your customers and leveraging rich data and insights, you can take better action and engage more consistently, driving lights out results. What are your thoughts? Have you tried to implement cross-channel marketing? What are your successes or failure? **Blog categories:** Data Quality, Omnichannel Marketing, Real-Time Personalization --- ### [Three Things Retailers Can Do with a Composable CDP to Build Customer Loyalty and Revenue](https://www.redpointglobal.com/blog/three-things-retailers-can-do-with-a-composable-cdp-to-build-customer-loyalty-and-revenue/) **Published:** August 8, 2024 **Author:** John Nash **Content:** Trust drives loyalty. Relevance reinforces trust. How, then, do retailers earn that trust? According to consumers, trust is earned through a consistent, personalized customer experience (CX) – with three of four retail consumers saying they are more loyal to brands with consistent customer service and experiences. According to [Deloitte’s 2024 Retail Industry Outlook](https://www2.deloitte.com/us/en/pages/consumer-business/articles/retail-distribution-industry-outlook.html), retail executives’ most cited growth opportunity for 2024 was strengthening loyalty programs, knowing that trusted companies financially outperform their peers up to 4X, and that customers who trust a brand are 88 percent more likely to buy again. Retailers, however, are not meeting lofty customer standards for what constitutes a modern, omnichannel CX. In the Deloitte study, for example, modern retail services that include buy online, pick-up in-store (BOPIS) and buy online, return in-store (BORIS) are widely available, but most retailers have a hard time pulling them off without friction. For example, just 1 in 10 retailers was able to offer an alternative pickup option, and a third failed to indicate how long it would take to receive a refund. ## **Composable CDPs Stay Ahead of Changes in Customer Expectations** Agility, specifically being able to quickly pivot to new and emerging CX use cases, is a key capability for meeting the level of personalization that customers expect. A need for agility extends to the entire marketing stack. A composable CDP provides brands with the ability to meet both new and emerging use cases. For a CDP, [modern and composable](https://www.redpointglobal.com/blog/a-composability-primer-everything-you-need-to-know/) means being able to activate your data to any end channel to meet your customers in ways that adapt to new channel innovations and consumer behavior changes. It means being able to bring in your own models so you can work with AI the way you want, without CDP restrictions. As customers change how they shop and how they interact – such as being more comfortable interacting with generative AI (genAI) applications and natural language processing (NLP) – brands can no longer afford to have monolithic, intractable systems or technology that are purpose-built to do one thing. According to the Deloitte research, half of retail executives are prioritizing AI-driven personalized product recommendations in 2024. Yet, only five in 10 are confident in their company’s ability to use AI effectively across their businesses, hampered by rigid technology that makes it costly to quickly spin up a new AI application. Composability helps solve issues of intractability. Best understood as a modular approach offering the freedom of choice to obtain best-of-breed components that complement existing investments, composability prioritizes agility and helps enterprise retailers more quickly reach untapped business value. A composable CDP that runs in a data cloud and performs core CDP functionality without having to replicate data – known as a “data-in-place” or “zero-copy” CDP – allows you to control your customer data behind your own security perimeter while connecting to all your MarTech touchpoints and enterprise data sources. By building a cohesive platform through API integrations, companies balance control with the flexibility to add or change components as use cases evolve. ## **Retailer’s Opportunity to Double Down on Loyalty** Consumers’ baseline expectations for seamless personalization include brands knowing who they are across channels, and catering to their individual preferences however they choose to engage. Because strengthening loyalty programs is such a key retail initiative, the key to success is to cultivate a detailed, up-to-date understanding of today’s and tomorrows consumers, and to use that deep understanding to provide a superior CX. With that in mind, here are the top three things retailers must consider when looking at a **customer data platform (CDP)** as the cornerstone of their CX strategy. #### **1. Dynamic Segmentation = Contextual Understanding = Loyal Customers** McKinsey lists “microtargeting” as one of four imperatives that retailers must prioritize to meet the consumer of tomorrow. By microtargeting, it means segmenting an audience to target customers who demonstrate a particular shopping behavior or preference, vs. a generic, static segmentation by age, gender or geo. The latter is inadequate for engaging a customer with a relevant CX in the moment of interaction because it does not provide a contextual understanding of the customer. A static segment is also likely to be outdated the moment that it is created. When a customer abandons a shopping cart, for instance, that action should immediately move a customer between segments because it materially impacts the brand’s response. Should the brand offer a discount for the abandoned item? Perhaps a discount on a complementary item? These are not one-size-fits-all decisions, but rather depend on what the brand knows about the customer. What is her lifetime value? Her average monthly spend? What is her cart abandonment history, i.e., does she eventually buy? Is she comparison shopping? Being able to dynamically move customers between segments in real time in concert with a customer’s real-time behaviors is a key capability for meeting a customer with a magical marketing moment – one that is hyper-relevant not just based on what you know about a customer and the customer’s intent, but also in the pitch-perfect cadence of a customer journey. An online browsing session, for instance, might trigger swapping out an outbound email. An in-store purchase might change what a customer sees on their next visit to the website homepage. For [dynamic segmentation](https://www.redpointglobal.com/segmentation-activation/) to keep up with the cadence of a customer, an enterprise CDP should provide a no-code environment for marketers to create granular segments that may be used in every channel without having to lean on IT or other external resources. Having to create and pull lists of customers or generate SQL are antiquated concepts that prevent brands from having the needed up-to-date understanding of a customer as the customer journey progresses. #### **2. An Agile MarTech Stack to Meet Your Evolving Use Cases** Dynamic segmentation speaks to a brand’s need for agility to engage a customer with relevance on any channel – at any time. A need for agility extends to the entire marketing stack. A composable CDP provides brands with the ability to quickly pivot to meet both new and emerging use cases. As customers change how they shop and how they interact – such as being more comfortable interacting with generative AI (genAI) applications and natural language processing (NLP) – brands can no longer afford to have monolithic, intractable systems or technology that are purpose-built to do one thing. For a CDP, [modern and composable](https://www.redpointglobal.com/blog/a-composability-primer-everything-you-need-to-know/) means being able to activate your data to any end channel to meet your customers in ways that adapt to new channel innovations and consumer behavior changes. It means being able to bring in your own models so you can work with AI the way you want, without CDP restrictions. According to the Deloitte research, half of retail executives are prioritizing AI-driven personalized product recommendations in 2024. Yet, only five in 10 are confident in their company’s ability to use AI effectively across their businesses, hampered by rigid technology that makes it costly to quickly spin up a new AI application. Composability helps solve issues of intractability. Best understood as a modular approach offering the freedom of choice to obtain best-of-breed components that complement existing investments, composability prioritizes agility and helps enterprise retailers more quickly reach untapped business value. A composable CDP that runs in a data cloud and performs core CDP functionality without having to replicate data – known as a “data-in-place” or “zero-copy” CDP – allows you to control your customer data behind your own security perimeter while connecting to all your MarTech touchpoints and enterprise data sources. By building a cohesive platform through API integrations, companies balance control with the flexibility to add or change components as use cases evolve. #### **3. Data Quality is a Key Component for a Contextual Understanding** How does a CDP approach the issue of data quality? Consumer data is inherently messy. The popularity of composability is one factor that brings the issue of data quality to the forefront, because some CDPs tout themselves as being composable that are little more than reverse ETL tools. Some do not even store customer data. Data quality – which should be core CDP functionality – is viewed as another application’s responsibility, with the problem being that it then becomes difficult to trust the quality of a unified customer profile. A CDP that sits on someone else’s Customer 360, for instance, may apply some very basic matching or cleansing before sending data downstream, but rudimentary measures do not yield a contextual understanding of a customer across channels. For instance, a basic deterministic match might break apart two different email addresses, but that is not the same as a real-time, updated unified profile that will actually tell a marketer or business user which email address to use when the customer self-identifies in one channel vs. another. For retailers to cultivate a detailed, up-to-date understanding of their customers, completing identity resolution and data quality processes as data enters the system is a critical step. Taking care of cleansing, matching, validation and data governance in real time at data ingestion eliminates the latency that derails a relevant CX in the cadence of a customer journey. To build loyalty and trust in an economy that has consumers prioritizing value, retailers must re-think their personalization strategies. With Redpoint, they have options. Dynamic segments that reflect an up-to-the-moment understanding of a customer that can be built without writing code. An agile marketing stack anchored by a composable CDP that will never lock you into a single approach or application. Data quality as a core competency to ensure that the resulting unified profile reflects the absolute latest and most accurate understanding of a customer. For more on why leading retailers turn to the Redpoint CDP as the most complete, composable CDP to take them to the next level on the personalization roadmap, click [here](https://www.redpointglobal.com/retail/). **Blog categories:** Retail --- ### [Master the Three ‘Rs’ of Personalization, Part 2: Relevance](https://www.redpointglobal.com/blog/master-the-three-rs-of-personalization-part-2-relevance/) **Published:** March 11, 2025 **Author:** Mike Ferguson **Content:** [Forrester research](https://www.forbes.com/sites/bradbirnbaum/2021/08/10/exceptional-customer-service-needs-elasticity/) reveals a stark truth: 63 percent of consumers will abandon a trusted brand after just one poor experience. But what defines “poor”? It’s subjective. Imagine receiving an offer for a product you just bought or being bombarded with upsell offers while trying to cancel a subscription. These are classic examples of irrelevance. Irrelevance is a common thread in poor customer experience. A brand may have all the data and be able to recognize the customer, yet still fails to deliver the next best action in the customer’s journey. The next best action is delivering a pitch-perfect offer, message or content that resonates with a customer in the moment of interaction. Inbound or outbound, it’s an engagement that perfectly captures a customer’s interests and intent. In a word, it’s relevant. In our [previous blog](https://www.redpointglobal.com/blog/master-the-three-rs-of-personalization-part-1-recognition/) on the Three ‘Rs’ of Personalization, we focused on recognition as the first of three pillars that constitute next-level personalization — **recognition**, **relevance**, and **right time**. Here, we turn our attention to the importance of relevance. It’s one thing to get your data right and know a customer inside out, but it’s another to use that knowledge of a customer – that deep, personal understanding – to enhance a customer experience with a next best action. ## **How to be Relevant** You have a Customer 360 (not the dreaded “Customer 90”). You recognize an individual customer in the context of a business or household. With a Golden Record, you know their past actions and current behaviour, giving a fair estimation of what they might want to do next. How well a brand applies the Golden Record across a journey is the secret to consistent relevance. Included in that calculation is a contextual understanding of the customer derived from preferences and other information, but it’s also derived from immediate actions within the current interaction, e.g., the click to cancel a contract, a comment to a call centre agent, etc. For instance, if a customer’s current behaviour indicates a high propensity to churn, it is not an ideal time for an upsell. External context also often influence customer behaviours and thus play an important role in being relevant. Factors such as weather, fashion, politics, pop culture, etc., all have the potential to steer a customer in a new direction. Relevance, in turn, often depends on how well a brand links these external factors to an individual profile. ## **The Customer Sets the Parameters for Data Usage** Delivering a relevant customer experience also means respecting privacy concerns. A great offer on an unwanted channel introduces friction. Relevant personalization must account for an individual’s stated preferences. Studies show that customers are willing to share more data when data is used respectfully within boundaries set by the consumer. This value exchange amplifies relevance, showing that it’s not just the right offer or message that counts, but also respecting an individual preferences. ## **AI and Relevance: Keep the Customer Top of Mind** Discussing relevance in personalized experiences is incomplete without AI. There are a few points to consider here. One is that when you’re using data to train AI models on generating a next best action, it’s important to take a step back and really think about what relevance means. One mistake a lot of companies make is that they think about relevance in terms of what’s relevant for the company vs. what’s relevant for the customer. When this is the case, it’s easy to introduce bias into AI models, steering decisions that will produce favorable outcomes for the company (enhanced lifetime value, upsell, cross-sell, etc.) instead of a primary focus on the ideal interaction for the customer. Another point to consider is the data you will present to your AI model once it is trained to make sure that the results are relevant. It seems obvious that the quality of the data is critical for success, but the current hype in the AI space appears to downplay this factor. ## **Relevance = Knowing When to Hold Back** Sometimes, relevance means taking no action. Personalization for its own sake can lead to poor experience. An action, offer or message may be hyper-relevant, perfectly capturing the context and ready to be delivered in the cadence of a customer journey, but if it’s the sixth message of the day it is intrusive. Sometimes we are creating so much noise in our attempt to personalize across so many channels that we are drowning out the one key message that we need to deliver. ## **Relevance, Making Customers Feel Special One Interaction at a Time** Relevance is the heartbeat of personalization. It leverages a Golden Record to create meaningful, timely interactions that resonate with each customer. By harnessing deep insights into a customer’s history, context, and preferences, brands can craft experiences that feel intuitive rather than intrusive. When every touchpoint is aligned with the customer’s current journey, it prevents the over-communication and misaligned offers, building a bridge of trust and loyalty. In an era where a single misstep can drive a customer away, focusing on relevance is not just an advantage, it’s an imperative for creating genuine, memorable connections. In the third and final blog in this series, we will focus on **right time** as the final pillar of a superior customer experience. For more on the role relevance plays in the creation and execution of a personalized CX, join Vice President of Marketing Beth Pfefferle and me in the second of a three-part webinar series on the Three “R’s” of Personalization. To view the webinar, click [here](https://event.on24.com/wcc/r/4778766/6AF94B6E6B63E0B3D2103B6259B56014/5828167?mr=s). **Blog categories:** 1:1 Personalization, Data Quality **Blog tags:** Data quality, Golden Record --- ### [Master the Three ‘R’s’ of Personalization: Part 1, Recognition](https://www.redpointglobal.com/blog/master-the-three-rs-of-personalization-part-1-recognition/) **Published:** February 26, 2025 **Author:** Mike Ferguson **Content:** Personalization is only as good as a brand’s ability to recognize its customers. Get it wrong, and you risk sending irrelevant offers, creating friction in service interactions, and ultimately driving customers away. According to a [Capco survey](https://www.capco.com/en/About-Us/Newsroom-and-media/Banking-Survey-Press-Release), 72% of customers expect brands to recognize them across all touchpoints, and rate personalization as “highly important.” Yet many brands still struggle to achieve this. Why? Because true recognition requires more than just a name; it demands a complete, unified view of the customer. Digging deeper into the capabilities required for a brand to recognize a customer, there are three components that constitute “personalization.” Those are r**ecognition**, **relevance** and **right time**. In a series of blogs, we will break down these three pillars of personalization, detailing how each contributes to a superior customer experience (CX) and why the combination of all three is essential in the era of dynamic customer journeys consisting of a multitude of online and offline touchpoints. ## **Recognition: Do You Know Who I Am?** Recognition. There’s a famous scene in the movie “Goodfellas” where the protagonist, trying to impress his date, skirts the enormous line outside the Copacabana nightclub by escorting her through the kitchen to the restaurant. From bouncers to busboys, everyone greets him personally, and the maître d’ orders that a table be set up for the couple directly in front of the stage. OK, so he’s not a model citizen, but the scene perfectly captures the perks that come from being recognized and the exemplary customer experience that goes with it. He and his date are made to feel important. Duplicating that type of experience in a digital setting is the goal for brands intent on competing on CX. Customers made to feel important become repeat customers. And it’s often the little things that customers notice. A push notification reminding us of items left in a shopping cart. An email from our health plan to inform us about a new benefit we’ve become eligible for. Product recommendations that perfectly match our physical and digital journey with a brand – what we’ve purchased, what we’ve expressed interest in, and what we may not have even considered but seems right up our alley. These and other personalized touches provide value to a customer in that they demonstrate a brand’s interest in knowing the customer beyond a transactional basis, and using that knowledge to deepen the relationship. It is all based on recognition, which itself is achieved through data. ## **Recognition 1, 2, 3** Recognizing an individual customer, up to and including recognizing an individual customer within the context of a household or a business, begins with collecting and connecting all available data. Customers leave digital signals every time they interact directly with a brand or elsewhere in the digital world; a social media trail, a visit to a third-party website, a chatbot encounter, a call center interaction, a browsing session, etc. Some engagements may even be anonymous, such as with an unrecognized device. Distinguishing one customer from another begins by collecting every signal and beginning to build a unified customer profile, also known as a Golden Record. A unified profile is the key to recognition because it contains all customer identifiers – devices, nicknames, addresses, email addresses, etc. The combination of a full contact graph with all data aggregations and an extensive list of attributes becomes a digital stand-in for a customer, a household, business or other entity that a company is trying to understand. To trust that a Golden Record is an accurate representation of a customer, data quality processes must be completed as data is being ingested and updates to the Golden Record made accordingly. A Golden Record is never static but rather mirrors the typical changes – big and small – that represent the typical customer, and the customer’s journey with a brand. That is, customers move, change jobs, break apart relationships and form new ones. An anonymous customer becomes known. Interests evolve. True recognition extends beyond distinguishing one customer from another to also understand a customer in the context of those changes. A contextual understanding refers to, say, a car rental agency knowing that a customer is renting a vehicle for personal travel or for business, or even reserving the car on behalf of a colleague. The same holds true for a hotel or travel company; is the person making the booking doing so on their behalf, or for an executive at their company? A Golden Record that is continuously updated as new signals emerge is the window to recognizing a customer not just as a name or identity, but as someone with specific interests, preferences, and a unique cadence. This deep understanding is often referred to as a Customer 360, which implies a brand has complete knowledge of a customer. ## **Recognition Barriers** Too often, brands still operate with less than a full customer understanding. Call it a “Customer 90.” Perhaps they recognize a customer only through association with an email address, and communicate in that one channel. Or they know what device is linked to a credit card and a POS system. A brand knows what the customer buys, but is uncertain about the customer’s return history and has no view into product reviews on social media or the brand’s website. But an email address – or even a transaction history – are not representative of a person, and certainly not reflective of a person’s interests or intent. A main barrier that prevents brands from having a complete, single customer view is the quality of the data. It is either incomplete, meaning there are important signals that are not taken into account, or data is siloed in various channels. Or it is inaccurate, which is a result of poor data quality, i.e., data that has not been cleansed, normalized, or otherwise been made ready for business use the moment it enters the system. Whether incomplete or inaccurate, the result is the same – a lack of true recognition. Perhaps a brand is 75 percent sure of the identity of a person making an online reservation. Or 80 percent sure that they’re engaging with the head of household. Anything less than 100 percent certainty, however, runs the risk of introducing friction into the customer experience. ## **Recognition and Relevance** The consequences of failing to accurately recognize a customer in the context of a customer journey brings us to the next pillar of personalization – relevance, or a lack thereof. In our next blog on the “Three ‘R’s’ of Personalization” we will highlight the many advantages that result from being able to recognize a customer in the moment, and to use that recognition to deliver a hyper-relevant experience. For more on the important role recognition plays in the creation and execution of a personalized CX, Vice President of Marketing Beth Pfefferle and I discuss the topic in the first of a three-part webinar series on the Three “R’s” of Personalization. To view the webinar, click [here](https://event.on24.com/wcc/r/4778762/3B2BB4029FC9FDB17DFE28FE67396C1C/5828167?mr=s). **Blog categories:** Data Quality, Identity Resolution, Single Customer View **Blog tags:** Golden Record, identity resolution --- ### [2026 Predictions: Trends that Will Redefine Customer Engagement (Part II)](https://www.redpointglobal.com/blog/2026-predictions-part-ii-trends-that-will-redefine-customer-engagement/) **Published:** December 19, 2025 **Author:** John Nash **Content:** *Editor’s Note: This is Part 2 of our 2026 predictions. You can read [Part 1 here](https://www.redpointglobal.com/blog/2026-predictions-part-1-three-trends-that-will-redefine-customer-engagement/).* The Association of National Advertisers (ANA) chose “agentic AI” and “authenticity” as its marketing words of the year. The dual nod – its first-ever in its 12th year of bestowing the honor – reflects a marketing landscape where, the ANA says, “success will come from navigating advanced AI capabilities without losing the trust, truth, and transparency that define strong brands.” According to [Mordor Intelligence](https://www.mordorintelligence.com/industry-reports/agentic-ai-market,), the agentic AI market is expected to grow from $7 billion to $43 billion by 2030, representing a CAGR of 44%. This growth is expected to be driven by a shift toward autonomous systems, breakthroughs in LLM reasoning and the maturation of multi-agent orchestration frameworks. In Part 1 of our 2026 predictions, we explored the reasons why data readiness is rapidly becoming essential for producing the best CX and AI outcomes – aligning with the need for hyper-personalized digital experiences. The second part of our predictions shifts the focus to agentic AI and how it impacts decisions about an organization’s underlying data foundation. 1. **Agentic AI Will Demand Innovative Data Readiness Solutions to Solve Persistent Data Quality Issues** According to Gartner, it is estimated that by 2028, 40 percent of agentic AI projects developed to support customer experience will fail due to issues with the quality and consistency of the data being used. **For agentic AI to play a key role in managing the relationships a brand builds with its customers, it must be supported by clean data fit for the intended use case**. Data readiness is a crucial element for providing the foundation of good data to fuel agents. But AI agents should not have to fix their own data. Ideally, the customer data will be ready before they need it – i.e. it will be right and fit-for-purpose – as we explored in Part 1 of our Predictions. Absent a good foundation, AI agents can now use key data services that already exist to get customer data ready for high-value autonomous use cases in CX, marketing and data products. These data services include identity resolution, data quality, matching, and data hygiene. MCP servers can provide best-in-class options, ensuring data is ready (by drawing on a complete, real time customer profile) allowing autonomous agents to operate accurately and efficiently in complex customer data processes. A channel activation agent, for instance, queried by a marketing user to understand an audience, to visualize segments, or to analyze product affinities, becomes a key marketing and CX asset by virtue of being underpinned by data readiness. The same holds true for a DataOps agent managing data observability and data quality, handling questions from data engineers that might include how ready a particular dataset is to support a specific use case or how source data quality is trending. The use of agentic AI will take off in the coming year as recognition builds that AI agents and MCP services can be used to resolve data quality issues. There will be widespread use of agents that continuously check for problems as data is created or updated, spot unusual activity, identify duplicate records, and automatically apply data quality rules without human intervention. By leveraging the capabilities of agentic AI, organizations will significantly enhance their approach to customer data management, ultimately leading to improved data quality and operational efficiency. - **Proof Point**: According to Martech for 2026, more than half (56 percent) of data teams say the biggest issue teams have with integrated AI systems is poor data quality. “AI,” the report says, “is the epitome of garbage in, garbage out.” 2. **‘Customer Agents’ and ‘Brand Agents’ will Collaborate in Breakthrough Ways** The Gartner five-year personalization outlook posits that a digital twin of the customer strategy (DToC) will become a staple of the personalization landscape. As such, there will be an even greater need to understand customer intent, expectations, and needs. Agentic AI expands the concept of a digital twin in CX, in which brands build AI agents representative of a customer that autonomously perform tasks on behalf of a customer, such as scheduling an appointment, initiating a return, booking a hotel room, etc. The more a brand knows about a customer, the better the CX that will be delivered through the agent as a proxy. **Agentic AI has the potential to redefine customer engagement as autonomous agents manage complex tasks and anticipate customer needs – underpinned by real-time, high-quality customer data**. As these complex tasks become more routine, we will see the concept expanded to where customer agents interact directly with brand agents. Customers will create agents to interact with their favored brands, providing the agent with the information needed to operate on their behalf. The more trusted the brand, the more information the customer will provide the agent – knowing the tradeoff will be a more relevant CX. One breakthrough CX use case may be for customers to begin using agents to start a customer journey. Instead of a product search on a website, for example, a customer might set rules for its agent and, using GenAI tools, empower it to negotiate with one or more brand agents to find the best deal. The continued integration of AI into customer engagement frameworks will revolutionize how customer agents and brand agents interact. By fostering autonomy, enhancing data integration, and promoting collaboration through advanced tools, organizations will see significant improvements in engagement strategies and customer service. - **Proof Point**: According to Gartner, AI agent machine customers will replace 20 percent of the interactions at human-readable digital storefronts by 2028. Agentic AI will eliminate the need to interact with websites and applications. Why bother when your AI agent can do it for you? - **Proof Point:** According to McKinsey, considering the growing availability and adoption of AI-powered discovery tools, along with moderate assumptions about merchant readiness for agentic commerce, by 2030, the US B2C retail market alone could represent an opportunity to orchestrate revenue in the range of $900 billion to $1 trillion. 3. **Data Management Leaders Will Bring Apps & AI to the Data (vs. Data to the Apps)** The rise of agentic AI places great importance on having all the data accessible and visible in one place, to avoid having the agent miss data or incur huge costs pulling the data in from multiple places. Particularly when a real-time, contextual understanding of a customer is needed. Enterprise companies can minimize this risk with a data-in-place strategy that not only limits unnecessary data exposure, but also performs needed data quality processes in a single platform to create a more timely, trusted, and accurate single view. By keeping customer data in a centralized data lake, such as Snowflake or Databricks, with tools and applications processing the data where it resides, the enterprise maximizes the value of its AI investments. Essentially this brings ‘applications to the data,’ vs. bringing ‘data to the applications,” making it more cost-effective and faster to power insights. Such an environment also allows for greater flexibility and agility, with organizations able to integrate best-in-class components that suit their specific data readiness needs – components that might include data ingestion tools, data quality, identity resolution, segmentation and activation systems, and real-time interaction modules. This environment allows for pieces to be added, swapped out, or customized as requirements and business strategies evolve. - **Proof Point**: According to a 2025 [report by CData](https://www.cdata.com/company/press/state-of-ai-data-connectivity-report/?utm_source=chatgpt.com), **only 6 percent of enterprise AI leaders say their data infrastructure is fully ready for AI**. ## **Conclusion** The role of data readiness in supporting effective CX and AI use cases – particularly to support AI initiatives with AI-ready data – will only become more pronounced with the continued rise of agentic AI. A key trend will be the embrace of agentic AI by the enterprise as a key CX tool for autonomously completing more and more complex tasks, for solving customer data problems, and for allowing customers and brands to interact in innovative new ways. In addition, with a goal of extracting more value from AI, more companies will transition to a data-in-place architecture that allows for more agility and quicker time-to-value in the delivery of relevant, real-time experiences. **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [2026 Predictions: Trends that Will Redefine Customer Engagement (Part I)](https://www.redpointglobal.com/blog/2026-predictions-part-1-three-trends-that-will-redefine-customer-engagement/) **Published:** December 16, 2025 **Author:** John Nash **Content:** *Editor’s Note: This is Part 1 of our 2026 predictions. Part 2 will focus on the role of data readiness in the rise of agentic AI as another key trend to watch in 2026.* Signs that we have reached a critical mass of digital engagement are everywhere. Consumers default to a smartphone as the primary device for banking, shopping, navigation, and entertainment. QR codes are now mainstream for countless customer experience use cases in retail, healthcare, travel, and customer service. Mobile wallets and tap-to-pay are driving the shift to a cashless economy, and digital identity, passkeys, and digital logins are now commonplace. It’s becoming almost impossible to leave your smartphone at home and still function as a productive member of society. This crossover to digital engagement frames several emerging trends that will take root in 2026, centering around data readiness as a key to meeting customer expectations for brand interactions. Here are three predictions based on the digital crossover: 1. **Hyper-Personalization Becomes the Norm** Digital-first customer interactions are the basis for a **tipping point in moving from basic personalization to hyper-personalization**. A study on healthcare consumer engagement from Engagys, a healthcare consulting and advisory firm, illustrates the urgent need for brands to respond to a rise in digital engagement with more relevant personalization. The study showed that digital engagement by healthcare consumers – use of portals, email, and text – has risen for two consecutive years, while non-digital channels saw a decline. Older customers are driving the change, with more than 80 percent of consumers between 60-and-69 using a smartphone as their primary communication device. To meet this digital tipping point, health plans are investing in personalization, with a focus on cost efficiency and member familiarity. Plans identify multichannel orchestration, preference management and “next best action” design as top objectives, reflecting a data-driven mindset. Healthcare payers are moving from the middle to the upper levels of the [personalization capabilities maturity curve](https://www.redpointglobal.com/blog/advance-on-the-personalization-maturity-curve/), where more advanced personalization – like Medicare age-in campaigns and activating members for annual wellness visits – take root. Similarly, retailers are advancing from basic personalization (persona-based product recommendations) to more advanced personalization (real-time recommendations and decisions; multi-stage, multi-trigger, multi-channel journeys). Driving the shift to advanced personalization is the recognition that data-driven personalization will yield out-sized gains in CX improvement, where the quality of experience is measured by greater loyalty and higher revenue. A Forrester study on the ROI of CX transformation showed that for every percentage point increase in the Forrester CX index, companies can earn from $10 to $100 million in incremental revenue. - **Proof point**: According to Gartner, the effective delivery of a personalized CX makes businesses 60 percent more profitable compared to companies that are not customer focused. 2. **Data Readiness is a Must-Have for Organizations to Compete** According to Gartner’s Evolution of Data Management survey, IT professionals identify investments in AI-ready data as their No. 1 priority over the next two-three years, with data quality and governance as a close second. One reason is that even as organizations generate reams of customer data, not enough is used to fuel differentiated AI or CX experiences. In an [Invesp survey](https://www.invespcro.com/blog/data-driven-marketing/), 87 percent of marketers said that data is their organization’s most under-utilized asset – with 54 percent claiming that a lack of data quality and completeness is the biggest challenge to data-driven marketing. **To achieve the hyper-personalization demanded for digital-first engagement requires data readiness** – making customer data ready (complete, accurate, timely) and fit-for-purpose (actionable, trusted, compliant) across the enterprise. Data readiness eliminates the persistent customer experience gap, measuring the distance between the experience consumers expect versus what brands can deliver. Applying the principles of data readiness to all customer data as it enters the system builds a unified customer profile that provides brands with a deep, contextual, real-time understanding of a customer. A persistently updated, complete profile makes the difference between fragmented, static experiences and truly orchestrated, omnichannel personalization. It gives brands the ability to act on fresh, accurate data in real time, anticipating customer needs, optimizing engagement across all touchpoints, and driving measurable growth. - **Proof point**: According to Gartner’s Personalization Customer Survey, **44 percent** of customers block a brand when it communicates in a way the customer perceives as irrelevant or annoying – and **40 percent** will stop doing business with the brand altogether. - **Proof point**: Enterprises that invest in data readiness, particularly in relation to AI, achieve [26 percent higher business outcomes](https://www.gartner.com/document/6634334) compared to those that do not prioritize this area. This includes improvements in revenue generation, cost optimization, and customer experience. 3. **Enterprises will Adopt a Best-in-Class Data Management Strategy as a Foundation for Data Readiness** According to Gartner, most enterprises have a dozen or more data management solutions with overlapping functionalities – a sign that existing architectures are fragmented and complex. With gaps and overlaps in functionality, it is no wonder that the biggest issue teams are running into with integrated AI systems is poor data quality (missing/stale/ inconsistent), with 56.3 percent reporting that as the top issue per a 2025 AI & Data in Marketing Survey by Chiefmartech. Facing these challenges, 35 percent of data and analytics leaders surveyed by Gartner said they need to “significantly overhaul their data management architecture.” **This need to simplify operations will drive the market toward a converged data management platform, complemented with best-in-class components to address key gaps in data readiness.** The old way to build, manage, and operationalize data stores with hundreds of data pipelines has been upended by the need for operational simplicity where success is not measured by data deliverables, but by data products that are easy to find, use, and maintain. As Gartner clearly states, “the question that matters is whether a data team can deliver AI-ready data on time and in full.” Data and Analytic leaders have challenging objectives in the need to 1) simplify their data management platform while 2) improving data readiness and 3) avoiding vendor lock-in and new data silos. This is why they need flexibility to swap out capabilities within their data management platforms with independent software vendor (ISV) solutions that are best in class, particularly to get data right and fit-for purpose. For example, incorporating leading components to perform customer data cleansing, matching and identity resolution is the best way to resolve complex data quality issues, better readying a company’s data for AI and CX use cases. - **Proof Point**: According to the 2025 Gartner Chief Data and Analytics Officer Agenda Survey, 56 percent of senior D&A leaders said that their primary responsibility is to optimize the technology landscape ## **Conclusion** The combination of a consumer-led digital engagement tipping point and the increasing familiarity and comfort level of consumers with AI interactions will result in several trends taking root in 2026. We will see hyper-personalization become a CX standard, and data readiness will be essential – particularly to support AI initiatives with AI-ready data. Finally, the clear need for data readiness – and operational simplicity – will put more pressure on enterprise companies to move toward simplified yet best-in-class data management, both for operational and cost efficiency and to allow the enterprise to deliver more relevant real-time experiences. **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [2023 Will Be a Watershed Year for Customer Experience (CX)](https://www.redpointglobal.com/blog/2023-will-be-a-watershed-year-for-customer-experience-cx/) **Published:** November 9, 2022 **Author:** John Nash **Content:** We are approaching the end of the first full calendar year in which most people accept that consumer data belongs to the consumer rather than the property of the company that collects it. The [“digital curtain”](https://hbr.org/2022/02/the-new-rules-of-data-privacy) was finally lifted, as it were, spurred largely by Apple’s [App Tracking Transparency](https://www.nytimes.com/2021/04/26/technology/personaltech/apple-app-tracking-transparency.html) privacy tool in its iOS 14.5 release and the [announcement by Google](https://www.cnbc.com/2022/02/16/google-plans-android-privacy-change-similar-to-apples.html) that it would follow suit. Even with Google also announcing that it would extend support for third-party cookies on Chrome until 2024, the die has already been cast: In 2023, brands across every industry vertical with a customer-facing dynamic will give consumers more control over how their data is collected, stored and used. The trend toward consumers having more control over first-party data will solidify for several reasons. First, brands will follow the tech giants and become more transparent, with opt-ins and explanations for how consumer data will be used. Second, because of the privacy changes, more consumers believe that their personal data belongs to them and that they – and they alone – can decide how it is used. Third, more brands will fundamentally grasp the concept of the data value exchange, in which consumers willingly provide additional first-party data when they trust that it’s in safe hands, i.e., that a brand will use it to enhance the customer experience, and not sell it to third parties or otherwise use it without a customer’s express permission. With consumer control over data a central theme for 2023, we predict a few more dominoes will fall as far as putting the customer at the true center of a holistic, omnichannel experience. One-to-one marketing, for example, will become both necessary and simpler as an executable strategy. From an industry perspective, a renewed focus on first-party data and the value exchange will have retail companies and healthcare organizations re-think business models. And with the value of first-party data skyrocketing, we will see organizations finally giving data quality its due. We will address each prediction in this space, starting with a little bit more about consumer control and the value exchange. ## **The Data Value Exchange** One thing to understand about Apple’s decision to provide an intuitive data sharing opt-in is that the decision wasn’t made in a vacuum. The writing has been on the wall that consumers have understood the value of their data for some time. Consider a [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/) from 2021 in which 66 percent of consumers surveyed said that they will give brands more information about themselves if the brand uses it to create a more valuable customer experience. For the customer, that value is measured by consistent relevance across an omnichannel customer journey. A brand must recognize an individual customer across every channel and engagement touchpoint, demonstrated by the delivery of a frictionless experience. From the same survey, 82% of consumers surveyed said they are loyal to brands that demonstrate a *thorough understanding* of them as a unique customer, across all touchpoints. ## **A Segment-of-One Tipping Point** It stands to reason, then, that with an expectation for seamless omnichannel personalization, brands will further prioritize a one-to-one marketing strategy. Long held as an almost mythical ideal, one-to-one marketing in 2023 will cross the bridge between a concept and an executable strategy as it is now necessary to reinforce the capture of first-party data which is more valuable in return as data it is less accessible to competitors. Also, one-to-one marketing will become simpler to execute because consumers now explicitly see the value, and today’s technology enables it in ways that were impossible until now. The transition will occur both because of its proven effectiveness with consumers, particularly with the millennial and Gen Z demographics, but also because technology has become far more sophisticated at segmentation. It is possible now to generate hundreds if not thousands of different segments, selections, offers and messages to package and send out that intelligently orchestrate a next-best action to a segment of one. Brands will realize that consumers simply no longer respond to age-based segments or other random data associations that are increasingly irrelevant to an individual customer journey. With self-training machine learning models that dynamically segment audiences and find commonalities based only on what is true in the data, marketers are now able to generate millions of relevant, hyper-personalized interactions that truly reflect a market of one, vs. building an interaction model around a broad segment. In this way, the brand experience adapts to the customer – as the customer actively engages with the brand, inbound or outbound, on any channel. ## **An Outcome-Based Approach to Healthcare** From an industry perspective, the increased focus on a personalized experience centered around a customer will manifest itself in healthcare as a drive toward improving outcomes rather than simply delivering services. In 2023, there will be a tremendous acceleration of a value-based care model that is more aligned with the healthcare consumerism model. There will be an enhanced focus on improving outcomes, lowering costs and improving patient satisfaction, all at the same time by putting the customer at the center of a holistic healthcare experience. To be successful with value-based care, healthcare organizations must have a deep understanding of a patient outside of a clinical setting. We must know all that is knowable about any one person and their situation, within the limits of privacy, to fully improve outcomes. When roughly [80 percent](https://carejourney.com/social-determinants-of-health/) of health outcomes are determined by a patient’s social determinants of health (access to a nutritious diet, economic stability, etc.), having a single patient view that recognizes those determinants is the key to orchestrating personalized experiences that drive improved outcomes. Just as technology makes one-to-one marketing a reality, an enterprise [customer data](https://www.redpointglobal.com/customer-data-platform) platform (CDP) capable of producing an accurate customer golden record using a healthcare organization’s own first-party data makes the single patient view a reality. For the entire healthcare ecosystem to adopt a consumer-centric model, data that is siloed across various systems of record must be integrated into a single view, giving marketers and business users a single point of operational control to improve engagement across the healthcare journey. ## **An Omnichannel Retail CX** In retail, 2023 will further separate the haves vs. the have-nots where the line of success will be drawn based on which brands will orchestrate a personalized, omnichannel experience centered around an individual customer. Based on the current hesitancy among brands to go “all in,” the expectation is that perhaps about 20 percent of brands will get the omnichannel strategy right. Those that do will have the same single point of operational control that will allow healthcare organizations to become consumer-centric. The difference, of course, being that the successful retail brands will instead be able to define and control experiences and customer journeys across the enterprise seamlessly and consistently – and in the customer’s cadence. Take curbside pickup as an example. More brands are rolling it out because customers now expect the convenience, and they expect the seamless interaction between digital and physical channels. Many brands, though, stop at creating the experience. They may offer curbside pickup, but the totality of a customer’s actions and behaviors that led to the transaction are not aggregated into a customer golden record, and other opportunities to engage a customer with relevance at the time of the interaction may be missed. In a true omnichannel experience, a brand is poised to deliver a next-best action at any point in time. ## **Give Data Quality its Due** One final 2023 prediction relates to all that precede it, in that a value exchange, a one-to-one marketing strategy driven by dynamic audience segmentation and customer-centric omnichannel experiences all share data quality as an underlying requirement. Meaning that the organizations that succeed in differentiating based on CX in 2023 will be those that give data quality its due. Perfecting an organization’s own first-party data with advanced identity resolution processes, data cleansing and enhancement at the moment data is ingested from every source is vital to ensuring that organizations interact with the *right* customer, household or entity. Relevant, valuable interactions depend on the organization really understanding who the customer is, and that requires high quality data sets that are both accurate and timely. In conclusion, 2023 will be a watershed year in the customer experience realm. Digital-first experiences now carry the day in every industry with a customer-facing dynamic, and consumers no longer tolerate it when brands discount the importance of blending physical and digital engagements into a singular, frictionless omnichannel experience across every touchpoint. In 2023 it will become crystal clear that time is running out for brands to transform CX or be left behind. **Blog categories:** Data Quality, Identity Resolution, Segmentation & Activation --- ### [How to “TRANSCEND” Marketing Operations with a NoSQL Document Database](https://www.redpointglobal.com/blog/how-to-transcend-marketing-operations-with-a-nosql-document-database/) **Published:** March 26, 2019 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/03/Data-Center_661113826-e1553521431157.jpg)In the race to personalize the customer experience, marketers who work with siloed data in traditional relational database management systems (RDBMS) have become practiced in hedging their bets. Knowing that a marketing program set to run in the morning may be irrelevant to a small cohort if data has yet to refresh, they will be intentionally vague with their message and send broadly personalized info based on segments. With this model, they are betting that there will be more conversions with this vague message than the number of intended targets frustrated by receiving an irrelevant message or offer (e.g. a discount on an item they recently purchased). The problem with this approach is that vague messages do not move the needle for customers, who now more than ever before expect to be recognized and treated as an individual. For marketers, delivering a hyper-personalized customer experience in line with customer expectations requires keeping pace with an always-on consumer. It requires meeting the customer at every touchpoint of an omnichannel journey, on any device, and in the precise context and cadence. Intentionally vague messaging may have sufficed just a few years ago, but today’s sophisticated customer easily sees through the gambit and will react to impersonal or, worse, irrelevant messages by taking their business to a competitor that offers the personalization they expect. In a recent Marketing Insider Group survey, [78 percent of consumers](https://marketinginsidergroup.com/content-marketing/content-marketing-personalization-imperative/) reported that personally, relevant content increases their intent to purchase. A NoSQL (Not Only SQL) document database transcends the limitations of an RDBMS that have prevented marketers from providing customers with personalization at the speed and scale necessary to deliver a real-time, dynamic customer experience across channels and devices. I will present a session about NoSQL databases at TRANSCEND19, Redpoint Global’s second customer conference, April 3-5 in Boston. During our session, “Use Cases and Practical Applications for NoSQL and Document Databases” (April 4, 10:30 a.m.), we will explore how the maturation of database technologies meshes perfectly with the marketing trend toward personalization, how a NoSQL document database delivers value and the key [document database use cases](https://www.redpointglobal.com/blog/how-to-transcend-marketing-operations-with-a-nosql-document-database/) for marketers using Redpoint Data Management (DM) or Red Point Integration (RPI) to unify inbound and outbound marketing with a NoSQL document database. **Marketing Transformation with Data Consolidation** While NoSQL databases were largely created to satisfy global distribution requirements, marketers quickly realized that the schema-less, unstructured data formats that NoSQL databases support could transform database marketing with significant performance increases. Specific to document databases, a major benefit of NoSQL is data consolidation; by having all customer data available in a single document and accessed without impacting other applications using the same database eliminates any practical need to have siloed data, eliminating the ETL constraints that introduced lag and prevented marketers from real-time customer engagements with the most recent data. Data consolidation enabled by a NoSQL document database allows marketers to achieve operational use cases such as personalization, a 360-degree customer view, and profile management with far greater confidence in the data. Not having to move data around strips away complexity, freeing marketers from having to work with raw data and build queries to create effective marketing programs. Further, a NoSQL document database gives marketers the confidence that any data they need is in the document at the time it’s needed. A segment out of a database, therefore, is guaranteed to be valid at the time of execution, eliminating the fudging and vagueness used to prop up a program that relies on siloed (read: old) data. Trust in marketing programs is solidified because the structure and hierarchy of a document in a document database is known to a marketer. If, for example, a marketer wants to know everyone who has purchased a certain product, any piece of relevant data in existence will be in the document, inspiring confidence in the result. With a SQL database, this might not be the case; a marketer building a query might misplace a command, skew the results, and inadvertently create a campaign marred by irrelevant messages. There are also many IT benefits of a NoSQL document database – a significant cost benefit with everything pushed into a single database and global distribution among them – but our TRANSCEND19 session will focus largely on NoSQL supporting marketers using Redpoint solutions to create personalized engagements in the context and cadence of the customer. **A Confluence of Trends Gives Marketers Strategic Control** Database technology is evolving in tandem with the maturation of marketing platforms and programs. Seasoned marketers may recognize a little bit of an old-school approach in how NoSQL document databases alter their day-to-day responsibilities. Roughly 20 years ago, SQL database technology was a basis for giving marketers more control over raw [data](https://www.redpointglobal.com/blog/first-party-customer-data-delivers-value-and-a-personalized-cx/); marketers had control over what they wanted to build based on the schema. But personalization requirements and the massive amounts of data that marketers must work with render this control almost meaningless, like a finger in a dike. A document database takes back some of the control with marketing requests pre-built by data scientists into a document, meaning the operational marketer only has to click on a drop-down to find what they need. At first glance, an outside view might be that this strips marketers of not just control, but strategic value. But the opposite is true; document database marketing allows marketers to be even more strategic by creating last-mile, segment-of-one engagements and letting machine learning and advanced analytics do the heavy lifting to activate the data. A pre-built document – a one-sheet for your customer – is more in line with marketing objectives than working off a schema because it values recency over volume. Aggregates that use archived data, sentiment, or anything that exists outside the document can be baked into machine learning algorithms. Just as a marketer doesn’t have to worry about where data is stored, they don’t have to worry about ranking data values, or constructing models. The data they need is at their fingertips; while a document database may strip them of control of raw data, it makes up for it in spades by giving marketers strategic control over how data is used to create a differentiated customer experience. **Learn More at TRANSCEND19** While a NoSQL document database has the potential to transform marketing operations, there is still ample room for a database platform with both NoSQL and SQL technologies. A relational database certainly meets a purpose for many use cases and data sets, and is a workhorse known for consistency, availability, and for handling well-defined, rarely changed schema. Introducing NoSQL into your architecture does not have to be an all-in proposition; many organizations explore hybrid implementations that optimize unique outcomes. We will discuss hybrid implementations during our TRANSCEND19 session. Customers who are accustomed to running database marketing on SQL may have concerns about adopting an unstructured DB. Redpoint makes the process of managing this data seamless – allowing people familiar with SQL to navigate a NoSQL tool without requiring a deep understanding of tables, keys, or transaction joins. This straightforward process lowers the entry barrier for many customers, even though in some cases there is still a short learning curve for those more familiar with schema formats. Any learning curve, however, must be measured by the transformative benefits of running Redpoint solutions on top of a NoSQL database. In a recent Veritas report, “[Realizing the Power of Enterprise Data”](https://www.veritas.com/form/whitepaper/realizing-the-power-of-enterprise-data) that polled 1,500 IT decision makers, 40 percent said that having too many different data management systems in use poses their biggest challenge, with 38 percent saying they have too many data sources to make sense of. A NoSQL database at the core of a database marketing strategy significantly mitigates these concerns. Register for [TRANSCEND19 today](https://events.redpointglobal.com/transcend19), and find out why nearly 90 percent of respondents in a recent customer survey said that NoSQL is either “important” or “critical” to their business. ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2018/08/SB_CDP_Transforming_Customer_med.jpg)](https://www.redpointglobal.com/wp-content/uploads/2018/06/Solution-Brief-CDP-Transforming-Customer-Exp-0418-01.pdf) **Blog categories:** Anonymous to Known, Customer Data Platform, Data Management, Data Quality --- ### [Commercial Excellence Begins with "Personalization Excellence"](https://www.redpointglobal.com/blog/commercial-excellence-begins-with-personalization-excellence/) **Published:** January 9, 2020 **Author:** Redpoint Global **Content:** A recent McKinsey article claims that a “tech-enabled transformation” across the commercial function for industrial companies could generate close to [$300 billion](https://www.mckinsey.com/industries/advanced-electronics/our-insights/accelerating-revenue-growth-through-tech-enabled-commercial-excellence?cid=soc-app) in additional revenues. A commercial function playbook details how an investment in analytics will result in commercial excellence across four sources of value: e-commerce and digital marketing, digital sales, advanced pricing, and analytics-driven marketing. While marketing directly accounts for two of the four value sources named in the article, it’s notable that analytics underlines each area as the driving force for achieving sustainable growth. A recognition that analytics must be accompanied by broader change, that it should be led by the business and not IT, and that the best-performing companies build small but highly skilled analytics teams are among several core tenets identified as key to a tech-enabled transformation. While the ideas presented are in the context of the commercial function, they mirror the efforts of data-driven marketing organizations that are using advanced analytics such as automated machine learning for excellence in the area of customer engagement. With a re-reading of the article in this lens, an argument can be made that McKinsey puts artificial constraints on marketing by limiting it to just two of the four value sources for growth potential. If we instead think about marketing as responsible for delivering a holistic customer experience across all channels and every interaction with a brand, it is impossible to separate this experience from commercial excellence. ## **Personalization is the Common Denominator** By accepting that a customer’s entire experience with the brand is inseparable from commercial excellence, then tech-enabled transformation should start with marketing because of the potential to drive growth across the enterprise. Customer experience, in this light, extends beyond a customer’s omnichannel journey across online and offline channels. It also entails interactions with a brand that have not been considered marketing-related in the traditional sense, such as customer service and account management. Customer experience, in a nutshell, extends across every aspect and covers every possible reason someone would interact with the enterprise. Personalization is the thread that ties all these interactions together, showing the customer that the brand recognizes they are the same customer across channels. It is why personalization is now considered table stakes. In the Harris Poll survey commissioned by Redpoint, [63 percent of consumers](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) said that personalization is a standard service they expect. Asked how a brand can demonstrate it recognizes a customer as an individual, 43 percent said it was important the brand recognize them across all touchpoints. From the marketing perspective, then, “commercial excellence” is better understood as “personalization excellence,” which means providing a customer with an unbroken chain of engagement across all touchpoints. According to Rusty Warner, principal analyst with Forrester, there are five key elements of real-time decisions. [Recognition, Context, Insight, Execution, and Optimization](https://go.forrester.com/blogs/15-07-29-lets_get_real_introducing_the_forrester_wave_real_time_interaction_management_q3_2015/) are all important characteristics of the personalized customer experience. While it may seem to be a simple list, each step requires serious thought and attention to achieve the personalization excellence that drives revenue. Any break in the chain interrupts the experience created by ubiquitous personalization, akin to seeing a Starbucks coffee cup in a Game of Thrones scene. It breaks the spell, and introduces friction into an otherwise seamless customer experience. ## **Start with Customer Data** To achieve the level of personalization that customers increasingly expect, a tech-enabled, data-driven transformation using automated machine learning needs to begin with customer data. To deliver a personalized customer experience across every touchpoint, brands need a broad, AI-driven platform for complete, holistic customer experience management. By ingesting data from every source and every type – structured, unstructured, semi-structured, first-party, second-party, and third-party – a brand has a unified customer profile, or golden record, that gives them a 360-degree customer view that includes all behaviors, preferences, transactions. Applying automated machine learning to the golden record using code-free data models tuned to optimize a customer experience across all channels provides marketers with a next-best action for a customer that is always in the context and cadence of a unique customer journey. **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Management, Real-Time Personalization, Single Customer View --- ### [How to “Insure” That Your Marketing Spend Targets the Right Customer](https://www.redpointglobal.com/blog/how-to-insure-that-your-marketing-spend-targets-the-right-customer/) **Published:** August 31, 2022 **Author:** Redpoint Global **Content:** Rising costs due to inflation, supply chain disruptions and severe weather events are among several economic and natural events hitting the insurance sector hard. On one hand, passing along cost increases and risk to new or renewed customers in the form of premium hikes creates acquisition and retention challenges. On the other hand, for existing customers – particularly those with a policy through a multi-line insurer – fixed premiums prevent insurers from making up the difference when claims payouts surpass short-term financial forecasts. According to a [July report from PolicyGenius](https://www.prnewswire.com/news-releases/home-insurance-prices-outpacing-inflation-in-many-states-average-increase-of-12-1-since-last-year-301584394.html), goods and services in the U.S. increased 8.6 percent year-over-year between May 2021 and May 2022, with home insurance premiums increasing 12.1 percent in the same timeframe. [Travelers](https://www.travelers.com/resources/home/insuring/why-did-my-homeowners-insurance-go-up) attributes rising rates to several factors, among them an 18.6 percent increase in material goods for new residential construction, asphalt roofing materials up 16.3 percent, lumber and wood products up 6.2 percent, and 20 separate $1 billion or more loss events (in 2021). Instability creates uncertainty, which is forcing insurance companies to be more strategic in distributing leads, working with underwriters and ultimately generating profitable policies. The mass advertising approach of the past is being phased out, replaced by tailored, data-driven marketing campaigns to both acquire and retain profitable customers. ## **Know Your Customer** Being data driven applies both to marketing to prospects and to retain existing customers. For prospects, it’s important to know everything there is to possibly know about an individual. If an insurer knew, for instance, that the prospect was making steep monthly payments on a high-end auto with traditionally expensive repairs, perhaps the insurer offers a high deductible plan or denies coverage outright. For another example, consider a multi-line customer with combined auto and homeowner policies. If an insurer knew the household had children reaching driving age and had been searching for used cars, it could upsell the customer much more effectively. Conversely, a different marketing campaign might be in store if that same customer had just filed a claim for an extensive home repair. Compiling as much first-party data on a prospect or customer as possible is the key to making sound decisions based not on a law of averages or an educated guess but on specific behaviors of an individual or household. Identity resolution is a foundational capability for generating data-driven insights and orchestrating a next-best action relevant for an individual in the context of the individual’s customer journey, either as a prospect or as an existing customer. ## **A Real-Time Element** Being relevant to a specific customer entails more than aggregating multiple data sources, it also contains a real-time element. More even than being aware of and reacting to various life stages, real-time entails moving with the customer through various channels in the context of an individual customer journey. When a prospect or customer appears online, contacts the call center, visits a branch, etc., an insurance company should know where that customer is in a customer journey – do they have an expiring contract, are they in the market for an annuity, have they recently filed a claim? What the customer searches for online, for example, and attaching that online session to an identity graph is just as important to delivering consistent relevance as knowing the customer has teenage children about to start driving. Knowing a full breadth of transactions and behaviors allows an insurer to carry on a consistent conversation with a prospect or customer over the course of a journey, and be more proactive in engaging with that customer with the most relevant information or content. ## **Identity Resolution Use Cases** Advanced identity resolution capabilities with Redpoint rg1 provides insurance companies with several key benefits: - Using a combination of deterministic (rule-based) and probabilistic (analytics-based) matching, advanced identity resolution minimizes or eliminates interacting with duplicate records and other waste. - Better understanding of a customer (household/entity) – Identity resolution in the building of a unified customer record is key to understanding channel preferences and behaviors of customers in order to target them more effectively. - Implementing controls – Frequency capping and suppression rules placed on a unified customer profile helps eliminate sending messages, offers or content that annoys or confuses customers. - Extending relationships to new devices and contexts – Identity resolution that brings in updated, real-time data ensures a customer record always reflects an up-to-date, complete profile of an individual/household/entity, allowing for consistent, personalized experiences across channels and devices, applied consistently and in line with customer expectations – online and offline. ## **Identity Resolution and Personalization** Omnichannel personalization as an outcome of advanced identity resolution is becoming a competitive differentiator in the experience economy. Recent [McKinsey research](https://www.mckinsey.com/business-functions/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) on the value of personalization found that 71 percent of customers expect a personalized customer experience, and 76 percent of customers become frustrated when brands fail to do this. The value of personalization as it pertains to retention and customer loyalty underscores why data quality is a critical element of identity resolution, and why simply matching records is just the tip of the iceberg for resolving identities across all touchpoints, records, devices, channels and identifiers. Because inaccurate matching, overmatching or undermatching all result in the likelihood of introducing friction into a customer experience with irrelevant or unwanted offers or communications. With a firm understanding of the importance of data quality, and perfecting data the moment data is ingested from multiple data sources, it becomes clear why identity resolution is a core component of a CDP. Many organizations mistakenly think of a CDP as primarily a data aggregation system meant to automate some personalization. The reality, though, is that a CDP is really only as good as its data quality processes – and when it tackles those data quality processes – in creating a complete, accurate golden record on the back of advanced identity resolution capabilities. To learn more about how Redpoint can help provide you with the most precise view of every customer, and ultimately use a personal understanding to eliminate waste, deliver a personalized experience and drive revenue, [click here](https://www.redpointglobal.com/customer-data-management/identity-resolution/). **Blog categories:** Financial Services, Identity Resolution --- ### [Elevating Data Quality as the Foundation for Enterprise-Wide Customer Intelligence](https://www.redpointglobal.com/blog/elevating-data-quality-as-the-foundation-for-enterprise-wide-customer-intelligence/) **Published:** March 16, 2026 **Author:** Amandeep Singh Khanuja **Content:** As enterprises move beyond the era when data volume provided an edge, the differentiator really is data quality, assurance of business-critical data being accurate, complete, and consumption-ready in real time. Modern digital ecosystems create data at speeds never before imagined; yet the ability to affirm, harmonize, and manage this data usually falls well behind. At the QKS Group, our research leads unequivocally to one conclusion: Data Quality has become the strategic foundation for enterprise-wide customer intelligence, operational resilience, and AI-powered innovation. This article highlights a forward-looking perspective toward evolving Data Quality from reactive cleansing to autonomous and intelligent data quality systems that can detect, prevent, and resolve issues before they impact downstream operations. Whether the use case in mind is real-time personalization, existing analytics, or emerging enterprise AI, the constant theme is clear: trusted data must come first. While many organizations remain in early stages of continuous data quality maturity, Redpoint Global is already laying the foundation for tomorrow’s AI-driven governance, without sacrificing high-performance delivery needs today. For enterprises modernizing legacy systems or accelerating digital transformation, this article provides key insight into what the future of Data Quality looks like and how leaders such as Redpoint Global enable that future with precision and confidence. ### The Convergence Gap: Elevating Data Quality in a Fragmented Data Landscape Modern enterprises operate on unmatched volumes of data; however, often their quality remains inconsistent, siloed, and unreliable. Traditional approaches to data quality have relied on batch processes and manual validation, which cannot scale in hybrid, fast-moving architectures. As organizations adopt more data sources, automation frameworks, and cloud-native pipelines, the distance from data creation to trusted insight only grows. Yet across this analysis, one trend emerges: manual data quality processes are no longer fit for purpose in modern data ecosystems. DataOps-driven environments with embedded quality check-points, automated rules, continuous profiling and instant anomaly detection are the order of the day. However, rapid delivery of data through pipelines continues to outrun the organization’s ability to validate and govern it in real-time. This creates what we define as the Convergence Gap: the disconnect between accelerating data flow and the ability to ensure its trustworthiness. Closing this gap requires bringing together two traditionally distinct capabilities: 1. **Enterprise-Class Data Quality**: Embedded automated profiling, validation, cleansing, and enrichment in every stage of the data lifecycle. 2. **Data Observability**: End-to-end and real-time view of pipeline health, freshness, completeness, and anomalies; and assurance that upstream issues do not contaminate downstream intelligence. By 2026, enterprises will demand continuous governance, real-time quality standards enforcement, AI-enhanced anomaly detection, metadata-driven rules, and observability of data health across the whole information lifecycle. The Data Readiness Hub from Redpoint Global is designed to address this convergence by making organizations DataOps-agile but with uncompromised data trust. ### Redpoint Global’s Data Readiness Hub: A Purpose-Built Foundation for Data Quality The Redpoint Data Readiness Hub, purpose-built for customer data, provides continuous quality, identity accuracy, and real-time refinement required for enterprise-scale customer intelligence. Core components include: **Identity Resolution & Golden Record Accuracy**: Redpoint applies advanced probabilistic and deterministic matching to consolidate the fragments of customer identities together with ML classification. That results in one golden record-a single, highly accurate representation of each customer. It removes duplication, resolves inconsistency, and hence provides a consistent data asset for Activation, Analytics, and AI. **Real-time Data Quality Enforcement**: Unlike traditional batch-oriented methods, Redpoint validates and standardizes the data upon arrival to provide for immediate cleansing and enrichment. Address verification, formatting rules, consistency checks, and completeness scoring ensure that only trusted data passes down. **Integrated Observability for Data Quality Health**: Every stage of the process is fully observable, from ingest to match to activate. Real-time metrics collected by Redpoint include: - Freshness - Completeness - Error rates - Transformation lineage - Quality scores Issues will trigger teams through contextual notifications to identify the root causes faster. **Metadata-Driven Governance & Automation**: Rich metadata, such as rule versioning, lineage records, and historic quality metrics that Redpoint automatically creates powers: - Automated remediation workflows - Intelligent anomaly detection - Compliance reporting - Audit transparency **AI-Ready Data for Advanced Use Cases**: Redpoint makes sure the AI pipeline gets trusted, privacy-compliant, high-quality data. The “bring your own model” capability and integration with MPC enable enterprises to embed predictive and privacy-enhancing logic directly into data processing flows. **Composable Architecture for Scalable Modernization**: Redpoint’s headless, API-driven, cloud-agnostic design easily fits into any enterprise stack and can modernize incrementally without disruption. Bringing these together will make Redpoint not just assess the quality of data but actually operationalize and enforce quality at scale continuously. Thereby, this real-time foundation of quality drives quantifiable business outcomes through better decisions, high accuracy in personalization, faster time-to-value, and lower risk. *“AI doesn’t fall short due to weak models; it falls short due to weak data. With real time decisioning and autonomous systems now defining competitive advantage, data quality has to be proactive, persistent, and built directly into the data lifecycle. Redpoint’s mission is to guarantee that customer data is trustworthy long before it fuels analytics or AI.”* Ian Clayton, Chief Product Officer, Redpoint Global ### Analyst Perspective: Redpoint at the Forefront of Data Quality Excellence QKS Group’s evaluation places Redpoint Global as a Leader due to its ability to unify identity resolution, data quality, and observability into a cohesive, enterprise-ready platform. Its strengths include: - Continuous identity resolution and golden record accuracy - ML-driven anomaly detection and automated quality enforcement - Deep metadata governance and transparent lineage - Real-time processing and low-latency integrations - A composable, API-first architecture - Strong alignment with DataOps and continuous data assurance principles Redpoint’s approach directly tackles the oldest problem in enterprise customer intelligence-trusted data at every system, channel, and interaction. ![Qks Blog Embedded Image](https://www.redpointglobal.com/wp-content/uploads/2026/03/QKS-Blog-embedded-image-800x430.png) ### Future Outlook: The Rise of Autonomous Data Quality and How Redpoint Is Ready to Lead It The cost of poor-quality data will increase exponentially as enterprises embrace AI, predictive analytics, and customer automation. Models trained on incorrect or incomplete data amplify risk and degrade decision-making. As a **Leader and Ace Performer** in the SPARK Matrix for [**Data Quality & Observability Tools 2025**](https://www.redpointglobal.com/resources/qks-group-spark-matrix-data-quality-and-observability-tools-2025/), Redpoint demonstrates that rare capability to handle the full complexity of customer data while enabling consistent trust at enterprise scale. Industry trends indicate one thing in no uncertain terms: - Generative AI amplifies the demand for correct, contextual, compliant data - Autonomous rule engines will replace manual data quality tasks - It will also ensure that self-healing pipelines detect quality issues and resolve them without human intervention. - Agentic orchestration: AI assistants will be integrated into data workflows. Redpoint Global is aligned with that future already: its anomaly detectors, rule engines, golden record accuracy, and composable design put it right at the leading edge of a new class of autonomous data quality systems platforms that learn continuously, self-correct, and optimize adaptively. As organisations move toward real-time, AI-driven operations, the capabilities of Redpoint furnish the trusted data foundation necessary for scalable innovation. **Blog categories:** Data Management, Data Observability, Data Readiness **Blog tags:** Data Observability, Data quality, Data readiness --- ### [Drive AI Success with AI-Ready Data](https://www.redpointglobal.com/blog/drive-ai-success-with-ai-ready-data/) **Published:** February 17, 2026 **Author:** John Nash **Content:** Gartner named AI-ready data as one of three pillars to support strategic AI opportunities in its CMO Leadership Vision outline for 2026 (**See Figure 1**). With AI “transforming the structure, strategy and scope of the marketing function,” success requires that CMOs shift to human-agent teams that redefine how marketing creates value, and earn trust through “emotionally relevant brand experiences.” Content and experience orchestration were named the other two strategic pillars necessary to capitalize on AI, with the overarching objective of driving growth through multichannel personalization. ### **What is AI-Ready Data?** [AI-ready data](https://www.redpointglobal.com/blog/all-successful-ai-projects-start-with-ai-ready-data/) is data that is transformed, cleansed and enriched immediately upon ingestion to be contextually usable for any AI use case. To be considered truly AI-ready, data must both be [“right” and “fit-for-purpose”](https://www.redpointglobal.com/data-the-defining-difference/). The right data is data that is complete, accurate, and timely. - **Complete**: It reflects [a full understanding](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/) of a customer, household, or business entity using data from all sources, and of all types. - **Accurate**: Identity is resolved at the [individual, household or business entity level](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/) using deterministic and probabilistic matching. - **Timely**: Unified profiles, attributes, and model scores are [continually updated](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/) and made available across the enterprise in the cadence of an individual customer journey. Fit-for-purpose data means that data is actionable, trusted and compliant. - **Actionable**: It is in the [appropriate form for consumption](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/) by downstream applications and users - **Trusted**: It is [observable and tunable](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/) so users can verify results - **Compliant**: PII data is secure with [managed permissions](https://www.redpointglobal.com/blog/ready-data-is-compliant-data-does-your-data-hold-up-to-scrutiny/) for sensitive information ### **The Data Readiness Process** The production of [AI-ready data](https://www.redpointglobal.com/blog/all-successful-ai-projects-start-with-ai-ready-data/) is the primary function of a data readiness hub, which cleanses, standardizes and matches data at the point of ingestion before it reaches downstream processes or third-party applications. The creation of a golden record – a unified customer profile – is a foundational [data product](https://www.redpointglobal.com/blog/everything-you-need-to-know-about-data-products/) produced by a data readiness hub. Validating that data are right and fit-for-purpose via autonomous data quality from ingress to activation, a data readiness hub gives data the needed context for AI. ![Reimagine Processes Equipped With Ai](https://www.redpointglobal.com/wp-content/uploads/2026/02/Reimagine-Processes-Equipped-with-AI.jpg)**Figure 1**: Strategic pillars for driving growth with multichannel personalization. A [data readiness hub](https://www.redpointglobal.com/data-readiness-hub/) provides the strong data foundation that is a prerequisite for an effective AI strategy. AI-ready data improves the accuracy and reliability of models, reduces bias, and saves costs by eliminating the need for manual workflows for data standardization. With this foundation, AI agents have the needed precision and updated context required to execute intelligent, autonomous actions and to provide more relevant, trustworthy outcomes. That precision and context is required for both human-driven workflows as well as for agents making sense of the data itself. ### **Agentic AI in Action** Referring to Gartner’s three-pillar pyramid for driving growth through multichannel personalization, one purpose for AI-ready data is to identify new segments, which bleeds into the second pillar: content, where they claim AI makes it possible to generate 10X more content (for all those new segments!) at no additional cost. The first two pillars are tightly intertwined, with AI-ready data as the common denominator. AI-ready data fuels more granular segmentation, which creates the need for more content to tailor to those segments and/or individuals. Consider, for example, a segmentation agent as a key agentic AI application, where the intelligent agent recommends and creates dynamic segments that can be immediately used to orchestrate customer journeys. Unlike a static audience list, these AI-driven segments adapt as AI evaluates contextual signals and customer data in real time. In a human-driven, prompt-based workflow (e.g., “build me a segment”), AI executes complex data queries and logic to generate an audience. In both human and data-driven use cases, the effectiveness of AI-powered segmentation is directly tied to the quality of the underlying data. The use of high-quality data (right *and* fit-for-purpose) powers a closed-loop cycle of improved outcomes; with high-quality data inputs, AI models generate more meaningful, trustworthy insights, which in turn lead to more accurate unified profiles and more precise audience targeting. In this manner, audience building is always based on the most current, accurate and “right” customer context. ### **A Composable Hub** Gartner posits that composable tech (including a CDP “hub”) is a foundational technology to support multichannel personalization. The reason is that in the world of agentic AI, even with a data readiness hub providing AI-ready data, AI agents do not operate on an island or in a closed system. Rather, they work in concert with other agents from multiple sources. A [composable data readiness hub](https://www.redpointglobal.com/blog/driving-composable-cdp-success-with-data-readiness/) allows the agents to work together in an interoperable way, with model context protocol (MCP) servers providing a common understanding – a single source of truth. Ideally, agents will sit near the data connecting with the data readiness hub and thus have access to a solid data foundation without having to continually repeat core processes, such as identity resolution. In bringing the application close to the data, the data readiness hub adheres to the principles of [data gravity](https://www.redpointglobal.com/blog/the-gravity-well-of-customer-data-why-ai-agents-and-real-time-cx-demand-data-proximity/) while still recognizing that agentic AI changes the calculus, requiring the need for distributed data. A data readiness hub sits at the intersection of data gravity, MCP servers, localized agents and orchestration, offering agents a consistent framework for human-driven and data-driven tasks without requiring constant data movement. A composable hub allows agents to orchestrate journeys using that solid data foundation. Just like humans interacting with GenAI, agents are asking questions of the data and retrieving answers where the decisioning is happening close to the data. ### **Agentic AI & Composability: a Framework for Growth** The combination of a composable data readiness hub and agentic AI technologies forms the backbone of effective multichannel personalization. By providing AI-ready data and fostering interoperability among agents, organizations can ensure that both human-driven and autonomous workflows are grounded in accurate, current and contextually rich information. This solid data foundation enables more dynamic segmentation and targeted content creation, and also supports the orchestration of customer journeys at scale, resulting in more relevant and trustworthy outcomes for businesses and their customers. **Blog categories:** Agentic AI, AI & Machine Learning **Blog tags:** Agentic AI, Data quality, Data readiness --- ### [Personalization in Practice: Driving Engagement and Revenue with Commercially Insured Patients](https://www.redpointglobal.com/blog/personalization-in-practice-driving-engagement-and-revenue-with-commercially-insured-patients/) **Published:** February 24, 2026 **Author:** Redpoint Global **Content:** Commercially insured patients are essential to the financial stability and clinical performance of today’s health systems. As explored in [Smarter Communication, Better Care: A Data-Driven Approach to Engaging Commercially Insured Patients](https://www.redpointglobal.com/blog/smarter-communication-better-care-a-data-driven-approach-to-engaging-commercially-insured-patients/), these patients often disengage in their care journey because communication feels generic, outdated, or disconnected from the care experience. Providers are also hindered in their ability to guide these patients to the right care, as they’re limited by fragmented data and inconsistent outreach, leading to missed screenings, follow-ups that never get scheduled, and patients seeking care outside of the health system. For health systems looking to build strong relationships with commercially insured patients, here are four steps to a more patient-centric, hyper-personalized outreach approach: ### **1. Build a Unified Patient Profile That Goes Beyond Clinical Data** Health systems rely on EHR and claims data to understand patients. While essential, these data sources alone don’t paint a complete picture of a patient’s health history or lifestyle, missing a key personalization opportunity that can impact how they engage with the system. Creating a unified, real-time patient profile from various data sources can help to better understand the patient as a whole and bolster engagement to help close gaps in care and maintain system loyalty. The unified patient profile should include: - Clinical and claims data - Social determinants of health - Behavioral and engagement signals - Communication preferences - Past response patterns A unified profile requires data that is accurate, current, de-duplicated, and consistently available across the system in order for provider teams to act. Without data readiness, personalization efforts can miss the mark. When a complete patient profile is centralized and accurate, teams can personalize outreach and avoid sending irrelevant messages that can lead to disengagement. ### **2. Align Communication with Patient Preferences** Commercially insured patients prioritize convenience and personalization. But just because *most* of your patients respond to text messages doesn’t mean it’s right for *everyone*. Messages sent through the wrong channel can make outreach feel impersonal, or worse, go completely unnoticed because some patients simply don’t engage on that channel. Providers can make improvements by: - Capturing patient communication preferences during appointments - Applying these preferences consistently across the system - Ensuring your entire team has access to these preferences to maintain consistent outreach practices Siloed systems lead to inconsistent or conflicting patient messages, but communication built from a unified patient profile ensures that every message reflects the most accurate information about where a patient is in their care journey and how they prefer to engage. ### **3. Use Predictive Insights to Guide Patients Toward Care** Health systems utilizing updated patient profiles can anticipate patient needs long before they appear in the chart. Providers can leverage predictive insights to proactively support patients by identifying those who: - May be at risk for condition progression before it happens - Are candidates for specialty programs based on previous care encounters - Fit Social Determinants of Health markers for future elective services - Have delayed recommended care - Are showing signs of disengagement For example, if a patient’s health signals and activity patterns suggest a future need for orthopedic care, the system can kick off a long-term, multi-channel drip campaign that highlights a COE facility and leading specialists before any injury occurs. In time, that “weekend warrior” athlete and obstacle course race enthusiast that previously only utilized preventive appointments would be familiar with in-network options when an acute or chronic condition requires care. This proactive approach can guide patients to stay within the health system rather than exploring options externally, driving service line acquisition. ### **4. Measure Engagement as a Core Performance Metric** Improved communication must drive patient action that can be tracked within the system to measure results over time. This requires a privacy-compliant persistent “key” that connects individuals to communications, engagement and outcomes for proper measurement. Teams can then accurately track campaign-level return on marketing investments and channel-attributable revenue across the full marketing funnel, even for long-term initiatives. Not only does this show the value generated for the system, it also leads to improved campaign strategy and performance over time. Tracking engagement to reveal true patient behaviors can include metrics like: - When a patient converts to care, and what communications triggered action - Appointment scheduling and completion rates over time - Preventive screening adherence - In-network specialty referrals - Granular multi-channel attribution - Patient response to outreach by channel - Repeat visits and long-term retention Centralized, ready-to-use data that meets HIPAA regulatory compliance requirements enables consistent analysis of patient campaigns, engagement, and appointment completion, giving leaders the visibility needed to scale what works. ### **The Path Forward: Hyper Personalized, Effective Care** Commercially insured patients drive the revenue that enables provider organizations to continue delivering high‑quality care for all. Personalization at scale is no longer optional; it’s a strategic lever for patient satisfaction, improved health outcomes and organizational sustainability. When providers pair strong data foundations with human‑centered communication, they can meet patient expectations while also improving operational efficiency and long‑term patient retention. This level of engagement doesn’t just elevate the patient experience—it strengthens the financial backbone that makes whole‑community care possible. The organizations that invest in personalization today will be the ones best positioned to sustain their mission tomorrow. Did you miss part one of this series? Read [Smarter Communication, Better Care: A Data-Driven Approach to Engaging Commercially Insured Patients](https://www.redpointglobal.com/blog/smarter-communication-better-care-a-data-driven-approach-to-engaging-commercially-insured-patients/). **Blog categories:** Healthcare **Blog tags:** Data quality, Data readiness --- ### [The Pros and Cons of Composable Architecture](https://www.redpointglobal.com/blog/the-pros-and-cons-of-composable-architecture/) **Published:** January 20, 2023 **Author:** Ian Clayton **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Blog categories:** Identity Resolution, Master Data Management --- ### [Why You Need Data Readiness in a Modern Data Cloud](https://www.redpointglobal.com/blog/why-you-need-data-readiness-in-a-modern-data-cloud/) **Published:** August 11, 2025 **Author:** Steve Zisk **Content:** Improved security, better business performance in a multi-cloud environment (without taking on more IT infrastructure), greater flexibility, reduced cost and complexity, and quicker time to extracting value from customer data are among the many reasons enterprise companies are pivoting to a data cloud architecture. However, without a clear strategy for ensuring data readiness, many of these promised benefits remain out of reach, turning cloud investments into expensive storage rather than engines of business value. A clear data readiness strategy reduces many gaps in infrastructure, applications, data and skills that have the potential to hinder business goals. Primary data readiness objectives – making data both right ([complete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/), [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/), [timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/)) and fit-for-purpose ([actionable](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/), [trusted](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/), [compliant](https://www.redpointglobal.com/blog/ready-data-is-compliant-data-does-your-data-hold-up-to-scrutiny/)) – are a prerequisite for effective cloud management, for migrating and managing data in the cloud, and for maintaining the quality, accessibility, and governance of customer and business data over its lifecycle. ## **Maintain Control of the Single Customer View** A data readiness platform that operates on a primary customer database within a data cloud and creates a unified profile of a customer, household or business entity is a key first step in cloud data management. By making the profile accessible and available in real time to any application that needs it, a data readiness platform provides the enterprise with an actionable single customer view while minimizing data movement and replication, leading to better performance at lower cost, with better governance – security and compliance, but also lineage, usage patterns, quality standards. Costs are controlled because data readiness produces clean, unified and fit-for-purpose data before it is activated, preventing unnecessary data movement or redundant processing within the data cloud. Minimizing compute and storage costs yields greater efficiency, ensuring optimal business outcomes both by not having to manage messy data, and because the business operates with a firmer understanding of the customer – fewer duplications, more targeted marketing, relevant CX, etc. In this way, the enterprise controls their data – and the cost of managing data – all while having control and visibility into the building of the unified profile. And, by maintaining a data-in-place environment, the enterprise is able to execute queries and processes directly within the data cloud, ensuring that no data persists outside of the environment. ## **API Accessibility** With a data readiness platform deployed on a data cloud, organizations can easily handle, store, and examine large amounts of customer data in a quick and adaptable way, helping them get immediate insights and customize customer experiences. And with near-infinite scalability in a cloud-based architecture, organizations ensure that their infrastructure grows with the business as CX, AI and other use cases evolve. Additional flexibility comes from a data readiness platform that offers bi-directional APIs from data ingestion through to activation. This capability ensures that data is ready and fit-for-purpose across the customer data lifecycle, available to any external touchpoint, outbound service provider, or marketing channel – and for modeling and analytics. ## **Data Readiness as You Define It** Another reason a data readiness platform is ideal for managing customer data in a data cloud is that composable services give organizations the option to use only what they need. When data quality, identity resolution, and audience/segment selection are core features of a data readiness platform, an organization does not have to outsource these activities to another application – or write code. Instead, with a platform that offers composable services and APIs for identity resolution, customer profiles, segmentation, data orchestration and real-time interactions, the platform brings together the various pieces needed to perform a complete business process or function in one environment. The platform then becomes the single source of truth for customer data, maintaining a consistent understanding of the customer, household or other entity across all applications connected to the data cloud. A robust, enterprise-ready data readiness platform should provide: - **Automated Data Ingestion and Data Quality**: Handling all enterprise data at the cadence of the customer. - **Identity resolution**: Tunable for any use case while delivering complex and accurate customer profiles using probabilistic, deterministic and machine learning techniques. - **Segmentation and activation**: Using selection rules and models to dynamically and precisely segment audiences and power superior CX at every touchpoint. Using these composable services, an organization can deploy data readiness capabilities where and when they are needed, including as part of enterprise data services or agentic workflows. Ready to maximize your data cloud investment with a data readiness strategy? Learn how Redpoint can help you deliver immediate CX and AI value with clean, connected, compliant data in your data cloud environment: [Data Readiness Hub – Redpoint Global](https://www.redpointglobal.com/data-readiness-hub/) - For information on the strategic framework between Redpoint and Snowflake, visit [“Redpoint and Snowflake: A Winning Combination.”](https://www.redpointglobal.com/resources/redpoint-and-snowflake-a-winning-combination/) - And to view the recording of “Redpoint & Snowflake: The Framework to Support an Evolving MarTech Stack,” click [here](https://event.on24.com/wcc/r/4414778/B97632EF49033C94E63EF573FD11F360?partnerref=rpgblog). **Blog categories:** Data Quality, Data Readiness **Blog tags:** Data readiness --- ### [How Data Readiness Powers Agentic Action](https://www.redpointglobal.com/blog/how-data-readiness-powers-agentic-action/) **Published:** August 1, 2025 **Author:** John Nash **Content:** Agentic AI is capturing headlines, promising intelligent agents that can autonomously take action, learn, and optimize customer experience (CX). But for agentic AI to deliver on its potential, it requires more than simply making data accessible through APIs. Many customer engagement technology vendors claim to be in the agentic AI game, but in reality they’re doing little more than making software components callable by an external agent – whether or not that external agent autonomously completes a task. True agentic AI requires clean, accurate, and timely data that is fit-for-purpose for agent use, making data readiness a requirement to maximize the value of agentic AI across the enterprise. More importantly, agentic AI requires a platform that does more than pass data to external agents; it actively participates in the agentic process itself. ## **Agentic AI Is More Than API Access** Simply exposing data via APIs to allow external agents to call data for their workflows is certainly important as a first step to agentic AI, which is why a data readiness platform – in addition to making data right and fit-for-purpose – should also make its software components callable by agents in ways that optimize agent knowledge, actions and workflows for success. To truly support agentic AI a platform must empower agents that act autonomously using high-quality data and the proper tools to carry out *actions* in any given context. The better and faster the callable components are, the better agents can optimize actions to reach their goals. This is a key difference between simply handing data off vs. becoming active participants in the agentic process. ## **Human-Centered vs. Data-Centered Agentic AI** Agentic AI can take many forms. Some agents are human-centered, acting on natural language prompts like “build me a segment” or “show me a journey visualization.” Redpoint enables these interactions through prompt-based workflows that allow human users to request insights, actions, or visualizations – delivering immediate, explainable outputs. But the future of agentic AI also includes data-centered agents that participate directly in workflows. These agents can: - **Perform identity resolution** autonomously when triggered by external systems. - **Cleanse, match, and standardize data** in response to requests from other agents. - **Monitor campaigns** and trigger actions based on results. - **Evaluate when customer data and contextual signals** are ready to launch a campaign or nurture workflow. Unlike traditional triggered actions that require rigid rule-setting, agents have the autonomy to determine when data is ready, when criteria are met, and when to act. They blend intelligence with action, reducing manual intervention while ensuring high-quality outcomes. ## **Building Toward True Agentic AI** There is a maturity curve to agentic AI that consists of three distinct parts. The first step is to allow agents to call and use high-quality data. Again, this is what some vendors are now claiming as full-on agentic AI vs. a stepping stone to the next step, which is data made actionable for agents. This means providing active metadata, contextual signals, and aggregations that make data understandable and usable by agents. The third step crosses the threshold into what can truly be called agentic AI: Developing intelligent agents that can act on human prompts or agent requests, making decisions based on context, and taking actions autonomously across the customer engagement journey. Examples of agentic AI supported by a robust data readiness platform might include: - A **segmentation agent** that recommends and creates dynamic segments that may be used in orchestrating customer journeys. - A **data observability agent** that monitors data health, signals when data is ready for campaign activation, and autonomously executes the process. - An **ID resolution agent** that ensures customer identities are updated in real time or households are validated before personalized interactions occur. - A **campaign readiness agent** that autonomously checks for campaign prerequisites, ensuring content, permissions, and triggers align before launch. - An **integration agent** that interacts with third-party systems like Braze, Adobe, or Salesforce to exchange data, optimizing timing and content to reduce campaign costs and increase reach and effectiveness. ## **Preparing for an Agentic AI Future** Agentic AI, when paired with AI-ready data, creates a new level of customer engagement – one that is responsive, intelligent, and optimized in real time. But it can only succeed when the agents have access to clean, trusted, and real-time data, and when the platform is designed to support autonomous action, not just passive data delivery. At Redpoint, we see agentic AI as a natural evolution of our data readiness mission: enabling businesses to engage customers with intelligence and precision while maintaining control, transparency, and trust. Redpoint’s architecture supports the transition from AI-ready data to embedded AI and now to agentic AI. As we continue to develop our agentic AI capabilities, we will share deeper insights into how Redpoint can help your organization harness autonomous, intelligent agents to optimize customer engagement at scale. **Blog categories:** Agentic AI, Data Readiness **Blog tags:** Agentic AI, Data readiness --- ### [Why Data Consolidation Is the Missing Link in Payers’ Value-Based Care (VBC) Strategy](https://www.redpointglobal.com/blog/why-data-consolidation-is-the-missing-link-in-payers-value-based-care-vbc-strategy/) **Published:** February 2, 2026 **Author:** Redpoint Global **Content:** The shift from fee-for-service to value-based care (VBC) payment models requires data that delivers contextualized, longitudinal member views for payers to quickly identify care gaps. However, payers and their data leaders often face a patchwork of siloed systems when addressing population health. Commonly, claims, clinical data, pharmacy, social determinants of health (SDoH), and behavioral health data are housed separately. When surveying the industry, Gartner found that complex data ecosystems not only lead to bad decision making but that poor data quality also costs the healthcare industry [$12.9 million annually. ](https://www.gartner.com/smarterwithgartner/how-to-improve-your-data-quality)Data consolidation is no longer only an IT goal, it’s a strategic imperative for payers to reduce avoidable utilization and improve medical loss ratio, outcomes, manage risk, and power VBC operations. Payers should prioritize efforts to create a centralized and actionable data environment that contextualizes data, making it accessible, accurate, and useful to reduce inefficiencies and support faster member engagement/campaign deployment. ### **Why Disparate Data Undermines VBC Performance** Data leaders know that fragmented, siloed data leads to incomplete member insights that negatively impact care, downstream outreach and balloon medical spend. They are continually up against these challenges when trying to sift through the member data to uncover care gaps, duplication, and delayed interventions. These pain points lead to internal operational inefficiency as data analysts spend their time cleaning and reconciling rather than analyzing member insights. Externally, the downstream impact affects both members and providers. Member experience suffers as disconnected data means disjointed care coordination and missed engagement opportunities, which can impact quality scores like HEDIS and Star Ratings. Payer-to-provider relationships can also strain since inconsistent data feeds and unclear performance measures weaken trust, further preventing payers and providers from hitting VBC benchmarks. In addition to fragmentation issues, health plans often find data to be riddled with quality issues, unresolved identities, incomplete care journeys, missing feedback loops, and lacking visibility into the member’s health status. What makes health data so challenging is that it’s not only scattered across electronic health records, claims systems, call centers, web interactions, third-party apps, but each source may use different formats, identifiers, and update cycles – making it difficult to unify, interpret, and act on the data in real time. However, by approaching data consolidation with data readiness best practices, payers obtain a deeper understanding of their members. The process enables access to contextualized member insight for situational awareness and behavioral signals that reflect where a member is on their care journey. Payers can personalize, predict, or optimize engagement based on the whole-member health story. Because payers understand members at personal levels, they are better equipped to use the data to fuel meaningful engagement. This outreach activates members to close open care gaps, which ultimately bolster VBC performance. ### **Limitations of Existing Solutions** To consolidate data, a focus on [data readiness](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/) can help fill the data fragmentation gaps that can arise from varying technology and workflow processes. Data readiness takes “dirty” data from disparate sources and makes it [clean, accurate, and fit for purpose](https://www.redpointglobal.com/data-the-defining-difference/). A common misconception is that data readiness can be addressed by using systems such as Master Data Management (MDM), Customer Data Platforms (CDPs), or data clouds; however, these systems have several flaws. First, they miss the dynamic aspects of member behavior, second, they may have flaws in aggregating and unifying upstream data, and thirdly, they don’t solve for identity or data quality. Without correcting the upstream data quality issues other platforms fail to address, health plans are not solving the core issues. Data readiness prepares member data to power any initiative across the enterprise. It delivers the most accurate and complete member record with data that is cleaned, enriched, and unified. With a strong data readiness foundation, health plans can then turn information into impact across the entire member journey with complete, accurate, timely, actionable, trusted, and compliant data. ### **Data Readiness is a Strategic Enabler** To accelerate innovation and competitive differentiation, data readiness must be prioritized. With fit-for-purpose data, member identities are resolved across technology systems, behavioral and transactional signals integrated, and longitudinal insights accessible in real time. Health plans can then rapidly launch new products, deploy engagement campaigns, personalize member experiences, and partner more effectively across the ecosystem. In this way, data readiness is not just a technical requirement, it is a strategic capability that enables plans to adapt, compete, and lead in a consumer-driven healthcare market. Redpoint achieves data readiness for payers through its Data Readiness Hub, which includes: - Automated data quality: Standardization and error correction that happen continuously and automatically, preventing errors from compounding downstream, keeping analytics and engagement tools running smoothly. - Advanced Identity Resolution: Deterministic (exact match) and probabilistic (likelihood-based match) techniques to fit specific use cases. It also understands data from anonymous-to-known journeys, enables a longitudinal view of each patient, and the ability to group individuals who share a household or financial relationship. - Contextual Profile Unification: Curate customer data into clear and actionable profiles — validated contact information, meaningful clinical and behavioral signals, and key interactions across the care journey. Patient profiles are automatically enriched with AI models, calculations, and trusted third-party data to communicate with speed, accuracy, and empathy. - Smart Activation: Where clean, connected, and contextual patient data becomes a strategic asset. Dynamic, real-time segments evolve as new data flows in, continuously orchestrated across multiple engagement channels—whether digital or physical—ensuring that every interaction is timely, relevant, and personalized. ### **The ROI of Data Consolidation** With [Redpoint’s Data Readiness Hub](https://www.redpointglobal.com/healthcare-payers/) in place, data is fit for purpose. Payers can turn data consolidation into a competitive advantage to achieve the most important goal of VBC operations: improved clinical outcomes. Redpoint serves as the connective tissue between technology systems. The Data Readiness Hub enables teams to centralize data from multiple tools, conduct usage audits, and eliminate redundant platforms while preserving core functionality through flexible orchestration and data continuity. Redpoint also creates a centralized, actionable data environment, enabling payers to make member data accurate, accessible and useful to reduce inefficiencies and support faster member campaign deployment. Data leaders can use the contextualized data to fuel population health strategy to ensure the organization can deliver personalized, effective engagement to close open gaps in care, better manage those with chronic disease, reduce hospital readmissions, and bolster member experience to secure VBC reimbursement. Data consolidation is about empowering payers to fulfill the promise of VBC. Payers who unify and contextualize their data gain a critical edge in improving member outcomes, strengthening provider networks, and driving operational efficiency. Interested in learning more? Check out Redpoint’s data readiness hub for payers: **Blog categories:** Healthcare **Blog tags:** Data quality, Data readiness --- ### [The CDP Reckoning: Why Customer Data Platforms Are at a Crossroads](https://www.redpointglobal.com/blog/the-cdp-reckoning-why-customer-data-platforms-are-at-a-crossroads/) **Published:** January 27, 2026 **Author:** Steve Zisk **Content:** For nearly a decade, the Customer Data Platform (CDP) promised clarity in an increasingly fragmented customer data landscape. Bring the data together. Unify it around the customer. Use it to drive better decisions, better experiences, and better outcomes. And for a time, that promise resonated. But today, the CDP category finds itself at a crossroads. Adoption is widespread, yet utilization often lags. The term itself has become overloaded, where it seems that almost any system that touches customer data now lays claim to the CDP mantle. At the same time, newer architectures, cloud data platforms, and AI-driven use cases are forcing enterprises to rethink what they *actually* need from their data. This moment is the CDP reckoning: not the demise of the category, but a necessary re-examination of its purpose, scope, and future. ### **The Original Vision: Data, Insight, Action** The original idea behind a CDP was simple: customer data would no longer live in disconnected silos across channels and departments. Instead, it would be mastered once, unified into persistent customer profiles, and made available for analytics, personalization, and engagement. The vision was philosophical just as much as it was technical. [Customer centricity would replace channel centricity](https://www.redpointglobal.com/blog/customer-centricity-begins-with-the-data/). Insights would continuously inform actions. Actions would generate new data, feeding a virtuous cycle of learning and optimization. At the time, this convergence was genuinely new. Campaign management, analytics, and data management lived in separate systems, often owned by different teams. The CDP promised to bring them together. What few anticipated was how difficult that convergence would be in practice. ### **When a Vision Is Too Big to Land** One of the defining challenges of the CDP era is that its vision crossed too many boundaries at once. Customer centricity required organizational change. Data mastery required deep technical rigor. Activation demanded operational discipline across channels. Ownership spanned marketing, IT, data teams, and compliance. No single buyer truly owned the whole problem. As marketing evolved, roles shifted. Marketers moved away from hands-on data work toward strategy, content, and orchestration. [Data readiness](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/) (entailing quality, identity resolution, governance, and persistence) became the domain of chief data officers and centralized data teams. The result was a structural gap. Marketers needed [trusted, usable data](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/) but were no longer equipped or incentivized to build and maintain it themselves. Meanwhile, data teams focused on platforms and pipelines, not downstream customer engagement. The CDP sat squarely in between. ### **From Clear Definition to Category Confusion** Over time, the clear definition of the CDP eroded. As the market heated up, more vendors claimed the label. In many cases, “CDP” came to mean little more than “a place where customer data exists.” At the same time, some technology providers – particularly those rooted in data warehousing – rejected the term entirely, declaring CDPs obsolete in favor of cloud platforms, data lakes, and [composable architectures](https://www.redpointglobal.com/blog/the-composable-cpd-faq-for-marketers/). Both sides were reacting to the same reality: technology had changed. Modern cloud data platforms made it far easier to centralize data than it once was. Organizations began asking why they needed a separate CDP if their customer data already lived in Snowflake, Databricks, or a hyperscaler ecosystem. That question is at the heart of today’s reckoning. ### **The Real Problem Never Went Away** Despite new architectures and new terminology, the core problem CDPs were designed to solve has not disappeared. Enterprises still struggle to: - Create a complete, accurate view of the customer across systems - Resolve identities across people, households, and entities - Maintain data quality at scale - Make customer data available in real time - Apply consistent governance and permissions - Use that data to drive relevant, contextual engagement Cloud data platforms excel at storage and processing. They do not, on their own, deliver persistent customer profiles, identity resolution, or situational awareness. Organizations can build those capabilities – but doing so turns them into software vendors, with all the cost, complexity, and maintenance that implies. The reckoning, then, is not about whether customer data unification is still needed. It is about *how* and *where* that work should happen. ### **From Labels to Capabilities: A Shift in Thinking** The most productive way forward is to stop arguing about labels and start focusing on capabilities. Instead of asking, “Do we need a CDP?” organizations must ask: - What customer data do we need, and why? - Which use cases actually matter? - Where does data need to live to support our use cases? - What services must exist to make data usable, trustworthy, and actionable? For some organizations, a packaged CDP remains an efficient way to deliver those capabilities. For others, extending existing data platforms with specialized services may make more sense. The right answer depends on strategy, scale, skills, and urgency – not on category definitions. This reframing also explains why utilization gaps exist. Many organizations bought CDPs before fully articulating their strategy, use cases, or operating model. Technology alone cannot compensate for unclear goals or insufficient process change. ### **Context Is the New Currency** Across successful implementations, one theme consistently emerges: [context matters more than content](https://www.redpointglobal.com/blog/does-your-customer-data-have-enough-context-heres-how-to-tell/). Personalization fails when there is a disconnect between an offer and the customer’s situation. Without situational awareness, such as a clear understanding of what the customer has done, what they need now, what constraints exist, etc., engagement becomes irrelevant or even damaging. With the right context, results can be dramatic: higher conversion, better adherence, fewer missed appointments, improved satisfaction. These outcomes are driven not by more data, but by better data. That is, data that are organized, unified, and applied at the moment of interaction. This is where customer data capabilities prove their value: closing the context gap between raw information and meaningful action. ### **AI Changes Everything – and Nothing** AI has accelerated the reckoning. Agentic systems require data that is [unified, governed, and ready *before* it is used](https://www.redpointglobal.com/blog/liberated-data-and-the-art-of-the-possible-data-readiness-for-ai/). Poor data quality and redundant processing quickly become cost and risk multipliers. Replicating data across systems for every AI use case is neither scalable nor economical. > With the right context, results can be dramatic: higher conversion, better adherence, fewer missed appointments, improved satisfaction. These outcomes are driven not by more data, but by better data. That is, data that are organized, unified, and applied at the moment of interaction. AI may become the new user interface. It may automate segmentation, decisioning, and execution. Whatever direction it takes, AI still depends on well-defined customer data, persistent identities, and clear intent. Data-in-place architectures, where processing happens close to where data lives, are emerging precisely because AI wants *all* relevant context without constant duplication. The future belongs to organizations that prepare their data once and reuse it across human- and machine-driven decisions. ### **What This Means for Marketers** As AI absorbs mechanical tasks, the marketer’s role becomes more strategic, not less. Customer strategy, value exchange, and experience design remain deeply human disciplines. No algorithm invents loyalty programs, reimagines service models, or balances brand trust with personalization at scale. What will not change is the need for high-quality, well-governed customer data. Whether delivered through a CDP, a customer data service, or an extended data platform, those capabilities are foundational, not optional. That is the true outcome of the CDP reckoning. The category is not disappearing. It is being forced to grow up. To hear the full discussion on CDPs, data readiness, AI, and the future of customer engagement, and explore the nuances behind this reckoning, watch the complete [*“The CDP Reckoning”* webinar](https://www.redpointglobal.com/resources/the-cdp-reckoning-what-will-the-future-demand-of-your-customer-data/), a discussion between Redpoint Global CEO Dale Renner and David Raab of the CDP Institute. **Blog tags:** CDP, Data readiness --- ### [The Gravity Well of Customer Data: Why AI Agents and Real-Time CX Demand Data Proximity](https://www.redpointglobal.com/blog/the-gravity-well-of-customer-data-why-ai-agents-and-real-time-cx-demand-data-proximity/) **Published:** January 22, 2026 **Author:** Steve Zisk **Content:** Data management guru [Dave McCrory coined the term data gravity](https://thectoadvisor.com/dave-mccrory/) in 2010 to refer to how data attracts applications, services, and other data to it, a black hole-like gravitational force. That force exerts more of a gravitational pull in the age of AI and autonomous AI agents where, needing a heavy volume of high-quality data, the question of where to run AI workloads becomes important. Should AI move to the data, or vice versa? For autonomous AI agents to deliver on their promise and deliver real-time, hyper-personalized experiences such as flawlessly negotiating the best deal on a car, or proactively managing a customer’s healthcare journey, agents require [data readiness](https://www.redpointglobal.com/blog/data-readiness-real-time-relevance-delivering-context-in-every-moment/) at scale and speed. This requirement precludes bringing data to the AI, with the latency concern just one of several reasons why moving data is not in the best interest of the enterprise. ### **What is Data Gravity?** Data gravity operates on a simple, Newtonian principle: **mass attracts mass**. This is because when your data is too large, too dense, and too interwoven with other systems, it is economically and technologically difficult to move. Your massive, constantly growing customer data (behaviors, transactions, affinities, events, predictions, campaigns, social posts) is the greatest mass, attracting associated workloads, applications, and services (including your AI agents and real-time CX decision engines) toward it. This “irresistible force” is what leads to the critical strategic decision of bringing AI and the compute to the data, not the data to AI. For any enterprise aiming to thrive in the age of autonomous agents, designing an architecture that respects data gravity is non-negotiable. ### **The Triple Threat: Why Ignoring Data Gravity Fails AI and CX** Attempting to move or copy massive datasets for every business process leads to what we call the “triple threat” to data readiness: 1. **The Latency Trap (Performance)** AI agents do not act in a single step. To accomplish a goal – say, rescheduling a complex service appointment – an agent must execute an iterative, continuous loop of planning, acting, and reflecting. A single, seemingly simple autonomous request can trigger over 10,000 inference cycles in the background. If an agent has to wait for data to be copied or transported across networks for *each* of those 10,000 steps, the resulting latency makes a real-time customer interaction impossible. The agent slows down, becomes unreliable, and ultimately fails to resolve the customer’s request. Low latency access to data is paramount for agent success. 2. **The Cost Killer (Economics)** The traditional method of solving data access problems has been to copy data from its core repository and push it out to operational systems (like a cloud-based LLM platform). This model is economically devastating. Although recent changes make it easier to migrate data away from cloud storage, cloud providers still charge punishing egress fees for day-to-day movement. For companies with petabytes of customer data, these costs spiral out of control. Gartner and IDC research indicate that egress charges often account for 10 to 15 percent of an organization’s total cloud bill, with some enterprises spending even more. This reality is driving major enterprise decisions. A significant portion of organizations (55 percent in some surveys) plan to move workloads *off* the cloud or into hybrid models once these data-hosting and computing costs hit a critical threshold. Ignoring data gravity is literally draining the budget needed for innovation. 3. **The Sovereignty Constraint (Governance)** In heavily regulated industries, moving customer data is not just expensive; it is illegal. Regulations like GDPR and strict data sovereignty rules require that certain categories of customer data remain within specific geographic boundaries or private data centers. By bringing the AI and the operational logic to the data and processing it within the secure environment, companies ensure continuous compliance. The agent is only permitted to export small, final, aggregated outputs (“result tokens”), keeping the raw, sensitive mass of data safe and compliant. ## **Moving the Decision Engine to the Data for Real-Time CX** The principle of data gravity is what separates the old-school marketing cloud from the future of customer data: a [composable data readiness hub](https://www.redpointglobal.com/blog/driving-composable-cdp-success-with-data-readiness/). The old model required copying, duplicating, and consolidating data from disparate silos into a single, separate marketing database. The new reality of AI agents and hyper-personalized CX requires a model that leverages the gravitational pull of your core data store. A truly data-ready architecture must place the core decisioning capabilities (the personalization engine, the next-best-action logic, and the agent’s brain) at the center of your data gravity well, which is where data readiness must be centered as well. This strategy allows you to: 1. **Exploit the Edge:** With the majority of enterprise data expected to be generated and processed at the edge (in stores, factories, and devices), CX logic must be able to distribute and execute decisions closer to the customer interaction. 2. **Unify and Activate:** A data readiness hub running directly in a “data-in-place” environment allows the AI agent to access the *unified customer profile* instantly, without moving the underlying data mass. This low-latency access is the only way to ensure the agent’s actions are relevant, personalized, and executed in real time. 3. **Future-Proof Investment:** By architecting systems to accept and operate with data gravity, the enterprise creates a foundation that can scale to meet the demands of truly autonomous AI, turning the cost and complexity of enterprise data into a powerful competitive advantage. The shift to autonomous, customer-controlled AI agents is here. The winners will be the organizations that respect the gravitational pull of their customer data, embracing an architecture where the intelligence is always ready, and where the compute chases the data – not the other way around. For information on Redpoint’s composable Data Readiness Hub, click [here](https://www.redpointglobal.com/data-readiness-hub/). **Blog tags:** Agentic AI, Composable, Data readiness, Data-in-Place --- ### [Why Data Scientists Should Care About Customer Experience](https://www.redpointglobal.com/blog/why-data-scientists-should-care-about-customer-experience/) **Published:** November 15, 2018 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2018/11/Data-Scientist_435537142-e1542230320405.jpg)Data scientists who support marketing must do more today than just build models and provide reports. It’s essential that they understand the importance of customer experience and can act as storytellers, translating data into insight that marketers can use to deliver a superior customer experience (CX) — one that is relevant and personalized. The fact is, customers expect and enjoy personalized customer experiences. The LoyaltyOne study “CX: Intention vs. Impact” states that [76 percent of consumers](https://www.loyalty.com/home/insights/article-details/the-importance-of-prioritizing-high(er)-value-customers) say receiving personalized discount offers based on their purchase history is important. Also, according to a study from [Marketing Insider Group](https://marketinginsidergroup.com/content-marketing/content-marketing-personalization-imperative/), 78 percent of consumers polled say personally relevant content increases their purchase intent. Data enables marketers to better understand customers and deliver the types of experiences customers expect. So, by extension, the data scientists and analysts that support marketers also need to be concerned with CX. Working together, data scientists and marketers can develop a holistic view of the customer using tools that create a single-point-of-control over data and interactions and provide marketers access to the data they need in real time. With the right customer-centric processes and technology in place, marketers are more likely to meet customers’ exacting demands for personalized experiences. **Why CX Now?** A recent [Gartner CMO Spend Survey 2018-2019](https://www.gartner.com/webinar/3890974) revealed that customer capabilities, such as customer experience (CX), dominate CMOs’ list of skills they feel are vital to deliver a strong, cohesive marketing strategy. Using CX to gain a competitive edge requires a consistent approach across the enterprise, and across all customer touchpoints. Hence the importance of greater collaboration between the analytics and marketing teams. The benefits are already evident. According to research firm Aberdeen’s report “Customer Experience Agenda 2018,” top performers in terms of CX retain 45 percent more customers year-over-year than other companies (85 percent versus 58 percent). And, top performers grow customer profit margins year-over-year by nearly 40 percent. Those top performers are in the minority of businesses, so now’s the time to make the changes needed to provide a superior CX to gain a long-lasting competitive advantage. The only way to achieve that CX-based competitive advantage and be a top CX performer is by delivering a consistent and contextually relevant omnichannel experience — because that connected experience is what most customers now want. According to the “[Wantedness](https://www.wantedness.com/)” study by marketing agency Wunderman, 63 percent of U.S. consumers consider great brands as the ones that exceed their expectations across the entire customer journey. This evolving competitive landscape and need for holistic customer experiences actually presents an opportunity: Deliver a superior customer experience across every interaction touchpoint and all along the customer journey by engaging on customers’ terms in their preferred channels. Marketers can’t do this without the help of data scientists. Data scientists and marketers must collaborate to create a single-point-of-control over data and interactions — one that provides a holistic customer view, connecting customer data from across sources: batch and streaming, internal and external, structured and unstructured, transactional and demographic. Creating a single-point-of-control over data allows data scientists to work with marketers to develop a deep understanding of the customer and deploy the models and personalization strategies that have maximum impact. Then marketers can align that insight with their omnichannel strategy and deliver highly personalized experiences at speed and scale. A single-point-of-control over interactions is necessary to orchestrate those interactions in today’s omnichannel environment, ones that span multi-stage real-time and offline customer journeys. **CX Foundations** Building this environment means that data scientists and marketers need to think more like customers: Customers have always viewed the companies they do business with as one unit, not a group of silos they have to interact with individually. Data scientists and marketers need to shift from a silo mind-set that leads to fragmented experiences to one that is centered on connecting disparate touchpoints in a way that weaves each discreet interaction into a cohesive whole. This new, connected reality has two foundational elements that will enable a deep understanding of customers’ needs, expectations, and channel preferences, so marketers can then deliver consistent and contextually relevant omnichannel experiences: customer insight and customer interactions. Customer **insight** to meet today’s CX expectations takes… – Holistic view of the customer – Real-time data access – Single-point-of-control over data Customer **interaction** to meet today’s CX expectations takes…. – Understanding where a customer is in their path-to-purchase – Decisioning for the next best action (e.g. offer, message or content) – Orchestrating the right actions in customers’ preferred cadence and channel(s) Optimizing and personalizing omnichannel customer experiences to meet customer expectations requires organizational, process, and technology changes that bridge silos and center on the customer. Again, the ideal method for enabling all this is to create a single point of control over data and for real-time interactions, and phasing in process and organizational changes over time. The single-point-of-control acts as a central hub from which data scientists can provide insight and marketers can make decisions that are then feed to the channels. It comprises a single-point-of -control for aggregating and understanding customer data, as well as a single-point-of-intelligent-control over customer interactions that spans across every enterprise touchpoint. The ideal technology to support a single-point-of-control is a customer engagement platform that comprises a Customer Data Platform (CDP) and an interaction hub. The combination of these tools allows data scientists and marketers know all that is knowable about their customers, so it’s possible to recognize a customer across multiple channels and interaction touchpoints. This allows marketers to then deliver relevant, hyper-personalized offers at any touchpoint in real time. Both the current competitive landscape and the rise of the empowered consumer have made it clear that data scientists need to take a new different approach to working with their peers in marketing. Using a single-point-of-control enables data scientists to help marketers get the insight they need to hyper-personalized customer journeys in the context and cadence of the customer and set their brand apart from the competition — a win/win for customer and company. ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2018/10/SB-ArtIntelUS1018-01-COVER-768x995-2.jpg)](https://www.redpointglobal.com/wp-content/uploads/2018/10/SB-ArtIntelUS1018-01-Artificial-Intelligence-hi-res.pdf) **Blog categories:** Customer Data Platform, Data Management --- ### [Why Data Quality Matters for IT and Business Stakeholders](https://www.redpointglobal.com/blog/why-data-quality-matters-for-it-and-business-stakeholders/) **Published:** January 17, 2019 **Author:** John Nash **Content:** ![data_quality_integration](https://www.redpointglobal.com/wp-content/uploads/2017/10/data_quality_integration.jpg)Data quality is a major factor in delivering personalized experiences to today’s customers. If your data is not up to par, hyper-personalization at scale runs the serious risk of all sorts of problems, from annoyed customers receiving an irrelevant offer to decreased marketing ROI. Poor data quality is a barrier to recognizing a customer in real time, which makes a personalized customer experience almost impossible. Yet according to a study by [Blazent and 451 Research](https://www.blazent.com/resources/state-enterprise-data-quality-2016/), only 40 percent of C-level executives are “very confident in the quality of their organization’s data.” This lack of quality – characterized by data redundancy, missing data, out-of-date data, among other issues – impacts every area of today’s business from strategic decision making to the delivery of the customer experience. Given the strategic importance of data in empowering businesses to deliver customer experiences and analytics teams to deliver new insights, it is now clear that substandard data is simply unacceptable. Less clear for many enterprises is how to resolve these issues, through combined efforts of IT and business line managers. Both are key stakeholders involved in ensuring data quality and they view the requirements of a customer data platform (CDP) and other data consolidation tools very differently. **The CDP’s Role in Data Quality** To create differentiated customer experiences – ones that are dynamic, hyper-personalized, multi-stage and omnichannel, brands need to implement technology like CDPs to pull data out of silos and into a single customer view. A robust CDP is able to link all knowable customer data to enable an always-on, always-processing view of a customer, which provides a unified and complete “[golden record](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/)” and makes that data available wherever and whenever it’s needed. A CDP ingests all sources and types of customer data, including batch and streaming, internal and external, structured and unstructured, transactional and demographic, to present the unified view of each customer. An optimal CDP should operate in real time, offering a golden record that is continually available at low latency to all applications and users who need access. CDPs play an important role in ensuring data quality by automating data transformation features such as contextual matching, standardization, normalization, deterministic and probabilistic matching, merging, purging, householding, de-duplication, and other profiling tasks to quickly and dramatically improve data quality. Enhanced confidence in data quality, and marketing access to that data enable companies to achieve performance gains from artificial intelligence (AI) and machine learning, which can support advanced real-time personalization and next-best-action recommendations. Feeding high quality data into advanced AI and machine learning-supported algorithms is the brass ring for marketing personalization in that it generates relevant next best action recommendations in the proper cadence at every stage of the customer journey. Importantly, this is accomplished without the usual pratfalls that keep marketers up at night when faced with insufficient data quality, such as damage to the brand, lost revenue, and inaccurate insights that hamper strategic decision-making. Automating data transformation also frees marketers and IT to focus on value-added activities instead of manual cleansing. An [IDC study](https://www.dmnews.com/data/data-management/article/21039974/tapping-data-for-marketing-insights?utm_source=DMN+Daily+Insider&utm_medium=email&utm_campaign=HCL190109018&o_eid=6556C9031534F6Z&rdx.ident%5Bpull%5D=omeda%7C6556C9031534F6Z&om_id=1016853002&ajs_uid=6556C9031534F6Z&oly_enc_id=6556C9031534F6Z&ajs_trait_oebid=3459D7928134B6M) revealed that businesses spend 75 percent of their time with data gathering and cleansing, leaving just 25 percent to analytics. Advanced technology enables organizations to flip that ratio, so that 75 to 80 percent of the time is spent on value-added analytics and delivering competitive customer experiences. **Line of Business (LOB) Executives: Utilizers of Data** As marketers increasingly design data-driven customer journeys, and customer experience solutions increasingly incorporate data science, AI, and machine learning, many marketers fear an over-reliance on IT to access and ensure data quality. In reality, if the technology is in place to ensure quality data in a single view, new solutions like the Redpoint Customer Engagement Hub™ can be used by marketers to access data, predictive analytics and insights while also personalizing omnichannel experiences that meet the expectations of today’s consumer – with little or no intervention from IT. LOB execs now need to be responsive to consumers that increasingly engage in real-time and expect a degree of personalization this is consistent across enterprise touchpoints. This requires a responsive IT organization that makes high quality data accessible in real-time and in ways that span traditional silos. Many enterprises have yet to create these bridges, leading to disconnect with the IT department or long wait times for prioritization of the requests from LOB. This disconnect is a reason that many future-forward organizations are appointing CDOs (Chief Data Officers and/or Chief Digital Officers) that have one foot in each area of the business. **IT Executives: Protectors of Data** Data quality is not only important for line of business executives and marketers. For IT, data quality has an important role in maintaining enterprise reputation and integrity. IT executives and teams are many times viewed as the guardians of data for the enterprise. Errors, omissions, inconsistencies, old data or other issues can reflect poorly on the organization. Mitigating risk with data quality can have a positive impact on the bottom line. As we’ve written before, data quality can often cover not only the cleanliness of the data in the system but also the governance of that data. Compliance and privacy are at the forefront of data quality today, with increasing pressure from consumers and government to protect sensitive data. The emergence of new regulations such as the General Data Protection Regulation (GDPR) and California Privacy Act governing the use of data related to individuals has added pressure on organizations to safeguard the information of individuals and to be more accountable when it comes to how the data is used and by whom. Ensuring data quality helps IT reduce the need for personnel to address data errors or go through multiple steps to access data and interpret insights. Many IT departments face a shortage of resources, especially when it comes to data scientists. Finding ways to ensure data integrity and access for marketers and other business line executives without manual IT intervention is of interest to most IT departments. **A Unified Data Quality Strategy** While there may be different perspectives on the ways to improve data quality, both technology and business executives understand that poor data quality can lead to all kinds of problems related to customer experience. Poor data quality is a serious obstacle to a superior customer experience, which many companies are showing can be a competitive differentiator. To deliver on a quality experience, it is imperative that the business, IT, and marketing are unified in defining their customer strategy and executing that strategy with data that protects the customer’s privacy and the enterprise reputation while at the same time helps create dynamic, personalized customer experience. ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **RELATED CONTENT** [3 Elements of Ironclad Customer Data Quality Management](https://www.redpointglobal.com/blog/3-elements-of-ironclad-customer-data-quality-management/) [![](https://www.redpointglobal.com/wp-content/uploads/2017/10/SB-HDQITUS0917-02-Data-Quality-IT_lo-res-1-814x1024.jpg)](https://www.redpointglobal.com/wp-content/uploads/2017/10/SB-HDQITUS0917-02-Data-Quality-IT_lo-res.pdf) **Blog categories:** Customer Data Platform, Data Management, Data Quality --- ### [What’s Needed to be a RealCDP?](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/) **Published:** July 31, 2019 **Author:** John Nash **Content:** The Customer Data Platform industry continues to grow as more buyers realize that CDPs offer to meet one of their most pressing needs: easy access to unified customer data. But the growth of CDPs has been accompanied by confusion as companies adopt the CDP label without providing what most people expect a CDP to deliver. To help clarify matters, the [CDP Institute](https://www.cdpinstitute.org/) recently launched its RealCDPTM initiative, which defines a set of features that CDPs must meet to earn the RealCDP label. The logic behind [RealCDP](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/) is that most people have an intuitive notion of what they expect a CDP to enable their company to accomplish and that CDPs need a specific set of features to make this possible. Of course, mind-reading is an inexact art but our research confirms what we suspected: When asked what they expect from CDP, people overwhelmingly say its job is to create a unified customer view. Digging a bit deeper, they understand this requires specific capabilities including [data](https://www.redpointglobal.com/blog/what-is-data-ingestion-why-context-matters-in-a-data-ingestion-strategy-for-cx/) collection from all sources, identity matching to create unified customer profiles, and access to full details of all data. The RealCDP requirements reflect these findings. It offers a five-point checklist: - Ingest data from all sources - Retain full detail of all ingested data - Store the ingested data as long as the user wants - Convert the data into unified customer profiles - Make the profiles available to all external systems ![](https://www.redpointglobal.com/wp-content/uploads/2019/07/realCDP2-300x220.png) The five points are designed to be a simple as possible, both so users can easily understand them and so we can objectively determine when a system meets them. But there are still nuances to consider. - **Ingest data from all sources.** We define this to include structured data, such as purchase transactions; semi-structured data, such as Web browser history; and unstructured data, such as call center transcripts. But should it extend to video and audio files? Is it enough to just store the data or must the system add metadata to make it searchable? What tasks are required to add a new data source or element within an existing source? How much time is allowed to make the ingested data available? Our answers to all these are based on what we think users expect, again subject to mind-reading limitations. For this particular set of questions, the general answers are: we expect the CDP to handle common types of marketing data but not newer ones like audio files, and we think it’s okay to require a fair amount of technical effort to set things up. - **Retain full detail of all ingested data.** Full detail is unambiguous, right? But it could mean the system stores an exact copy of the inputs or transforms them in a way that lets users reconstruct the detail if necessary. Transformation may sound less desirable but it’s often required to make the data usable for subsequent tasks, so there’s good reason to do it from the start. Similarly, there’s a need to impose structure on the imported data, so this topic includes questions about data models and deriving structured elements from unstructured data. In addition, it’s often necessary in practice to decide which details to retain, so we need to assess capabilities to sort through the inputs and keep only the good parts. Answers to these questions may not determine whether a system meets the requirements but they’re still important to users who might buy a system. So we want to gather them during the RealCDP process. - **Store the ingested data as long as the user wants.** There’s no question that this one is vague. But user needs do vary and the core purpose of RealCDP is to ensure that each user gets what she needs. So we need to look at controls the system provides over data retention, including policies based on data source, age, risk, consent, and more. These questions spill into security and privacy requirements such as encryption, security certifications, consent gathering, permission compliance, and access audits. - **Convert the data into unified customer profiles.** This gets to the essence of a single customer view and it raises many questions. What’s a customer? What kind of identity resolution is required? Does a profile need direct access to every bit of ingested data or is it okay to pre-select a subset of attributes that are available? What’s required to add model scores, segment assignments, and other derived features? How quickly must the profiles be updated after new information is ingested? The bar to pass this item can be set fairly low but we’ll want to clarify many of nuances so buyers can understand what they’re getting. - **Make the profiles available to all external systems.** How are the profiles shared? Is a periodic file extract enough or do you need a real-time API? What functions are built into the API and what does it take to build a custom connector? Can the system need to create specialized extract formats such as database tables or AI training sets? How quickly must the system respond to a profile request? Does the system need a segmentation interface that builds complex selection statements? Does it need to accept complex external queries? Should the system include event-triggered and scheduled audience exports? Coming back to user expectations as a guide, it’s likely they expect something more than periodic file extracts but it’s not clear how much more. ![](https://www.redpointglobal.com/wp-content/uploads/2019/07/realCDP3-300x220.jpg) The purpose of RealCDP is to reduce confusion, not increase it, so we’ll give yes or no answers for each of the five items. But we’ll also present some of the underlying details to help users understand what’s really included in a given system. We’ll also clarify that there are other important topics for marketers that often come up, but are not covered by RealCDP. Real-time data processing is a big one; integrated identity resolution is another. Marketing applications such as predictive modeling, personalized message selection, and message delivery are also on the list. They’re not core to the RealCDP for various reasons but some users will find them essential. So we’ll at least indicate which systems provide some serious support in those areas without getting into the fine details. **RELATED ARTICLES** [Demise of the DMP, Long Live the CDP](https://www.redpointglobal.com/blog/demise-of-the-dmp-long-live-the-cdp/) [Building vs. Buying a CDP? Why That’s Not the Only Question You Should be Asking](https://www.redpointglobal.com/blog/building-vs-buying-a-cdp-why-thats-not-the-only-question-you-should-be-asking/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Data Quality, Identity Resolution, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [What is Real-Time Interaction Management (RTIM)?](https://www.redpointglobal.com/blog/what-is-real-time-interaction-management-rtim/) **Published:** April 1, 2019 **Author:** Redpoint Global **Content:** Real-time interaction management (RTIM) plays a critical role throughout the entire customer lifecycle. As hyper-personalized experiences become table stakes, brands must rise to meet customer expectations and deliver relevant omnichannel experiences – in real time. Let’s get real about RTIM and the steps you must take to deliver effective real time interactions that maximize engagement, lifetime value, and revenue. ## **Level Setting: What is Real Time Interaction Management?** Perhaps you buy a drill that you plan to pick up in-store (BOPIS). You arrive at the store 15 minutes later and at checkout, the cashier makes you an offer for a portable charging station instead of drill bits because you already purchased those online last week. That’s just one example of RTIM. [Forrester](https://www.forrester.com/report/Now+Tech+RealTime+Interaction+Management+Q1+2019/-/E-RES142812) defines RTIM as enterprise marketing technology that delivers contextually relevant experiences, value, and utility at the appropriate moment in the customer life cycle via preferred customer touchpoints. Essentially, real time interaction management is all about finding relevance at the intersection of your customer, your brand, and time. To achieve this relevance, you must understand: - Customer context - Customer cadence Customer context encompasses recent transactions, behaviors, preferences, and – most importantly – intent. Understanding your customer’s unique context will enable you to deliver the most relevant next-best offer. And as the old adage goes, “timing is everything.” The right offer has to be combined with the right timing, or cadence of the customer. It’s important to note, “real time” could mean different time frames for different brands. For instance, if your customer buys online and picks up in-store, you want to make her the next-best offer at checkout, which might mean minutes or hours. But if your [customer](https://www.redpointglobal.com/blog/commercial-excellence-begins-with-customer-experience/) contacts your call center about a problem, the customer support representative must have all her information in milliseconds to make the next-best offer over the phone. ## **Why Prioritize Real Time Interaction Management?** Customers are in charge of their unique buying journey, and they expect seamless, [personalized](https://www.redpointglobal.com/challenges/personalization/) experiences. In fact, [52 percent of consumers will switch brands](https://www.martechadvisor.com/articles/customer-experience-2/three-enterprise-leaders-driving-personalized-cx/) if one fails to meet their expectations. Getting RTIM right reduces your risk of losing customers to competitors and increases retention rates. Effective RTIM contributes to other key engagement metrics; average order value, share of wallet, and [customer](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/) lifetime value all benefit from your brand executing highly relevant customer experiences. One consumer packaged goods (CPG) company saw an increase of 144% in revenue after overcoming data silos and orchestrating personalized real time experiences across their omnichannel environment. To keep up with your consumers and competitors, RTIM isn’t a nice to have – it’s table stakes. ## **Barriers Between You and Effective Real Time Interaction Management** The biggest challenges for marketers to overcome lies in customer data. In a recent benchmark study conducted by Harris Poll Research and Redpoint, the barriers preventing marketers from meeting customer expectations are: - Customer data is available but lacks the depth required to produce new levels of performance gains - Fragmented engagement systems that fail to connect or deliver a unified view of customers across touchpoints - Marketing is the only team fully invested in data-driven customer strategy; other teams and stakeholders are not on board - Unable to keep pace with customers’ expectations for real-time omnichannel engagement - Siloed customer data that remain inaccessible across the entire organization The proliferation of digital touchpoints and the endless surge of marketing technology contributes to the situation we find ourselves in today. The average marketer utilizes 10 engagement systems (i.e., CRM, marketing automation, mobile, eCommerce, website, and more), and 65 percent of marketers say the number of engagement systems makes it harder to provide a seamless customer experience. Without precise and accurate [identity resolution](https://www.redpointglobal.com/challenges/identity-resolution/), we can’t understand customers across all touchpoints. Attaining a holistic, 360-degree view of each customer is the first step toward effective RTIM and without it, we risk alienating customers with irrelevant experiences. ## **Capabilities for Effective Real Time Interaction Management** Forrester also notes that the three critical capabilities for RTIM are: speed and agility, data-driven personalization, and cross-channel optimization. Speed and agility refer to your ability to serve relevant messaging at the customer’s moment of engagement or interaction. Data-driven personalization encompasses the ability to integrate online and offline touchpoints and automate messages that treat each consumer as a segment of one. Lastly, cross-channel optimization refers to leveraging machine learning to run A/B/multivariate tests regularly to determine ideal strategies and continuously improve engagement. How do you achieve these abilities? Once again, the answer lies in your customer data. In order to orchestrate personalized messages across the omnichannel in real time, consider your ability to: - Connect all data sources and types - Identify customers across channels to create a Golden Record - Leverage machine learning to analyze customer circumstance (context and cadence) and predict intent - Determine next-best actions While the customer controls their journey, you need a single point of control over data, decisioning, and interactions so you can execute the personalized experiences customers expect. These analytical capabilities are now available to marketers from within customer engagement tools – so there’s no need to hire data scientists or rely on IT for support in these areas. A customer engagement hub helps marketers surmount the obstacles that customer data presents. These platforms connect and aggregate data across your existing martech stack, then cleanses and processes that data – accurately resolving identities across touchpoints – to give you a unified view of every customer. Next, the customer engagement hub analyzes transactions, behaviors, and preferences while leveraging machine learning and advanced modeling tactics to determine and automate next-best actions for every customer. Actionable insights are made available across the entire organization, informing every team that interacts with customers. Finally, this solution enables brands to deliver those actions, offers, or messages in the right channel at the right time, creating the seamless and hyper-personalized customer experiences that increase engagement, retention, and lifetime value. ## **What’s next?** The path ahead isn’t as daunting as you think. RTIM is achievable with the right approach. Start by addressing your customer data and build a Golden Record – the always-on, always-updating view of your customer. Consider starting small, using available data to execute personalized outbound campaigns through one channel. Next, progress from a single channel to multichannel approach, testing, tuning and optimizing as you go. Finally, analyze your inbound and outbound strategies and focus on your ability to recognize customer context and cadence across the lifecycle. A solid foundation of integrated, connected customer data and machine learning models will enable you to successfully manage real-time interactions. The Redpoint Customer Engagement Hub™ solves your fragmented, siloed data issues and helps marketers intuitively run advanced analytics that power personalized, real-time omnichannel customer journeys. Leveraging this solution transforms how you deliver customer experience, driving critical engagement metrics and increasing revenue for your business. **Blog categories:** Real-Time Personalization --- ### [What is Marketing Automation?](https://www.redpointglobal.com/blog/what-is-marketing-automation/) **Published:** September 10, 2020 **Author:** Steve Zisk **Content:** What is marketing automation? Broadly defined as systems designed to help marketers capture leads, nurture them in the funnel and analyze lead behavior and campaign performance, [marketing automation in this context](https://www.cmswire.com/marketing-automation/what-is-marketing-automation-and-how-does-it-help-marketers/) is more closely associated with B2B marketing. This traditional definition holds that marketing automation software – from the “classical marketing” sense – is designed to manage leads or prospects and make nurture marketing work better. ## Marketing Automation for B2C In Context – a Personalized CX If the B2B context is definition 1A in Webster’s, an evolving 1B definition will recognize the growing importance of marketing automation for B2C marketers, where marketing automation in this context extends beyond the classic use case to include providing customers with hyper-personalized experiences at scale. This big tent view of marketing automation elevates marketing to a mission-critical line of business, recognizing that a personalized customer experience (CX) is a critical revenue driver. In a [2019 Harris Poll sponsored by Redpoint](https://www.redpointglobal.com/wp-content/uploads/2020/07/Addressing-the-Gaps-in-Customer-Experience.pdf), for instance, 37 percent of consumers surveyed said they will not do business with a company that fails to offer personalization. And 62 percent said that a personalized CX is part of the standard service they expect. In this light, marketing automation software that serves B2C use cases becomes as important as traditional B2B use cases. General-purpose lead nurturing marketing software that facilitates drip email campaigns, in other words, might be a good start, but in today’s world, basic marketing automation is table stakes. Because it is now vital to keep up with a dynamic customer journey and deliver consistent relevance, in real time, with every interaction across channels, marketing automation must also provide for omnichannel [journey orchestration](https://www.redpointglobal.com/orchestration) and the ability to manage every detail of the customer record. ## Marketing Automation is One Piece of the Martech Puzzle Additional confirmation that the traditional understanding of marketing automation is evolving comes from the fact that some of the biggest standalone marketing automation platforms have, in recent years, been acquired by marketing cloud vendors and incorporated into their broader martech tool sets. This further clouds the distinction between classic B2B marketing automation and marketing automation software intended to improve customer engagement – customer data platforms, multichannel marketing, marketing hubs, marketing clouds, etc. A discussion of [marketing automation for B2C](https://www.redpointglobal.com/blog/what-is-marketing-automation/) in the context of the broader martech environment may also lead to rethinking what problems marketing automation intends to solve — its overall importance or overarching benefits. This blog will attempt to answer some of these questions in light of marketing automation’s role in providing a relevant, personalized CX throughout an omnichannel journey. ## Marketing Automation Needs a Broader Set of Tools Marketing automation tools were designed to help with basic linear, single-channel marketing campaigns including, for example, automating more timely and effective emails, generating leads, managing pipeline, email analytics, and shortening the sales cycle with CRM integration. Automating individual tasks and channels is a worthy goal, but marketing teams must ask at what cost. Does the complexity of a growing martech stack warrant adding more and more point solutions to achieve incremental improvements to channel-centric campaigns? Effective and timely emails, while important, do not by themselves generate deep insight into customer behavior. A more fruitful approach to marketing automation considers a customer journey as a holistic experience across channels, necessitating tools that intelligently orchestrate omnichannel interactions. Taking a step beyond the traditional realm of marketing automation to gain those deep customer insights requires instead generating a complete unified customer profile, to make smarter decisions at scale using automated machine learning and orchestrating those decisions far beyond the email world. The difference between what today’s always-on, connected consumer expects in terms of a personalized experience and the one delivered by most brands is the customer experience gap. Some of the chasm results from the limitations of lead nurturing marketing automation tools compared with a platform able to generate and act on the deep customer insights that deliver relevant and timely personalization. ## Marketing Automation for B2C vs. a CDP In the Harris Poll, for instance, consumers are 2.5X more likely than marketers to say their expectations for a personalized CX are not being met – and they rank omnichannel consistency twice as high as marketers in terms of importance to the overall experience. Asked what prevents them from closing the gap, marketers list the complexity of technology solutions (39 percent), a lack of cross-functional commitment to strategy (34 percent) and an inability to integrate new capabilities to existing processes or technology (33 percent) as the top three challenges. The core assets required for marketing automation are a marketing database to track leads, an email service provider to drive drip campaigns, and a basic analytics platform to track campaign effectiveness. Together, these basic core assets generally fail to solve for the challenges that prevent marketers from closing the CX gap. To achieve omnichannel orchestration, these core assets need to be augmented with a CDP that brings all customer data together across both the marketing automation platform and other martech and adtech systems. The growing importance of providing a personalized CX across an omnichannel customer journey does not lessen the importance of marketing automation. Instead, it positions marketing automation as one step along the path to orchestrating the entire customer experience rather than just automating individual pieces of a journey. **Blog categories:** Customer Data Platform, Data Management, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Segmentation & Activation, Single Customer View --- ### [What Does Customer Centricity Mean?](https://www.redpointglobal.com/blog/what-does-customer-centricity-mean/) **Published:** May 22, 2019 **Author:** Redpoint Global **Content:** **![](https://www.redpointglobal.com/wp-content/uploads/2019/05/shutterstock_598921298-e1558462923370.jpg)What is customer centricity and why is it important?** The [customer centricity definition](https://www.redpointglobal.com/orchestration/customer-centricity/) is simply a strategic approach to customer experience, leveraging data to provide highly relevant experiences across the entire lifecycle. It’s more than providing satisfactory customer service over the phone or in store. We’re living in an [omnichannel](https://www.redpointglobal.com/challenges/omnichannel-marketing/) world and customers expect personalization at every moment of interaction. Rather than promoting products and services in a batch and blast manner, [customer](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/) centricity is the idea that every moment of interaction a consumer has with your brand is extremely relevant. Gaining insight into customer context and cadence enables you to maintain that level of hyper-personalization. Ultimately, it’s a value exchange: if you treat each individual as a segment of one, they will reward you in revenue. Prioritizing a customer-centric approach to customer experience (CX) increases acquisition, retention, and lifetime value. **What Does it Mean to be a Customer-Centric Organization?** Achieving customer centricity means that you’re able to provide customers with dynamic, contextually relevant experiences. Gartner notes that the maturity of your data impacts your ability to become customer-centric. ![alt =“customer centric data maturity chart” ](https://www.redpointglobal.com/wp-content/uploads/2019/05/Customer-Centric-Chart-1024x479.png) By incorporating more than just historical and transactional data in your approach, you can enhance CX and increase engagement. Integrating all your data across every touchpoint and running analyses on that data provides you with actionable insights you can leverage to deliver customer-centric experiences. You know your brand is customer-centric when: - You understand the context and cadence of every customer - You can treat each consumer as a segment of one - You can personalize not just the path to purchase, but their experience across the entire lifecycle Of course, this requires you to know all that is knowable about your customer. To achieve this level of maturity in customer understanding, it’s essential to: - Ingest and integrate all types and sources of customer data - Accurately resolve customer identities across every touchpoint - Build a Golden Record: a combination of all customer behavior – from anonymous to known stages – that persists over time - Analyze customer interactions and determine next-best offers or messages at the speed of your customer **Challenges of Implementing a Customer-Centric Strategy** If you’re like most marketers, attaining a complete and persistent view of your customer is anything but easy. Traditional approaches to customer experience are limited by data. Without insight into every customer’s unique journey, it’s difficult to treat them as individuals and deliver the personalized, customer-centric experiences they value. ![alt=”challenges to achieving customer centricity" ](https://www.redpointglobal.com/wp-content/uploads/2019/05/Barriers-to-entry-1024x804.png) Harris Poll Research confirms that the [biggest barriers to customer understanding](https://www.redpointglobal.com/wp-content/uploads/2019/03/Gaps-in-CX-Infographic_FINAL-1.pdf) that marketers face are all related to data. Lack of depth, lack of access, and siloed data majorly impact your ability to keep pace with customers and personalize their entire journey. Fragmented data also hinders your ability to keep pace with your customers and interact with them in real time. Without insight into customers’ interactions and the ability to analyze and determine the next-best offer, marketers are challenged to meet customer expectations for real-time engagement across the omnichannel. Connecting your data is the first step in surmounting the challenges you face when executing customer-centric experiences. **Best Practices: 3 Steps to Becoming More Customer-Centric** As customer experience becomes the new battlefront for brands, now’s the time to take steps towards achieving the level of customer centricity that engages and retains consumers. Start by addressing your data, then advance your decisioning capabilities and finally, orchestrate the customer-centric omnichannel experiences your audience expects. ![marketing engagement systems](https://www.redpointglobal.com/wp-content/uploads/2019/05/engagementsystems-300x293.png) **1. Build a Golden Record** Marketers today have no shortage of resources at their fingertips, yet the amount of engagement systems within our martech stack creates data silos and hinders our customer understanding. By leveraging solutions like a [customer data platform](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/), you can connect all your existing systems to gain an accurate, persistent, and always-updating customer profile. **2. Recognize customer intent** By adopting machine learning, you can automate offer testing and optimization in a scalable way. Deploying models to predict what an individual might need next helps you determine the next-best message for every customer – across anonymous and known stages. Supported by this capability, you can serve the most relevant next-best message at the speed of your customer. **3.Understand customer context to deliver relevant experiences** Customer context includes information like: - Transactions - Behavior - Preferences - Intent This level of insight allows you to move beyond transactional data and provide customers with an experience that spans their entire journey, rather than focusing on just the latest interaction. Once you take these steps, you’ll be ready to orchestrate customer-centric experiences. Every moment of interaction – no matter when or where the customer shows up next – will be personalized and seamless. **Ready to take the next step?** [**Book a meeting**](https://redpointglobal.secure.force.com/cts/TimeTrade_SF1__RoutingQuestionnaire?id=a2A4400000CeWxz) **to discuss how Redpoint helps you improve your data maturity and decisioning capabilities so you can orchestrate customer-centric experiences.** What Does Success Look Like?** With great customer centricity comes great engagement. Provide customers with personalized experiences they value, and they’ll reward you in return. Investing in your CX can pay dividends and regardless of your barometer for success, money talks. Customer-centric experiences increase: - Engagement - Retention - Share of wallet - Customer lifetime value A leading global insurer [raised retention rates by 20%](https://www.redpointglobal.com/wp-content/uploads/2017/10/CA-INUS0917-02-Anon-GlobalIns-lo-res.pdf) after transforming their data management capabilities. A consumer packaged goods brand [increased revenue by 144%](https://www.redpointglobal.com/wp-content/uploads/2017/10/CA-CPGUS0917-02-Anon-CPG-lo-res.pdf) by leveraging a customer engagement hub which provided them with a single point of control over data, decisions, and interactions. Implementing a customer-centric approach to CX takes time, but the sooner you take the first step, the sooner you’ll see a return. ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization --- ### [The Telltale Signs Your Business Needs a CDP](https://www.redpointglobal.com/blog/the-telltale-signs-your-business-needs-a-cdp/) **Published:** June 4, 2020 **Author:** Steve Zisk **Content:** While there is no firm and fast roadmap for how companies will emerge from the current economic slowdown, there is a consensus that the situation will produce widespread change in customer behaviors. McKinsey’s recent [Rapid Revenue Recovery](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/rapid-revenue-recovery-a-road-map-for-post-covid-19-growth) paper on post-COVID-19 growth postulates that to adapt to broad change, business response will include five core elements. Among them are an enhancement and expansion of digital channels and a rethinking of customer journeys – designing new use cases and customer experiences based on insights from having a deeper understanding of customer values. To build this deep customer understanding for success in the new environment, companies must eliminate any operational roadblocks that prevent this goal: data siloes that limit an understanding of a customer to a single channel, models unable to accurately predict customer behavior, and stale data that cannot be traced or linked to a customer. These challenges are telltale signs that your organization stands to benefit from a [customer data platform (CDP)](https://www.redpointglobal.com/customer-data-platform). These challenges are not new, of course. As customer journeys have become more dynamic and unpredictable, marketers have recognized the operational inefficiencies that stem from the common roadblocks. They expose a customer to ads for an already-purchased item because the point of sale system is not linked to the ad network. They provide product recommendations on an e-commerce site based on week-old data because the system is not integrated with other customer data sources. Or they can’t institute a buy online, pick-up in-store service because the website has no visibility into real-time inventory. What’s different is the increased level of urgency to solve these challenges in the wake of highly unpredictable and changing customer behaviors. The need for a CDP to address operational inefficiencies and engage with customers in real-time, with real-time data, and with a complete 360-degree view of the customer has never been greater. ## **Not All CDPs are Created Equal** An inability to collect, unify and use customer data to provide a consistently relevant customer experience in the context of a dynamic customer is a sure sign that a business will benefit from a CDP. It’s incredibly important, though, that a business understands and articulates the use cases they’re trying to solve, because not all CDPs tackle customer data problems the same way. Having a real-time picture of customer data is a foundational requirement. A CDP that connects all sources of customer without taking the step of providing a real-time, unified customer profile will fail to provide relevant experience at any stage of a customer journey. Take a CDP that relies on batch processing. If a customer shows up at an e-commerce site, and a marketer wants to pull web browsing history to generate a product recommendation – if the history is even a minute old, there’s a good chance a product recommendation will not be relevant to the customer’s current experience. The same holds true for identity resolution capabilities, where not all are created equal. Basic matching and de-duplication may be sufficient for persona-based marketing, but fall short of being able to recognize an individual customer throughout an omnichannel journey. A CDP vendor may promise identity resolution, in other words, but it’s wise to check under the hood. Online and offline data hygiene, and probabilistic and deterministic advanced identity resolution are important not only to bring all customer data together, but to bring customer data together in a way that gives marketers confidence that they have an accurate, complete, up-to-date picture of an individual customer. ## **Common Challenges, a Unique Solution** Even before the sudden, massive changes to consumer behaviors, customers were already on record with their expectation that brands know who they are across channels. In a 2019 Harris Poll commissioned by Redpoint, 73 percent of consumers surveyed said that brands struggle to meet their expectation for a personalized experience. Asked to define personalization, 43 percent said it was a brand knowing they are the same customer across all touchpoints, with 42 percent saying it was receiving relevant product recommendations based on recent viewing history. Any brand or business unable to fulfill these expectations due to limitations for how their martech stack collects, unifies, and prepares customer data will benefit from implementing a CDP that does the difficult things well. The ability to provide a consistently relevant customer experience that drives new revenue depends on it. **Blog categories:** 1:1 Personalization, Customer Data Platform, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [The Right to Be Forgotten as a Vehicle for Building Customer Trust](https://www.redpointglobal.com/blog/the-right-to-be-forgotten-as-a-vehicle-for-building-customer-trust/) **Published:** September 12, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/09/shutterstock_1097930213-300x210.jpg)*Editor’s Note: This is the second of a two-part series on the Right to be Forgotten clause of the General Data Protection Regulation. [Part 1 ](https://www.redpointglobal.com/blog/why-the-right-to-be-forgotten-is-one-of-the-hardest-parts-of-gdpr/)focused on Erasure 101, compliance and complexities, and ongoing considerations.* The right to be forgotten, also known as the right to erasure, detailed in [Article 17](https://gdpr-info.eu/art-17-gdpr/) of the General Data Protection Regulation (GDPR) is only about 400 words, broken down into three sections. The first outlines the conditions under which data controllers must erase personal data “without undue delay” upon request from a data subject. The second requires the original data controller to notify other data controllers who may be processing the personal data of the data subject’s request. The third outlines exceptions, listing five conditions where keeping personal data supersedes a subject’s right to erasure. Straightforward, right? Well, for data controllers – including retail and brand marketers responsible for safely guarding personally identifiable information (PII) – Article 17 is rife with potential pitfalls, many of which arise from how marketers interpret rules for compliance. A [previous blog](https://www.redpointglobal.com/blog/why-the-right-to-be-forgotten-is-one-of-the-hardest-parts-of-gdpr/) in this space detailed the right to be forgotten requirements and identified many of the difficulties with compliance. Since GDPR doesn’t present a handy guide for how retailers and brand marketers can overcome compliance challenges, we want to share a roadmap for how to successfully navigate the right to be forgotten complexities while showing customers that you value their data privacy. **Data Privacy and the Value Exchange** In a [Harris Poll survey](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) commissioned by Redpoint earlier this year, consumers were unambiguous in their opinions about transparency and control in the use of personal data. Strong majorities said that it was at least “very important” that companies tell the consumer what information is being collected (74 percent) and how the information is being used (73 percent). Concerning authorization, 71 percent of consumers said it was very important that they must be allowed to give a company explicit authorization for how data is being used, and 68 percent said it was very important that they be allowed to set specific preferences. While consumers demand transparency, they also understand that companies have valid reasons for the request and use of personal data. In the same survey, 54 percent of consumers said that they will share personal data in exchange for a personalized customer experience. But if the company or brand misuses or is careless with personal data, nearly 90 percent of consumers said they would likely switch brands. The consumer/brand value exchange is an opportunity for brands to minimize the percentage of customers who exercise the right to be forgotten – and to deliver a more personalized customer experience. The more a brand demonstrates transparency in protecting personal data, the more data a consumer is willing to share, and the more personalized the experience becomes. This self-fulfilling cycle is a win-win for marketers; they deliver on consumer expectations for privacy, ensure compliance and documentation, build brand trust, and strengthen customer lifetime value (CLV) by building customer loyalty with relevant, personalized engagement. **Anonymous and Known Considerations** Transparency addresses many of the challenges in addressing the right to be forgotten, because in addition to satisfying the customer it also establishes a trail, if you will, that documents the consent-based relationship. But there can also be legitimate business purposes for anonymizing a known record, or using aggregated information for segmented campaigns, while still honoring the contract with the customer. To personalize a programmatic ad for a cookie-based, anonymous record, for example, a marketer must have a source for third-party demographic data, which can be combined with anonymous and known records to deliver personalized content. By stripping PII away from a known record – anonymizing the data – before conducting a campaign, marketers accomplish several objectives. They ensure they’re not breaking the trust of a consent-based relationship, they ensure they’re not in violation of the law, and they avoid having an unsuspecting prospect or customer wonder why a brand knows so much about them (the dreaded ‘creep factor’). A failure to properly segregate anonymous and known data will increase the risk that a data subject files a request for erasure. **Automate Compliance with Data Lineage** How, then, do marketers document all the steps they take to ensure they’re complying with the right to erasure for every data subject, while still using relevant data to deliver a personalized customer experience? Marketers need a platform that manages the operational considerations for every GDPR requirement, including consent collection, data rectification, and the integration of that data with enterprise systems. Redpoint’s partnership with PossibleNOW marries the latter’s enterprise consent and preference management platform with Redpoint’s proven data lineage capabilities in the [Redpoint Customer Data Platform (CDP)](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/). A robust data lineage solution documents the origin and use of any data that touches an enterprise system; how the data was acquired, its permissions, its current and future use, and movement. It includes mechanisms to mask or forget data, and to inform other systems that data has been masked or forgotten. It is a hub, if you will, to inform other systems of every aspect of GDPR requirements – the right to be forgotten, channel preferences, portability, the right to connect, and others – and the basis for ensuring the accuracy and currency of data used for high-quality customer records that are in turn used to deliver personalized customer experiences. The right to be forgotten presents challenges for marketers, but it also presents a golden opportunity to better understand your customers. Honoring a customer’s preferences for how their data is collected and used strengthens a personalized customer experience because the currency in the exchange – trust – is more valuable to the customer than getting their product preferences right. **RELATED ARTICLES** [Why the Right to be Forgotten is One of the Hardest Parts of GDPR](https://www.redpointglobal.com/blog/why-the-right-to-be-forgotten-is-one-of-the-hardest-parts-of-gdpr/) [What You Need to Know about Consumer Data Privacy Compliance](https://www.redpointglobal.com/blog/what-you-need-to-know-about-consumer-data-privacy-compliance/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2019/09/0906X-SB-GDPRPossUS-13x10_at72dpi-1-239x300.jpg)](https://www.redpointglobal.com/wp-content/uploads/2018/04/SB-GDPRPossUS0318-01-PossibleNow-hi-res.pdf) **Blog categories:** Anonymous to Known, Customer Data Platform, Data Management, Identity Resolution --- ### [The Middle Survey Says... Healthcare Personalization Has Room to Grow](https://www.redpointglobal.com/blog/the-middle-survey-says-healthcare-personalization-has-room-to-grow/) **Published:** February 19, 2020 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/02/Healthcare-personalization_126288149-scaled.jpg) While some consumers equate their healthcare experiences with waiting in line at the DMV, the upside is that there is tremendous opportunity to change the experience with today’s patient population. By removing friction and offering personalized care and communications, providers and insurers can increase patient satisfaction and improve the overall healthcare journey. The DMV reference is not just a random analogy. For key insights on the patient experience and how it can be improved, Redpoint [surveyed 1,000 U.S.-based adults via Dynata](https://www.redpointglobal.com/?post_type=news_cpt&p=10784) about their healthcare experiences. More than half of the consumers surveyed said that their experience with health insurance companies is so poor they would rather visit the dentist, deal with jury duty, sit in the middle airplane seat on a long plane ride or – you guessed it – wait in line at the DMV. With 47 percent of consumers surveyed reporting that they do not receive a holistic experience from their healthcare provider, the lesson is that there is ample room for improvement. **Frustrations Abound** Consumers provided many reasons why the healthcare experience is less preferred than a dentist visit and other typical life frustrations. Complaints included long wait times, impersonal visits, challenges scheduling appointments, a confusing or complex process to access care, and the inability to communicate digitally on multiple channels with their provider or insurance company. The majority of consumers (71 percent) reported at least some frustration with the healthcare experience. More than half (54 percent) of consumers also feel that their healthcare providers and insurers don’t have all the contextual information they could to personalize healthcare recommendations. The lack of personalization tops the concerns of most patients, with 75 percent wanting deeper personalization in healthcare and some 61 percent that would visit their provider more often if the communications experience felt personalized to them. **Prescription for Better Healthcare: Communication** Hope isn’t lost. Consumers are telling healthcare providers and insurance what they can do to improve. Beyond eliminating friction with simple tools like offering better digital communications options, patients want the experience to be more personalized and catered for them as individuals. Healthcare payers and providers can help cater to the individual patient needs via a [golden record](https://www.redpointglobal.com/challenges/single-customer-view/) that provides them with a deep understanding of the individual patient. With the golden record, both payers and providers can coordinate patient information to hyper-personalize messages, offer healthcare recommendations and coordinate other logistical elements of providing healthcare to help close the gap between expectations and the current patient experience. **Value-Based Care Starts with Personalization** In 2018, [Redpoint partnered with Lucerna Health](https://www.redpointglobal.com/blog/redpoint-global-and-lucerna-health-join-forces-to-advance-consumerism-in-healthcare-for-payers-and-providers/) to advance the market’s transformation to value-based care through personalized engagements with the healthcare consumer. A single view is the foundational requirement for delivering personalized engagements that are in the context and cadence of an individual healthcare consumer’s journey. With every available data point about the consumer compiled into a golden record, healthcare organizations like Lucerna have the deep understanding of each consumer required to drive the model. Combined with in-line analytics to determine next-best actions and an intelligent orchestration layer to consistently deliver messages in any channel or moment of engagement, healthcare organizations have a single point of control with which to guide an individual journey. In practice, this leads to lower costs, better patient engagement and satisfaction, and better outcomes because consumers are presented with information and recommended actions that are the most relevant to them as individuals. Inefficiencies that result from siloed data and an unintegrated system of various providers (primary care, lab, pharmacy, specialists, etc.) are eliminated, which greatly help the consumer navigate through the healthcare system. **RELATED CONTENT** [Take a Data-Driven Approach to a Consumer’s Healthcare Journey](https://www.redpointglobal.com/blog/take-a-data-driven-approach-to-a-consumers-healthcare-journey/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* ![](https://www.redpointglobal.com/wp-content/uploads/2019/10/1003-SB-CUSEXPHCUS0318-02-CusCentricHealth.jpg) **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Management --- ### [The Importance of Identity Resolution in Healthcare](https://www.redpointglobal.com/blog/the-importance-of-identity-resolution-in-healthcare/) **Published:** August 25, 2022 **Author:** Sarah Lull **Content:** After listening to the Q2 earnings calls of several health insurers, Forrester discovered some commonalities in terms of their priorities and investment strategies. Among its [key takeaways](https://www.forbes.com/sites/forrester/2022/08/02/key-takeaways-from-health-insurers-q2-earnings-calls-lessons-on-the-future-of-healthcare/?sh=187bd006f933) was that there is a growing pressure for health insurers to “up their game” in the area of customer experience, spurred by the entry of retail giants Amazon, CVS and Walgreens in the space, but also because of consumer expectations that the overall health experience more closely resemble the retail experience. That is, a demand from consumers for ease, immediacy, control of data and transparency that is pressuring payers – and providers – to be more proactive in guiding the consumer healthcare journey. One challenge for healthcare organizations to meet these expectations is that healthcare lags behind retail in terms of creating seamless digital experiences that recognize a patient or a member as the same individual across multiple channels, and integrating those experiences with offline journeys. Whether online or offline, inbound or outbound, healthcare consumers want caregivers, payers and other stakeholders vested in their well-being to know them as the same person. This recognition includes personal information as well as medical history, chronic conditions, prescriptions and medication adherence, care gaps, etc. To fulfill consumer expectations for a consistent, omnichannel personal understanding, healthcare organizations are increasingly interested in identity resolution. Key use cases for deploying identity resolution capabilities include omnichannel orchestration (delivering a consistent experience across channels) and next-best actions (real-time decisions activated in concert with an individual consumer healthcare journey). **Knowing all that is Knowable** For a basic example of why healthcare organizations are interested in identity resolution for the purposes of omnichannel orchestration and delivering a next-best action, consider a member of a healthcare plan who engages with a provider for a digital visit (i.e., telehealth or online chat with a provider). With the verified identity the provider can personalize content and next best actions, like closing a care gap or scheduling a wellness visit. If we think of a holistic healthcare experience as an ongoing conversation between a consumer and everyone vested in the consumer’s care – primary care providers, specialists, payers, pharmacists, etc. – identity resolution is one of the foundational requirements for all stakeholders to have the same view of the consumer. The delivery of a real-time, personalized experience might also mean that in addition to having an updated, real-time view of claims and clinical data, a provider can also see information about the individual’s risk tolerance, disposition to telehealth options, access to transportation for the purpose of scheduling appointments, the member’s ability to pay, and other social determinants of health. Providers have exceptional clinical analytics for world-class diagnostic and treatment capabilities, but outside of a clinical setting the majority of providers know little about their patients. It’s not that they don’t care; of course the health of their patients is paramount. The issue is that they don’t have the behavioral, preference and consumer data to better inform an omnichannel next-best action that factors in the entirety of a healthcare journey. Identity resolution is a key component for creating a golden customer record, a single customer view that combines a full identity graph with full contact history, all attributes and all aggregations. A Golden Record provides healthcare professionals with up-to-the-moment, highly accurate and reliable information about a healthcare consumer that extends beyond clinical data. Channel preferences, social determinants of health and risk tolerance are among the signals included in a persistently updated Golden record that, once activated, yields personalized experiences on any channel, at any time. **One Identity, One Platform** From a technology standpoint, providers and healthcare systems may have multiple electronic medical records (EMR) systems that are siloed by care units, networks or hospitals. But when it comes to marketing, the majority of health systems use solutions or platforms that are not HIPAA-compliant, meaning they’re aggregating personal, non-PHI data. A personalized experience, then, is limited to a member’s name, age, address, etc., precluding health information that would otherwise provide marketers with a full identity graph, allowing them to deliver a more personalized experience and, ultimately, drive better outcomes. Consider another example of a healthcare organization wanting to close a care gap for colorectal cancer screenings. Aggregating an audience off a marketing database, the organization sends an email to men and women turning 50 urging them to schedule an appointment. The email may meet the low standard of “personalization,” but it could improve outcomes considerably with a complete identity graph; matching the recipient to a specific provider, recommending optimal times, offering transportation options to someone who doesn’t drive, or providing payment arrangement information to a low-income member. Perhaps there are other care gaps that need to be addressed, or the member has a child at home due for an annual wellness check. Furthermore, perhaps email is not the ideal channel for a percentage of the audience that responds better to a call, an SMS or a video consultation. When a health organization knows all that is knowable about a patient or member through a Golden Record, it can leverage advanced segmentation rules based on current health condition, social determinants of health, etc., to allow for a more efficient and effective personalized message to key segments that return the most value for a health plan or provider group. This ability is of particular importance for value-based care (VBC) reimbursement models. If a member’s financial value to a health plan provider is worth a certain amount in annual premiums, the insurer may contract with a provider for a smaller financial reimbursement (e.g., 20% of the overall maximum value) if the member stays healthy; that is, she schedules preventive screenings, takes prescribed medications as directed, follows a nutrition plan, etc. In a VBC model, there is a direct revenue link between hyper-personalized content through advanced segmentation and improving health outcomes. **A Next-Best Action with Every Engagement** Identity resolution enables healthcare organizations to accomplish both omnichannel orchestration and the delivery of next-best actions by providing them with everything there is to know about a healthcare consumer’s identity in one marketing platform, including first-party data, channel preferences, devices used, household status, life stage, etc. Communication with a patient or member is based off an entirety of signals and behaviors, and real-time engagement is enabled across an entirety of channels as the healthcare consumer traverses them. Importantly, triggered events can be on an inbound or outbound dynamic. The email example is a common outbound marketing tactic, but a full identity graph that includes a consumer’s devices, social media, phone numbers, online session behavior, etc. can also identify an inbound consumer. Perhaps a member has received a IVR call to schedule an appointment but decides to schedule online instead. With a single customer view, a payer or provider recognizes the individual – even without logging into a member portal – and can personalize the online content, making it hyper-relevant for an individual health care journey. **Drive Better Outcomes with Personalization** In a [2021 Harris Poll](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) on customer experience, consumers ranked healthcare third (behind retail and financial services) in terms of providing a consistent experience that demonstrated a personal understanding of a consumer. Yet when asked which industries *should* provide the most consistent experience and customer understanding, healthcare polled first. In addition, in a recent survey from health technology vendor Welltok, [80 percent of consumers](https://www.medicaleconomics.com/view/patients-want-more-well-being-support-doctors) said they are more likely to follow a treatment plan if it is personalized. These and similar results show why identity resolution is emerging as an important capability in the healthcare space. More organizations are adopting VBC business models where it becomes important to maintain a consistent dialogue with a patient or member across physical and digital channels in order to deliver next-best actions optimized for an individual healthcare journey. Maintaining a consistent dialogue requires that a digital healthcare experience is similar to an experience a healthcare consumer would receive in a clinical setting, such as an appointment with a provider. That’s the expectation consumers have. To meet it, healthcare organizations must orient a seamless experience around the consumer – using all relevant data that form a single view of the healthcare consumer. For more on how identity resolution can help your organization deliver better health outcomes with a more personalized customer experience, click [here](https://www.redpointglobal.com/customer-data-management/identity-resolution/) or [here](https://www.linkedin.com/showcase/redpoint-for-healthcare/). **Related Redpoint Orchard Blog** [Healthcare Consumers Weigh In: Personalized Experiences are a Must](https://www.redpointglobal.com/blog/healthcare-consumers-weigh-in-personalized-experiences-are-a-deal-breaker/) [Align the Healthcare Experience Around the Consumer: How an Omnichannel CX Delivers Improved Health Outcomes](https://www.redpointglobal.com/blog/align-the-healthcare-experience-around-the-consumer-how-an-omnichannel-cx-delivers-improved-health-outcomes/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Healthcare, Real-Time Personalization --- ### [The Impact of Real Time on CDP](https://www.redpointglobal.com/blog/the-impact-of-real-time-on-cdp/) **Published:** August 14, 2019 **Author:** John Nash **Content:** Is real-time processing a core requirement for Customer Data Platforms? Some customers say yes and some say no; not surprisingly, the answers correlate closely to their own use cases and needs. The real answer depends on the use cases your company expects a CDP to support. But no one likes “it depends” as an answer. A general answer might turn on what fraction of buyers have use cases that require real-time. CDP Institute research finds that real-time profile delivery ranks just beneath requirements we’ve included in the [RealCDPTM ](https://realcdp.com) checklist. This puts it right on the border. ![](https://www.redpointglobal.com/wp-content/uploads/2019/08/Raab-CDP-8-13-300x220.jpg) Yet the question is more complicated than that. There’s more than one type of real time processing associated with a CDP. Our survey asked two questions: one about real time profile delivery and another about real-time recommendations. A more comprehensive view would include at least two more topics: real-time updates and real-time identity resolution. Let’s look at all four of those in turn. - **Real-time updates**. This refers to ingesting data from source systems at speed and scale, loading it into the CDP, using it to update customer profiles, and making those profiles available. A typical use case is making events in one system visible to another system, such as letting a call center agent see an online buyer’s shopping cart. This usually needs new information to be posted in one or two seconds, although some people might allow longer. Completing the ingest/load/update/share process that quickly requires engineering optimized for individual transactions. It also requires complementary capabilities outside the CDP: source systems must be able to send real-time updates as events occur and target systems must either ingest real-time updates from the CDP or read the live [real time CDP](https://www.redpointglobal.com/blog/the-impact-of-real-time-on-cdp/) data directly. The good news here is that’s often a middle group between true real-time updates and a periodic batch update. For example, a lag of several minutes is acceptable in many re-targeting scenarios, where the task is to send a follow-up message after an event has occurred or to remove someone from a follow-up list. - **Real-time lookup**. This is letting an external system read the customer profile as it currently exists in the CDP. The access usually involves API call that includes a unique identifier such as a customer ID and specifies which data elements to return. In cases like showing customer history to a call center agent, the CDP may simply display its profile on a user screen. In other cases, the profile data may be extracted so the calling system can use it in a process such as Web site personalization. A CDP may support real-time lookup even if it only updates the underlying profiles at intervals such as nightly. The data available for real-time lookup is often copied from the main CDP data store into a special format optimized for real-time interaction, such as an indexed file or in-memory database. This means users must specify in advance which elements are made available. The acceptable response time for real-time lookup depends on the use case: there’s little problem in making a call center screen or Web site wait one full second but programmatic ad bidding might require response in less than 50 milliseconds. - **Real-time identity resolution**. Real-time updates and profile requests usually include a unique identifier such as a customer ID, device ID, or email address. This lets the CDP quickly find the related customer profile by searching for an exact match. But when the input lacks a clear ID, the CDP may need to run an identity resolution process that looks for matches based on a combination of data elements (name, address, etc.) and/or behavioral information (location, time of day, device type, etc.) This can require considerable processing to test different matches, apply different match methods, and to look up external reference data or identity graphs. A comprehensive identity process would also reassess existing match sets (groups of IDs assigned to the same person) after each update to see if the new data implied a split or merger. This sort of identity resolution can be difficult even without time constraints. So it’s important to understand what kind of matching is needed when judging whether a CDP can support a particular use case. - **Real-time interactions**. This refers to processes where the CDP is updated with new data during the course of an interaction. It combines real-time lookup with real-time updates. A typical use case is website personalization, where the CDP receives an initial message with customer information, finds the customer profile, and returns an offer recommendation. The customer then takes an action which is reported back to the CDP, which updates its profile and returns a new offer recommendation. This cycle continues until the customer leaves the Web site. Depending on the details, the CDP may only update its profile with the new information or it may do more work such as rescoring a predictive model. These interactions are usually executed by copying the current profile into memory at the start of the interaction, updating the in-memory profile as the interaction proceeds, and then copying the final profile back into the main CDP data store when the interaction is complete. One drawback to this approach is that customer actions taken through a different channel may not make their way into the in-memory copy of the profile. Which of these real-time capabilities do you need? Again, the answer depends on your situation. Whatever you decide, ensure the system you buy can do what you expect. Remember that CDPs vary widely and it’s not safe to assume that any particular system has any particular capability. Take your time to do thorough research: not only will you avoid mistakes, but you’ll be better prepared when you acquisition is complete and it’s time to start deployment. **RELATED ARTICLES** [What’s Needed to be a RealCDP?](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/) [Building vs. Buying a CDP? Why That’s Not the Only Question You Should be Asking](https://www.redpointglobal.com/blog/building-vs-buying-a-cdp-why-thats-not-the-only-question-you-should-be-asking/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform --- ### [The Data-Driven Acceleration of the Consumer Healthcare Journey](https://www.redpointglobal.com/blog/the-data-driven-acceleration-of-the-consumer-healthcare-journey/) **Published:** April 23, 2020 **Author:** John Nash **Content:** In the 2020 [Industry Pulse Report](https://healthpayerintelligence.com/news/payer-consumerism-strategies-rely-on-data-sdoh-simpler-language) from Change Healthcare, the survey of nearly 500 payers and providers reveals that all have a consumer-centric strategy either in place or in development. Among the metrics the survey uses to measure expansion of healthcare consumerism strategies are increasing commitments to sharing data with consumers, as well as enhancing the consumer healthcare experience. The report is contributing evidence of the growing trend toward healthcare consumerism, which recognizes the empowered consumer at the center of a dynamic healthcare experience. A seamless, holistic experience spans a consumer’s interactions with payers, providers, and other health professionals across multiple channels. Importantly, it is diametrically opposed to a traditional static ecosystem marked by siloes of engagement that introduce friction and create a cumbersome experience. While healthcare consumerism has been in motion for some time, the current healthcare crisis is likely to greatly accelerate the trend, particularly as consumers transition to a more digital experience and healthcare professionals shift to digital channels to adapt to evolving consumer healthcare journeys. There are of course the immediate and serious effects of COVID-19, from caring for the sick to securing supplies and mitigating the spread of the virus. Consumer education is also important; letting consumers know how to access treatment or testing, what’s covered under their plan, whether certain copays are waived, etc. In one study from mid-March, [70 percent of healthcare consumers](https://healthpayerintelligence.com/news/52-of-members-surveyed-saw-no-member-engagement-on-coronavirus) were unfamiliar with the status of their coverage for coronavirus-related situations. **A Connected, Personalized Experience Starts with Data** The ongoing health emergency serves as a stark reminder that a dynamic consumer healthcare journey touches a multitude of stakeholders who are vested in improving health outcomes and consumer satisfaction through a variety of channels and engagement touchpoints. Increasingly, this entails recognition that digital communication is an important to improving the consumer experience. Consider a 2019 Consumerization of Healthcare Study by Econsultancy, which found that [75 percent of consumers](https://econsultancy.com/reports/the-consumerization-of-healthcare/) wanted the healthcare experience to be more like other industries – infused with highly personalized and connected experiences across physical and digital channels. Specific examples from [Accenture’s 2019 “Future of Healthcare” survey](https://www.accenture.com/_acnmedia/pdf-94/accenture-2019-digital-health-consumer-survey.pdf) include 70 percent of consumers preferring a provider who offers follow-up texts or emails, and 29 percent of consumers taking advantage of virtual care when offered – an 8 percent increase since 2017. To meet the consumer expectation for a connected, personalized experience and to help guide the healthcare consumer through a dynamic journey, healthcare professionals need access to relevant data. Claims and clinical data that belong to the same consumer record must be accessible and updated in real time, providing healthcare professionals with a single view of the healthcare consumer. **Eliminate Siloes with a Single View of the Consumer** The Redpoint Golden Record provides a persistently updated single view by combining data of every type and from every source, and making it accessible in real time. With advanced identity resolution capabilities, the Redpoint platform resolves a consumer’s identity to the individual level across multiple devices and known and unknown journeys. Rules-based automated machine learning models power a real-time decisioning engine, which generates a next-best action in the context of the consumer’s unique journey. Across a diversity of industries that include retail, finance, and hospitality, Redpoint clients use the platform to drive new revenue with a single control over all data, decisions, and interactions. Real-time decisioning ensures that data is actionable the moment it’s needed, and for optimal impact. A CPG company, as one example, uses the platform to generate real-time web-based product recommendations, generating a [79 percent increase](https://www.redpointglobal.com/wp-content/uploads/2019/07/cpg-casestudy.pdf) in conversions and 3X ROI in the first year. According to research from Gartner, [“customer-centric”](https://www.gartner.com/smarterwithgartner/is-your-organization-customer-centric/) brands that combine transactional, preference, and historical data with behavioral data, IoT and sentiment analysis, and first-party, second-party, and third-party data across an anonymous to known record can achieve a conversion lift of 20 percent or more. Conversely, “persona-centric” brands that stop at using transactional, preference, and historical data usually cap revenue lift at 10 percent. A single view of the healthcare consumer helps healthcare professionals reach the “customer-centric” level, with results that include communicating with a member or patient in the preferred channel, with information that is most relevant to their current situation. When access of care, benefits navigation, and other aspects of a healthcare journey are coordinated through a single point of control, the healthcare consumer is met with a seamless, consistent experience across digital and physical channels. **Communicate with Relevance, in the Right Context** In the current situation, for example, this could mean a payer knowing that a consumer searched for testing locations on a payer website and alerting a provider, who then follows up with an SMS message. Or a payer with access to a provider’s appointment availability proactively notifying a consumer about a change to a provider service and offering to schedule an appointment. These use cases depend on having the relevant data, and on being able to communicate in real time in the channel where a message or action will produce the desired effect. Digital transformation in healthcare is not a recent development. The trend toward healthcare consumerism is, in many ways, a recognition of the changing nature of the consumer healthcare journey toward a dynamic, digital-first, experience that mirrors a consumer experience in other industries. Now, with consumers altering daily behaviors in ways never before seen, this trend will only accelerate. Healthcare professionals who are prepared to deliver a consistent experience and guide a consumer through the complex, dynamic healthcare ecosystem will benefit from higher retention, improved acquisition efforts, and higher consumer satisfaction rates. Redpoint and Lucerna Health will co-host a webinar, “Payer Marketing in a Challenging Environment” on May 13 that will expand on these themes. The second in a series on healthcare consumerism, the webinar will touch on the impact of social distancing on this year’s marketing campaigns, the impact on providers, and how to optimize personalized offer journeys in an increasingly digital marketplace. *Editor’s note: A follow-up blog will focus on ways to manage the dynamic customer healthcare journey.* **RELATED CONTENT** [Advancing Value-Based Healthcare One Patient Engagement at a Time](https://www.redpointglobal.com/blog/advancing-value-based-healthcare-one-patient-engagement-at-a-time/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Customer Data Platform, Healthcare, Omnichannel Marketing, Real-Time Personalization --- ### [The Advantages of Real Time Customer Segmentation](https://www.redpointglobal.com/blog/the-advantages-of-real-time-customer-segmentation/) **Published:** November 25, 2020 **Author:** Redpoint Global **Content:** ## **The Key Benefits Of Customer Segmentation** Even the most loyal customers can be unpredictable. Today more than ever, their behaviors shift frequently, their needs change often, and their preferences vary widely. Customer profiles need to be fluid, too. As a result, the definition of a customer profile is evolving — and using them is now crucial because customer experience is increasingly a key area of differentiation for businesses across nearly every industry. It’s also time to rethink how to build and maintain customer profiles to get the most from them, and, more importantly, deliver the most value to your customers. Most business leaders think of real time customer segmentation as everything that defines a group of customers captured in one detailed description. Generally, companies build customer profiles to support personalization, segmentation, targeting, along with other customer experience– and marketing-related activities. Often, this type of customer profile underlies buyer personas. In some cases, a customer profile is based on an assumption of the type of customer that would benefit the most from a product or service. This type of profile is usually based on broad insights such as behaviors, demographics, and psychographics, but is static. Once it’s created, it stays the same for some extended time frame. As a result, its value is limited. [Customers are changing; the customer profile isn’t](https://www.redpointglobal.com/blog/top-10-benefits-of-rules-based-orchestration/). Ideally, customer profiles focus on individual customers, rather than a group or segment, and encompass real-time insights from across channels, devices, and internal and external data sources. In this case, the customer profile matches the actual customer from moment to moment. These profiles are invaluable because they can help not only with personalization and targeting for groups or segments, but also for individual customers. As a result, the customer experience, customer journeys, and marketing communications and offers will be more relevant and valuable than ever. ## **Real Time Customer Segmentation Is Crucial** Organizations today must be prepared to interact with customers at an individual level at any time and in any channel. Static customer profiles simply aren’t sufficiently able to support this because they lack the currency and breadth that businesses need if they are to move at the cadence of each customer. What’s more, today’s omnichannel interactions require real-time cross-channel insight that static customer profiles can’t provide. Companies need insight into more than just what they know about a customer in a single channel (e.g., mobile, website); they need to have access to and be able to leverage customer insights across many channels and touchpoints in real time—allowing them to use a customer’s behaviors in one channel to inform their actions when reaching out to that customer in another channel during the next interaction. This holistic view builds living, fluid customer profiles uniquely able to reveal the [optimal next-best action](https://www.redpointglobal.com/real-time-decisions/). Rather than have one overarching, static customer profile—or even a handful that represent key groups and segments—companies need to build and maintain a comprehensive view of each and every customer (e.g., a Golden Record). Simply put, this enables companies to be more relevant when interacting with individual customers. Real-time customer profiles provide a host of benefits in the moment and over the long term: ## **Deeply Understand Customers** - Combine all customer data—internal and external; behavioral, demographic, financial, geographic, operational, psychographic, transactional—into a distinctive customer profile - Gain a holistic view of individual customers - Keep current on customers and their changing needs and preferences - Continuously identify customers’ value over time and respond accordingly ## **Uniquely Personalize the Customer Experience** - Build more relevant and engaging customer journeys - Personalize experiences at each touchpoint throughout the journey - Unify the customer experience across channels - Use your deep understanding of customers’ current and potential future value when you reach out to them with communications and offers - Improve conversion rates - Eliminate waste chasing the wrong customers or interacting at the wrong time or in the wrong channel ## **Quickly Access Comprehensive Customer Data** - Connect internal and external, online and offline, and first-party, second-party, and third-party data in real time to create a unique, robust view of each customer - Ensure that customer profiles are always current and correct - Identify prospects most likely to become high-value customers - Better predict what new products/services are most likely to launch successfully Imagine taking every benefit of a standard, static [customer](https://www.redpointglobal.com/blog/commercial-excellence-begins-with-customer-experience/) profile and revving it up with real-time insight. Everything from product development and positioning strategies to prospecting and loyalty schemes is enriched with real-time insights. As a result, you deliver more value to customers and they, in turn, become more valuable to your business. ## **Elements of Real Time Customer Segmentation** Real-time customer profiles comprise disparate customer data linked from many sources and included in a single location that users can easily access. Building comprehensive real-time customer profiles starts with bridging internal data silos and creating pathways to ingest external data. Ideally, a real-time customer profile is continually pulling data from relevant internal and external sources. These could include first-party, second-party and third-party sources of behavioral, demographic, financial, geographic, operational, psychographic, and transactional data. The sources could be as varied as contact center and marketing interaction data or device and IoT data. What is specifically included in a customer profile will vary depending on a company’s industry, the types of customer data important to their strategy and goals, and their business objectives. In a retail environment, an example of a [customer](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/) profile might include lifetime value, website behavior, mobile and email interaction history, preferences, and intent, as well as purchase history. An example of a customer profile that a financial services company builds might include data such as products owned, life stage, balance history, and third-party intent data (e.g., is a customer looking at houses or new cars). In both situations, companies focus on gathering and using customer data specifically meant for taking action—whether making strategic decisions, determining next-best actions, guiding a customer along an optimal journey, or delivering the ideal marketing communications, offers, or promotions. When building customer profiles, consider what data is most important to your business: 1. **What customers do:** What channels do they use to interact with you and when? How long do they spend on your site or using your app? What are they doing while there? Are there any notable changes in their behavior? What is a typical journey for them? How do they generally respond to your experiences, interactions, and offers? 2. **What customers buy:** What do they purchase and how often? Are there any clear product or service preferences? Have they tried related products? Are there any changes to what they usually purchase in terms of value or frequency? When was the last time they bought something from you? 3. **What customers say:** Are your customers providing you direct or indirect feedback? If so, what input are they sharing? Do they participate in a brand community? What are they saying about your brand, customer experience, marketing or ads, and products or services on social media? Are their sentiments shifting positively or negatively? 4. **What customers like:** What do they spend time doing on your site? Are they viewing specific products, adding to a wish list, reading your blog? Who and what do they follow and share on social media? What’s in their social interest graph? 5. **What experience customers have:** What is it like for a customer to do business with you? Can they easily get the information they need about your products and services? Is the customer journey simple and optimized based on their specific interests and preferences? Is there a point in the journey where the customer is likely to leave? 6. **What customers in your industry value:** How does your customer expect you to support them and their needs that’s specific to your industry? How relevant is their intent behavior in another industry to your interactions with them? When selecting the data to include in these customer profiles, remember: Real-time customer profile data is continually updated, so companies can make quick decisions on what actions to take or offers to present to specific customers at specific times in the best channel. For instance, if the retail and financial services companies in the examples above were sending email campaigns, the retailer will likely want to highlight products relevant to the customer’s areas of interest (or purchase history); whereas, the financial services company will want to promote auto loans or mortgages—depending on what a customer has shown interest in. Ultimately, having a real-time customer profile—a Golden Record—enables companies to be more relevant every time they interact with individual customers. ## **How to Build a Real-Time Customer Profile** There are many ways to build customer profiles, but there is one ideal method: establish an underlying support system that enables you to build and maintain a real-time comprehensive view of each and every customer as well as a dashboard to access that data. Being able to do this—at scale—requires extensive data management skills. So, ideally, implement a platform that does the heavy lifting for you. Look for one that enables you to not only bridge disconnected data sources across the enterprise, but also ingest data from second-party and third-party data sources. It also should provide access to data in real time to allow you to create living, breathing customer profiles that change as your customers’ and their interests, needs, and preferences change. When laying the foundation for building, maintaining, and using real-time customer profile, take these steps: 1. **Start with your “why”:** Determine your overarching business objects, as well as your specific customer experience and marketing strategies and goals. 2. **Select the supporting data:** Consider what you’ll need to include in your customer profiles to meet those goals. Determine what industry-specific data should be included in the profiles, as well. 3. **Implement a foundational platform:** Creating and maintaining real-time customer profiles would be nearly impossible without a platform to bridge data silos and automate key processes, such as creating segments, determining next-best actions, adjusting customer journeys on the fly, and presenting real-time communications and offers. 4. **Get clarity:** Use your connected data to build holistic real-time customer profiles. Then rely on your platform to continually cleanse, match, and update each and every customer record in real time. Harness the insight from your living, breathing customer profiles to better understand your customers and deliver unique and relevant experiences and interactions. 5. **Act fast:** Static customer profiles quickly become irrelevant. Real-time customer profiles enable you to take immediate action. As a result, the customer experience improves, conversions and loyalty increase because customers see more value in doing business with you, and you see more value from each customer. Don’t fall into the trap of assuming that your static customer profiles are good enough. In today’s customer-centric business environment, they lack the accuracy, comprehensiveness, and recency needed to deliver on customers’ expectations for experiences that provide value to them—by simplifying their journey, being contextually relevant, and helping them to solve a problem or improve their life in some big or small way. Only real-time customer profiles enable you to achieve all that, and more. **Blog categories:** Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Super Bowl Commercial Scorecard: Which Brands Scored a Touchdown](https://www.redpointglobal.com/blog/super-bowl-commercial-scorecard-which-brands-scored-a-touchdown/) **Published:** February 5, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/02/shutterstock_1291088032-e1549376411353.jpg)Offense was in short supply during the New England Patriots’ hard-fought [13-3 victory](https://www.youtube.com/watch?v=wZKsHkkBLHI) over the Los Angeles Rams in Super Bowl LIII on Sunday. While many found the defensive battle “boring” – note the many “Maroon 5, Rams 3” memes – the same cannot be said for the commercials, which delivered more memorable moments than the game had touchdowns (one). As always, it’s just as fun to dissect what transpired off-the-field than what happened between the lines. While the ads certainly sparked a lot of discussion, other morning-after topics include the shirtless Adam Levine fronting Maroon 5 in a widely panned halftime show, the glaring lack of African-American drummers during the halftime drum line show, Big Boi’s fur coat, and the Rams’ male cheerleaders, to name a few. Social media [weighed in](https://www.ajc.com/news/local/live-updates-super-bowl-2019-buzz-social-reactions/PWJqlhiZ65c2ZgoHCf4FfL/) on all of the above and more, if you were too engrossed in the game to notice. Personally, one of my favorite ads was the NFL’s two-minute spot to promote its 100th anniversary, featuring a who’s who of past and present NFL stars tackling each other in a ballroom – prompting many internet jokes about Rams’ running back Todd Gurley rushing for more yards in the commercial than he did in the game. Some of the other memorable ads from a brand perspective included: - WalMart’s [“Famous Cars”](https://www.youtube.com/watch?v=4PDhMoFoUGY) ad that featured famous fictional cars – the “Scooby Doo” van, the “Back to the Future” DeLorean, and the “Knight Rider” Firebird, among others – to promote its buy online, pick-up curbside grocery service, an offshoot of the popular buy online, pay in-store (BOPIS) model. An awed customer, impressed by the reserved pick-up lanes, white glove service – and the flying DeLorean – asks “what’s happening” and is told by Knight Rider’s Kitt “It’s the future, Michael.” It was a clever way to let more than 100 million viewers know that WalMart is betting big on next-level customer experience as a differentiator. - The Pringles [“Sad Device”](https://www.youtube.com/watch?v=tDakI68u2xE) ad promoted the brand’s many varieties with a voice-controlled smart speaker upset that it (she?) has no hands or mouth to build or taste the “flavor stacks”. The ad was one of many that highlighted AI not as a futuristic, nebulous concept but as part of our everyday lives. - SimpliSafe’s [“Fear is Everywhere”](https://www.youtube.com/watch?v=_rnrEQBieIQ) ad falls into the same category. Yet rather than a “sad” device, the commercial for the company’s home security system featured a “spying” device as one of the modern fears we as consumers have. It poked fun at our fears, real or imagined, about porch pirates, delivery drones, and robots coming to take our jobs. SimpliSafe is telling us that it understands the “creep factor” is real in how brands interact with us, and the ad is meant to reassure us that its product has our best interests in mind. - Microsoft’s [“We All Win”](https://www.youtube.com/watch?v=CM2QJO2IDFo) commercial for the Xbox Adaptive Controller, designed to help gamers with limited mobility. Children spoke openly and honestly about overcoming their physical disabilities and the importance of being treated as equals. While comedy seems to be the default genre for creating memorable Super Bowl ads, Microsoft took a gamble by showing us our own humanity, betting that by making us feel good about ourselves we’ll feel good about the product. It also showed us the power of technology for helping to better our lives. I would argue that these commercials were well worth the estimated $5.2 million cost (for a 30-second ad) that brands willingly pay to reach more than 100 million Super Bowl viewers, more than double some of the highest-rated sitcom finales of all time. (Last year’s game averaged about 108 million viewers). There is also ample evidence that the ads are effective. According to a [National Retail Federation (NRF) survey](https://www.marketingcharts.com/featured-82221) that spanned 10 years of Super Bowl viewing, last year 19.3 percent of respondents said that Super Bowl ads made them more aware of advertiser brands, up almost 3 percent since 2008. It was about the same increase for respondents who said that the ads influenced them to search online for more info (up 3.4 percent) and influenced the viewer to buy more products from the advertisers (up 4.3 percent). **Cross-Over and Personalization** An effective ad, as measured by share of digital voice, social impressions, and online views, is one that is talked about, analyzed, and dissected as much or more than a game-changing play, and that’s why we are now seeing brands coming up with innovative new ways to engage with the audience. Methods include cross-over promotions, personalization, and integrated marketing campaigns with strong social media tie-ins. ![](https://www.redpointglobal.com/wp-content/uploads/2019/02/Bud.png "Photo by William Thomas Cain")Bud Light and HBO teamed up for [“Joust”](https://www.theverge.com/2019/2/3/18209844/game-of-thrones-ad-bud-light-dragon-mountain-super-bowl-2019) that featured the Bud Light Knight doing battle with The Mountain from “Game of Thrones” to promote the HBO program’s upcoming final season. If you’re a Bud Light drinker who also watches “Game of Thrones” – and it’s a safe bet that the brands wouldn’t have shelled out $10 million for the 60-second spot without knowing precisely who their target audience is – then you probably viewed the commercial akin to a personalized invitation. The ad was an example of brands targeting consumers based on preferences – the more a brand knows about its customers, the more effective it can be in delivering a relevant experience. T-Mobile crossed off the personalization and the cross-over boxes with a series of ads – one each quarter – in its [\#AreYouWithUs](https://www.youtube.com/watch?v=Kxwp3WuWwxY) campaign. The commercials featured text message exchanges that people can relate to – a conversation about dinner plans, a daughter confounded by her technology-challenged dad – that the mobile carrier tied into its long-standing T-Mobile Tuesdays promotions. After the dinner exchange – Sushi? Tacos? – the commercial revealed a Taco Bell giveaway. Another commercial ended with an offer of a $10 Lyft discount. Of course, to redeem the freebies customers must download the app. With the offers, T-Mobile is actually putting a price tag on what it’s willing to spend in exchange for your personal data. **And the Winners Are …** Early results of Super Bowl LIII ad effectiveness are in, and one of the biggest winners was Audi, whose [“Cashew”](https://www.vox.com/the-goods/2019/2/3/18209864/audi-super-bowl-53-cashew-ad-etron-gt) ad used the Heimlich maneuver to promote its e-tron GT electric sedan. The ad boosted traffic to the e-tron GT site by 13,678 percent according to [Drive Time](https://www.mediapost.com/publications/article/331478/hyundai-wins-super-bowl-while-kias-somber-spot-p.html), with a 116 percent spike in traffic growth. Toyota’s [“Wizard”](https://www.youtube.com/watch?v=u1LSwcwnpo4) ad (featuring the Who’s “Pinball Wizard”) to promote the new Supra saw traffic to the Supra website jump 13,000 percent. Among car manufacturers who advertised during the game, the commercial garnered the highest percentage of online activity following its ad. Maybe the low-scoring defensive slugfest between the Patriots and the Rams drove viewers to the Internet, more excited to conduct digital research than to watch punt after punt after punt. Not every Super Bowl is going to be an offensive juggernaut with teams trading touchdowns, but those tend to be the ones that live on in our collective memories. We measure the success of Super Bowl ads the same way – if it’s remembered, it worked. I’d love to hear what you thought of the Super Bowl ads. What struck a chord? What flopped? Are you now in the market for an Audi e-tron GT? Leave us a comment below to keep the conversation going. ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Real-Time Personalization --- ### [Shattering the Myth of the Single-Vendor Marketing Suite](https://www.redpointglobal.com/blog/shattering-the-myth-of-the-single-vendor-marketing-suite/) **Published:** April 19, 2018 **Author:** Steve Zisk **Content:** ![the myth of the marketing cloud](https://www.redpointglobal.com/wp-content/uploads/2018/04/marketing-cloud-myth.jpg)The idea behind a single-vendor marketing suite is a laudable one: unify all your needed technologies into a single toolset with a single login and a single model. Once that’s done, you will have a single solution that accomplishes everything you need to meet the omnichannel consumer in their channel of choice with the most relevant messaging ever. There is only one problem with this vision. It’s an outright myth. Single-vendor marketing suites never accomplish their stated goal of being a central solution. This is because most single-vendor suites are a “Frankencloud” of disjointed technologies that have been assembled through acquisition and don’t play well together. Moreover, it is incredibly complicated to update a single-vendor suite to keep up with the pace of change in marketing technology. A pace, incidentally, that has only increased over the past few years. ### **No Solution Can Do It All** ChiefMartec identified 5,381 distinct marketing technologies in its most recent [Marketing Technology Landscape](https://chiefmartec.com/2017/05/marketing-techniology-landscape-supergraphic-2017/). These distinct solutions are offered by more than 4,890 unique vendors in *49 product categories.* That is 49 separate categories of product, each classification covering its own business case. This is nearly 1,000 new technologies over the 2016 landscape, which featured 3,874 unique solutions. Single-vendor suites can’t possibly operate in all the marketing technology product categories. Expecting any single solution to achieve everything possible in 49 categories is a recipe for disaster. Among the 21 percent of marketers who use single-vendor suites – according to [Walker Sands research](https://www.walkersands.com/State-of-Martech) – only 16 percent stay within their suite. This is a clear indication that the marketers who do leverage suites understand they can’t fulfill every possible need and that no one has best-of-breed throughout its suite. ### **The Cost of a Single-Vendor Suite** Single-vendor suites have problems beyond not achieving their stated goals. One of the most substantial is the actual budgetary outlay. Because of how a single-[vendor suite](https://www.redpointglobal.com/blog/shattering-the-myth-of-the-single-vendor-marketing-suite/) is constructed, implementing one requires a significant amount of time and money. The reason is that most single-vendor suites involve ripping-and-replacing most, if not all, of your existing marketing stack. CMO Council and Redpoint Global research found that 44 percent of marketers spend more than 25 percent of their budget on replacing existing solutions. This is just the financial impact and doesn’t even begin to consider the length of time you would spend on implementing the single-vendor suite and integrating it with your company database. In most cases, the timeline involves several months of work because your organization’s entire infrastructure needs to be changed over for the single-vendor suite to function properly. And if a new solution arises that your single-vendor suite doesn’t support, you must either wait until the vendor integrates the capability or implement the capability as a separate solution. This creates the same situation you started with: a set of disconnected solutions that don’t share data and don’t communicate. The idea behind a single-vendor marketing suite is a worthy one; a single solution, enabling one login, that provides everything a marketer needs. But the reality is that no single solution can hope to keep abreast of all the changes coming down the pipe in terms of marketing technology. This is especially true for larger organizations, who need functionality that can scale to meeting the needs of innumerable customers. ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/redpoint-global-inc/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Real-Time Personalization --- ### [Everything You Need to Know About Data Products](https://www.redpointglobal.com/blog/everything-you-need-to-know-about-data-products/) **Published:** January 6, 2026 **Author:** Steve Zisk **Content:** One recognition of customer data’s value as an enterprise asset is the emergence of data product contracts, documents in which the data producer and data consumer agree on measurable guarantees about what data will accomplish. To better understand what a data product contract will stipulate, it is first necessary to understand the data product itself. What is a data product? In short, it is a packaged, reliable, documented, and evolving bundle of data that is built, delivered, and supported the same way you would manage a regular product. Like a smartphone or even a car, a data product is subject to updates and changes to help the data consumer better accomplish their goals. It turns data from a messy, ever-changing project into a stable, reliable product that users can depend on. ## **Constraints that Necessitate a Data Product** A data product addresses the three main challenges, or constraints, that companies face when trying to use data effectively: 1. **Architectural** – With data fabrics, data mesh, etc., companies are rebuilding how their data are organized. There’s a conflict between changing the data to meet new requirements and keeping it static to avoid breaking everything that depends on it. 2. **Developmental** – Companies continually build data pipelines and create new use cases for their customer data, but data has not traditionally been subject to developmental practices that govern requirements definitions, an agile framework, CI/CD, etc. Data is not treated with the same discipline as software, leading to misunderstandings in how to build and maintain data. 3. **Communication** – Data consumers and data teams have different understandings and/or different ways of communicating their expectations around data. Miscommunication is common, and it leads to data that doesn’t match what the business really needs. *The idea behind a data product is to satisfy those three constraints*: - I need to fit my customer data (and pipelines) into a comprehensive enterprise architecture. - I need my customer data to be explicitly managed with the same agile design thinking as for any development project. - I need to align and define communications between data consumers and data producers to make data robust: safely controllable / changeable by the producer, and continually usable by the data consumer. ### **How a Data Product Satisfies the Architectural Constraint** A data product satisfies the architectural constraint by defining a set of deliverables that guide the availability and use of the data, without tightly coupling that framework with other deliverables. Those deliverables include the data definition (schema, semantics, metadata, linkages), delivery/access method (API, tables, files, messages), and metrics (SLAs for cadence, reliability, quality, etc.). Defining a data product gives developers the freedom to do all of this work in enterprise architecture that provides performance, stability, and efficiency without changing what the data consumer is expecting. The data product provides a clear, stable interface, where the inside can evolve and be rebuilt, but the outside remains predictable for the consumer. Think of it like electrical wiring. Behind the wall, wiring can be updated or replaced. But the outlet stays the same and all your devices just keep working. If an “outlet” (some set of details for the data product) needs to be changed, upgraded, or removed, that can happen, too, as long as the producer and consumer agree on when and how the change happens. ### **How a Data Product Satisfies the Developmental Constraint** Defining data as a “product” allows data ops, cloud ops, and developers to start looking at the various aspects of a specific data product through the same lens as other development practices. They can define measurements, QA metrics, and other processes that dictate raw data’s transformation to being ready and fit for its intended purpose. A data product treats data not as a random pile of files, but as something that is built, tested, and maintained – like an app on a smartphone that gets updates and support. For data engineers, applying a product lens to the data allows the team to use a predictable and manageable process for building, releasing, and changing the data product. And the tools and artifacts of the software developer – stories, sprints, backlogs, tickets – can be used to guide the work. ### **How a Data Product Satisfies the Communications Constraint** Resolving a communications gap is where the actual data product contract comes in, spelling out what a data producer will deliver, how it will be delivered, and what characteristics it will have. The contract is an inherent part of the data product as the description of what will be delivered. And, for the data consumer, it describes what the consumer can do with the product being delivered. Sticking with the smartphone analogy, the contract is like the service plan (contract) that comes with a smartphone (the product). The plan covers what the consumer gets, what the product can do, and what the manufacturer promises. ## **Coming Next: A Data Product and Data Readiness** If we look at the creation of a Customer 360 as an [example of a data product](https://www.redpointglobal.com/profile-unification/), one important thing to understand is that a data product has the capacity to evolve and change. The data producer might for instance optimize how the Customer 360 is created to help the data consumer better accomplish the intended use case, such as adding a data source, or creating a new pipeline or API. In this light, it makes sense that a data product would evolve in line with the regular product development process, while also solving a lot of misunderstandings and separating the goings-on in the IT world with what is being delivered to business users. Once the leap is made to treating data as a product, a natural follow-up is to address the concerns of the business user around [trust, accuracy and the overall readiness of customer data](https://www.redpointglobal.com/data-the-defining-difference/) to accomplish their business goals, AI, and CX use cases. Addressing those concerns is the heart of the data product contract. In a follow-up piece, we will examine the [concept of data readiness](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/) as it applies to data products and as a foundational component of a data product contract that stipulates how customer data will be made right and fit for its intended purpose. **Blog categories:** Data Product **Blog tags:** Data readiness --- ### [Shape the Future of Customer Experience with Customer Analytics](https://www.redpointglobal.com/blog/shape-the-future-of-customer-experience-with-customer-analytics/) **Published:** June 7, 2019 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/03/Gartner.png)This spring, Gartner Senior Director and Analyst Melissa Davis published a paper “Shape the Future of Customer Experience with Customer Analytics.” In the piece, she writes, “Customer analytics is the No. 1 planned technology investment to transform the customer experience (CX). Data and analytics leaders must understand how real-time responses and continuous intelligence, customer journey analytics, and AI will shape the future of customer experience in digital business.” *Through 2022, 50 percent of large organizations will have failed to unify engagement channels, resulting in the continuation of a disjointed and siloed customer experience that lacks context.* Davis also outlines three key challenges: - Real-time is becoming table stakes as organizations attempt to meet customer expectations - Customer-centric contextualized experiences based on the customer journey are becoming a competitive differentiator in CX - AI provides new opportunities and challenges for data and analytics leaders to innovate CX **RELATED ARTICLES** [What is Customer Centricity?](https://www.redpointglobal.com/blog/what-is-customer-centricity/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Journey Orchestration --- ### [Say Goodbye to the "Participation Trophy" Era of the CDP](https://www.redpointglobal.com/blog/say-goodbye-to-the-participation-trophy-era-of-the-cdp/) **Published:** April 15, 2020 **Author:** Steve Zisk **Content:** Forrester’s recent report on the state of the customer data platform ([CDP](https://www.redpointglobal.com/)) highlights how much the product and the market have evolved in the 18 months since its last report, in October of 2018. Buoyed by the entry of traditional marketing clouds, including Adobe and Salesforce, the CDP – according to Forrester – can no longer be shoehorned into its original definition as a solution that “centralizes customer data from multiple sources and makes it available to systems of insight and engagement.” Calling the entry of martech cloud vendors a “seismic shift” in the market, the report’s authors state that it’s the end of the “participation trophy” era, where simply announcing your presence in the arena was enough to lay a credible claim to the CDP mantle. Rather, with the increased competition along with a heightened urgency for marketers to deliver a hyper-personalized customer experience, a CDP today must clearly demonstrate a valuable purpose. It must, as the authors state, “justify its existence”. ## **No More “Shifting Buckets” of Manual Work** While the report lays out what is now expected of a CDP in the wake of marketing clouds entry into the market, a careful reading between the lines shows that there are, in fact, CDP vendors today – Redpoint among them – that satisfy the core functions required for inclusion into the CDP Institute’s [RealCDP](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/) designation. These functions include ingesting and storing data, data management, [identity resolution](https://www.redpointglobal.com/challenges/identity-resolution/), real-time access to a customer record, and omnichannel orchestration. The key point that takes the CDP out of the “participation trophy” realm is that it must do all these things not in isolation, but in service of a goal beyond data centralization and one that spans the entire customer journey. Further, it must include automation and intelligent capabilities that ensure a CDP is not just replacing one set of manual work with another. I would extend the “functional competency” mandate to include requirements that a CDP must be [enterprise-ready](https://www.redpointglobal.com/blog/is-your-marketing-platform-ready-for-prime-time/) and cloud-first, meaning that to excel at the core functions those core competencies must be built and usable for the enterprise rather than cobbled together, or adapted from another piece of software or repurposed to fit a CDP label. ## **Shortcomings of Marketing Cloud Vendors** As the report ticks through the core competencies of a CDP, it details the elements that Redpoint included in a ground-up, purpose-built solution that provides marketers with a single point of control to orchestrate customer journeys across an omnichannel environment. The contrast between what Redpoint provides versus what marketing cloud vendors offer becomes clear with a closer look at how marketing clouds justify their entry into the market. One key difference is that marketing clouds are often built by accretion, meaning that what they’re calling a CDP is actually a cobbling together of several cloud platforms, each with siloes of data, operations, and interactions. These siloes are a tremendous obstacle in terms of a marketer’s ability to bring together all the data needed to orchestrate a customer journey and manage those orchestrations. Cloud siloes also usually increase latency in the CDP process, contributing to stale data and decisions and impacting relevant engagement. Second, another major issue for marketing cloud vendors is the problem of choice. There is a general recognition in the CDP market, and among martech buyers in general, that accepting whatever a marketing cloud offers in terms of personalization, ad management, social engagement, or even in terms of channels and ecommerce, doesn’t make sense. Marketers understand that they must retain the power to choose what best fits their business goals – and marketing clouds do not make this easy. By contrast, Redpoint’s open garden architecture is infrastructure agnostic. In an open garden environment, an organization is able to connect to any and all data sources and technologies, essentially future-proofing an infrastructure when business objectives pivot. ## **This is CDP’s Time in the Spotlight** The report’s authors also detail important must-haves prior to implementing a CDP to avoid future headaches. These include a unified omnichannel marketing strategy, organizational coordination, and a data management strategy. The caveat I would add to this sound advice is that an organization does not have to boil the ocean, if you will. Rather, it is entirely possible to test the waters with one or two actionable use cases that deliver quick wins and a provable ROI. This strategy allows an organization to grow into its competencies, capabilities, and maturity as it slowly but surely centers its customer engagement strategies around the CDP as the core of its martech stack. The bottom line when it comes to assessing a CDP’s capabilities is that the entry of marketing cloud vendors in the CDP market drives the point home that there is no market for a CDP for a CDP’s sake. Their entry signifies the increasing importance of a CDP as the default technology to understanding customer behavior across all channels and devices. If you would like to see how Redpoint’s solutions provide a scalable, unified single point of control where all customer data is connected and every customer touchpoint is intelligently orchestrated, we invite you to include a [Redpoint demo ](https://www.redpointglobal.com/request-demo/)as part of your evaluation process. **Blog categories:** Customer Data Platform --- ### [Redpoint Global Profiled in the 2020 Data Quality Magic Quadrant & Critical Capabilities Reports](https://www.redpointglobal.com/blog/redpoint-global-profiled-in-the-2020-data-quality-magic-quadrant-critical-capabilities-reports/) **Published:** August 6, 2020 **Author:** Redpoint Global **Content:** As a niche player in the 2020 Data Quality Magic Quadrant, Redpoint focuses on [data quality](https://www.redpointglobal.com/data-management/) for customer experience management. Gartner frames the importance of this marketplace writing, “Trusted, high-quality data is a vital component for the success of digital initiatives. Organizations are accelerating the speed of their digital transformations by introducing digital products, adopting cloud computing, modernizing their business processes and embracing distributed infrastructure to leave the data at edges.” In this year’s report, Redpoint was praised for customer satisfaction, ease of installation, upgrade and use, and overall performance and scalability. In the Magic Quadrant, Gartner states, “Redpoint retains a loyal customer base and is among the highest-rated vendors in this Magic Quadrant for customer satisfaction. Some of the company’s reference customers comment that Redpoint has been a great partner for more than 10 years and have seen consistent improvement in Redpoint’s product features and collaborative efforts to meet their business requirements.” The reference customers expressed strong satisfaction on ease of installation, deployment, and use – scoring among the highest of the 16 competitors. Customers especially highlighted user-friendliness telling Gartner that Redpoint’s automations were easy and intuitive. Redpoint also scored above the average for the performance and scalability of its data quality products, specifically in supporting real-time data processing in Hadoop environments with billions of transaction records. **Critical Capabilities Assessment of Data Quality Vendors** In the supplemental Critical Capabilities for Data Quality Solutions report, Gartner notes that Redpoint extends its platform beyond data quality, with capabilities for data integration and master data management. Redpoint’s approach is to seamlessly marry these customer data capabilities with machine-learning-based decisioning and intelligent optimization. As strengths of the solution, Gartner highlights customer feedback of core functionalities to support all data quality use cases, with Redpoint scoring 4.0/5.0 or higher in data integration, data migration, and AI/machine learning use cases. The term “data quality” relates to the processes and technologies for identifying, understanding, and correcting flaws in data that support effective information governance across operational business processes and decision making. The packaged tools available include a range of critical functions, such as profiling, parsing, standardization, cleansing, matching, enrichment and monitoring. Redpoint powers the resolution of messy, poorly normalized data sources into one, cohesive, clean, consistent Golden Record that can be operationalized to provide custom, real-time experiences at scale. The volume and cadence of data quality is possible with low latency and real-time decisioning, no matter the data source. **Redpoint’s Scores** Of the nine Critical Capabilities evaluated in the research report, Redpoint received a score of 4.0/5.0 or above (considered Excellent by Gartner) in seven criteria. This includes the highest score for Parsing & Standardizing & Cleansing and Role-based Usability, and Redpoint was among the top scores for Matching & Linking & Merging, and Scalability & Performance. Of the seven use cases evaluated in the report, Redpoint received scores of 4.0 or more in six categories. We were also one of the highest-rated vendors for Analytics & BI, Data Integrations, Operational/Transactional Data Quality, and AI & Machine Learning. **Redpoint’s Perspective: Data Quality Matters for CX** While Redpoint is classified as a Niche vendor in the Gartner Magic Quadrant for Data Quality Solutions, at Redpoint, data quality is just one of many core competencies. Unlike other vendors evaluated in these data reports, we provide a technology platform that enables companies to orchestrate exceptional customer experiences – at scale – across any channel. In that regard, we often compete against vendors that call themselves Customer Data Platform (CDP) – companies who in their marketing material claim to provide superior data management capabilities, including data quality. What is interesting is that Redpoint is the only CDP included in these recent research reports from Gartner. Redpoint also is one of the only companies that is also ranked in the Multichannel Marketing Hubs Magic Quadrant who has prioritized data quality enough to take part in the data quality reports. Like any analyst report, these documents are only one tool used to research a vendor. Based on business objectives, functionality, use cases, and many other factors, the right solution for one company may not be ideal for yours. You should also evaluate other research and reviews to obtain a more complete understanding of competing options. We recommend you also consider: [CDPi’s vendor comparison tool](https://www.cdpinstitute.org/resources/cdp-vendor-comparison/), the [Gartner Peer Insights](https://www.gartner.com/reviews/market/data-quality-tools/vendor/redpoint-global) evaluations, and OVUM’s Market Radar report to name a few. *\** *Gartner Magic Quadrant for Data Quality 2020 by Melody Chien and Ankush Jain, published 27 July 2020 and Critical Capabilities for Data Quality Solutions, published 3 August 2020.* *\*\*Gartner does not endorse any vendor, product or service depicted in its research publication, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as a statement of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.* **RELATED CONTENT** [What is Data Hygiene?](https://www.redpointglobal.com/blog/what-is-data-hygiene/) [Data Privacy, Data Quality, and Data Governance: Experts Weigh In](https://www.redpointglobal.com/blog/data-privacy-data-quality-and-data-governance-experts-weigh-in/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Management, Data Quality --- ### [Redpoint Global Moves Up in Challengers Quadrant of the 2020 Gartner Magic Quadrant for Multichannel Marketing Hubs](https://www.redpointglobal.com/blog/redpoint-global-moves-up-in-challengers-quadrant-of-the-2020-gartner-magic-quadrant-for-multichannel-marketing-hubs/) **Published:** May 27, 2020 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/05/shutterstock_520783261-scaled-1.jpg)For the second year in a row, Redpoint Global is profiled as a Challenger in the 2020 Gartner Magic Quadrant for Multichannel Marketing Hubs\*. Being noted as a Challenger is yet another proof point that we are highly capable of executing our vision and that our vision is aligned with client business needs – while also inching ever so closely to the Leaders quadrant. This year’s report is particularly relevant considering how the business world has changed since the beginning of the year. For many companies, a customer-centric approach underpinned by the freshest, most complete view of the customer– across many different touchpoints – is not just critical to success but, in many cases, necessary for survival. Marketers are seeking solutions that meet their business needs today along with the adaptability to support new and innovative customer experiences in the future. That is exactly what we do. **Gartner’s Evaluation Process** For the 2020 Magic Quadrant for Multichannel Marketing Hubs, participating vendors were required to answer an extensive questionnaire, submit a recorded product demonstration, provide customer references, and hold a briefing with the analysts. In total, Gartner evaluated 19 technology vendors across two dimensions (ability to execute and completeness of vision). Each dimension consisted of a number of criteria (e.g., overall viability, marketing strategy), which were weighted high, medium, or low. Based on these criteria, the Gartner analysts scored and plotted the vendors on their widely used Magic Quadrant. In this year’s Magic Quadrant, reference customers praised vendors’ overall product capabilities. As marketers deploy MMH solutions to address increasingly complex customer journeys and use cases, vendors that support marketing teams with a focus on customer success, technical support and simplifying usability can set themselves apart. **Redpoint’s Strengths Highlighted** Gartner specifically noted Redpoint’s ability to “facilitate powerful multichannel marketing efforts” with its rich feature set. Further, Redpoint was one of the only companies to be acknowledged for data management and identity resolution, which is a foundational component for executing [multichannel marketing strategies](https://www.redpointglobal.com/blog/multichannel-vs-omnichannel-marketing-and-keeping-up-with-a-customer-journey/). Redpoint’s “powerful machine learning” capabilities were also noted as strengths in this year’s report. While Gartner does not endorse one company over another, they wrote that “enterprise B2B and B2C marketers seeking strong identity and data management, cloud-agnostic deployment, and advanced ML should consider Redpoint.” Particularly for ambitious marketers, it is important to select a solution that supports differentiated customer experiences. Of the solutions evaluated in the 2020 report, Redpoint is the only company that offers all the following capabilities for enterprise users: - Comprehensive data management solutions for obtaining a contextual understanding of each customer no matter where they are in the customer journey (a “Golden Record”), - Automated machine learning for adapting to ever-changing customer needs in proper context, - Intelligent orchestration for delivering messages and offer across all touchpoints and journeys, - An open garden approach that leverages existing technology resources and last-mile delivery solutions, - Flexible deployment options that enable companies to maintain their security perimeter around customer data, their most valuable asset. While we are pleased to be favorably recognized in this year’s report, it is important to keep in mind that the 2020 Magic Quadrant for Multichannel Marketing Hubs is just one tool to research vendors. It is best to clearly define your business objectives, target metrics, required functionality, supported by specific use cases, and security requirements before doing a deep analysis of any one solution. It is also wise to cross-reference research and reviews to obtain a more complete understanding of competing options. We recommend you also consider: [CDPi’s vendor comparison tool](https://www.cdpinstitute.org/resources/cdp-vendor-comparison/), the [Gartner Peer Insights](https://www.gartner.com/reviews/market/data-quality-tools/vendor/redpoint-global) evaluations and OVUM’s Market Radar report to name a few. *\** *Gartner Magic Quadrant for Multichannel Marketing Hubs, By Noah Elkin, Adam Sarner, Benjamin Bloom, Joseph Enever and Colin Reid, May 12, 2020. Gartner does not endorse any vendor, product or service depicted in its research publication, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as a statement of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.* **RELATED CONTENT** [Digital Transformation Just Became Accelerated](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) [Cadence of the Customer and the Corner Store Experience](https://www.redpointglobal.com/blog/cadence-of-the-customer-and-the-corner-store-experience/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization --- ### [Redpoint CTO to Present at MarTech Virtual Training Event](https://www.redpointglobal.com/blog/redpoint-cto-to-present-at-martech-virtual-training-event/) **Published:** March 10, 2021 **Author:** Redpoint Global **Content:** On Tuesday, March 16, Redpoint Global CTO and Co-Founder George Corugedo will present a session at the [MarTech Training](https://martechconf.com/spring/agenda/?utm_source=mtc&utm_medium=social&utm_campaign=mtc+spring+2021+sponsor&utm_content=redpoint#sessId-727) virtual event. During the 30-minute presentation, Corugedo will rebut the premise that “Every CDP Does Everything,” demystifying the perception that all customer data platforms that provide data, insight and action capabilities are essentially the same. Topics will include: - An exploration of the nuances between data/insight/action capabilities as they relate to providing a differentiated customer experience. - The benefits of starting with an outcome-driven approach with an ultimate focus on creating new revenue streams; per Gartner analysis that marketing leaders measure and operate at an outcomes level. - A look at the market and why creating a personalized experience matters in an increasingly digital and contact-free economy. - A drill-down into data layer functionality and why many CDP vendors stop short at addressing data complexity (such as transformations). - An examination of how a single operational “brain” (a single point of operational and data control in a marketing environment) aligns with customers expectations for a holistic CX that matches their perception of a brand. - Why a channel, process and data siloes create a structural inconsistencies and a gap between the experience customers demand and what brands deliver. Corugedo will also present a deep dive into the Customer Data Management, Automated Machine Learning (AML) and Intelligent Orchestration components of the Redpoint rg1 customer experience platform, breaking down why many customers consider the platform the top revenue-generating application in their environment. Highlights include: - Customer data ingestion from every source, format, structure or lack of structure to create a [Golden Record](https://www.redpointglobal.com/blog/use-a-golden-record-to-enhance-customer-experience-cx/) with an Identity Graph and any information marketers need to tell a story based on a unified customer profile. - How a wizard-driven, template-driven AML application simplifies the building and deploying of code-free, self-training machine learning models that reduce the friction of marketing – without the need for data scientists. - An intelligent orchestration layer with a direct connection to all channels through open garden connections, with ingestion to execution measured in milliseconds through a single point of operational control. This [martech training](https://www.redpointglobal.com/blog/redpoint-cto-to-present-at-martech-virtual-training-event/) session will draw directly from recent case examples of real-world implementations at Redpoint’s customers, including CVS, Ralph Lauren, Keurig, GoDaddy, Blue Cross/Blue Shield and many other household name brands. Attendees will come away with a new appreciation for what they need to consider in the search for the perfect CDP – as well as to know what to look out for from vendors that often over-promise and under-deliver. To view the full MarTech virtual event agenda and to register for Corugedo’s session, click [here](https://martechconf.com/spring/agenda/?utm_source=mtc&utm_medium=social&utm_campaign=mtc+spring+2021+sponsor&utm_content=redpoint#sessId-727). Scroll down for a brief description of George’s session, scheduled for Tuesday, March 16 at 2:45 p.m. *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Identity Resolution, Journey Orchestration, Real-Time Personalization --- ### [Old vs. New: Updating the Clienteling Concept for an Omnichannel CX World](https://www.redpointglobal.com/blog/old-vs-new-updating-the-clienteling-concept-for-an-omnichannel-cx-world/) **Published:** January 19, 2022 **Author:** Steve Zisk **Content:** As digital-first customer journeys that combine both physical and digital touchpoints become routine, the concept of clienteling is evolving. Traditionally understood as a technique used by retail sales associates to establish long-term relationships with customers based on data about their basic preferences, behaviors and purchases, clienteling has broadened its reach. If we accept that a core component of clienteling is to build a long-term relationship with a customer, it stands to reason that the concept should extend beyond retail to include every industry and involve every employee who interacts with customers. When clienteling began to gain traction, a typical use case was for a store associate to use a tablet to access customer data while personally assisting a customer during an in-store visit, like a digital concierge. An updated vision instead anticipates use cases and templates for customer information that translate to healthcare, travel and hospitality, financial services or any industry or organization with a brand-consumer dynamic that has multiple customer-facing departments. Regardless of industry, every use case shares a common goal of using relevant customer data to strengthen the relationship a brand has with a customer. What differs is the data needed to optimize the relationship, how that data is presented to the customer-facing associate, and how the data is used – particularly in a bidirectional manner. A doctor, for instance, could use an accurate, up-to-date profile of a patient to help develop a care plan during an in-person or virtual appointment, while also collecting real-time information to help continue the conversation. ## **Recreate the Corner Store Experience** If optimal business practices require that all customer-facing employees must knowledgably engage with customers in real time, with the same familiarity as the proprietor of a corner store, traditional clienteling models will not suffice. Establishing or strengthening a long-term relationship with a customer shouldn’t need its own terminology; continuing a natural conversation supported by data should be par for the course for every business. In other words, why wouldn’t a company make best use of customer data at the precise moment it’s needed to engage with a customer in real time as a natural extension of a customer journey, however and wherever it unfolds? New research from [Harris Poll](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) in a Redpoint-commissioned survey makes it clear that customers expect brands to have a deep, personal understanding of them wherever and whenever they engage. Further, customers understand that such an understanding comes from the sharing of data. Consider that 69 percent of consumers surveyed said that in the wake of the global pandemic it is more important that a brand know their individual needs and preferences. And 65 percent say that a personalized experience that reflects a deep understanding is now a standard expectation. As for the data exchange, 66 percent of consumers surveyed said that they will provide more information about themselves if the brand or organization uses it to create a more personalized experience. ## **Customer Data: Every Interaction & Every Channel** A broadening of the clienteling concept comes as expectations for digital omnichannel customer experiences solidify. Almost every major retailer today has in-store kiosks, virtual agents, self-service pick-up amenities for orders placed online, etc. In healthcare, secure online portals can serve as a one-stop digital experience for a virtual patient visit linked to automated prescription refills and other follow-up care steps. The basics of clienteling are quickly becoming omnipresent, but a key element in the success of these programs will center on the data provided to the customer-facing service providers. To deliver holistic omnichannel experiences, brands and organizations need to have a real time, [single view of the customer](https://www.redpointglobal.com/single-customer-view/), unbound by channel or data siloes. In a recent retail-focused Forrester report on the [future of digital](https://www.forrester.com/report/the-future-of-the-digital-store/RES176283), 73 percent of decision-makers at global retailers have either implemented or started to implement data management and analytics initiatives to improve their complete view of the customer across channels. According to the report, typical use cases include the integration of in-store and online tracking and engaging customers “by tapping a lucrative omnichannel view of the customer using zero- and first-party data sources.” A focus on [zero party data](https://www.redpointglobal.com/learn/zero-party-data) sources – data willingly provided by the customer – validates the Harris Poll findings where a majority of consumers will provide personal data in exchange for a personalized experience. That, in a nutshell, speaks to the future of clienteling: the unfettered use of customer data by every customer-facing person in the organization to continue or build a long-term customer relationship with data as the foundation of a value exchange. The relationship is built on trust, with the understanding that the data provided by the customer will be transparently used in accordance with a customer’s wishes to create a seamless, omnichannel experience every time the customer interacts with the brand. ## **Related Content** [What is Clienteling and How Does it Impact Personalization?](https://www.redpointglobal.com/blog/what-is-clienteling-and-how-does-it-impact-personalization/) [Clienteling and the In-Store Personalized Experience](https://www.redpointglobal.com/blog/clienteling-and-the-personalized-in-store-experience/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Identity Resolution, Real-Time Personalization, Retail, Travel & Hospitality --- ### [Now is the Time to Set Ambitious Marketing Goals](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/) **Published:** May 26, 2020 **Author:** John Nash **Content:** Resistance to new ideas is a universal concept, famously espoused by author and sometime [inventor](https://www.thoughtco.com/what-were-mark-twains-inventions-740679) Mark Twain, who once said “a person with a new idea is a crank until the idea succeeds.” Marketers know all too well that institutional pushback is part and parcel of devising a novel way to engage with customers. Yet despite the many barriers and limitations – lack of budget, resources or buy-in – good ideas always seem to break through. In trying times like today, marked by rapidly changing and unpredictable customer behaviors and digital transformation acceleration, ambitious marketers can take solace knowing that good ideas are needed more than ever. What does it mean, though, to be an ambitious marketer? While having the mettle to break through limitations and turn ideas into reality is important, innovation is a key condition – which gets to the essence of the Twain quote. A new campaign idea that is just a new way to reach an existing audience may have a nominal impact. But an idea that results in competitive advantage and drives new revenue has the power to elevate marketing into a mission-critical line of business. Here, resistance comes from all corners; the usual skepticism, mixed with fear of being taken out of one’s comfort zone. Breaking through the status quo is a strategic imperative. According to the Harris Poll sponsored by Redpoint, [73 percent of consumers](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) say that brands are not meeting their expectation for a personalized, omnichannel customer experience. **Relevance and the Differentiated Experience** Competitive advantage derives from creating something in the market that is completely differentiated. Making a curbside pickup service nominally faster, for instance, may result in an uptick and short-term gains, but is unlikely to deliver sustainable, long-term growth. True differentiation stems from offering a relevant experience to an individual customer at any stage of their omnichannel customer journey. In a curbside pickup service, that at a minimum would mean offering a seamless experience from the online purchase to the actual pickup, and coordinating logistics so that the right product is available at the right time. Relevance within the context of the journey, however, could mean using geolocation data to send an SMS message alerting an arriving customer of the closest available parking spot. It would mean having an up-to-date record of every customer transaction and behavior combined with every customer identifier – all devices, emails, addresses, social, etc. By possessing this single customer view, a differentiated experience might entail a relevant offer for an accessory item – sent on the customer’s preferred channel and device – at pickup. Curbside pickup is but one example used to illustrate the broader point that customer journeys are becoming more unpredictable, with more channels, devices and options than ever before – combined with the onset of changing behaviors brought on by today’s uncertainty. The same deep understanding of an individual customer is required to deliver a relevant customer experience wherever the customer chooses to interact. To some extent, ambitious marketers have always tried to better understand their customers – with various degrees of success. The problem is that dynamic and increasingly digital customer journeys have outpaced traditional customer engagement strategies, which rely on personas, the calendar, batch processing and other antiquated methods that fail to account for today’s truly innovative customer journeys. **Move at the Speed of the Customer** Achieving an understanding of the customer has always involved having the right data. What’s different today is not just the sheer volume of data that’s available, it’s having the right technology in place to turn data into insight. Importantly, making insight actionable with a next-best action must be done at the speed of the customer. The window to provide relevance, in other words, is a short one; a highly relevant interaction could mean making a specific offer based on a web page the customer visited two seconds ago, with a different offer if the customer visited a different page. Real-time data orchestration and real-time decisioning require automated machine learning to deliver a consistently relevant, individual customer experience at scale. If we circle back to the curbside pickup scenario, we see the importance of not only having real-time data with an updated customer record, but also having intelligent orchestration capabilities that provide the ability to deliver a next-best action wherever the customer appears next in the customer journey. What this describes – analyzing behavioral, transaction or IoT data across devices, using sentiment analysis, and first-party, second-party, and third-party data across a customer’s anonymous and known customer journey – is the difference between being customer-centric as opposed to persona-centric, which stops at transactional data, preferences and perhaps some survey data. According to research from Gartner, customer-centric brands can achieve up to [20 percent lift](https://www.gartner.com/smarterwithgartner/is-your-organization-customer-centric/) vs. roughly 6 to 10 percent for persona-centric brands. New ideas may always be met with resistance, and some of it may come from within – marketers whose vision of a perfect customer engagement is blocked by frustration over common technology pitfalls. Or a failure to execute a new idea because of data siloes that mask a deep understanding of a customer. But when ambitious marketers are met with pushback, with Redpoint they will have an important ally to break through any limitation; a platform that provides a single point of control over data, decisions and interactions that gives them the power to create innovative customer experiences relevant for today’s dynamic journeys. The strategic imperative to bring new ideas for engaging with a customer to fruition has never been more urgent. Redpoint is the one solution that empowers brands to transform and deliver customer experiences that succeed in the new reality. It is how ambitious marketers lead markets. **RELATED CONTENT** [Digital Transformation Just Became Accelerated](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) [Cadence of the Customer and the Corner Store Experience](https://www.redpointglobal.com/blog/cadence-of-the-customer-and-the-corner-store-experience/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Data Quality, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Leveraging Customer Profiles to Increase Engagement](https://www.redpointglobal.com/blog/leveraging-customer-profiles-to-increase-engagement/) **Published:** May 3, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/05/Customer-profiles-e1556908997223.jpg)Customer experience is a competitive advantage for brands who can deliver highly relevant, hyper-personalized journeys to individual consumers. Customer profiling is the method marketers have used in the past to [segment consumers](https://www.redpointglobal.com/blog/augment-customer-segmentation-with-a-personalized-cx/). But to achieve the level of personalization customers expect today, we need to set a new standard. First, let’s start with the basics. What is customer profiling? Customer profiles are a way of describing one customer, or a segment of customers, with specific demographic, geographic, and psychographic information, in addition to details like purchase history or buying patterns. **Traditional Approaches to Customer Profiling** Customer profiles allow you to segment consumers and tailor messaging to specific audiences. And as every marketer knows, the more relevant the content, the better the experience. The traditional approach to customer profiling typically includes five steps: 1. *Gathering data on your customers*: Your data comes in all sources and types from all databases and engagement systems. CRM, POS, eCommerce, mobile, call center – leverage every interaction touchpoint that you possess. Of course, not just any data will do. It’s essential to ensure and maintain data quality and identify customers across each of your channels, or ‘last miles’ to the consumer. 2. *Analyzing path-to-purchase behavior*: Now it’s time to start making meaning out of your precise, accurate, and complete data. Customer journeys are no longer static and linear. They are dynamic, multi-event and multi-channel. Every path to purchase is unique, and customers expect to be treated that way. 3. *Identifying patterns:* Discovering patterns in the buyer’s journey helps you realize strengths and weaknesses in the customer experience you deliver. Leverage technology like artificial intelligence (AI) and machine learning to classify and predict customer behavior and intent, and to automate these processes. This allows you to allocate your time to more meaningful tasks like segmenting customers and determining the next-best action, offer, or message to serve them. 4. *Segmenting customers*: Armed with insights, you can now create customer profiles and categorize consumers according to demographic, geographic, psychographic, and buying behavior information. How you segment is dependant on your goals and objectives. Are you looking to increase customer lifetime value? Your segments will probably focus on behavior and intent, like propensity to buy or average order value. Are you looking to acquire more new customers? Those segments might be based more on demographic and psychographic information and will differ significantly from customers you are trying to grow and retain. 5. *Prioritizing which segments to target*: Depending on your strategy and goals, you can now identify which customer profiles are most important. Deliver the most relevant messages for each audience segmentation and tailor customer journeys accordingly. And once this process is complete, it’s time to deploy a campaign in the relevant channel, measure results, and optimize future iterations. That’s customer experience done and dusted, right? Not quite. **Challenges with Yesterday’s Customer Profiling Approaches** While this process has sufficed in the past, traditional segmentation and customer profiling is no longer enough. Customers expect personalized experiences, but 75 percent of consumers say brands are struggling to meet their expectations. Something’s gotta give. In order to deliver the most personalized omnichannel customer experiences, the way we build customer profiles has to change from a segment-of-many mindset to a segment-of-one. The blocker for most organizations is the data. Today, [60 percent of marketers](https://www.redpointglobal.com/wp-content/uploads/2019/03/MarTech-Infographic_FINAL.pdf) use at least 10 martech systems. That means that customer data is siloed and it’s extremely difficult to: - Aggregate all your data - Identify customers across every touchpoint (including matching, de-duplicating, and householding capabilities) - Build a single customer view Without these abilities, it becomes more challenging to analyze quality data and determine next-best-actions for individual customers. Additionally, delivering relevant offers and messages in your customer’s preferred channel – in cadence with their unique journey – is a tough hurdle to surmount without the right foundation. Gaining the level of understanding necessary to develop comprehensive customer profiles – and then act on those insights in a meaningful, personalized way – is not scalable. That’s why traditional customer profiling results in the segment-of-many approach, rather than a segment-of-one. If you need help integrating customer data to build accurate, complete, and up-to-date customer profiles, **book a meeting** to find out how Redpoint can help. **Best Practices for the New Standard** As customer experience becomes the battleground for brands, customer data becomes more important. To stay competitive and deliver the experiences customers expect, customer profiling must evolve. Aggregating and integrating all data into one place – regardless of data source or system – is a major step towards advancing customer profiling capabilities. Attaining a single point of control over customer data, along with identity resolution capabilities such as matching, cleansing, and householding, helps build a Golden Record for every customer. The Golden Record is the new standard for customer profiles. This version of a profile combines all behaviors, transactions, preferences, and intent information and persists across the customer lifecycle. Since customer journeys are no longer linear and static, the Golden Record encompasses the dynamic buying process of each consumer so you can tailor their customer journey. Robust, up-to-date profiles give you the base you need to make accurate next-best action decisions for each individual customer. Finally, leveraging your Golden Record will inform the most relevant touchpoint and timing to deliver the offer or message. Embracing the evolution of customer profiling has a major impact on your organization; when you deliver superior customer experiences, increases engagement, retention rates, and customer lifetime value will follow. **RELATED ARTICLES** [CDP Myths Debunked: More Than Just Another Data Platform](https://www.redpointglobal.com/blog/myths-debunked/) [Data Matching and Identity Resolution: Keys to Hyper-Personalization](https://www.redpointglobal.com/blog/data-matching-and-identity-resolution-keys-to-hyper-personalization/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Omnichannel Marketing, Single Customer View --- ### [If Your CX Strategy Doesn’t Include BOPIS, You’re Doing it Wrong](https://www.redpointglobal.com/blog/if-your-cx-strategy-doesnt-include-bopis-youre-doing-it-wrong/) **Published:** January 23, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/01/shutterstock_433819123-e1548253731234.jpg) If you are a typical retail consumer, you’ve likely bought an item online and picked it up at a store sometime in the past year. What’s commonly referred to as [BOPIS](https://www.redpointglobal.com/blog/if-your-cx-strategy-doesnt-include-bopis-youre-doing-it-wrong/) (buy on-line pick-up in-store) hits all the right notes for today’s sophisticated consumer. It combines the convenience and control of a self-guided online experience with the instant gratification and cost-effective benefits of same-day pickup, with the added bonus of not having to worry about the dreaded porch pirates. It is no wonder, then, that an estimated [60 percent of U.S. consumers](https://www.foresee.com/blog/5-useful-bopis-stats-every-retailer-know/) used BOPIS in 2017, up from just 35% in 2015. From the customer’s perspective, “click and collect” appears fairly easy to execute all around – I click “buy now,” I jump in my car and go pick up my new TV, how hard is that? Many large retailers agree – including Walmart, Kohl’s, Home Depot, and Best Buy – and have rolled out BOPIS strategies with varying levels of success over the past few years. Retailers are beginning to adopt the practice to meet customer expectations and to enhance the overall customer experience. Retailers that do not offer the service should be aware that customers notice; [one survey revealed](https://www.proshipinc.com/about/news/why-do-customers-bopus) 50 percent of consumers who have used BOPIS said that if they wanted an item and the retailer didn’t offer the option, they would choose a retailer who did. The online experience is so ingrained in today’s culture that consumers can’t compute not being able to go to a local store to pick up an item they’ve bought online. As for enhancing the customer experience, customers [embrace BOPIS for many reasons](https://www.proshipinc.com/about/news/why-do-customers-bopus). Most cite avoiding shipping costs (88 percent) and because it’s easier to browse for products (77 percent), but other reasons including immediacy, reliability, and flexibility of an in-store pickup. BOPIS done right is a tremendous opportunity to generate a satisfied (read: loyal) customer. A customer “wowed” by the experience is likely to tell a friend or two, and statistics also show that once at the store the customer is likely to make an additional purchase. BOPIS was well represented with sessions at the [recent NRF 2019, Retail’s Big Show event](https://www.slideshare.net/NationalRetailFederation/bopis-20-transforming-the-customer-experience-with-selfserve-automation) in New York. At one session, Jude Reter, VP of Digital Experiences at Express, discussed the store’s pilot BOPIS roll out where Express used lockers for the in-store pick-up location. Reter said that the use of lockers resulted in a 21 percent increase in weekly BOPIS orders at the pilot locations, with a 19 percent revenue increase. Also, 25 percent of BOPIS customers made an additional purchase vs. just 8 percent when lockers were not used. **Great for the Consumer, Challenging for the Retailer** Executing a friction and frustration-free BOPIS implementation is more difficult to pull off than most customers – and even some retailers – appreciate. The truth is, a seamless, end-to-end BOPIS process is a delicate dance wrought with potential missteps, each with the potential to derail a positive customer experience. Rather than resulting in referrals and additional purchases, a poor BOPIS experience runs the risk of shared horror stories and a lost customer: A wait in line, a misplaced item, the wrong item, an item that’s not ready when promised, an item delivered to the wrong store, or – worst case – a rude store clerk who has no record of the transaction. Other potential roadblocks exist behind the scenes and have just as much potential to introduce friction into the customer experience. These include supply chain tracking and other logistic concerns, staff training costs, and storage considerations. Decisions for retailers include whether to offer curbside pickup or dedicated parking, where to put the pick-up location, what inventory will be eligible for BOPIS, and how soon to make pick-up available. There are also endless considerations that pertain to the online experience – is it easy for the customer to find the in-store pick-up option on your website, what is the confirmation process, what information will you collect, do you offer guest checkout? Advanced BOPIS capabilities include expanding the omnichannel experience to include features such sending a push notification to a customer when they’re in route to the store, letting them know where to go and what to expect, or using geo-fencing to send a cross-sell discount offer for in-store redemption. With so many avenues to potential customer friction, it’s no wonder that according to a [2017 JDA survey](https://retail.emarketer.com/article/bopus-remains-challenge-many-retailers/59dbf40cebd4000aa48d8e6d) that 75 percent of US store managers say they have difficulty implementing a successful BOPIS strategy, and just one-third are able to offer a discount to a customer who does choose the BOPIS model. **Data’s Role in Bringing BOPIS to Life** A major reason why BOPIS poses so many complex challenges for what the consumer appears to be a straightforward, two-step process (click, collect) is marketing silos. For starters, if your ecommerce system isn’t integrated with your POS systems, effective BOPIS is a non-starter. It seems obvious, but providing a retail location with real-time knowledge about a customer’s online purchases is BOPIS 101. A customer data platform (CDP) breaks through marketing silos and provides the organization with a single point of control over customer data that is the baseline for implementing a successful BOPIS strategy. A CDP ingests data from any customer data source, providing a unified customer view that is accessible across the enterprise and allowing marketers to move at the pace of the customer – such as being prepared to fulfill an online order at a physical location and other omnichannel orchestration efforts. Armed with the unified customer view and the ability to recognize a customer at any online or offline touchpoint at every stage of the customer journey, the enterprise can take BOPIS to the next level by offering a next-best action throughout the entire process. Hyper-personalized interactions delivered in the right cadence are made possible with machine learning and artificial intelligence (AI) algorithms that take into account every single data point about the customer – first-party, second-party, and third-party data – to generate the next-best action that has the best chance to enhance the customer experience. In a successful [BOPIS](https://www.redpointglobal.com/blog/if-your-cx-strategy-doesnt-include-bopis-youre-doing-it-wrong/) program, this could mean providing a free TV mounting with the purchase of mounting hardware at pick-up, or perhaps offering a 5 percent discount on take-out at a partner restaurant that can be redeemed on the way home from the store. From the customer’s perspective, a next-best action generated during a BOPIS experience should appear as natural as buying an item online and picking it up at the local store; if the customer doesn’t think twice about it, you know you’ve done something right. While the technology is there to ensure the elimination of any potential friction in a well-designed BOPIS strategy, the real danger is inaction. Because let’s face it, today’s sophisticated customer accustomed to an omnichannel experience will find it far-fetched to believe that any top retailer lacks the capability to execute a flawless “click and collect” process. The biggest source of friction a retailer must worry about is not that the BOPIS strategy is less than perfect, but that a customer buys from a competitor because you don’t offer it at all. **RELATED ARTICLES** [How to Personalize Retail Experiences Without Being Creepy](https://www.redpointglobal.com/blog/how-to-personalize-retail-interactions-without-being-creepy/) ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** AI & Machine Learning, Identity Resolution, Journey Orchestration, Omnichannel Marketing, Single Customer View --- ### [How to Shorten the Redpoint Data Management Learning Curve](https://www.redpointglobal.com/blog/how-to-shorten-the-redpoint-data-management-learning-curve/) **Published:** February 14, 2020 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/02/2-14-DM-blog--300x188.jpg)Redpoint Data Management (RPDM) is a feature rich, enterprise [data management](https://www.redpointglobal.com/customer-data-management) application that functions across both traditional and Big Data architectures all within a single interface. This and other key features and capabilities are why the [Redpoint solution](https://www.redpointglobal.com/wp-content/uploads/2017/10/DS-DMUS0216-07-RPDM-lo-res.pdf) consistently ranks high in customer surveys for processing speed, data quality, data integration, and ease of use. Yet even with a single, intuitive interface that makes the design and execution of data management processes easy and fast, there is still a learning curve. While the tool is indeed intuitive for users experienced working with data, it’s never wise to underestimate the complexity and comprehensiveness of the data and the processing required. Just as the volume and variety of data and data sources are expanding at an unprecedented rate – and the need to extract insight and make sense of the data in ever-shrinking timeframes – so too is the number of [data management](https://www.redpointglobal.com/customer-data-management) solutions that tend to overpromise and underdeliver. **A Treasure Trove of Resources** With that in mind, Redpoint makes available several resources, within the application, to ensure that RPDM users are better prepared to build and maintain their solutions. Context sensitive help functionality provides users with a searchable repository of expansive documentation covering all facets of the RPDM application. In order to aid users in getting started with their implementation, RPDM provides sample projects covering common operations performed in the application. Redpoint realizes that some of its capabilities – data parsing, data hygiene, and matching to name a few – may raise user concerns around the ability to integrate these processes into their solution due to a perceived complexity of the processes themselves. While Redpoint does provide the basic building blocks (tools) for users to build these processes from the ground up, we also provide a series of pre-built templates that obfuscate the logic, resulting in an exercise of configuration rather than development. Configuration rather than development results in reduced implementation and testing timeframes with an increased confidence in the results. These pre-built templates are provided as part of the application and are based on a combination of best practices and industry expertise. ![](https://www.redpointglobal.com/wp-content/uploads/2020/02/2-12-blog-DM-300x199.png) **An Ally in the Battle to Turn Data into Insight** To further help simplify that learning curve, Redpoint offers online, on-demand training. On-demand training incorporates self-paced, classroom learning through videos with hands-on exercises in Redpoint’s virtual lab training environment. The ability to interact with a live environment with set data helps to facilitate concept retention without sacrificing a production environment. On-demand training is a prerequisite to live training. Formal live training programs can be customized on a client or project basis. Further, understanding the need to be up-and-running quickly, Redpoint can make qualified personnel available as needed to fill an immediate project need, or to provide experience for new users on an existing project. Consultants are always available to educate users on best practices, or in a peer review capacity to provide feedback. We support our clients by providing the Redpoint Help Center and Community portal where users can submit tickets for issues or product enhancement requests, access reference guides, read knowledge base articles, downloads or interact with a community of Redpoint users. The Help Center offers both community feedback and a knowledge base repository filled with useful tips and documentation from the Redpoint team and from users on a range of projects, offering solutions that address many common challenges. Redpoint hosts Orchard Talks, a monthly seminar covering a specific topic related to our software products. On February 27, Redpoint will host a webinar to discuss Advanced Matching in Redpoint Data Management. In addition, we host webinars with every new RPDM release to go over new features and functionality. Lastly, Redpoint customer conferences are a great forum to meet other customers, learn about specific RPDM use cases, and attend advanced training sessions. At TRANSCEND19 for example, the Redpoint 2019 customer conference in Boston, Redpoint solution engineers Todd Hinton and Ian Clayton held a [session on use cases and practical applications for NoSQL](https://www.redpointglobal.com/blog/how-to-transcend-marketing-operations-with-a-nosql-document-database/) and document databases, examining how new database technologies deliver value for key use cases for marketers using RPDM. This year’s customer conference will be held in early November in Boston (invitations forthcoming!). It has been said many times that data is the new currency in the business world. Trafficking in this new currency requires collecting, cleansing, and transforming data into insight. RPDM empowers organizations to capture, cleanse, and integrate structured, semi-structure, and unstructured data from any source. By taking advantage of best practices, learning functionality and advanced features, and using helpful resources, RPDM users will be that much closer to delivering hyper-personalized customer experiences that move the revenue needle. **RELATED CONTENT** [Real-Time Data Aggregates for Today’s Dynamic Customer Journeys](https://www.redpointglobal.com/blog/real-time-data-aggregates-for-todays-dynamic-customer-journeys/) [Why Data Veracity is the Foundation for a Personalized Customer Experience](https://www.redpointglobal.com/blog/why-data-veracity-is-the-foundation-for-a-personalized-customer-experience/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2019/11/1029-SB-MDMUS0917-02-MDM-Silos-238x300.jpg)](https://www.redpointglobal.com/wp-content/uploads/2017/10/SB-MDMUS0917-02-MDM-Silos-lo-res.pdf) **Blog categories:** Customer Data Platform, Data Governance & Security, Data Management, Data Quality, Master Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [How Marketing Can Compete in an IoT World](https://www.redpointglobal.com/blog/how-marketing-can-compete-in-an-iot-world/) **Published:** February 6, 2018 **Author:** Steve Zisk **Content:** ![marketing-iot-world](https://www.redpointglobal.com/wp-content/uploads/2018/02/marketing-iot-world.jpg)In just the next two years, according to [Business Insider Intelligence](https://www.businessinsider.com/how-the-internet-of-things-market-will-grow-2014-10), 34 billion devices will be connected to the Internet and only 10 billion of those will be the smartphones, tablets, and smartwatches that dominate the market today. By 2021, BI says that almost $6 trillion will be spent on Internet of Things (IoT) solutions like connected devices in the home. All this means that marketers will have new channels through which to compete, because customers don’t invest in a connected thermostat or coffee maker so it can work just like the old offline version. They want something more. The need to create a unique experience will transform marketer’s lives. In this new world, the customer journey doesn’t end with a purchase, but instead the purchase becomes part of an ongoing relationship between the customer and the brand. The best IoT devices interact in two directions and the most transformative among them will take advantage of both inbound and outbound channels. As a result, users are quickly growing to expect these devices to be contextual all the time. This means marketers need to react to data feeds that are larger, and do so faster than they ever have before. ## **Moving from Subscriptions to Information** Take the example of a company that sells coffee pods. With a traditional model, customers buy a subscription to deliver coffee on a regular basis. This model ignores consumption, and instead assumes that a customer needs coffee at regular time intervals. The result is either a backlog of coffee, or running out before the next subscription arrives. Either way, customers often grow frustrated and cancel the subscription. A connected brewer that understands what beverages are being consumed and when they are being consumed can deliver the right pods when they’re needed. With an added layer of machine learning, the brewer can start to understand additional factors, such as day of the week, time of year, or any number of other data points. So, for example, the company could deliver hot chocolate when the weather predicts snow, or tea during a particularly dreary stretch. Now the full customer experience is not just about having coffee, but having the pods they like when they’re needed. Importantly, these decisions shouldn’t be left to the device and the intelligence behind them. They need to start as suggestions – “A new auto delivery will ship in the next 2 days – would you like to delay this until next week?” The intelligence is in knowing the question to ask, and confirming easily with the customer right then, right there. Frictionless relationships win the revenue and the loyalty in interactive IoT. ## **Data: The Key to a Transformative Customer Experience** To deliver on this kind of functionality, and help establish a high level of customer reliance, the devices must provide a truly transformative experience. That means having the right data and information at the right time. Ultimately, those companies offering the best quality experience through their IoT devices will emerge as winners. This is no small task. When you add up the information coming from devices, and combine that with the existing information flowing from all digital channels, you end up awash in data in multiple volumes, of multiple varieties, at multiple velocities. Add to this the need to react to these devices and you have a situation in which marketers must feed the cycle of data, insight, and action to meet customer expectations of instant gratification. Often this means bringing in outside information. Data points like real-time weather, which affects consumption of goods and services, are becoming key to the data mix. The same is true for data from other lines of business, like bringing in inventory demand data and shopping point data. Because if you’re trying to deliver product offers in real time, the promise you made to the consumer can only be kept if you let them know that you can deliver the product in a couple of days. To make all this work you need two key components: A customer data platform that puts all the right data in the same place and a layer of orchestration, which is where the real-time decisioning occurs. ## **Customer Data Platforms and Orchestration Are Key to Marketing’s Future** The customer data platform is about having all the data in one place, and then extracting the right data at the right time, and at the right level. This allows it to be consumed by other processes, messaging systems, and orchestration systems, and optimize that messaging over time based on business unit knowledge. You also need to combine this core data with data from other sources as well. This means creating more than just a marketing database. You must ingest this data, run data quality on it, and then persistently identify sessions, cookies, devices, people, and accounts, then link them together so the data can be used effectively. On top of this, you need an orchestration layer that includes analytics, business intelligence related to marketing operations, and other levels of planning. Then, the journeys and interactions can be coordinated across all these different channels, whether they’re IoT, digital, or even direct mail. The information about a person and their device behavior changes so rapidly, it’s no wonder that machine learning is playing an ever-growing role in marketing offer decisions. In truth, the adjustment for marketers will be more about complexity. This is about delivering the right message through the right channel – or multiple channels – using the right media at the right time. And only then can you deliver the experience customers demand while also helping increase the bottom line. *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform --- ### [How Can Data Privacy Laws Improve Personalized Marketing?](https://www.redpointglobal.com/blog/how-can-data-privacy-laws-improve-personalized-marketing/) **Published:** June 7, 2018 **Author:** John Nash **Content:** ![Data Privacy can have an Impact on Personalized Marketing](https://www.redpointglobal.com/wp-content/uploads/2018/06/data-privacy-personalization.jpg)Tighter data privacy laws are a golden opportunity for forward-thinking brands. One of the essential changes necessary to comply with modern [data privacy regulations](https://www.redpointglobal.com/wp-content/uploads/2018/04/SB-GDPRPossUS0318-01-PossibleNow-hi-res.pdf) is heightened data quality across the organization. As data quality increases, so does the understanding brands have about their customers’ preferences, needs, and wants. This deeper understanding presents an opportunity for brands to communicate with more relevance and context with their customers. As a result, brands drive greater personalized communications that increase the customer experience and, over time, customer loyalty. This enviable byproduct of tighter data privacy rules is exactly what consumers want and marketers are looking to achieve. In a recent survey of senior marketing executives, 58 percent believed the need to provide deep relevance and contextual experiences that are meaningful and valued by an individual customer is driving advancement and innovation. How can communicating the way the customer prefers to be communicated with be a bad thing? Better data quality enables brands to be more relevant to their customers’ lives and reduces friction in the path to purchase cycle. While there’s certainly an associated cost burden for any data privacy regulation, brands focused on the negatives miss that these regulations provide an opportunity to ultimately improve the customer experience. The high-quality customer data that results when complying with privacy regulations is most valuable to the enterprise when it’s incorporated into the customer experience value chain. To provide contextually relevant customer interactions, brands need to know all that is knowable about their customer; as it is one of the pillars of outstanding customer experience. This requires working from a single canonical record that collects every data point about an individual customer across the organization into one continuously updating record – commonly known as a unified customer profile, or “golden record.” ### **Identity Resolution in the New Data Privacy Environment** The era of passive opt-in, where consumers who visited a brand’s website automatically agreed to data collection, is fading away. In writing new data privacy rules, regulators worldwide have emphasized that customers need to be involved in the decision-making process about their personally identifiable information. Who can use each customer’s data? What can it be used for? These are questions brands increasingly need to let customers answer. Consumers are willing to share their data if they get something in return. Accenture recently found that 83 percent of consumers will share their data in exchange for a personalized experience, but only as long as businesses are transparent about how the data is used and that customers retain control of the information. Brands need to show that they respect the customer’s wishes and actively participate in a two-way value exchange. Identity resolution is a key facet of that. If brands are unable to recognize consumers across all touchpoints, then they are unable to provide the relevant communications consumers deserve and expect. The right technology is key to resolving customer identities, especially given the volume and variety of data being created every day. Data lakes and data warehouses lack the capabilities necessary to link the disconnected pieces together. Instead, brands need a purpose-built solution designed expressly to unify data across silos, build and maintain the golden record, and provide centralized control from a single portal. The only solution capable of achieving this level of identity resolution across the organization is a customer data platform. ### **Adopt a Customer Data Platform to Build the Golden Record** [Customer data platforms](https://www.redpointglobal.com/blog/5-ways-to-tell-if-you-have-a-customer-data-platform/) (CDPs) are purpose-built to integrate multiple varieties, velocities, and volumes of data. The better ones provide persistent key management that powers identity resolution across any current and future engagement touchpoints. The futureproofed nature of a customer data platform is key in the new data privacy environment. Regulations (and data technologies) change constantly, so the flexibility of a CDP proves invaluable to the compliance process. CDPs provide a central point of control for maintaining golden customer records. With a well-deployed customer data platform, brands can manage customer communication preferences across the organization as well as consent from within a single portal. The centralization of data control possible with a customer data platform can be transformative. As data management and identity resolution practices improve, brands with CDPs can more readily personalize the customer experience and manage preferences because they understand the customer more closely. The age of passive consent is waning. Consumers now pay closer attention to which brands have their data and how it is used. Brands who use data to crystallize their understanding of customer preferences, and to personalize interactions, will be rewarded with higher loyalty and increased revenue. They will share in some $800 billion in revenue [recently predicted](https://www.bcg.com/publications/2017/retail-marketing-sales-profiting-personalization.aspx) to shift over the next five years to the top 15 percent of organizations who provide the best personalization. Leveraging customer data in the new data privacy environment is good for all consumers and for every brand who gets it right. ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Governance & Security, Data Management, Data Quality, Real-Time Personalization --- ### [How Can CMOs Meet the Connected Customer?](https://www.redpointglobal.com/blog/how-can-cmos-meet-the-connected-customer/) **Published:** May 8, 2018 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2018/05/The-State-of-Engagement_FINAL_001.jpg)The job of the chief marketing officer (CMO) has changed along with the nature of the customer’s path to purchase. As consumers have moved away from the traditional linear journey in favor of a highly-personalized path to purchase, CMOs have added leading the execution of the enterprise-wide customer experience strategy to their list of responsibilities. This is a new area for many CMOs, who have historically focused on crafting the most innovative messaging and creative media production. [![](https://www.redpointglobal.com/wp-content/uploads/2018/05/CMO-report-chart-1.jpg)](https://www.redpointglobal.com/wp-content/uploads/2018/05/CMO-report-chart-1.jpg)Success remains elusive for the newly empowered CMO. Forty-seven (47) percent of marketers admit they are failing to deliver on the customer expectation of [personalization and contextual engagements](https://www.redpointglobal.com/challenges/personalization/). That so many marketers haven’t delivered on customer expectations is troubling, especially because consumers will increasingly flee brands that they feel don’t meet their needs. This realization is among the conclusions of the new report from CMO Council, “The State of Engagement: Bridging the Customer Journey Across Every Last Mile.” Redpoint Global partnered with CMO Council to produce this research, which surveyed more than 200 senior marketing executives across multiple industries. ### **What Skills Do CMOs Need in This New World?** With the added responsibility of the customer experience, CMOs need to add some new skills to their repertoire. Which ones? For starters, the CMO needs to understand more closely the technologies in their marketing stack. This is partly in response to the in-sourcing trend of the past few years. Even 10 years ago, CMOs could reliably outsource data management or campaign execution and still generate business results. That approach can’t keep pace with a customer as they interact with a brand in real-time across any and all the channels that they choose to interact in, so more outsourced functions have started to come back in-house. Understanding what solutions are used is especially important. The marketing technology stack is fragmented among distinct operational silos, each with their own specialized engagement system. This is a problem. Siloed operations limit the brand’s ability to engage consumers at the right time in the right place, which is why a single point of control over customer data and interactions is so important to understanding a consumer uniquely and where they are in their path-to-purchase to deliver the right next offer or action that’s in the proper context and cadence. CMOs also need to understand the customer experience more acutely than they have, including how consumers interact with the brand. Once CMOs understand the how and when of consumer interaction, they can more readily deliver the kinds of highly personalized experience customers expect. But to understand that, CMOs first need to solve the attribution problem. ### **Solving the Modern Marketer’s Attribution Problem** According to [the CMO Council report](https://www2.redpointglobal.com/report-the-state-of-engagement-pr), only 10 percent of marketers can reliably attribute the customer experience to business impact. Understanding how each consumer wants to uniquely interact with a brand or retailer is a vital component in understanding the path to purchase. ![](https://www.redpointglobal.com/wp-content/uploads/2018/05/CMO-report-chart-2.jpg) Customers don’t all respond to the same message in the same way, or even at the same time. An SMS coupon delivered when someone breaches a geofence at a retail store, such as a coupon for drill bits at a hardware store, will be acted on differently than the same offer received at 10 p.m. If that coupon for drill bits is delivered to someone who has never purchased a drill or shown any intent for buying a drill, then it will at best be ignored as irrelevant. Brands with accurate attribution capabilities are able to determine the success or failure of each campaign and narrow that down to the specific customers who responded. Brands who can make that determination are the ones who will deliver the personalized experiences consumers demand. In an age where delivering a contextually relevant customer experience is the indicator of long-term success, marketers need to fix their attribution problems to thrive. Part of fixing the attribution problem is having a single point of operational or data control that can unify online and offline customer information. That’s important, because knowing all that is knowable about customer helps to understand attribution. Attributing success to the right message or right interaction touchpoint is fundamentally about understanding the customer in totality. To deliver impactful customer experiences, 42 percent of respondents said systems that leverage real-time data to deliver relevant, contextual experiences were the most critical piece of this puzzle. This acknowledgment is heartening, as is that only 30 percent of marketers said that budget was a necessity to meeting their goals. That so few CMOs said budget was needed makes it clear that more brands have acknowledged the importance of fully funding marketing’s efforts. Despite understanding what’s necessary to achieve their goals, the fact that marketers can’t is a problem. Brands need to be able to tie marketing’s efforts back to business results. Adding more interaction touchpoints such as a mobile app or in-store beacon technology or IoT sensors is only going to make this challenge more complex. The only thing that can reliably improve the customer experience, and allow for optimization, is deploying a solution that can function as a single point of control over the data aggregation, analysis, and interactions. The forces of commoditization sparked a change in market dynamics that made traditional methods obsolete. More products, more channels, and lower prices will not save brands today. It’s all too easy for prices to drop to zero, and the sheer number of options for different products means there is almost always someone offering the same for less. Customer experience is the only way forward in this new world. The brands and retailers focused on delivering highly personalized engagements along the customer’s path to purchase are the ones who will succeed in the long term. Download [the CMO Council report](https://www2.redpointglobal.com/report-the-state-of-engagement-pr) today for more. ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![The State of Engagement](https://www.redpointglobal.com/wp-content/uploads/2018/05/AD-Banner0518-01-StateEngage-FINAL.jpg)](https://www2.redpointglobal.com/report-the-state-of-engagement-pr) **Blog categories:** Data Quality, Identity Resolution, Real-Time Personalization --- ### [Goals, Breadth & Depth: Three Dimensions of CX Strategy](https://www.redpointglobal.com/blog/goals-breadth-depth-three-dimensions-of-cx-strategy/) **Published:** September 24, 2020 **Author:** Steve Zisk **Content:** A customer experience (CX) strategy encompasses three dimensions, [the three dimensions on which services can differ are](https://www.redpointglobal.com/blog/goals-breadth-and-depth-three-dimensions-of-cx-strategy/): goals, breadth and depth, and is not to be confused with a CX plan, which is the execution of the strategy. According to Gartner, CX strategy answers the questions of where an organization intends to play – which customers, geographies, etc. – how it intends to win, and the resources needed to get there. When an organization sets specific goals and determines the breadth and depth of CX considerations, a [CX strategy](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty) that answers the questions of where to play, how to win and what resources are needed begins to take shape, providing a roadmap to follow in subsequent planning stages. Looking more closely at goals, breadth and depth as dimensions of a CX strategy reveals the pervasive nature of [customer experience](https://www.redpointglobal.com/blog/limitless-data-integration-unlocks-a-superior-customer-experience/), touching every possible interaction a customer may have with an organization. CX strategy, then, will naturally differ from business to business and industry by industry, although a common trait will be determining what data matter according to the [metrics an organization is trying to drive](https://www.redpointglobal.com/blog/what-is-automated-machine-learning/). ## **CX Strategy Dimension No. 1: Goals** Setting CX strategy goals is a fairly straightforward exercise. Some will be common across organizations; the desire for customers to have consistent, relevant and valuable interactions, for instance. Some will vary by organization, maturity, industry and even the type of relationship a brand or business is trying to build with a customer. Defining CX goals ultimately determines what data needs to be collected, calculated or inferred, depending on whether net promoter score (NPS), customer lifetime value (CLV), customer sentiment, customer satisfaction, another metric or any combination thereof is the priority. Consider, for instance, a consumer who purchases a kayak from a sporting goods company. Regardless of metric, the company will obviously want the customer to have a positive experience across all digital and physical channels, and will take the necessary steps to ensure as much. But if NPS is prioritized, letting the customer schedule a delivery time may supersede sending a discount offer on a complementary item, which might be the preferred course of action if CLV was the overarching goal. Whether in healthcare, retail, financial services or another industry, there are countless ways to define and measure a positive customer experience. CX strategy goals largely rest on how the experience is measured on an industry-by-industry basis. ## **CX Strategy Dimension No. 2: Breadth** One way to understand breadth in terms of customer experience is to consider the array of potential channels and touchpoints that constitute all stages of an [omnichannel customer journey](https://www.redpointglobal.com/omnichannel-personalization/), and enumerate the variables and relations required to measure how different types of customers choose to interact. In the buying journey, for instance, a prospect has different reasons for visiting a website, contacting a call center, researching reviews or engaging with social media than someone further along in the specific buying journey. A repeat customer may behave completely differently than a new customer, even if both are engaging with the same channels. Likewise, an older customer demographic may have different expectations for what constitutes a positive CX than a younger demographic. Incorporating breadth in setting CX strategy recognizes that customers have different motivations for engaging with various touchpoints. A one-size-fits-all CX, in other words, may delight some customers while frustrating others. Paying attention to the breadth of experiences helps alleviate potential friction before it arises. ## **CX Strategy Dimension No. 3: Depth** Depth is perhaps the most interesting dimension of a CX strategy in that it can be a proverbial “rabbit hole” situation where once you start exploring and experimenting with various levers, many more possibilities arise for extending the CX through line. It’s fairly well understood that a positive CX entails far more than integrating customer data into a [single customer view](https://www.redpointglobal.com/single-customer-view/) and using this unified profile to put the right offer in front of a customer. The far end zone, if you will, is harder to gauge. The picture becomes clearer with an understanding of how people, processes and technology affect an overall customer experience at each possible touchpoint. Website design, for example, becomes integral to CX goals. A range of considerations – design, images, ease of navigation, etc. – must be decided not only according to the experience on a landing page, but also as to how a website visit unfolds. A website visit is its own subset of the greater customer journey; the various stages and rhythm within a series of clicks foretell the overall CX as greater than the sum of its parts. The same is true for every touchpoint, and for managing interactions and the overall experience throughout all aspects of a customer journey. When a kayak arrives at its destination, is it easy to unbox? Are instructions for affixing a seat or adjusting footrails the first thing a customer sees? Is there perhaps a guide with directions to local lake and river boat ramps? Does it include a thank you note? Cleaning instructions? ## Breadth vs Depth Marketing Considering depth in CX strategy is essentially making decisions pertaining to where and when efforts to delight a customer end. If opportunities are indeed finite, what is the natural ending point? Because it is possible – and relatively easy – to make the wrong decision. The dreaded “creep factor” is a prime example. Sending a customer an SMS about an item left in a shopping cart might be intended as helpful to guide a journey forward, but if it arrives five minutes after the item is placed in the cart the recipient may be more appalled than thankful. Cadence is another element that contributes to the depth of customer understanding. Cadence that aligns with a customer’s wants and needs helps provide a seamless CX and avoid friction pitfalls. Sending a customer a coupon for 10 percent off of a product they just paid full price for highlights the importance of getting cadence right. Similarly, choosing how often to send an email, display a banner ad, or pop up an offer to chat can affect both customer experience and marketing cost. Cadence is intricately related to having a [real-time capability](https://www.redpointglobal.com/real-time-interactions/); a brand must be as real time as a customer’s journey dictates, dialing it up or down as needed to create a delightful, personalized CX synchronized to the cadence of the customer. In addition to real time, cadence is also related to frequency. Important considerations include not just frequency of messages, but on which channels, what time of day, how much detail to include, when to include a follow-up communication, etc. Setting goals and determining the breadth and depth of [CX strategy](https://www.redpointglobal.com/blog/if-your-cx-strategy-doesnt-include-bopis-youre-doing-it-wrong/) essentially boils down to striking the right balance between an organization’s goals and a customer’s goals. A strategy must recognize the breadth and depth of every customer touchpoint – online, in-store, mobile app, social, call center – and determine how to manage a holistic customer experience that factors in a customer’s aspirations while providing the pitch perfect amount of personalization. ## **Related Content** [A Personalized Customer Experience (CX) Delivers Loyalty](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty) [What is Data Ingestion? Why Context Matters in a Data Ingestion Strategy for CX](https://www.redpointglobal.com/blog/what-is-data-ingestion-why-context-matters-in-a-data-ingestion-strategy-for-cx/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization --- ### [DTC Evolution as CPG Companies Branch Out](https://www.redpointglobal.com/blog/dtc-evolution-as-cpg-companies-branch-out/) **Published:** April 3, 2019 **Author:** John Nash **Content:** Consumer Packaged Goods (CPG) companies have traditionally relied on their retail partners to create the last mile to the consumer, and therefore lacked a deep understanding of consumer behaviors and preferences. Previously the transaction was wholly a trade-off; while the CPG brand did not have to invest in the last mile, the retailer reaped the rewards of capturing customer data to establish a direct connection with the consumer. For some CPG companies, the traditional relationship with a retail partner is fracturing. A bevy of fault lines is spurring CPG brands to make those last-mile investments and establish a direct relationship with the customer. Among them are what some refer to as the “retail apocalypse” marked by store closings, including the shuttering of an estimated [8,600 brick-and-mortar locations](https://fortune.com/2017/10/26/a-record-amount-of-brick-and-mortar-stores-will-close-in-2017/) in 2017. A brand today cannot rely on a single distribution channel. Even with a strong retail partner, the concept of go-to-market exclusivity is fading. In today’s omnichannel environment, where an empowered consumer has an unlimited choice about how and when they interact with a brand, brands need to engage with customers at any point of a customer journey. Establishing direct-to-consumer (DTC) channels such as a website, mobile apps, email marketing automation systems, call centers, physical direct mail, and SMS texts provide a brand with a wealth of customer data that it can use to achieve a better understanding of consumer behaviors, preferences, and intent. Savvy brands that know everything there is to know about a customer can then begin to compete on customer experience (CX) by providing a personalized experience across channels. ## **A Direct Connection, Direct Results** DTC trailblazers are seeing results. Nike CEO Mike Parker recently reported that the company achieved its [first-ever $1 billion quarter](https://www.mediapost.com/publications/article/333583/as-sales-disappoint-nike-sees-new-power-in-d2c.html) in digital sales, up 36 percent from the previous quarter. Much of the success, Parker said, is attributable to Nike creating a “seamless bridge from physical shopping to digital efforts” that rewards Nike+ members with personalized offers and messages. Establishing a direct consumer relationship has also helped spur retail sales, with more than half of transactions at the company’s flagship “Innovation” retail outlets now from Nike+ customers. Nike is far from an anomaly. Its experience just highlights how far consumer expectations have evolved. In a recent infographic “[Customer Engagement: CPG Direct to Consumer](https://www.redpointglobal.com/wp-content/uploads/2019/03/infographic-CPG.pdf)” Redpoint outlines several trends driving these expectations. According to [Interactive Advertising Bureau (IAB)](https://www.iab.com/news/iab-study-shows-consumer-economy-growth-shifting-to-direct-brands/), two-thirds of consumers expect direct brand connectivity and more than half of consumers go directly to a brand’s website with an intent to buy. Establishing DTC channels enhances the customer experience by the mere fact of providing customers with the omnichannel experience they expect; the Nike experience also shows that real differentiation comes from the personalization of the experience with relevant offers, notifications, or recommendations at the cadence of the customer across the omnichannel journey. Personalization is the “secret sauce” that attracts and keeps customers beyond merely providing additional purchasing outlets. In the recent “Gaps in Customer Experience” Harris Poll commissioned by Redpoint Global, 53 percent of consumers surveyed said that they expect a brand to know their buying habits and preferences and should be able to anticipate their needs. Further, 37 percent said they would stop doing business with a company that doesn’t offer a personalized experience. Differentiation is derived from utilizing data for a personalized customer experience. Direct brand interactions provide a brand with valuable information about customer buying patterns and preferences. Consumers willingly provide this data in exchange for a personalized experience that recognizes them at the moment of interaction, regardless of channel. A successful DTC approach understands that the channel itself – whether a website, a mobile app or other DTC avenue – is just a component of a larger strategy that includes real time as a central pillar. ## **The Evolution of the Brand-Retailer Partnership** Because DTC done right is an all-in commitment, some brands exercise caution because of a concern that a DTC engagement model will kill the golden goose by irrevocably harming the partnership with a retailer. The risk of store closings alone should be enough to overcome the tug of loyalty. Yeti, the outdoor recreational equipment and lifestyle brand, is a good example. In its IPO prospectus, the brand cited the 2016 bankruptcy of Sports Authority and ensuing price liquidation on Yeti products, along with inventory stockpile from other retail partners, as partly responsible for a 22 percent revenue decline in 2017. Yeti then re-doubled its DTC focus, leading to a 48 percent increase in DTC sales in 2018. Store closings aside, the caution is largely unfounded. While on its face it might appear that a direct purchase on a brand’s website will cannibalize a sale from a retailer, a non-linear customer journey is not a zero-sum game. Omnichannel buying journeys are dynamic; a customer may visit a brand website before purchasing at a retail location. Or a customer might buy online before picking up the product in-store (BOPIS), generating an additional sale when they receive a personalized and timely offer. DTC augments the in-store experience, creating additional foot traffic where consumers still spend roughly [90 percent of their retail dollars](https://www.emarketer.com/content/here-s-why-stores-still-matter-in-the-digital-age?ecid=NL1014). This past holiday season, more than 40 percent of millennials (and more than 30 percent of Gen Xers and Boomers) engaged in webrooming – researching a product online before buying at a physical store. In addition to helping increase in-store traffic, DTC can help a retailer by the brand providing invaluable insight and market test data to a retail partner, which turns the previous relationship on its head. Now, it’s the brand that has a single view of a customer from data collected across an omnichannel path-to-purchase experience, data that behooves both the brand and the retailer and holds more value to the retailer than a potential loss of direct sales to the brand. To minimize any potential conflict with a retailer, CPG companies must have a clearly defined strategy for what they wish to accomplish by exploiting every last mile available to the consumer. The most important consideration is that a DTC channel provides clear value to the customer and does not introduce friction to the customer journey. ## **Taking Control of the Customer Journey** Many retailers wonder how or where to start with providing a differentiated DTC experience, at least beyond basic e-commerce or mobile app offerings. A single view of the customer with a customer data platform is an integral piece. A golden customer record does more than integrate data sources, however. Rather, it’s a continuously updated record that includes any information pertaining to a customer’s behaviors, interests, preferences, needs, purchases, and intentions compiled from multiple customer engagement systems and data sources (anonymous to known, structured to unstructured, online and offline) to create a complete, 360-degree view that persists over time. Armed with a single view, a brand can optimize engagements with the Redpoint CDP, which leverages in-line analytics and real-time decisioning to drive personalization at scale. Redpoint provides a brand with a single point of control over data, decisions, and interactions that hyper-personalize a customer experience and make the customer want to keep coming back for more. The fluid, omnichannel buying journey is here, and unless a brand shows a customer that it cares about them along every mile of a dynamic journey, the customer may have switched brands by the time that all-important last mile comes into play. **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization, Retail, Segmentation & Activation --- ### [Don’t Surrender to Non-Linear Customer Journey Complexity](https://www.redpointglobal.com/blog/dont-surrender-to-non-linear-customer-journey-complexity/) **Published:** July 6, 2020 **Author:** John Nash **Content:** Just as there are endless permutations to a [nonlinear customer journey](https://www.redpointglobal.com/blog/dont-surrender-to-customer-journey-complexity/), there are endless ways to measure a marketing campaign’s influence on customer behavior throughout the stages of a journey. How do you track the impact on revenue when combinations of triggers become astronomical? In the not-so-distant past when customer journeys were linear, fairly predictable and limited to a few channels, it was easier for a marketer to set and track success metrics through traditional research, evaluation and purchase phases than it is today. With increasingly digital, omnichannel journeys now the norm, it’s more difficult to pin down not only which channel a customer may appear in next, but which phase of the journey they’re in and how they are best influenced. The disruption caused by coronavirus increases the difficulty; [digital transformation is accelerating](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) at a blistering pace, and customers are changing some entrenched patterns and behaviors. **Compounded Complexity** Marketers have many tools at their disposal to influence consumer behavior and drive revenue, with many different metrics to gauge effectiveness. Click-through rate, time on page, revenue per sale, frequency of sales/transactions, retention rates, conversions and segment level revenue are but a few of the metrics used to determine a campaign’s influence on moving a customer – or segments of customers – forward in a customer journey. I was reminded of the complexity involved with measuring the success of guiding a customer journey in a recent discussion with a Redpoint customer, a retail executive, who said that at any one time his company is likely to have roughly 20 active campaigns (across rewards, retention, etc.) with about 50 active offers for as many as 80 different customer segments. With an average of seven touchpoints for each campaign, that’s hundreds of thousands of different combinations or into the millions when accounting for the various channel dimensions (mobile app, SMS, email, direct mail, in-store). At first glance, it seems too unwieldy for a marketer to manage all of those combinations – identifying and maximizing the revenue part of the equation for an individual customer as it pertains to [customer lifetime value (CLV)](https://www.redpointglobal.com/blog/to-optimize-revenue-growth-tune-in-to-customer-lifetime-value/). **Define Your Objectives** The complexity and uncertainty that define today’s non-sequential, [non-linear customer journeys](https://www.redpointglobal.com/blog/dont-surrender-to-customer-journey-complexity/) make it more difficult yet more important than ever for marketers to define which metrics will yield the intended business outcomes. To maximize revenue while minimizing costs, marketers must have clearly defined goals in mind. If the aim is for the organization to lift revenue for top decile segments, that will require different metrics than a goal to improve retention across the entire customer base even though both are customer-centric approaches. Whatever the overarching goal, an urgent need for visibility underscores whichever metric or metrics a marketer chooses to focus on. Visibility into the different levers used to guide a customer journey, and visibility into the results, are essential to optimize customer experiences. The emerging [customer journey analytics](https://www.cmswire.com/customer-experience/gartner-report-highlights-emerging-customer-journey-analytics-market/) market is crowded with point solutions that focus on a narrow competence, thus providing limited visibility only as it pertains to a compatibility for a pre-determined objective – such as optimizing an individual touchpoint. Complete visibility and a holistic understanding of customer journey KPIs requires knowing not just how a metric influenced a customer or segment for one campaign or one journey, but rather over time and in relation to every other journey. A closed-loop process, granularity to the customer level and journey stage tracking across channels (i.e. not limited to channel-specific metrics) provides marketers with the insights they need to understand what is effective in creating a superior customer experience. This is only possible by tracking customer, offer and response data (those millions of combinations) at a detailed level. **Tame Complexity with a Digital Experience Platform** To handle the complexity of both the data and the journeys, organizations need a platform that provides a single point of control for the customer experience with built-in automated machine learning. The retail customer who highlighted the complexity of having to manage the myriad combinations said that he chose the [Redpoint rg1 solution](https://www.redpointglobal.com/one-platform/) in large part because of its proven capability to build and track dozens of segments, using several dozen attributes, while easily managing all types of customer data from multiple sources. With automated machine learning models guaranteeing the delivery of a next-best action at an individual customer level – with no duplicate or conflicting offers – the platform’s reporting templates provide marketers with unmatched granularity into the effectiveness of specific campaigns. Being able to analyze what’s most impactful on revenue from an individual customer standpoint gives marketers a powerful tool for testing and optimizing customer journeys at scale. The closed-loop process provides marketers with peace of mind knowing that, whatever their objective, they’ll never have to rely on guesswork for guiding a customer through an impactful customer journey that drives revenue. **RELATED CONTENT** [To Optimize Revenue Growth, Tune in to Customer Lifetime Value](https://www.redpointglobal.com/blog/to-optimize-revenue-growth-tune-in-to-customer-lifetime-value/) [What is Customer Centricity?](https://www.redpointglobal.com/blog/what-is-customer-centricity/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization --- ### [Digital Transformation Just Became Accelerated](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) **Published:** April 8, 2020 **Author:** Dale Renner **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/04/4-8-20-blog--300x198.jpg)As we hunker down practicing safe social distancing, doing our part to flatten the curve and hopefully slow the spread of coronavirus, there is speculation that the pandemic will eventually [give rise to a new economy](https://www.washingtonpost.com/business/2020/03/21/economy-change-lifestyle-coronavirus/) once schools and businesses re-open and we return to a semblance of normalcy. To be sure, everyone’s health and safety takes priority, and the main concern is for those directly impacted or with a family member or loved one who has contracted or is recovering from the virus. Beyond the massive impact on health and healthcare, societal ramifications are already being felt far and wide. Retail, restaurants, sports, and the travel and entertainment industries are among the most visibly affected, but this unprecedented challenge weighs heavily across industry for any business or enterprise that interacts with consumers. **Prepare for a Shifting Landscape** These organizations will face a new reality when we emerge into a new economy where consumers are likely to carry over new habits while accelerating digital and omnichannel engagement. Companies will have to adjust to this new reality by finding new ways to connect and engage with customers. Retailers for one will likely see a significant change in physical store configuration. There is already evidence of an uptick in [curbside pick-up](https://www.digitalcommerce360.com/article/coronavirus-impact-online-retail/), with some prominent retailers limiting transactions to only buy online, pick-up in-store (BOPIS). In the healthcare industry, telemedicine will more rapidly emerge driving digital engagement to new heights as the influence of in-person education and marketing events wane. One result of consumers changing how they interact with businesses will be the need to completely and concisely understand and measure the value of every customer interaction. It will be incumbent on brands to intermediate each interaction and seize the opportunity to take control of engagements and strengthen their relationships with customers in real-time ubiquitously across the enterprise and at an individual and granular level. **Top Down Transformation and a Customer-Centric Approach** Chief marketing officers and digital transformation leaders have faced pressure for some time now to use an influx of customer data to generate growth, with marketing increasingly viewed as a [mission-critical organization](https://www.wsj.com/articles/average-tenure-of-cmo-slips-to-43-months-11559767605) responsible for driving new revenue. The current crisis will likely hasten the progress of this trend; as consumers alter behaviors, marketing will have to take the lead in devising innovative customer engagement strategies that reflect the new way consumers will live, shop, work, and interact. A customer-centric approach is defined by individual engagements with highly personalized moments of interaction, as well as real-time and event-driven engagements that are unbound by channel. Industry research show that best practices in customer centricity can yield greater than 20 percent increases in response rates. Using the Redpoint platform, our clients have achieved triple-digit increases in revenue and at the same time more than 40 percent reductions in customer touches thereby reducing interaction costs and customer fatigue. In the new reality, there will be an increased urgency to deploy capabilities that enable brands to connect with customers across all digital channels delivering meaningful, relevant engagements in context and in real-time for each unique customer journey. Real-time decisioning breathes life into highly personalized moments of interaction, ensuring that a brand is consistently in step with a customer throughout the complete customer lifecycle – spanning multiple channels and multiple events over time. Brands that achieve being relevant to each individual do so by demonstrating a deep understanding of the person at each interaction. This capability is the foundation of perfecting superior customer experience delivery. **Relevant Personalization Starts with Data** A superior, personalized, and highly relevant customer experience is only made possible with data that provides the right setting – specifically, having a single customer view that tells a company everything there is to know about a customer. The Redpoint Golden Record is exactly that because it includes data from every source every type and every contact. This holistic view combines behavioral and transactional data with everything known about the customer across an anonymous to known environment; it infuses every customer interaction with context, the key for data-driven marketers to deliver relevance in real time – as the customer is engaging with the brand, and coordinate consistent messaging across a customer’s entire digital and physical journeys. Everyone is understandably looking forward to putting this uncertain time behind us and returning to work or school. Even a simple in-store visit would feel great right about now. These may be simple hopes, but similar to the adage about staying healthy, we don’t recognize how important they are until they’re taken away. In the coming weeks and months, we will begin to return to what passes for the new normal. As always, companies will strive to connect with customers. But if, as expected, customers alter how they engage and interact with a brand, it makes sense to prepare for that world now. Redpoint can help. **Blog categories:** 1:1 Personalization, AI & Machine Learning, Data Management, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization --- ### [Data Modeling in a Lights-Out Environment](https://www.redpointglobal.com/blog/data-modeling-in-a-lights-out-environment/) **Published:** September 26, 2019 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/09/9-26-George-AI-blog-300x186.jpg)Many companies that use artificial intelligence (AI) to power a business process do so with a “keeping up with the Joneses” philosophy. It’s like buying a 70-inch smart 4K TV because you saw the neighbors bring one home, only to hook it up to your VCR. It’s the same materialism with AI; there’s a notion that modernism in the enterprise requires AI, but there’s less appreciation for its true purpose and potential. The irony is that using AI merely to “keep up” and maybe impress customers or investors is that it devalues a core principle of using analytics and data modeling in the first place, which is to build models that solve for a specific, well-defined business problem. AI’s value does not come from being a status symbol, and to think otherwise runs the risk of relegating the technology to the dustbin of overused, commoditized technologies. In a [previous blog in this space](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/), I wrote about Redpoint’s evolutionary programming modeling and how the automated machine learning models differentiate from everything else in production today. Here, we’ll take a closer look at how the keeping up with the Joneses philosophy is holding companies back from realizing the full potential of advanced analytics, and why it is ultimately costing them revenue. **Not All Machine Learning Models are Built the Same** Redpoint does not, of course, have a monopoly on machine learning models that run through a host of algorithms to determine winners and losers in creating a personalized customer experience. There are other software companies out there trying to optimize a customer experience with models custom built for that purpose and many, to their credit, have the automation part locked down. The difference, though, is that we’re streaming 4K video while the other guys are rewinding a VHS tape. Code-based, hand-written models are simply not purpose-built for the requirements of a hyper-personalized customer experience, which is 24/7 evolutionary programming that runs without human intervention. The key benefit of Redpoint proprietary automated machine learning models is that data scientists are not a prerequisite; this puts the power of machine learning into marketers, while lights-out models never stop looking for opportunities in a unified customer profile to monetize the data with next-best actions that are perpetually in sync with a customer’s omnichannel buying journey. There are two reasons why code-based models aren’t up to the task. First, they cannot keep up with today’s always-on, continuously connected customer, who has a countless number of channels and touchpoints with which to interact with a brand, both as an anonymous and known record. When a new channel or new data source emerges and data scientists have to reconfigure their algorithms and build new models, bad things happen. The brand wastes valuable time that would otherwise be spent keeping pace with the customer, and a new model in production loses context of the customer’s overall journey. Second, code-based, hand-built models fail to meet AI qualification standards. They are built on predictive rules, which are not dynamic and become stale over time. Without an in-line, optimization engine, code-based models require human intervention to refresh, with similar negative results. A dynamic customer journey will always be at least a step ahead of a hand-built model based on a predictive ruleset. **Break Free from Conventional Wisdom, Put the Customer First** With so many drawbacks to relying on code-base models built by data scientists, it begs the question why. Why are companies reluctant to explore the full potential of automated machine learning as the foundation for a mission-critical, revenue-producing engine? Why are they still looking at advanced analytics as a cost center, akin to sinking money on a new TV without a purpose other than to impress the neighbors? Some of it is a failure of imagination; they’ve yet to formulate a use case for an innovative, hyper-personalized customer experience, or they don’t fully appreciate the significant potential it has to impact the bottom line. According to a Harris Poll survey commissioned by Redpoint, 37 percent of consumers said they will no longer do business with a company that fails to offer a personalized experience, and a majority (63 percent) said that personalization is an expected, standard service. The other reason is simple stubbornness. For a variety of reasons, many companies just aren’t ready to accept the democratization of access to modeling tools. Perhaps they have a lot of data scientists on staff who for some odd reason seem busier than ever trying to outrace the customer. Perhaps they had great results in the past. Or perhaps they’re just skeptical, thinking that automated machine learning that produces a hyper-personalized customer experience that’s perfectly aligned with a customer journey sounds too good to be true. If so, prying them from the conventional wisdom will probably take a competitor to lead the way. If they’re not too intractable, it might even be in time to save the business. **Blog categories:** Customer Data Platform, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Customer Journeys are Dynamic, Your Engagement Technology Should Be as Well](https://www.redpointglobal.com/blog/customer-journeys-are-dynamic-your-engagement-technology-should-be-as-well/) **Published:** November 1, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/10/10-31-nash-blog-300x200.jpg)Published in 2007, Tom Davenport’s groundbreaking work [*Competing on Analytics: The New Science of Winning*](https://www.amazon.com/Competing-Analytics-New-Science-Winning/dp/1422103323) explored the then relatively new science of harnessing customer data for competitive advantage. *CIO Insight* lauded it as one of the top 15 most groundbreaking management books. The first chapter focuses almost exclusively on Netflix as an example of a data-driven company that parlayed business intelligence into a (then) innovative new business model – mailing DVDs to customers and recommending titles for their queue. As we now know, the book’s publication pre-dates several more innovations from Netflix, which underscores the rapid pace of digital transformation and the danger of inaction (see Blockbuster). **Rules-Based, Automated Machine Learning Models** The current entertainment streaming wars further illustrate how much has changed since the incipient times of 2007 with the use of analytics to compete on customer experience. With price and product largely commoditized, companies in retail, finance, healthcare, travel, and industries across the board recognize the urgency to use data and analytics to create a personalized omnichannel customer experience. Creating a personalized customer experience for the always-on, connected customer requires two-way communication across every channel. The traditional approach to analytics that created a static list of customers for an outbound marketing campaign based on a fixed data model is incapable of keeping pace with an omnichannel customer journey. To support a consistent, personalized customer experience across channels, decisions need to be rules based, not list based. While this entails a real-time element, which requires that a brand take the optimal action at the precise moment a customer appears in a channel, it also entails dynamic flexibility. Unlike static lists that that cannot respond to new or changed data, rules-based decisions can now account for the totality of customer behaviors up to the customer appearing in a given channel. All of this is made possible through real-time data, or a golden record, for each customer coupled with automated machine learning. Lights-out, [evolutionary programming](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/) makes dynamic flexibility possible. With a continual ingestion of customer data from every source and of every type, automated machine learning models (code-free) are tuned to optimize a dynamic, personalized customer journey without human intervention across channels. **Competing On Customer Journeys and Experience** Dynamically managing a customer journey is required to provide customers with the type of online and offline experiences they increasingly demand from the brands they frequent. Results of a 2019 Harris Poll survey commissioned by Redpoint Global clearly show that competing on customer journeys and experiences is an imperative; brands that ignore this reality run the risk of alienating core customers. According to the survey, 63 percent of consumers agree that personalization is part of the standard service they expect. For instance, they expect a brand to know they are the same customer across all touchpoints (in-store, email, mobile, social, call center, etc.). Further, consumers were unsparing in their critique of brands that fail to deliver personalization, with 37 percent claiming that they will stop doing business with a brand that does not offer a personalized experience. It’s easy to see why static lists, which are familiar to any marketer who has run an outbound, drip campaign based on segments, are unable to keep pace with a dynamic customer journey. A typical non-sequential, non-linear customer journey includes many touchpoints, and an ability to deliver a personalized experience throughout the journey, across any channel, requires knowing everything there is to know about the journey. Consider a customer who logs in to a retailer’s website. Which pages the customer views and page durations vary with each visit and for each customer. A list derived from an analysis of past behaviors will not account for subsequent behavior that may fundamentally alter the next-best offer, recommendation, or action, and thus will not by synchronized to a customer journey. Personalization failures, which an [Accenture study estimates](https://newsroom.accenture.com/news/us-consumers-turn-off-personal-data-tap-as-companies-struggle-to-deliver-the-experiences-they-crave-accenture-study-finds.htm) costs US firms $756 billion annually, clearly do not go unnoticed – or unpunished – by the savvy consumer. A reliance on static, list-based analytics could easily result in a customer receiving an offer for a recently purchased product, or a recommendation that is irrelevant to the journey in that precise moment in time. **Keep Up with a Dynamic Customer Journey** Rules-based approaches eliminate these types of customer frustrations by being in pitch perfect sequence with a customer journey regardless of which direction it takes. One Redpoint customer, a specialty retailer, uses automated machine learning within the Redpoint Customer Data Platform to deliver an innovative, personalized buy-online, pay in-store (BOPIS) experience. Within minutes of purchasing a product on-line, the retailer is able to deliver a relevant, personalized experience to the customer even before the customer arranges for the in-store pick-up. Personalization touches include emails with pick-up instructions (directions, store hours, etc.), relevant offers for product accessories, and website content tailored to the customer’s preferences. Another Redpoint client, a web services company, uses the platform’s millisecond response time to know everything there is to know about a customer in real time. When a customer calls into the call center, for example, an agent will be aware of every action that customer has taken – up to and including the call. If the customer is currently having an administrative issue on a hosted website, the call center agent will have a record of it and have the information needed to resolve the issue. These outcomes stem from the ingestion of all customer data – every source and type – in real time, and rules-based, hands-free automated machine learning models that offer the same dynamism present in every customer journey today. In 2007, the year it launched its streaming service, Netflix customers still happily waited a few days to receive DVDs in the iconic red envelopes. Today, of course, customers expect to be able to stream content of their choosing at any time on any device. Relying on list-based analytics is the equivalent of mailing red envelopes and hoping your customers are okay with an experience that may have been delightful a decade ago, but is archaic by the standards of modern technology. Customers demand a relevant, personalized, and seamless experience across channels. Meeting this expectation results in more satisfied, loyal customers which directly translates into revenue. **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Management, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [How Data Readiness Activates Annual Wellness Visits](https://www.redpointglobal.com/blog/how-data-readiness-activates-annual-wellness-visits/) **Published:** January 9, 2026 **Author:** Redpoint Global **Content:** Annual Wellness Visits (AWVs) are one of the most strategic levers health plans have to improve population health outcomes, care accuracy and member satisfaction. But despite their impact on risk adjustment, care gap closure, and member experience, only about [25 percent of eligible Medicare Advantage members](https://www.healthaffairs.org/doi/10.1377/hlthaff.2017.1130) completes an AWV each year. Behind every missed AWV is a breakdown in data. When member information is fragmented, outdated, or incomplete, it becomes difficult to reach members with information that’s tailored to their unique healthcare needs and experience. Data gaps prevent precise targeting, timely outreach and meaningful personalization, all of which are needed to motivate members to act. Many health plans rely on traditional referral processes or broad, one-size-fits-all approaches to drive outreach for AWVs. Often these tactics rely on inaccurate or incomplete member data. There are often missing contact fields or outdated demographics that can make it difficult for health plans to properly identify and reach out to members who are due for their AWV. This context gap can limit a plan’s ability to personalize outreach, predict which members are more likely to respond to specific tactics, and optimize their outreach accordingly. Generic reminders to “schedule your wellness visit” often fail to motivate action because they overlook the personal drivers of each member’s health history, preferences, and engagement patterns. Reversing this trend starts with a data-first strategy, built on a foundation of clean, connected, and trusted member data. With this information, health plans are able to unify fragmented sources, resolve identities, and enable precision outreach that meets members where they are. A strong data foundation supports: - Predictive targeting that prioritizes members most likely to act or benefit. - Personalized engagement that reflects member behavior, language, and preferences. - Provider activation that integrates AWV promotion into daily workflows. - Omnichannel orchestration that ensures outreach feels coordinated and consistent. When contextual member data connects these pieces, AWVs become an active and measurable engagement opportunity that provides benefits to health plans, providers, and members. Plans using contextual member data have achieved up to 3X higher engagement rates, improved risk score accuracy, and reduced disenrollment among targeted populations. The outcome is not just higher AWV completion, it is a better member experience that drives trust, retention and financial performance. Improving AWV performance does not have to be overwhelming. The most successful organizations start with focused use cases, like enriching contact data or testing personalized campaigns, and scale from there. Each small step strengthens data integrity, engagement precision, and measurable ROI. Embracing a data-first strategy can help health plans to turn Annual Wellness Visits from an overlooked metric into a meaningful engagement tool to close care gaps and build lasting loyalty. Discover how to activate your members and drive measurable results. Download the eBook: [Increasing Annual Wellness Visits: Turning Member Touchpoints into Meaningful Action](https://www.redpointglobal.com/resources/increasing-annual-wellness-visits/) **Blog categories:** Healthcare --- ### [Consumer Safety and the Data Privacy Value Exchange](https://www.redpointglobal.com/blog/consumer-safety-and-the-data-privacy-value-exchange/) **Published:** July 8, 2020 **Author:** Mike Ferguson **Content:** The UK public’s outcry and mistrust over its government’s efforts to build a coronavirus-tracing app is a microcosm of consumer data privacy issues. A [timeline](https://www.bbc.com/news/technology-53114251) breaks down the months-long saga. To recap, the initial concept was to use Bluetooth technology to track device proximity. If a user who had opted-in tested positive for the virus and updated their status on the app, a signal would alert owners of every device that had been within a two-meter radius during the impacted timeframe. One of the core issues was whether the collected data would be decentralized – similar to a privacy-preserving technology jointly launched by [Apple and Google](https://www.apple.com/covid19/contacttracing) – or stored in a central database for use by the UK National Health Service (NHS) and “strategic leaders” (read: government officials) who claimed that the data was necessary for stringent monitoring of outbreaks. Promises by public health officials that the data would only be used for NHS care and research and that it would “be handled according to the highest ethical and security standards,” were met with a healthy dose of skepticism. A professor of security engineering at the University of Cambridge said, “I have 25 years’ of experience of the NHS … repeatedly breaking their privacy promises.” **Value Exchange** The potential trade-off between an increase in safety and security vis-à-vis lowering the threat of infection, and exposing one’s personal data to risk mirrors the value exchange that is top of mind for businesses in every industry that try to balance consumer rights with a personalized experience that drives new revenue growth. In the 2019 Harris Poll survey commissioned by Redpoint, 54 percent of consumers said that they are willing to share personal data with companies to achieve a more personalized experience – with the percentage trending higher for younger demographics (72 percent for Gen Z, 70 percent for millennials). The sharing of personal data, however, comes with certain expectations. In the Harris Poll survey, 40 percent of consumers say that it is “absolutely essential” that a company be transparent about what information is being collected, and how the information is being used (another 34 and 33 percent, respectively, said that it is “very important”). And, if those conditions are not met, consumers will take their business elsewhere. In the same survey, a large majority of consumers said they’d be likely to abandon a brand if their basic personal information was hacked or compromised (89 percent), or if a brand sold their data for marketing/ad purposes without permission (86 percent). **Finding A New Data Privacy Equilibrium** Concerns are not limited to the abandoned rollout of the UK track and trace app. With both GDPR and CCPA raising data privacy concern issues to the forefront, the pandemic’s [impact on data privacy is felt worldwide](https://www.seyfarth.com/news-insights/the-impact-of-covid-19-on-the-california-consumer-privacy-act-2.html). What will retailers, restaurants and other businesses do with temperature check data, for instance? In the US CCPA took effect in January 2020, with enforcement supposed to start the 1st of July. The California attorney general’s office recently said that coronavirus will not delay enforcement, despite pleadings from companies around the world. Rather, the office recommended companies be mindful of a “heightened value of protecting consumers’ privacy online” and the need for stringent data security. Consumers, too, will have to reconsider the value exchange balance. If conceding to a temperature check before entering a mall is a requirement, will it turn customers away? Beyond the direct health connection, will consumers be more willing to share data for a contactless experience? For a seamless curbside pickup, for instance, will a consumer share their cell phone number so they can receive an SMS to alert them where to park, or to pop their trunk because an associate is coming out with their product? Previous blogs in this space have covered the acceleration of digital transformation in the wake of COVID-19, from an [overall perspective](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) and its effect on the [consumer healthcare journey](https://www.redpointglobal.com/blog/the-data-driven-acceleration-of-the-consumer-healthcare-journey/). This unprecedented acceleration combined with rapidly changing consumer behaviours make data privacy compliance a top priority for brands today. Those that practice transparency and compliance, while demonstrating that they are using consumer data in accordance with an individual customer’s preferences, will establish trust. As we’ve seen from Harris Poll, trust is rewarded with loyalty, which translates to revenue growth. **RELATED CONTENT** [Data Privacy, Data Quality and Data Governance: Experts Weigh In](https://www.redpointglobal.com/blog/data-privacy-data-quality-and-data-governance-experts-weigh-in/) [An Avalanche of State Privacy Laws is Coming: What You Need to Know](https://www.redpointglobal.com/blog/an-avalanche-of-state-privacy-laws-is-coming-what-you-need-to-know/) [How Can Data Privacy Laws Improve Personalized Marketing?](https://www.redpointglobal.com/blog/how-can-data-privacy-laws-improve-personalized-marketing/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Governance & Security, Data Management, Data Quality, Master Data Management, Omnichannel Marketing --- ### [Beyond the Chatbot: RPA’s Role in Automating Customer Engagement Processes](https://www.redpointglobal.com/blog/beyond-the-chatbot-rpas-role-in-automating-customer-engagement-processes/) **Published:** October 8, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/10/rpa-blog-10-8-2-300x200.jpg)A chatbot is the “face” of the automation revolution (pun intended) because it represents how most customers experience business automation. There are, however, multiple notions of how a ‘bot’ is involved in a business process, which is more commonly referred to as robotic process automation (RPA). The [Redpoint Customer Data Platform](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/) (CDP) is known mostly as a martech solution that ingests customer data from every source, and of every type, to create a unified customer profile that marketers use to hyper-personalize a customer journey. While the unified customer profile, or [golden record](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/), is an integral component of the platform that provides marketers with unprecedented visibility and control over a dynamic customer journey, RPA is another component of the platform that is just as vital for creating personalization at scale and automating tasks and processes that keep campaigns on track. **Campaign Automation at Scale** It is difficult to bake segment-of-one personalization into a marketing campaign at scale if the campaign is managed manually. Human input simply cannot account for a campaign’s endless permutations to a natural conclusion. Having to handhold a campaign, if you will, is an inefficient allocation of resources that devalues results by introducing potential inaccuracies in the data, or by limiting a marketer’s ability to analyze a large enough data set to make an informed conclusion. Automating the process to reduce the amount of human intervention aligns perfectly with the ultimate purpose of RPA as a ‘bot’ that operates in a rules-based fashion behind the scenes to change the characteristics of an ongoing campaign. Dynamic audience management, as well as A/B testing that automatically selects a campaign winner in the field after a predetermined amount of time both strip time-intensive work away from marketers, who can focus instead on creating new campaigns. **Automation of Data Management Tasks** A second, behind-the-scenes RPA use case is to automate the various data management tasks that marketers and data analysts are responsible for. These tasks range from the obvious – automated approvals when a customer changes preferences – to the more obscure cases that include automated data quality and automated matching of customer records. In the context of customer engagement, RPA is effectively a set of bots inside processes at the data level that automatically choose the best records, the best pieces of data, or the best information. Or, when the bot is unable to do so, it is programmed to automatically place the information in front of a data steward or someone else who can resolve any rules conflict or other discrepancy. Just as in automated campaign management, there are a series of data-level automations that are geared toward reducing the amount of time a marketer spends getting their hands dirty, if you will, in intricate and endless data lineage tasks, and reduces the time that data scientists must spend on issues like data prep. **Automated Machine Learning at Work** A third class of automation is the deployment of bots inside the automated machine learning process to tune and pick the best model, to automatically re-train and re-deploy models based on time constraints or fitness constraints, and to automatically recalculate and update characteristics of customers to include customer lifetime value (CLV), propensity to churn, and other metrics that make up the most accurate, up-to-date, 360-degree view of a customer. With these three classes of RPA for the purposes of customer engagement, the Redpoint CDP leaves no stone unturned in eliminating inefficiencies that stand in the way of continuously producing accurate, relevant, and up-to-date offers that are perfectly in sync with the context and cadence of an individual customer journey. RPA ensures that at every stage of the process – from data ingestion through producing a golden record, and from producing models through producing and deploying campaigns – automation is done in service of both the marketer and the consumer. While a chatbot might still be the face of the automation revolution for a customer, bots inside the Redpoint Customer Data Platform support personalized engagement at every stage of a dynamic, omnichannel customer journey. Being met with a “Hi, How May I Help You Today?” is just the tip of the iceberg for how RPA is a strong ally for marketers with hyper-personalizing a customer experience at scale. **RELATED CONTENT** [The Impact of Real Time on CDP](https://www.redpointglobal.com/blog/the-impact-of-real-time-on-cdp/) [No Data Left Behind: Analytics, Orchestration, and Making Data Work for You](https://www.redpointglobal.com/blog/no-data-left-behind-analytics-orchestration-and-making-data-work-for-you/) [What is Data Lineage and Why is it Important?](https://www.redpointglobal.com/blog/what-is-data-lineage-and-why-is-it-important/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2019/10/1004-Solution-Brief-CDP-Transforming-Customer-Exp-0418-01-1-239x300.jpg)](https://www.redpointglobal.com/wp-content/uploads/2018/06/Solution-Brief-CDP-Transforming-Customer-Exp-0418-01.pdf) **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Become a Campaign Genius with Connected Customer Data](https://www.redpointglobal.com/blog/become-a-campaign-genius-with-connected-customer-data/) **Published:** November 2, 2020 **Author:** Liam Eckert **Content:** Shift the focus from bragging rights to the campaign battlefield. If that opening sentence has grabbed your attention, great – it was intended to. This blog is a summary of a presentation by Mark Weninger from Weninger Haus and Redpoint CSMO, John Nash, where they discussed tangible ways of connecting customer data to become the next campaign genius. In your next campaign planning meeting, rather than discussing aloud what the competition is doing and what your reaction to it should be, focus on the customer data you have access to and what you can do with it to deliver truly innovative, memorable and value-creating campaigns. Boil your use cases down to two to three and then determine what data you need to connect to create campaigns that will address those use cases. Rather than look outward to what your competitors are doing, the answer for how to win is hiding in plain sight: customer data. Customer data is often scattered across dozens of sources. These sources are siloed in systems across virtually every channel. The apparent sheer complexity of integrating the data you have on your customers may well explain a marketer’s penchant for eyeing the competition. Connected customer data, though, reveals insights for marketers to put the customer at the beginning, middle and end of every campaign. This blog looks at what constitutes [ambitious, data-driven marketing campaigns](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/) and the steps needed to get there before looking at one company using best practices and innovative technology to solve for real-time personalization, one of the bigger challenges facing marketers today. ## **Customer-Centricity Delivers Results** Data-driven, ambitious marketers with integrated customer data from every source may be less concerned about the competition than most because, being customer-centric, they know precisely how to create campaigns that are uniquely meaningful to *their* customers and prospects. Unlocking previously unattainable insight from connected customer data is almost magical in its power to create innovative campaigns that infuse meaning to one customer at one moment of an individual customer journey. When customers see their preferences, behaviors, and values reflected in a campaign, that’s a lift that cannot be duplicated by modeling a campaign after whatever someone else is doing. Research from Gartner calculates that a customer-centric brand produces a revenue lift of [20 percent or more](https://www.gartner.com/smarterwithgartner/is-your-organization-customer-centric/). The difference between customer-centric and persona-centric, which may produce a 10 percent lift, is that while both use transactional, preference and historical data as the basis for forming a personalized engagement strategy, customer-centric brands go further and also analyze this data across devices, incorporate IoT and sentiment analysis, and analyze device usage and first-party, second-party and third-party data across anonymous to known records. ## **Familiarity + Authenticity are Keys to Success** Connected data and a customer-centric approach help guide marketers to every campaign decision they make and hew to the principles that most successful campaigns have in common: familiarity and authenticity. Campaigns that seem to be scripted, stilted, robotic, or controlled are, by definition, impersonal. Their rigidness is exacerbated by today’s dynamic customer journeys that are spontaneous, informal and uncontrolled. The experiences that marketers deliver with customer-centric campaigns must align with the same ease with which customers move between channels and unscripted journeys. With customer behaviors changing rapidly, the need for flexibility becomes even more pronounced. Authenticity refers to ensuring that personalization is integral to every campaign. In a Harris Poll survey, [63 percent of consumers](https://www.redpointglobal.com/wp-content/uploads/2020/07/Addressing-the-Gaps-in-Customer-Experience.pdf) surveyed said that personalization is now a standard service they expect. They ask the question, whether overtly or subliminally, “Is the experience that is delivered real, to me, as a consumer? Does it align with not only my preferences and behaviors, but also my values?” Current market conditions heighten the need for authenticity because in addition to changing consumer behaviors, there has also been a shift in how customers extract meaning from an engagement. More attention is paid to what a brand stands for in a socioeconomic realm, and customers will note whether a brand shares its values. Being personal shows the customer that not only does a brand recognize them as an individual, but that it respects their values. ## **A “Good” Campaign is No Longer Good Enough** Standing at a hypothetical whiteboard to design the makings of a creative, innovative campaign that is both familiar and authentic, marketers must prioritize ethics over aesthetics and be hyper-focused on creating a great campaign vs. a run-of-the-mill or good campaign. What’s the difference? Whereas a good campaign may respect the customer, a great marketing campaign connects with a customer. It allows customers to easily see themselves in the campaign, which in turn makes the brand meaningful to a customer at the moment of engagement. A good campaign supports a brand, a great campaign energizes it. And while a good campaign may meet expectations, a great campaign challenges assumptions. > A good campaign supports a brand, a great campaign energizes it. It is difficult for a merely good campaign to gain traction with today’s always-on, connected customer who expects their preferences and values to be reflected in each and every brand engagement. A great campaign that meets this expectation will deliver a remarkable customer experience that produces a more satisfied, more loyal customer. And the secret to delivering a great campaign is leveraging connected customer data. ## **Optimize Connected Customer Data** Returning to our hypothetical whiteboard, there is a reason some marketers settle for good vs. great, or look for slight improvements in existing campaigns – either their own or a competitor’s – rather than strive for the art of the possible. Because it is hard. Difficult, though, does not mean impossible. The truth is, the tools exist today for marketers to create campaigns and moments that matter, that are responsive to customer needs, and that are driven by data using innovative technology that yield profitable revenue growth. Creating a great campaign entails four key steps. Crossing all four off a checklist will ensure the optimal use of connected customer data in any campaign. Marketers will have a clear window into customer intent and be able to layer context into every engagement, delivering relevance to the customer through a deep understanding of all that is knowable about a customer in the moment of engagement. - **Connect All the Data** The Gartner definition of the difference between persona-centric and customer-centric campaigns underscores the importance of data collection. Stopping at transactional, historical, and behavioral data may have sufficed for a traditional, linear customer journey. But with an explosion of channels, dynamic customer journeys, and a transition to [digital-first experiences](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/), to know everything there is to know about a customer requires having social data, sensor data and anything else that contributes to a unified profile. Social data analysis, for instance, opens a window into a customer’s values – vital information to learn how a customer attaches meaning to an experience with a brand. - **Forge the Golden Record** A key element in impactful customer experiences is the creation of golden record. The golden record provides context to a [single customer view](https://www.redpointglobal.com/single-customer-view/) by combining customer data of every type and from every source with every customer identifier – addresses, emails, devices, phone numbers, etc. Built on [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) capabilities, a golden record is persistently updated in real time, which means that it delivers context beyond an understanding of each discrete interaction in a customer’s journey. Rather, it analytically derives an understanding of the relationship between various interactions and decision points. Real-time aggregations such as time since last purchase, average purchase, lifetime value, etc. combine with a full identity graph to provide a 360 degree of a customer – as the customer is moving through an omnichannel journey. ![Redpoint Golden Record](https://www.redpointglobal.com/wp-content/uploads/2020/11/1027-RG-Graphic-01-300x169.jpg) - **Analytics** The insight afforded by a golden record enables marketers to deliver a [next-best action](https://www.redpointglobal.com/next-best-action/) for a customer optimized for a precise moment in the customer journey, on any channel. A next-best action is an offer, a message, content, education – any interaction that is hyper-relevant to the customer’s current situation, optimized for a singular moment during a journey. A next-best action will, by definition, have the best chance of resonating with a customer and guiding a journey to the desired conclusion. A next-best action, derived from the golden record, is made possible with [analytics. ](https://www.redpointglobal.com/machine-learning)Code-free, AI or self-training models that are deployed in-line put the power of machine learning in the hands of the everyday, operational marketer – not data scientists. Algorithmic optimization ensures that whatever the metric, a winning model will be the one most likely to succeed at the exact moment the action is rendered. Applying AI-powered analytics to a golden record to deliver a next-best action unlocks insight – at the exact moment it will be most effective for an individual customer. - **Intelligent Orchestration** [Intelligent orchestration](https://www.redpointglobal.com/orchestration) is the design of a customer journey that links every last mile to the customer (digital, physical, call center, etc.), leveraging the next-best action optimization approach for each last mile – and across last miles. It is the guiding mechanism that keeps a customer on the right path, while also ensuring that marketers are tuned to the cadence of the customer. Whether a customer is inbound, outbound, or engaging with a brand through a combination of both, an intelligent orchestration hub coordinates optimization by taking the insights and action from one channel to trigger personalized engagement and experiences on every other channel. Intelligent orchestration is the secret sauce that makes a customer feel like a brand [recognizes them as an individual](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/). A trusted friend, if you will, holding their hand as they navigate the winding path of a dynamic customer journey. ## **Deliver Compelling, Remarkable Marketing Moments** The four capabilities outlined above provide marketers with a single point of control over data, decisions, and interactions that is essential for bringing remarkable campaigns to life with highly personalized omnichannel experiences to individual customers, in real time. The [Redpoint rg1 customer experience platform](https://www.redpointglobal.com/rg1/) delivers the capabilities that bring remarkable campaigns to life with highly personalized experiences – delivered to individual customers – in real time and in an omnichannel environment. The single point of control ensures frictionless experiences exactly as a brand intends, and as perfect as customers expect them to be. With rg1, brands close the customer experience gap – matching personalized experiences precisely to the expectations of the digital-first, always-on consumer. ## **rg1 in Action – Competing on CX** Returning to our whiteboard one last time, one of the big challenges for marketers who try to compete on the basis of customer experience with innovative, data-driven campaigns is real-time personalization. Even for brands who have taken steps toward consolidating their customer data and transitioning from a channel-centric approach, real-time personalization is often a stumbling block. For an industry leading do-it-yourself retailer, rg1 solved the real-time challenge to enable the company to create a best-in-class buy-online, pick-up in-store (BOPIS) service, also known as click-and-collect. The art of the possible, for this retailer, was a five-minute service level window to be able to synchronize an order from any device to a physical retail outlet. This had not been possible before because some data they needed to rely on had lagged by days. Abandoned shopping cart data, for instance, was not integrated with POS data, and a pilot BOPIS program was beset by irrelevant offers. But with rg1 providing identity resolution at an individual level, a golden record for real-time contextual insight, analytics to determine a next-best offer and intelligent orchestration to ensure delivery at an appropriate touchpoint (channel and device), the retailer was able to create a remarkable, frictionless and hyper-personalized experience in the cadence and the context of a customer’s journey. Ultimately, the retailer achieved a 99 percent compression cycle from data to insight to action – satisfying all requirements for what it considered the art of the possible. ![rgOne and a single point of control](https://www.redpointglobal.com/wp-content/uploads/2020/11/1027-RG-Graphic-02-300x169.jpg) ## **Make the Art of the Possible a New Reality** rg1 demonstrates that data and technology capabilities support the creation of innovative campaigns that market to an audience of one. rg1 enables moving from broad to individual segments, from batch to real-time, from channel-centric to omnichannel, from persona-centric to customer-centric, and from linear to dynamic [customer journeys](https://www.redpointglobal.com/blog/multichannel-vs-omnichannel-marketing-and-keeping-up-with-a-customer-journey/). rg1 customers are on record saying that not only does the platform significantly narrow the customer experience gap, but that it also in many cases it is the organization’s top revenue-producing platform. rg1 turns data into revenue for ambitious marketers who turn the art of the possible into reality. ## **Related Content** [The New Normal and the Retail Imperative](https://www.redpointglobal.com/blog/the-new-normal-and-the-real-time-imperative/) [Data as a Revenue Engine: Monetize Your Data with a Single Point of Control](https://www.redpointglobal.com/blog/data-as-a-revenue-engine/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Augment Customer Segmentation with a Personalized CX](https://www.redpointglobal.com/blog/augment-customer-segmentation-with-a-personalized-cx/) **Published:** August 21, 2020 **Author:** Steve Zisk **Content:** What is customer segmentation? [Customer segmentation](https://www.redpointglobal.com/blog/new-birds-of-a-feather-real-time-dynamic-audience-selection/) is broadly defined as the process of dividing customers into groups based on common characteristics so companies can engage, market, reach and communicate to each group more effectively and appropriately. While there are many kinds of customer segmentation models, including device, referring source, B2B, etc., the traditional “birds of a feather” description generally segments customers four different ways: demographic (age, gender, income), geographic (ZIP, city, urban or rural), behavioral (purchase or spending habits) and psychographic (interests, attitudes, values). Customer segmentation is defined, at its core, as finding similarities in customer data and exploiting those similarities to achieve business goals. Acquisition, retention, loyalty, and CLV are among the goals of discovering and marketing to customer segments based on similar situations, interests, location and/or behaviors. ## CRM Segmentation Models and “Zero Segment Marketing” Because a personalized [customer experience](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty) has been shown to [drive new revenue](https://www.redpointglobal.com/blog/for-marketing-to-drive-revenue-say-goodbye-to-lists-forever/), there is one school of thought that personalization supersedes segmentation; if marketing to a customer based on gender or income is not relevant to a customer’s journey, the thinking goes, the only way to provide a relevant, personalized experience throughout a journey is with a “zero segment marketing approach”. While it is certainly true that a personalized customer experience drives revenue and is central to a brand’s ability to acquire and retain high-value, loyal customers, what this school of thinking confuses is that personalization is not a zero-sum game. Rather, it occurs within segmentation models. The term zero segment, then, is somewhat misleading; it’s not that there are no segments, it’s that they are so granular that they no longer fit into neatly defined delineations. Age, gender and income may be a starting point, but technology now makes it possible to segment an audience 100 or more ways for a far more accurate “birds of a feather” approach. [Customer segmentation models](https://www.redpointglobal.com/blog/augment-customer-segmentation-with-a-personalized-cx/) without channel restrictions enhances personalization for the simple fact that [engagements with an audience](https://www.redpointglobal.com/blog/leveraging-customer-profiles-to-increase-engagement/) will always be relevant for each customer within a segment in any channel – or more precisely wherever a customer happens to be in an omnichannel customer journey. Customer segmentation analysis will provide additional visibility into which channels are used most often by your defined customer segments. ## Benefits & Limitations of Traditional Customer Segmentation When customer journeys were predictable, linear ([research, evaluation, purchase](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-consumer-decision-journey)) and limited to a few mostly physical channels, customer segmentation and personalization were for the most part thought of as identical. A sporting goods store in Cleveland with excess inventory of Cleveland Browns game jerseys might create a segment of 25-to-34-year-old mails and target that customer segment with a promotional email based on an assumption that young men are more likely to be football fans. The segment may have even been created based on some rudimentary analytics. This type of segmentation was thought of as “personalized” – and to those who clicked on the email and redeemed the offer, perhaps it was. Maybe the campaign even outperformed its open-rate average. For that type of segmentation, false assumptions were accepted as the cost of doing business. I might live near the store and fall into the age demographic, but maybe I despise the Browns because they never make the playoffs. Or maybe I’m a Cleveland transplant, and I’m a die-hard Bears fan. Or perhaps I just saved up and paid full price for a home jersey of my favorite player and receive the email a week later once the jersey’s already in my closet. Customer segmentation modeling done the right way can still be effective, but dynamic, omnichannel customer journeys require far greater sophistication to ensure relevance and to guard against introducing friction into a customer’s holistic experience with a brand. ## Adtech Customer Segmentation The sporting goods example describes a typical martech engagement using basic customer segmentation analysis. Adtech customer segmentation works in much the same way. The sporting goods store might even outperform an email blast and secure a higher ROI with a paid TV advertisement for the same promotion that airs during a Browns’ game, putting the offer in front of the eyeballs of fans who are actively watching the game. A diehard Bears fan or someone who has given up on the Browns championship hopes is probably not watching. In the digital advertising world, an ad placed on ESPN.com could be segment-based by running the ad on the devices of known customers in that age demographic (assuming you have house-holding done right). The downside is a comparative lack of tracking compared with an outbound email campaign, and less of a personalized experience relevant to a customer or prospect’s relationship with the brand. The store might run the TV ad on consecutive Sundays, or until the excess inventory is sold, but really won’t have true insight into the ad’s effect on KPIs such as retention or CLV. Likewise, the digital ad campaign on a single channel might not be personalized or relevant to a customer’s experience across every channel with the brand. ## Cross-Channel Customer Segmentation One reason for the misconception about the synergy between segmentation and personalization is that marketers struggle to create a segment that can be used across channels. Traditionally, siloed data and siloed channels prevent an audience segment created for the purposes of, say, an email marketing campaign to be carried through to visitors to a landing page. Hence, many marketers have the mistaken belief that personalization must be bound by the same restriction, and they fail to grasp its power to create a holistic, omnichannel experience. With cross-channel audience segmentation, [omnichannel personalization](https://www.redpointglobal.com/omnichannel-personalization/) is enhanced because restrictions are lifted. Dynamic [audience segmentation](https://www.redpointglobal.com/orchestration/segmentation) that is unbound by channel is the Redpoint differentiator with the [rg1 solution](https://www.redpointglobal.com/one-platform/). Once a segment is defined and created, it is universally applicable to a campaign on any channel dynamically. This is really what is meant by “zero segment marketing”. It’s not just that micro-segments are far more granular, it’s also that there are fewer restrictions because the customer segment is unbound by channel. Zero limitations is a better description; removing siloes and channel restrictions allows for personalization within a segment throughout an omnichannel journey. This means that an audience can be segmented almost infinitely precisely because selection is applied dynamically. ## Rules-Based Customer Segmentation With next-level customer segmentation with Redpoint, audience selection is not only unbound by channel, dynamic selection also means that segments are rules-based, not list-based. As customers move through a customer journey, various rules apply depending on an action taken or not taken, or the inbound or outbound channel of engagement. A customer, then, moves in and out of a segment as a journey is ongoing. Consider what this means for offering a relevant experience. A basic customer data platform (CDP) may claim to offer segmentation, but once a list-based audience segment is created it is usually exported to a CSV file, uploaded to an email service provider or another channel of engagement, and sent from there. It is not difficult to see the fault; the moment the list is created its relevance wanes because it does not accurately capture the entire audience – either those leaving or joining – as the audience interacts with a brand across channels. A simple example is receiving an email promotion for a product a customer may have just purchased on the mobile app. Enhanced customer segment analysis is therefore required to improve the experience along the customer journey. ## Customer Segment Validation Regardless of customer segment size – from a large cohort to a micro-segment of one – testing and validation are key to ensuring the accuracy of predictions. Testing confirms whether you’re engaging with a customer segment in a way that is not just relevant a specific point in a customer journey, but relevant in terms of what is most likely to trigger a positive response. Testing is a prioritization mechanism; a customer [Golden Record](https://www.redpointglobal.com/single-customer-view/) provides a single view of the customer – preferences, behaviors, transactions, devices, IDs, social, etc. – and as customer segments before more defined, stringent testing will reveal which behavior, interest, trait or combination of traits form the best response predictor. This is true for any channel, particularly as the customer segment changes throughout a customer journey. ## Next-Level Segmentation with AML [Automated machine learning (AML)](https://www.redpointglobal.com/machine-learning) makes dynamic audience selection across channels possible, with algorithms that render a next-best action or offer for a customer according to the rules in place in whatever direction the customer journey unfolds. [AML is the key](https://www.forbes.com/sites/forbestechcouncil/2019/02/08/the-role-of-artificial-intelligence-and-machine-learning-in-driving-customer-experience/#5a4c37a1c13c) to finding granular segments for analysis at scale. A manual selection of cohorts is usually fairly basic; with AML, marketers can drill down to find and create segments that are better aligned to profitability goals – members most likely to respond to an offer, most likely to churn, etc. The Redpoint AML solution facilitates audience selection with visualizations, Venn diagrams that find and narrow an audience from nearly any number of sets of data points. This capability puts audience selection in the hands of the everyday marketer in a sandbox environment without having to pull from an information database or complicated tables or spreadsheets. Clustered audiences is another capability of the Redpoint AML solution, enabling companies to identify sub-segments within a larger set of customers – discerning granular patterns that would otherwise remain unknown left to the devices of the everyday marketer. The misconception that customer segmentation modeling is going by the wayside, replaced by the urgent need for a personalized omnichannel customer experience, is understandable to a degree. Dynamic audience selection immediately and universally available and applicable to all channels, governed by rules that change an audience in real time according to the vagaries of a customer journey, is heady stuff. Hiding the complexity with AML models that create and segment audiences on the fly is so powerful it almost seems segmentation isn’t happening at all. In reality, it allows for an unparalleled personalized and relevant experience across the entire customer journey. That’s the power of Redpoint. It’s why ambitious marketers choose Redpoint to help them lead markets. ## Frequently Asked Questions \[ultimate-faqs include\_category=’customer-segmentation’ \] ## RELATED CONTENT [What is Automated Machine Learning](https://www.redpointglobal.com/blog/what-is-automated-machine-learning/) [Clear the Hurdles for a Personalized Customer Experience](https://www.redpointglobal.com/blog/clear-the-hurdles-for-a-personalized-customer-experience/) [Customer Journeys are Dynamic, Your Engagement Technology Should Be as Well](https://www.redpointglobal.com/blog/customer-journeys-are-dynamic-your-engagement-technology-should-be-as-well/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Data Management, Omnichannel Marketing, Real-Time Personalization, Segmentation & Activation, Single Customer View --- ### [An Avalanche of State Privacy Laws is Coming: What You Need to Know](https://www.redpointglobal.com/blog/an-avalanche-of-state-privacy-laws-is-coming-what-you-need-to-know/) **Published:** October 15, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/10/blog-hinton-ccpa-10-16-300x205.jpg)The Westin Research Center periodically updates a comprehensive [state privacy law comparison](https://iapp.org/resources/article/state-comparison-table/), a one-page document that breaks down the status of data privacy laws across the United States, and lists each state’s approach to various consumer rights and business obligations. While just three states (California, Maine, Nevada) have passed privacy laws, legislation is pending in another 12. For enterprises with customers in every state, the flurry of new data privacy legislation is cause for concern, especially because of the significant differences between the laws. The consumer’s right to opt-out and the business obligation for transparency are the only common threads across each of the state laws. The Illinois bill in cross-committee, for example, does not include a right to deletion. Minnesota, Washington, and New York, meanwhile, have bills in committee that have an eyebrow-raising consumer right against solely automated decision-making as it pertains to their personal data. **Fail to Prepare, Prepare to Fail** What does this mean for a marketing organization that must prepare for and support the California, Maine, and Nevada bills that have been signed into law, as well as the potential for many more, even beyond those already in committee? For starters, of course, it means that organizations do not have the luxury of treating every customer the same; a one-size-fits-all approach to data privacy that fails to honor requests on an individual customer basis will not only run afoul of the law, it will introduce friction into a customer journey. Respecting data privacy is an imperative for brands. According to the Harris Poll survey commissioned by Redpoint, 40 percent of consumers say that is “absolutely essential” that a company tell them what information is being collected about them and how it is being used. Further, 38 percent said it’s essential they have complete control over how their data is used, and 32 percent said the same about being able to set specific preferences. **Tame Complexity with MDM** A recent [blog](https://www.redpointglobal.com/blog/why-customer-permissions-must-be-applied-dynamically-in-the-customer-lifecycle/) in this space highlighted the importance of applying customer permissions dynamically over the complete customer lifecycle. This capability will take on more urgency as state privacy laws expand. The challenge for marketing organizations, however, is the sheer amount of preference center complexity that different state privacy laws introduce. Handling the complexity requires a data management solution that can process changes in real time and push changes out to every system impacted by an updated customer preference. What sets the Redpoint Customer Data Platform apart as an enterprise-grade CDP is an integrated [master data management (MDM)](https://www.redpointglobal.com/solutions/redpoint-data-management/) component that processes every privacy data point at an individual customer level. Because the CDP ingests customer data from every source and of every type, a customer’s preferences and permissions for how their data is stored and used becomes part of a persistently updated unified customer profile, or golden record. **Privacy Compliance Starts with a Golden Record** The golden record, as it pertains to data privacy, is like a lockbox that updates and stores a customer’s preferences. MDM, however, activates any change across every enterprise system to ensure compliance at an operational level. A customer could make an opt-out request by contacting the call center, for example. Unless that information is distributed to every customer-facing application across the enterprise, there is a risk that a preference will not be honored accordingly – introducing friction into a customer journey. Any change could potentially affect a customer journey. An address change is an obvious example, as a customer moving from California to Nevada will fall under a new state privacy law jurisdiction. MDM disperses changes to subscribers in real time to ensure that marketers can keep pace with a customer’s changing permissions and other changing data elements as the customer engages in an omnichannel journey with the brand. Sophisticated heuristic and probabilistic data matching and dynamic updates to an aggregate ensure that a record is consistently updated in real time, providing marketers with confidence that campaigns are perpetually in sync with the persistent view and tracking of a customer’s preferences. **Stay Nimble, Stay Compliant** The fluidity of current state privacy laws, combined with the prospect of additional legislation, requires a flexible approach to compliance. Complexity will of course rise with more state laws impacting more customers, requiring that the enterprise has the technology in place to not only scale, but to handle the nuances of the various consumer rights such as the right to erasure, the right to portability, and the right to recertification. With an extensive set of APIs, the Redpoint CDP enables organizations to build applications pertinent for any variable. Consumer rights in California differ from those in Nevada, for example, and setting up data lineage processes accordingly will ensure there is no reliance on a one-size-fits-all approach to answer important questions such as how customer data is acquired, how it has been shared, its future use, or other questions about the data. Answering these questions on a state-by-state basis helps ensure compliance now and in the future, and provides transparency for customers, auditors, lines of business, regulators, and anyone else with a vested interest in an organization’s compliance. With a dozen privacy laws winding their way through state legislatures, and perhaps more on the horizon, it is imperative that organizations prepare accordingly. In addition to becoming compliant with the law, the benefit to honoring customer preferences is meeting customer expectations for a superior customer experience. In the Harris Poll survey, consumers rank privacy as the most important component of customer experience, just ahead of personalization. Meeting these expectations is a direct line to revenue, with 57 percent of consumers saying they would be less likely to shop or use services from a company that fails to protect their personal information. Conversely, 54 percent of consumers said they will share more personal data for companies that offer a more personalized experience. And 37 percent said they will flat out stop doing business with a company that does not offer a personalized experience. Companies have an obligation to comply with various state privacy laws, but compliance is also an opportunity to please the customer with a superior customer experience that drives revenue. **Blog categories:** Customer Data Platform, Data Management, Identity Resolution, Omnichannel Marketing --- ### [A Single View of the Customer Is Essential to Your Success](https://www.redpointglobal.com/blog/a-single-view-of-the-customer-is-essential-to-your-success/) **Published:** July 17, 2017 **Author:** Steve Zisk **Content:** ## What is a Single View Of The Customer? *A single view of the customer, or golden record, collects all that is knowable about a customer into a single point of control that is easily accessible across the organization and used by customer-facing marketers and associates to drive further brand engagement.* The modern customer creates a veritable treasure trove of data through dozens of digital and physical touchpoints, all of which can be actioned to craft more relevant messaging and power more engaging experiences. The problem is that much of this data is split into internal silos based on specific technologies and closely guarded by internal departments. It’s because of these data silos and fragmented solution portfolio that brands have largely failed to maximize the business value of their customer data. This must change. Brands can ill afford to continue the current state of disconnected data and point solutions. What brands need, and what the Redpoint team and I will showcase at Microsoft Ready in Las Vegas this week, is a solution that can unify disparate consumer data into the single view of the customer that will power contextually relevant experiences regardless of channel. ## **The Power of a Single View of the Customer** Consumers live a connected, omnichannel life with seamless transitions between digital and physical touchpoints. The same customer who might browse the racks at a retail store could simultaneously be checking competitors’ websites for a better price or searching for coupons on the retailer’s own site. Adding to this environment’s complexity is that consumers expect to have a consistent brand experience across all channels and touchpoints; they also want to be recognized as loyal, repeat customers and gain all the benefits that entails. Native online retailers have already accomplished this goal, and consumers expect that every brand should be able to do the same. Legacy brands across all industries have failed to create a common brand experience across online and offline engagements. This failure is tied to the lack of a unified view of the customer. Recent CMO Council research bears this out, with only seven percent of brands able to deliver real-time data-driven engagements across online and offline touchpoints. The single view of the customer, also known as a “golden record,” is vital for success in the modern business landscape. Most brands store customer data in functional and channel-specific silos tied to solutions that don’t share information. Having a single view of the customer means breaking down internal silos and blending anonymous and known data into one composite customer profile that showcases a consumer’s entire interaction history with the brand. Once this customer data is unified, brands can more readily deliver the right interaction at the right time, just like how becoming one with the Force allowed Luke Skywalker to fire the proton torpedoes that destroyed the first Death Star. Brands may not be fighting a Galactic Empire, but the lack of ability to understand customer behaviors and preferences does have a dramatic impact on revenue. [Boston Consulting Group recently found](https://www.bcg.com/publications/2017/retail-marketing-sales-profiting-personalization.aspx) that personalization in retail, healthcare, and financial services will push a revenue shift of $800 billion over the next five years to the 15 percent of companies who get it right. Brands can’t personalize effectively, and capture that revenue bump, if they lack a single view of the customer. ## **Customer Data Platforms and the Single View** The desire to unify customer data into a central location is not a new one. Legacy [data management solutions](https://www.redpointglobal.com/solutions/redpoint-data-management/), such as data warehouses and data lakes, already accomplish this goal for a variety of data types and structures. But it needs to be said that collecting data into a central database is not the same thing as making that data *available* to business users. A single view of the customer is functionally useless if it can’t be easily accessed by the departments who need it most. ![single view of customer](/wp-content/uploads/2017/07/SCV-300x146.png) This is where [customer data platforms (CDPs) ](https://www.redpointglobal.com/cdp/)come in. The idea behind a CDP is to maintain an always-on, always-processing customer golden record that is available at low latency throughout the enterprise. CDPs ingest any type of data, regardless of structure, at batch and streaming cadences and unify it into a central point of data visibility and control. The true power of a CDP lies in making this unified data accessible by the solutions and departments that need it most. What this means from a functional perspective is that business users can access the data they need without making endless requests to the IT team. This enables IT to focus on tasks of greater strategic value to the organization, while also empowering business users to act at the speed of the customer. Because of its ability to unify data regardless of source, type, and cadence, CDPs are a foundational component of an effective omnichannel customer engagement strategy. The prowess of a CDP at connecting customer data also opens the door to effective real-time customer engagement, which is increasingly vital for the modern connected consumer. ## **Unified Data Leads to Engagement Success** [McKinsey recently found](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/from-touchpoints-to-journeys-seeing-the-world-as-customers-do) that 50 percent of customer interactions now happen during a multi-event, multichannel journey. For most companies, this data is spread among multiple departments and multiple technology silos. This siloed data environment no longer functions as well as it once did; now, brands that can unify customer data will succeed where those who retain internal silos will falter. CDPs are the technological solution to this problem, enabling brands to craft contextually relevant messaging that reaches consumers in the moment of need through the channel they prefer. Achieving a single view of the customer is an important goal for the modern brand. Customer data platforms achieve that goal and, more than that, enable companies to operationalize their customer data in such a way that they more effectively engage with the connected consumer. Consumers have already moved on to a life with seamless integration between the digital and physical worlds. Brands must follow if they wish to retain their customer base and enhance revenue in the coming years. **Blog categories:** Single Customer View --- ### [A New Playbook: Super Bowl Ads Go Deep on Empathy and Rising Above](https://www.redpointglobal.com/blog/a-new-playbook-super-bowl-ads-go-deep-on-empathy-and-rising-above/) **Published:** February 9, 2021 **Author:** Steve Zisk **Content:** The highly anticipated scoring onslaught between Tom Brady and Patrick Mahomes in Super Bowl LV never really materialized, with Brady’s Bucs rolling to a rather anticlimactic 31-9 victory over Patrick Mahomes’ Chiefs on Sunday night. While Brady did his part with three touchdown passes, one running joke that lit up the internet was that an on-field streaker had more yards of offense than the Chiefs. Or that the National Anthem sign language dancing guy had more moves and energy than any Chiefs player. From an advertisement standpoint, a lot of the pre-game “hype” focused on Budweiser, Pepsi and Coke all sitting on the sidelines – choosing not to spring [$5.5 million for a 30-second spot](https://www.sportingnews.com/au/nfl/news/super-bowl-commercials-cost-2021/pjjs1nsfot5k1s34qf9v59wor). Come game time, however, Budweiser was prominently featured in an Anheuser-Busch ad featuring many of its products, and Bud Light shelled out roughly $22 million for two 1-minute spots, including [“Bud Light Legends”](https://www.vulture.com/article/2021-super-bowl-commercials.html) featuring a familiar cast of characters and one of the more humorous ads of the night, featuring [Bud Light Seltzer Lemonade](https://youtu.be/g6CVKs77X74) commenting on the lemon of a year that was 2020. Among the most-talked about spots include Bruce Springsteen appearing for Jeep in [“The Middle”](https://www.washingtonpost.com/entertainment/music/springsteen-sells-out-jeep-ad-superbowl/2021/02/07/b9bd1fa0-6986-11eb-9ead-673168d5b874_story.html) about unity and a chapel in Kansas (was Jeep announcing a 2024 run for president?) and Paramount never letting us forget that its new streaming service debuts on March 4, with not two or three, but [four 1-minute ads](https://www.nfl.com/videos/paramount-stars-celebrate-at-the-summit-of-paramount-mountain) with a who’s who of Paramount characters hiking to the top of Mt. Paramount. An Alexa ad with Michael B. Jordan appeared to be promoting infidelity, dreaming of a future design that left little to the imagination, a “flat” Matthew McConaughey pitched 3-D Doritos, and a Tide ad used the face of Jason Alexander of “Seinfeld” fame to promote cleanliness. ## **Less on CX, More on Meaning** As for Super Bowl commercials with a focus on customer experience (CX), customer engagement or personalization, this year’s crop of ads fell as flat as McConaughey. Unlike the past few years, there was nothing about [curbside pick-up](https://www.redpointglobal.com/blog/personalization-scores-a-big-hit-in-super-bowl-liv-commercials/), for instance, an interesting omission considering the service’s rise in popularity over the past year; one study shows that [85 percent of consumers](https://www.businesswire.com/news/home/20200924005537/en/85-of-Shoppers-Have-Increased-Curbside-Pick-Up-Since-COVID-19-79-Say-a-Contactless-Store-Pickup-is-Very-Important-to-Them) have significantly increased curbside pick-up orders. Pandemic fatigue, one could argue, made some brands shy away from reminding the roughly 100 million viewers of anything to do with the adjustments we’ve all had to make. That said, without directly addressing COVID-19, many brands played on tangential social issues, highlighting resolve, fortitude and solidarity. The 2-minute Jeep spot, for instance (the No. 3 most-searched for ad according to [Google Trends](https://www.9news.com/article/sports/nfl/superbowl/top-searched-super-bowl-ads-on-google/507-373eba94-9a94-497b-9336-a9bd4a70f12b)), narrated by Springsteen, touted connectivity, common ground and crossing the divide. Not to be outdone, Ford’s [“Finish Strong”](https://www.youtube.com/watch?v=XrQhFUCBhBg) ad discussed sacrifice, holding the line, staying strong and fighting for one another. Job-searching network Indeed also got into the act with “The Rising,” playing on hope, inspiration and strength of those trying to get back on their feet. Ads like those did play on customer experience in a way, with a tacit acknowledgement that what a customer care about today is very different than what it was a year ago. How we extract value from the brands we interact with is less about product than it is having a meaningful relationship. We covered this as a growing trend in our [2021 predictions blog](https://www.redpointglobal.com/blog/feelings-meet-facts-infusing-customer-experience-with-meaning-will-define-2021/). Value, we wrote, “is not measured by a price tag, but rather by respect, empathy and understanding of an individual customer’s unique needs and wants.” In the Ford commercial, for instance, the automaker did not show a single vehicle, focusing exclusively on joining together to overcome adversity. Our 2021 predictions blog also mused on the new ways of interacting with customers in the healthcare space. Dexcom, a healthcare company that makes devices for diabetics, used Nick Jonas as a pitchman in its first-ever Super Bowl ad to promote the [Decom G6](https://www.youtube.com/watch?v=zN8naTqX3TI), a device that uses a sensor on a user’s skin to measure blood sugar levels, which is then transmitted to a smartphone app for monitoring. The advertisement coincides with the growing trend of [healthcare consumerism](https://www.redpointglobal.com/blog/why-cx-is-the-right-rx-for-healthcare/), which refers to consumers being in control of their own health data through wearables and apps, and increasingly in control of the experiences that constitute a coordinated healthcare journey. In case you either missed the game – or just the commercials – here’s a [compilation of every ad](https://www.vulture.com/article/2021-super-bowl-commercials.html) from Super Bowl LV. As for the game itself, all the buzz is about Brady and his growing list of [Super Bowl records](https://www.patspulpit.com/2021/2/8/22272025/tom-brady-super-bowl-records-patriots-buccaneers) from his 10 appearances (and seven wins). For a sense of how long Brady’s been winning Super Bowls, prominent brands which aired commercials during his first Super Bowl win in 2002 include Blockbuster, AOL, Gateway Computers and a [Circuit City ad](https://adland.tv/adnews/circuit-city-broadband-breakfast-2002-30-usa) promoting the ease of finding this strange new thing called “broadband.” ## **Related Content** [Personalization Scores a Big Hit in Super Bowl LIV Commercials](https://www.redpointglobal.com/blog/personalization-scores-a-big-hit-in-super-bowl-liv-commercials/) [Super Bowl Commercial Scorecard: Which Brands Scored a Touchdown](https://www.redpointglobal.com/blog/super-bowl-commercial-scorecard-which-brands-scored-a-touchdown/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* --- ### [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) **Published:** March 25, 2020 **Author:** Dale Renner **Content:** A rushed greeting when you pass an acquaintance on the street is one of life’s common interactions we can all relate to – even if many are now on hold as we practice social distancing. We recognize the difference in how it makes us feel versus stopping to have an actual conversation, when you chat about each other’s kids, a new job, or vacation plans. One is a fleeting, all-too brief interlude that is usually quickly forgotten. The other establishes a far deeper, meaningful connection. The difference between the fleeting interaction and the engaging conversation is analogous to surface level identity management versus the Redpoint approach to identity resolution that underpins the delivery of a superior, hyper-personalized customer experience layered with context and meaning that brings the interaction to life. Advanced identity resolution and deriving customer context is the Redpoint competitive differentiator. **Walking in Step with the Customer** The key to delivering unprecedented context to a customer relationship is a single customer view, or [golden record](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/), that lets a brand know everything there is to know about a customer. A persistently updated golden record includes data of every type and from every source. Built upon singular identity resolution capabilities, it combines every customer identifier – addresses, emails, devices, social, phone numbers – with complete behavioral and transactional data to deliver the context that gives the single customer view its value. Context goes beyond understanding each discrete interaction in a customer’s journey. It is created by an analytically derived understanding of the relationship between the various interactions and decision points, which in turn creates testable predictions about future interactions and customer decisions. Thus, a contextual customer relationship is the key to optimizing each engagement in the context of the entire customer lifecycle, guaranteeing that a brand is always – always – in sync with the customer. **Simple Identity Management Misses the Mark** Surface level identity management, by contrast, could be using a cookie match or a device identification as the basis to provide a customer a specific offer or content. The difference is that this approach relies on matching at just one moment in time – the fleeting interaction. It might inform a brand that a user of a device likes golf (an assumption made because the device was used to purchase golf equipment), and the brand will serve up golf content. This is not true identity resolution, however, because it fails to take into account that consumers have different identities in different channels, or identities in digital and physical locations, they have households, and they have names that can be spelled and matched in a variety of ways. Neglecting variables results in a limited, narrow, and incomplete view. A brand that uses this view to serve up content or offers has an extremely high likelihood of introducing friction into the customer experience. It would be like greeting a person on the street and asking about a non-existent partner. A simple match also ignores the trove of behavioral and transactional data, resulting in missed opportunities: perhaps the brand offers a discount on a golf bag that the consumer just bought on another channel; or it serves up an ad for a destination golf vacation even though the customer posted a scathing Yelp review on the same resort. An incomplete view of the customer, which can be caused by over-matching or under-matching records and/or not having all transactional and behavioral data, handcuffs marketers who must offer relevance with every engagement. Mis-matching identifiers disconnects behavioral, transactional, and demographic data associated with that identifier. The result: diminished relevance, increased customer friction, and lost opportunity. **Identity Resolution Drives Profitable Growth** Delivering a personalized and meaningful customer experience is important for the simple reason that it is what today’s always-on, continuously connected customer expects. Consider the [2019 BRP Unified Commerce Study](https://brpconsulting.com/download/2019-unified-commerce-survey/), where 87 percent of consumers surveyed said that it is important to receive “a personalized and consistent experience across channels.” A 2019 Harris Poll survey commissioned by Redpoint drives the point home, with 63 percent of consumers surveyed claiming they expect personalization as a standard service, and 37 percent claiming they will flat-out not do business with a company that fails to personalize customer experiences. Pleasing a customer is a tremendous benefit in and of itself, but even more so as a means to an end. Ultimately, superior customer experiences drive revenue. According to [Gartner research](https://www.gartner.com/smarterwithgartner/is-your-organization-customer-centric/), “persona-centric” brands may see a revenue lift of 6 to 10 percent by using transactional, preference, and historical data to form a personalized engagement strategy. Yet brands that use transactional, preference, and historical data while also looking at behavior across devices, analyzing device usage, IoT and sentiment analysis, and first-party, second-party, and third-party data across an anonymous to known record – those brands are considered “customer-centric” and produce a conversion lift of 20 percent or greater. Providing a customer with a perfectly timed offer based on the entirety of interactions across the customer lifecycle is how a brand engages a customer with a meaningful, lasting impression. This rich, contextual relationship lets a customer know that their value to a brand is defined less by what or how much they buy – or don’t buy – and more by a furtherance and deepening of the relationship. A customer’s lifetime value, in other words, is more important – and ultimately more profitable – than a transactional relationship. The most complete and continually updated in real-time identity resolution is essential to creating the context that is necessary for exemplary customer experience delivery, and no one understands or delivers this context better than Redpoint. **Blog categories:** AI & Machine Learning, Customer Data Platform, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [A CDP Implementation Reference Guide: What You Need to Know](https://www.redpointglobal.com/blog/a-cdp-implementation-reference-guide-what-you-need-to-know/) **Published:** January 30, 2020 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/01/zisk-1-30-blog-2-300x191.jpg) Implementing a CDP is a little bit like setting up a smart TV. If your goal is to binge watch “Stranger Things” or a trending new docuseries on the new big screen, you don’t need to immediately connect to *every* streaming service within minutes of opening the box. Likewise, implementing a [CDP](https://www.redpointglobal.com/blog/a-cdp-implementation-reference-guide-what-you-need-to-know/) starts with deciding on a specific use case. You may not need all the functionality on day one, but it’s still there when you’re ready to explore more features. The recognition that a CDP can be used to achieve a specific use case is an important distinction to make, even though it may seem counter-intuitive to the aspirational notion that a CDP’s purpose is to generate a 360-degree view of the customer – a single source of truth for customer data. Another counter-intuitive concept is that a CDP, from day one, does not have to hold all customer data in a single database. Deciding on a use case helps dispel these counter-intuitive notions, because it is more than possible to extract immediate value from a [CDP platform](https://www.redpointglobal.com/customer-data-platform) with a “180-degree view” and having data live in separate databases. If the use case is customer acquisition, for example, it may not be necessary to connect to a point-of-sale system. As far as holding all customer data in a single database, real-time unified access to data is what’s important. Instead of moving data from systems of record to a CDP, a live, real-time connection offers value. There can be cleansed, master information about the customer in the CDP and reference data in other systems, like a CRM system of record. For the marketer, of course, a lot of these machinations will be hidden from view – as they should be. The marketer’s concern is to create [hyper-personal customer experiences](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty/), without having to worry about the technical underpinnings. If customer data is available, visible, and in real time, the database of origin is not the marketer’s concern. For the initial CDP implementation, however, decisions about data structure and availability are important. Each use case will determine how to strike the optimal balance between fast-moving and slow-moving data, depending on how recalculations impact the desired goal. **How Will You Be Using the CDP?** Stipulating that it is possible to extract value from a CDP without immediately completing a 360-degree view, there are other important considerations before implementing a CDP that are more intuitive, and common to other martech integrations. First, the CDP must meet whatever IT requirements are placed on it – particularly for highly regulated industries at risk of data leaks. Whether an organization maintains control of its own [security perimeter](https://www.redpointglobal.com/blog/marketing-data-is-too-important-to-cede-control-of-the-security-perimeter/) is one of several important decisions to make about how to satisfy IT requirements. The configuration and location of the CDP must meet IT’s criteria for security, privacy, performance, and control, and those requirements might not be satisfied for a SaaS solution where sensitive customer data resides in someone else’s cloud infrastructure and a third-party defines the IT architecture. Second, the CDP itself must share implementation between marketers and IT personnel. And if the CDP includes a [machine learning modeling capability](https://www.redpointglobal.com/blog/automated-machine-learning-one-size-does-not-fit-all/), collaboration must include third parties like data scientists. A CDP design, in other words, must recognize that different stakeholders must be allowed to perform their roles, while still providing an end-to-end workflow for marketers to deliver personalized customer experiences along the entire customer journey. Examples include seamlessly providing data quality functions under the hood and putting those functions under control of IT without marketers ever having to be concerned with them. Likewise, this means providing a data model repository that allows data scientists to control data cleansing, training, model validation, and optimization – while simultaneously allowing marketers to see, deploy, and measure the models. Third, a CDP must meet the marketers’ (and use cases’) required cadence and connectivity to other applications, a requirement that may eliminate a slew of CDPs that cannot orchestrate in real time, for example. Similarly, it might disfavor a CDP that cannot interactively push information out to a call center, a clienteling app, a bank branch tablet, or any other opportunity to interact with a client or customer that relies on inter-application connection. These three considerations indicate that marketers should define long-term requirements, as well as detailing their immediate use cases, to avoid choosing a CDP that meets minimum needs but cannot grow to match aspirations. **Everything’s Downstream from the Use Case** Choices in the purchase phase – such as private or public cloud, SaaS or hosted, clustering controls, or how the CDP will be configured in the martech stack – all have downstream consequences for the actual implementation phase. Will reporting and campaign activation need to be done inside the CDP, for example? Every choice or decision about what needs to be within the CDP will have implementation consequences. Those choices are tied to the ultimate use cases. If a use case involves attribution, measurement, or optimization, elements of the implementation will need to reflect those requirements to ensure that the CDP does not stop short of the last mile connection to the customer. **RELATED CONTENT** [Show Me: The Importance of Proving Out a CDP Use Case](https://www.redpointglobal.com/blog/show-me-me-the-importance-of-proving-out-a-cdp-use-case/) [How Does Your CDP Stack Up? The Real-Time Difference](https://www.redpointglobal.com/blog/how-does-your-cdp-stack-up-the-real-time-difference/) [Demise of the DMP, Long Live the CDP](https://www.redpointglobal.com/blog/demise-of-the-dmp-long-live-the-cdp/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2019/10/1004-Solution-Brief-CDP-Transforming-Customer-Exp-0418-01-1-239x300.jpg)](https://www.redpointglobal.com/wp-content/uploads/2018/06/Solution-Brief-CDP-Transforming-Customer-Exp-0418-01.pdf) **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Management --- ### [5 Ways to Tell If You Have a Customer Data Platform](https://www.redpointglobal.com/blog/5-ways-to-tell-if-you-have-a-customer-data-platform/) **Published:** April 24, 2018 **Author:** Redpoint Global **Content:** ![5 Ways to Tell If You Have a Customer Data Platform](https://www.redpointglobal.com/wp-content/uploads/2018/04/customer-data-platform-ways-to-tell.jpg)Customer data platforms (CDPs) are still emerging as a solution class. The resulting fungibility from this status has led to extensive confusion in the marketplace, with some vendors claiming that the capabilities of a CDP are already fulfilled by other solutions. That isn’t true, but it hasn’t stopped the claims from being made. The emerging nature of customer data platforms also means they lack a market-standard definition, which allows some vendors to rebrand older solutions, such as tag management software, as a customer data platform. The situation with CDPs is reminiscent of retail in the early 20th century, when the law of the land was “caveat emptor” or “buyer beware.” Companies who think they need a [customer data platform](https://www.redpointglobal.com/customer-data-platform) – and many can, in fact, benefit from the solution – must be vigilant in evaluating vendor offerings. Many solutions branded as CDPs are limited in scope and functionality. This means that the vendor offering a customer data platform may not be selling brands the tool the organization thinks they’re getting. In my experience, there are generally five key ways to tell if a solution is really a CDP. To be considered a customer data platform, a solution must: 1. **Accept data of any structure or cadence** – Customer data platforms function as a single point of access and visibility for data silos throughout the organization. The high variance in structure and cadence of data throughout most companies – streaming unstructured data from social media, structured batch data from the CRM, etc. – means that any CDP worth the name needs to accept any structure or cadence of customer data. This is especially vital because [Northeastern University](https://www.northeastern.edu/levelblog/2016/05/13/how-much-data-produced-every-day/) recently found that 2.5 exabytes of data are produced every day. As volumes increase, CDPs need to have the ability to ingest broader varieties of information and still leverage it. 2. **Resolve customer identities across the anonymous-to-known lifecycle** – Customer data platforms need to possess robust identity resolution capabilities. From a functional perspective, this means blending anonymous behavioral data with known information about customers into a coherent customer identity. With identity resolution capabilities, a CDP should be able to build and maintain a golden customer record from everything that is knowable about the average consumer. 3. **Manage customer data in real time** – CDPs are designed to build and maintain the golden customer record. Any CDP worth the name must be able to perform the necessary tasks to accomplish that goal at the speed of the customer. While the tasks are often done at a real-time pace, the definition of “real time” is sometimes fungible based on business needs. Despite this, the fact remains that any CDP needs to be able to update the unified customer profile at a moment’s notice. 4. **Empower business users with access to unified customer data** – The unified customer profile of a CDP is worthless if no one has access to that data. CDPs need to allow marketers and other business users to access customer records with minimal IT assistance; this is partly what makes CDPs so different from traditional data management technologies. Business users who can access customer data at their moment of need can do their jobs quicker, which leads to greater responsiveness to customer signals. 5. **Function on any deployment model and database technology** – A [customer data platform](https://www.redpointglobal.com/customer-data-platform) needs to be flexible in its deployment model. Some companies prefer on-premises deployments, others are fine with cloud-based software, and still others tend toward a hybrid deployment. The type of deployment shouldn’t matter. In terms of database technology, many brands have legacy databases in place that they don’t want to transition away from. A CDP shouldn’t force any organization to deploy a new database – the solution is designed to maximize investments in data technologies, not replace them. Customer data platforms are a powerful solution class with a potent ability to ensure brands can deeply understand their customers. With a fuzzy definition in the marketplace, however, it’s crucial that companies know what to look for when evaluating solutions. If the solution being evaluated doesn’t accept all forms of data, handle real-time updating, make data accessible, have a flexible deployment model, or resolve identities across the anonymous-to-known customer lifecycle, then chances are the solution isn’t really a CDP. ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/redpoint-global-inc/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Data Quality, Identity Resolution, Omnichannel Marketing, Real-Time Personalization --- ### [5 Things Credit Unions Should Do to Attract Millennials](https://www.redpointglobal.com/blog/5-things-credit-unions-should-do-to-attract-millennials/) **Published:** May 14, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/05/shutterstock_408104194-e1557779133451.jpg)Credit unions face an uphill battle to attract millennials, the roughly 80 million Americans born between 1981 and 1996 whose youngest members are now mostly out of school and on their own, launching careers, and beginning to think about long-term financial security. As a recent [Credit Union Journal survey](https://www.cujournal.com/opinion/millennials-really-dont-understand-credit-unions) makes clear, simply educating millennials on the merits of credit unions is a pressing concern. According to the survey polling more than 500 millennials, when asked to describe a credit union, 45 percent said they “offer a more human, less digital experience”, while 31 percent said they were “small banks with limited offerings”. Worst of all for credit union marketing teams, 25 percent of millennials fessed up to having “no idea how to describe one.” It is imperative that credit unions overcome this challenge to replace an aging customer base, with half of all credit union members now [53 or over](https://www.cujournal.com/opinion/millennials-really-dont-understand-credit-unions). With only [24 percent of millennials](https://www.cujournal.com/opinion/millennials-really-dont-understand-credit-unions) currently belonging to a credit union, the silver lining is that there is plenty of runway to attract new customers. There is more good news for credit unions that do solve the education problem; millennials value many of the experiences and services that credit unions are traditionally known for, including a personalized customer experience, a community-based ethos, and exemplary service. Large banks rate poorly on delivering some of these valued services, giving credit unions another opening to attract younger customers with a differentiated experience. Consider the 2019 Digital Banking Report, where [94 percent of financial institutions](https://thefinancialbrand.com/74986/banking-personalization-targeting-trends/) rated themselves as unable to deliver on the “personalization promise” that customers expect. Ample opportunity exists for credit unions to attract millennials by addressing the customer experience gap and providing localized service that larger banks can’t match. In addition to personalization, the four other issues credit unions need to address are regulations (particularly privacy), community, mobility, and accessibility. **Personalization – Do you know me?** In a survey conducted by[ Boston Consulting Group](https://www.creditunions.com/articles/the-3-key-drivers-of-the-member-experience/#ixzz5c1WApJGn), 54 percent of customers said personalization influenced their decision to become a customer of a financial institution, and 68 percent said that personalization influenced them to buy more products or services from an existing one. Personalization requires knowing everything there is to know about a customer beyond the basics such as banking preferences, and then providing relevant information and offers. As it pertains to the millennial population, personalization could mean knowing how much student loan and overall debt a customer carries, if the customer is looking to buy her first home, or whether she’s started retirement planning. More so than older generations, millennials are looking for financial planning support; offering a personalized experience such as a customized financial plan is one way for a credit union to distinguish itself from the one-size-fits-all approach more likely to be found at a retail bank. Providing this level of personalization requires that a credit union have a single view of the customer, a persistently updated [golden record](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/) that includes customer data from a range of sources. An accurate, complete understanding of the customer and their financial situation brings together data from internal applications and systems, and across a range of touchpoints, like mobile apps, payment networks, and social sites. A single view gives credit unions the ability to organize themselves around the customer to create customer-centric experiences that recognize members as individuals (a “segment of one”), and overcome typical product and channel siloes that introduce friction into the customer experience. Benefits include the flexibility to provide product recommendations at different stages of a customer journey – acquisition, expansion, loyalty, and retention. **Regulation – Can I trust you?** With General Data Protection Regulation (GDPR) and California Privacy Protection Act (CCPA) putting a spotlight on data privacy, the rights of consumers to control how, when, and where their personally identifiable information (PII) and other customer data is used has become a burning issue for every industry. While financial institutions have always prioritized the protection of customers’ core financial data in conforming to federal and local regulations, new data privacy regulations present credit unions with an opportunity to reconsider the firewall safeguarding personal data, and using it with a customer’s permission to enhance the customer experience. According to a Harris Poll survey commissioned by Redpoint Global, 70 percent of millennials said they are willing to share personal information in return for a more personalized experience, more than GenX (59 percent), Baby Boomers (39 percent), and Seniors (34 percent). By responding creatively to the privacy mandate, credit unions can create a personalized customer experience matching each customer’s opt-ins and other preferences with the caveat that they are transparent about how they are collecting, storing, and using customer data. The data value exchange is a win-win for the credit union and the customer; the more transparent a credit union is in how the data is used, and the more personalization it offers, the more customers – particularly millennials – are willing to offer personal data. **Community – How do you impact my community?** Attracting millennials by amplifying the community angle is an ace in the hole for credit unions, lending an aura of authenticity that national banks cannot match. According to a [Harris Poll study](https://kasasa.com/documents/executive-summary/Nielsen_Executive_Summary_2016.pdf?Source=MKTO&Medium=Email&Link=Txt-Top&Campaign=2016-Q4-Trigger-Gated-Millennials&mkt_tok=eyJpIjoiTUdObE16UTVZelJpTlRrMiIsInQiOiJPQm1wejJuTnpEaW9FY2xHTm4yR29Na0VcL054MlN5a3g1MXFSUmlJMW9ETUNpdWYwa0lhOHNhU1wvXC90N2lvOVZsRmZiZHVMVGlqa2VKN3BhM2ZOc1ppV1k1T0RNVDN4bWJcL09qUmxzeE5EMTdFRFwvNTlraU1qYzEyZVJCVmd2NHVNIn0%3D#_blank) commissioned by Kasasa, nearly half of millennials think it’s important to do business with locally owned businesses, while more than 60 percent said that would consider a credit union if they were changing financial institutions. Embracing a millennial as a customer and community member requires bringing the online and offline life of the customer together into a demographic, behavioral, and transactional history. By incorporating community-based demographic data into a unified customer profile, marketers can enhance the personalized experience with local flair – such as touting social responsibility programs, local drives, or other community enrichment programs. Local relevance helps foster loyalty and trust and enriches personalization, giving credit unions an edge over large retail banks. With banking products becoming commoditized, financial institutions are swept up in competing instead on customer experience. According to [Capgemini](https://www.capgemini.com/service/invent/), only 37 percent of banking customers believe their financial institution understand their needs and preferences adequately. Inflecting local knowledge into personalization efforts can be a powerful tool for credit unions to show a level of understanding that larger banks can’t match. **Mobility – Can I bank how and where I want to bank?** As much as millennials value loyalty, trust, and sense of community, one of the more effective ways a credit union can convince millennials to join their ranks is to provide the digital banking options they favor. According to the [2018 FIS Performance Against Customer Expectations](https://www.cujournal.com/opinion/credit-unions-are-losing-the-war-for-millennials) study, millennials rank digital self-service as the most important attribute in their banking relationships – far ahead of trust. The Harris Poll and Kasasa study reports similar findings, with [77 percent of millennials](https://kasasa.com/documents/executive-summary/Nielsen_Executive_Summary_2016.pdf?Source=MKTO&Medium=Email&Link=Txt-Top&Campaign=2016-Q4-Trigger-Gated-Millennials&mkt_tok=eyJpIjoiTUdObE16UTVZelJpTlRrMiIsInQiOiJPQm1wejJuTnpEaW9FY2xHTm4yR29Na0VcL054MlN5a3g1MXFSUmlJMW9ETUNpdWYwa0lhOHNhU1wvXC90N2lvOVZsRmZiZHVMVGlqa2VKN3BhM2ZOc1ppV1k1T0RNVDN4bWJcL09qUmxzeE5EMTdFRFwvNTlraU1qYzEyZVJCVmd2NHVNIn0%3D#_blank) claiming they will only consider financial institutions that offer online banking as well as physical locations, and 65 percent said they’d be more willing to switch to a community bank if it offered a mobile app or mobile check deposit. Digital self-service extends beyond mobile banking to include avoiding human contact with online and mobile services such as voice banking, account opening, bill pay, and loan origination, and to embrace peer-to-peer payments through integration with apps like Venmo and Zelle. One challenge for all financial institutions is that many of these channels didn’t exist five or 10 years ago. To provide millennials with the range of digital touchpoints they expect requires marketers to adopt an omnichannel approach. It starts with a single customer view that includes a customer’s physical and digital interactions, preferences, behaviors, and transactions. Real-time decisioning is the activation layer that unlocks the power of customer data, providing marketers with the ability to personalize the customer experience in the context and cadence of the customer across all touchpoints. For a millennial, this will likely mean engaging with them across the digital channels they prefer, and credit unions must be prepared to deliver this functionality for banking and non-banking activity alike. **Accessibility – Can you accommodate my needs?** Credit unions across the country are facing [discrimination lawsuits](https://www.boia.org/blog/how-credit-unions-respond-to-ada-website-accessibility-lawsuits) based on the Americans with Disabilities Act (ADA). Because the non-profit entities are considered “public accommodations” under Title III of the ADA, they are prohibited from “excluding or discriminating against people with disabilities” when conducting business. Many of the lawsuits allege that credit union websites are or have been “insufficiently accessible” to serve people with disabilities. As these lawsuits move through the courts, credit unions are understandably anxious about the ramifications. Many provide [accessibility statements](https://www.firsteaglefcu.com/website-accessibility-statement/), asking anyone who has difficulty using or accessing any part of the website to contact them, and reiterating their commitment to a “positive experience” for all members. Accessibility is especially important to attract millennials, who according to one study have a [higher rate of disabilities](https://www.inc.com/sylvia-ann-hewlett/millennials-with-disabilities-a-large-invisible-talent-cohort-with-innovative-potential.html) than Boomers or Gen-Xers, with roughly 30 percent of working millennials having a disability. A positive experience may not mean that a person with a disability has the same experience as a person without a disability, but it does mean that they should be able to accomplish all of the same tasks, whether that’s a mobile check deposit, checking a balance online, or transferring funds. To create the same personalized experience and understanding for a customer with a disability that every other customer enjoys, a credit union must obviously first know about the disability and make sure that it is part of the unified customer profile. More importantly, it must incorporate the customer’s preferences on an individual basis. Voice banking might be a preference for one visually impaired customer, while another may prefer accessibility options for setting up automatic bill pay. Treating all customers with the same disability the same way fails to recognize or engage with the customer as an individual. **Meet Sky-High Expectations, Reach Deep Pockets** One millennials study estimates that by 2020, the largest living generation will have [$1.4 trillion in spending power](//www.lexingtonlaw.com/blog/credit-cards/millennial-spending-habits.html) each year. That’s a lot of incentive for credit unions to bring millennials into the fold. The five points we’ve outlined for how a credit union can attract millennials really all boil down to the same important point: treat each millennial as an individual customer. Thinking that all millennials share certain traits and marketing to them as such is a sure-fire way to drive them to another financial institution. While it may seem counter-intuitive, the most effective way to attract millennials is to not think about them as millennials. **RELATED ARTICLES** [Personalization in Banking: How Banks Can Do Better by Overcoming Assumptions](https://www.redpointglobal.com/blog/personalization-in-banking-how-banks-can-do-better-by-overcoming-assumptions/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![cover- credit union solution brief](https://www.redpointglobal.com/wp-content/uploads/2019/03/Solution-Brief-Credit-Union-801x1024.png)](https://www.redpointglobal.com/wp-content/uploads/2019/03/Solution-Brief-Credit-Union.pdf) **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Omnichannel Marketing, Segmentation & Activation --- ### [3 Reasons to Ditch Your Marketing Cloud](https://www.redpointglobal.com/blog/3-reasons-to-ditch-your-marketing-cloud/) **Published:** May 17, 2018 **Author:** Steve Zisk **Content:** ![Ditch Your Marketing Cloud](https://www.redpointglobal.com/wp-content/uploads/2018/05/ditch-marketing-cloud.jpg)The hype surrounding marketing clouds hasn’t entirely faded, but I constantly meet marketers who are disenchanted with the reality. They know exactly what they want to achieve: true omnichannel marketing in real or near-real time that consistently delivers the right offer or message, based on a dynamic understanding of each individual. But they’ve come to see just how difficult it is to “get there from here” with today’s marketing clouds. A while back, my colleague George Corugedo predicted [some of the obstacles](https://www.redpointglobal.com/blog/marketing-technology-needs-consciously-uncouple-frankencloud/) they would face. George quickly recognized that many leading marketing clouds were better described as “frankenclouds.” They had been cobbled together from acquired subsystems never designed to work together, so their multiple code bases integrated poorly. These integration problems couldn’t be papered over with a single interface: they would likely lead to major problems with performance and scalability. George proved right. And, with two years’ more experience, we can identify three killer problems with most of today’s leading marketing clouds. These problems are baked into the DNA of today’s marketing clouds, and deeply problematic to marketers ### **1. Narrow and limited access to your own data** Marketers have long understood that their data contains great riches, if it can be mined appropriately. By now, that’s a cliché – which doesn’t make it any less true. But you can’t glean the diamonds unless you can explore your data without limitation and use it without friction. First and foremost, full access to your data is needed for your machine learning, analytics, and data science applications. Unfortunately, most marketing clouds provide a very narrow portal into your data. It’s like looking through a submarine periscope. Marketing clouds lock down your data to make sure you can’t see anyone else’s. Every query is pre-filtered by your own ID: you can’t see your raw data tables. It’s difficult or impossible to use the tools you prefer. To go beyond the provider’s pre-built queries, you’ll need custom development – typically by a consultant who may charge $200 an hour or more. Even if you have all the money in the world, it’s harder to move quickly or innovate. You bought into the cloud to improve agility, but the exact opposite is happening. As George predicted, layers placed between you and your data – combined with poor integration across the stack – can also kill performance, especially as you scale, or identify the need to offer highly personalized responses to customers in milliseconds. That’s one key reason many companies have grown increasingly skeptical about marketing clouds. ### **2. Today’s marketing cloud stacks compromise too much** Marketing cloud providers desperately want you to purchase their entire stack. Unfortunately, their stack is never best-of-breed in all areas, and it’s certainly not best-of-breed in every function you care about. As soon as you decide to use a third-party tool, you’re back in the integration game whether you like it or not – usually requiring an expensive consulting project. The alternative, of course, is to buy the whole stack, sacrificing functionality and/or investments you’ve made in automation, templates, and training. Most companies don’t like that tradeoff at all. According to Walker Sands, among the 21 percent of marketers who use single-vendor suites, [only 1/6 stay entirely within the suite](https://www.walkersands.com/State-of-Martech%22%20/t%20%22_blank). All this assumes you’re given the choice. Sometimes you’re not. For example, most large enterprises have chosen their own tools for delivering emails, reflecting their own requirements. But one stack provider recently told customers it will ban third-party email products, requiring its own. That makes life harder for customers. It’s no way to make friends, but presumably, the stack provider thought it had the whip hand in those relationships. Which brings us to Reason #3 … ### **3. You’re the passenger, not the driver** If you’re a marketing cloud customer – even a very large one – you have little leverage over where your service provider is taking you. What happens if your business strategy and theirs diverge? What if they decide to de-emphasize your industry for their own competitive reasons? What if you’re dependent on the current implementation of a feature they want to change? Someone else is driving the bus. You have to hope they keep heading in your direction. ### **A Better Alternative: The Open Garden** If marketing cloud stacks create more cost, restrictions, and risk than they’re worth, what’s a better alternative? An “[open garden](https://www.redpointglobal.com/blog/what-is-an-open-garden-approach-to-martech/).” Companies that integrate a true [customer data platform](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/) (CDP) with a modern orchestration layer can integrate *all* their data sources. Open garden solutions can make it possible for companies to ingest data of any variety at any velocity or cadence, keep using the best-of-breed they’ve invested in, and host anywhere it makes business sense – whether in a preferred cloud, a datacenter, or a hybrid environment. With an open garden as a foundation, companies can establish a current and persistent golden record that leverages all that’s knowable about every prospect and customer. Then, they can use *all* their knowledge to drive analytics, predictive models, and segmentation for next best offers and messages – and do it in-line with customer experience, across all touchpoints. They can finally “get from here to there,” *without compromise*. **RELATED ARTICLES** [Shattering the Myth of the Single-Vendor Marketing Suite](https://www.redpointglobal.com/blog/shattering-the-myth-of-the-single-vendor-marketing-suite/) [What Is an Open Garden Approach to Martech?](https://www.redpointglobal.com/blog/what-is-an-open-garden-approach-to-martech/) [3 Powerful Reasons You Need a Customer Engagement Hub Right Now](https://www.redpointglobal.com/blog/3-powerful-reasons-need-customer-engagement-hub-right-now/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/redpoint-global-inc/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Quality, Real-Time Personalization --- ### [The Data Readiness Advantage: Turning Trusted Data into Business Value](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/) **Published:** April 21, 2025 **Author:** John Nash **Content:** The new trend in data is … readiness. As in, data readiness. In the rush to take advantage of generative AI (GenAI), agentic AI, real-time analytics and other creative new ways for managing and processing data, businesses have neglected data quality at their own peril. The absolute need to get data right and fit-for-purpose has always been a struggle for enterprises and in most cases they settled for “good enough.” AI is now exposing the flaws in that approach. Customer data drives a wide range of use cases from CX to AI to operations, and all generally presumed quality was taken care of as a matter of due course. For the most part companies didn’t worry about it, or they operated under the mistaken impression that basic data quality was the function of the MDM solution, the DMP, the CDP, the data lake, the data warehouse, etc. But now that data quality is understood to be a critical factor in successful CX and AI initiatives, companies across all industry verticals are giving it far more attention. Data readiness, however, extends beyond generic, basic data quality. The framework for having data that is right *and* fit-for-purpose consists of six distinct characteristics that together constitute the requirements needed to extract value from enterprise customer data. (**See Figure 1**) ![Data Readiness Slide Right + Fit For Purpose New 4 10](https://www.redpointglobal.com/wp-content/uploads/2025/04/Data-Readiness-slide-right-fit-for-purpose-NEW-4-10.jpg)**Figure 1**: *Data readiness makes enterprise customer data right – and fit for purpose* Together, these six pillars ensure enterprise customer data produces the desired business outcomes. Enterprises find that with data readiness in place, those use cases are easier to implement, easier to measure and yield better results. Having data that is right and made ready for business use sets the enterprise up for the best chance of success. The reason data readiness succeeds where so many data technology solutions fail is because it addresses the issue of data quality in a holistic way rather than as a minor nuisance that is viewed as the responsibility of point solutions. The core elements of data readiness consist of making sure that data is right – it is **complete**, **accurate** and **timely** – and that it is fit-for-purpose – it is **actionable**, **trusted** and **compliant**. ## **The “Right” Data = Complete, Accurate, Timely** Making data “right” encompasses all the processes that fall under the broader data quality umbrella – everything required to build a real-time unified customer profile. When data readiness is achieved, the enterprise is assured that all enterprise customer data meets a high standard for what it means to be **complete**, **accurate** and **timely**: - *Complete*: All relevant enterprise data is collected in the building of the customer profile – also known as a Golden Record. Data from every source across the enterprise, and data of every type – behavioral data, transaction data, permissions data, identity data; structured, unstructured, semi-structured, etc., – is unified to provide a broad and deep understanding of each customer. - *Accuracy*: Automated processes need to deal with the inherent messiness of customer data – multiple identities, misspellings, errors at capture points, changes to contact points – requiring cleansing, comprehensive matching and providing context, i.e., householding and other relationships such as business entities. - *Timeliness*: *D*ata readiness promises that a unified profile is continually updated as data is ingested. Continuous, real-time updates to model scores and attributes ensure that the profile reflects the absolute latest understanding of a customer as changes happen, ensuring that enterprises keep up with the customer’s cadence. ## **Data Fit for Purpose = Actionable, Trusted, Compliant** Making data fit for purpose incorporates the elements of data readiness that make sure enterprise customer data may be integrated appropriately into a process to deliver the desired business outcomes. Suitable data is **actionable**, it is **trusted**, and it is **compliant**. - *Actionable*: Actionable data implies that a unified profile is accessible across the enterprise when and where an application or a user needs it. Updating a profile as the customer understanding evolves is made worthwhile only when that profile is accessible in the time needed – up to and including in real time – and in a structured way that endpoint technology can easily access. - *Trusted*: Data observability allows businesses to understand the quality of the data they are working with before they use it. This includes viewing every step of the process between raw data coming in, and a unified profile being created. It also includes transparency to see the components of a profile, to see why a match was made, or understand the makeup of a household. Trusted data also gives users the ability to make desired changes to fit within the parameters of the intended use case, i.e., tunable matching. For example, some enterprises may be more comfortable with loose matching rules for marketing purposes, but tight matching for operational purposes. - *Compliant*: Data readiness enables compliance through enhanced visibility into the underlying data as well as putting controls in place for specific aspects of compliance that align with enterprise objectives governing security, control/access, and permissions/consent. Attaching the right identity to data is part of this, as is a choice of where the data resides and type of database (private cloud, on-prem, etc.) Data readiness further recognizes a consultative approach to compliance, i.e., are all the proper controls in place and the right data being used to advance the business goals and use cases. ## **Does *Your* Customer Data Meet This Standard?** The six pillars of enterprise data readiness are important facets that every company must ask about its data. An honest assessment may reveal some built-in limitations of existing customer data technology, because there are very few systems designed to address the full breadth and range of what it means to get data right – and ready for business use. > With data readiness in place, enterprise use cases are easier to implement, easier to measure and yield better results. Having data that is right and made ready for business use sets the enterprise up for the best chance of success. When data readiness is neglected – or assumed to be handled elsewhere – what invariably happens is that errors left undiscovered create issues downstream. It’s the dreaded customer data debt problem that will eventually have to be repaid. Consider, for example, a situation where a company stores incoming customer data in a data lake. Perhaps a CDP vendor provides an assurance that incoming data has been matched. Yet that data may include multiple records for one customer, one under one identifier and a separate one under a different identifier. A simple deterministic match in time is also untethered to a previous understanding of a customer – which then lessens the effectiveness of updated attributes. That’s customer data debt. Problems having to do with the lack of data quality, governance, security, a lack of data standardization and a lack of repeatable processes tend to accrue over time, which is what happens when solutions are not purpose-built to handle data readiness. Solutions that perhaps excel at reverse ETL, AI, analytics, etc., may tout how well they move enterprise customer data from Point A to Point B, but that niche solution neglects comprehensive matching that guarantees the accuracy of a unified profile. ## **Enterprise Customer Data Readiness with Redpoint** Having the right data to support use cases for AI, for CX, or another business imperative is too important to leave to an unknown of where or how data quality is taking place. With a solid data readiness foundation in place (your data is both *right* and *fit-for-purpose*) your organization can deliver more relevant and personalized customer experiences; enable trusted, AI-powered insights with confidence; and empower teams across the business with real-time access to clean, connected data. Your CDP won’t save you. Your data cloud won’t save you. Only ready data – data that is made complete, accurate, timely, actionable, trusted and compliant – can give you the agility and intelligence your business needs to win, driving revenue growth and operational efficiency. The Redpoint Data Readiness Hub is the only solution built from the ground up to ensure that your enterprise customer data is always ready to power your most important use cases. To learn more about the Redpoint approach to data readiness, click [here](https://www.redpointglobal.com/). **Blog categories:** Data Readiness --- ### [Transform Patient Engagement Through Strategic Alignment](https://www.redpointglobal.com/blog/transform-patient-engagement-through-strategic-alignment/) **Published:** December 4, 2025 **Author:** Stephanie LaFazia **Content:** The kickoff room is filled with smart, energetic people excited to start the project. I ask, “What are we trying to change in the next 90 days?” The question is met with brief silence …. and then a flurry of excited responses: “Launch a preference center.” “Create a unified patient view.” “Stand up new campaigns.” Sound familiar? ## **Start with an Idea of the Finish Line** Too often, once the deal is signed, the kickoff jumps straight to technical to-dos. The team dives into integrations and deliverables without a clear answer to the crucial question: What are we actually trying to change? Implementation teams bring horsepower. Business teams bring outcomes. But they’re not always aligned from the start. If we start building without a clear “why,” we burn cycles, miss opportunities, and complete work that doesn’t move the numbers that matter. So we pause. We align. And we anchor the work to a shared North Star, a measurable business outcome everyone understands and supports. ## **A Recent Example** We were onboarding a healthcare provider that wanted to move beyond a technical checklist and make patient engagement real. The intent was clear: truly understand patients, their history, context, and needs, so the organization would be known for personalized care at every touchpoint. But the first draft plan? A list of builds, feeds, campaigns, and connectors, without the “why” in the room. So we stopped. And aligned. ## **How We Aligned (Before Any Build)** #### **1. Put business and implementation in the same room.** We asked these teams to answer two simple questions: - When this works, what changes? - What will you say to your CFO in one sentence? The answer: “More patients engaged in preventive care, fewer missed appointments.” That became our North Star. #### **2. Translate the goal into a one-page cause-and-effect map.** Key points to map: - Business goal: Increase preventive engagement; reduce no-shows. - Leading signs: More preference captures; higher reminder response rates. - Levers we control: Unified profiles, consented channels, timely nudges across email, SMS, portal, and call center. #### **3. Prove the data can be trusted.** Yes, there were millions of golden records and dozens of feeds, but we validated identity resolution and reachability first. If the data isn’t reliable, everything else is a guess. #### **4. Define “done” as measurable.** “Preference center launched” does not count as a deliverable. “Preventive engagement up 18%, no-shows down 12%” is a concrete, well-defined metric. #### **5. Work from one page, reviewed weekly.** Data → Activation → Impact. If the bottom row isn’t moving, we adjust the middle row now, not next quarter. ## **What Happened Next** Same tools, different anchor. We built the preference center, activated mammogram reminders, and orchestrated omnichannel outreach. Patients received timely, personalized nudges aligned to their preferences. Engagement rose 18 percent, and no-shows fell 12 percent, with compliance staying tight. Less tech talk. More movement on the metric that mattered. ## **Closing Thoughts** When you start with alignment and real cross-team collaboration, everything changes. Instead of chasing technical milestones, you anchor to a measurable outcome. That’s how you turn momentum into meaningful movement, and why every implementation should begin with one simple question: *What are we trying to change?* **Blog categories:** Healthcare --- ### [Reflecting on Redpoint’s 2025 Healthcare Trends: Has Personalization Reshaped Patient Experience and What Else Came to Be?](https://www.redpointglobal.com/blog/reflecting-on-redpoints-2025-healthcare-trends-has-personalization-reshaped-patient-experience-and-what-else-came-to-be/) **Published:** December 11, 2025 **Author:** John Nash **Content:** Last year, we outlined our predictions for the year ahead with a focus on how healthcare’s evolution will put providers at critical crossroads in how they deliver care, engage patients, prioritize personalization and navigate shifting industry dynamics. A driving force, as we predicted, were trends such as value-based care (VBC), increased outpatient services, advances in AI technology to reshape patient expectations, the patient-provider relationship, and the rise in behavioral health and chronic care management needs, presenting new challenges – and opportunities – for health systems. Let’s revisit the five key trends that we thought would shape healthcare providers’ strategies in the coming year and what we think lies ahead for 2026. 1. **2025 Prediction:** *With an Increase in VBC Models, Providers will Need a More Holistic, Comprehensive View of Patients \[* **2026 Outlook:** An Ongoing Battle\] McKinsey data found that by 2027, there will be 90 million patients in VBC models, more than double the number in 2022. VBC transitions take time, and so does this prediction. The goal of VBC is to promote integrated care, delivering high-quality care services and optimizing costs using a patient-centered approach. It involves care teams working together to address all health needs, including mental health and social determinants, tailoring an individualized care plan and providing continuous monitoring, education, and support, which entails open lines of personalized and consistent communication. To accomplish this goal, providers still require a deeper understanding of a patient and a patient’s ongoing needs. For example, health systems can adopt a holistic approach to managing chronic diseases, focusing on patient outcomes and cost reduction. The Centers for Disease Control and Prevention [found that](https://www.cdc.gov/chronic-disease/about/index.html#:~:text=Chronic%20diseases%20such%20as%20heart,have%20at%20least%20one%20condition.) three in four American adults have at least one chronic condition, and over half have two or more chronic conditions. Meaning that this collaborative approach to care will need to continue. A multidisciplinary team, including physicians, dietitians, and specialists, would work together to create personalized care plans. These plans, for diabetes, for example, feature continuous glucose monitoring for real-time data, comprehensive patient education to empower self-management, and coordinated care to address comorbidities. Behavioral health support would also be provided to manage the psychological impact of diabetes. Additionally, providers can connect patients with community resources, such as local gyms and nutrition programs, and address social determinants of health (SDoH) by ensuring access to affordable medications and healthy food. This integrated approach not only improves diabetes management but also enhances overall quality of life, reduces emergency care needs, and lowers long-term healthcare costs. To provide such comprehensive care and support requires a deep patient understanding. As the trend toward value-based care grows, more health systems will prioritize the collection and interpretation of all patient data to deliver a personalized healthcare experience that is a hallmark of the value-based care movement. This is also where the industry’s focus on data readiness becomes foundational, helping health systems improve data quality and accessibility so that more of their patient data can actually be used to drive individualized care. 2. **2025 Prediction**: *A Shift to Outpatient Care will Heighten the Need for Dynamic Patient Journey Personalization. \[* **2026 Outlook**: *The Urgent Care Market Keeps Growing, Fueled by Systems!\]* More than 430 urgent care centers opened in new locations in the first half of 2025. Nearly 40% of those centers are affiliated with hospitals, [according to data](https://www.modernhealthcare.com/providers/mh-ardent-health-honor-health-upmc-urgent-care/#:~:text=Key%20Takeaways%20*%20Health%20systems%20see%20urgent,after%20the%20heights%20of%20the%20COVID%2D19%20pandemic.) from Urgent Care Consultants and shared by the organizations President Alan Ayers with *Modern Healthcare*. Urgent care is more than patient convenience; it is expansion opportunity for health systems to retain and attract patients. The key is data. The more a health system knows about the patient or prospect, the better care it can provide, both at the point of care and overt time, while creating opportunities to introduce new service lines that support acquisition goals. Comprehensive, contextual patient data can help providers understand if the patient needs a PCP, or if there are any underlying, and potentially, unaddressed health conditions. These insights create opportunities for deeper hyper-personalization, ensuring the system not only captures a patient’s immediate needs, but anticipates future ones, guiding them intentionally into longitudinal care. Diversified care access is allowing health systems to cast a wider net for patients while engagement strategies deepen understanding of those they serve. These efforts not only ensure continuity of care but also safeguard financial sustainability and VBC alignment in a rapidly evolving healthcare landscape. Unified and actionable medical and behavioral data enables urgent care centers and their affiliated systems to execute targeted marketing campaigns and act as critical entry points for attracting and converting new patients. This integrated approach delivers personalized, closed-loop care journeys, differentiating organizations from competitors and fostering long-term patient loyalty. 3. **2025 Prediction**: *Behavioral Health and Chronic Care Management Will Change the Patient-Provider Dynamic \[* **2026 Outlook**: *Care Alignment for Mental and Physical Health is Key\]* We previously highlighted that roughly 25 percent of the U.S. population is expected to utilize behavioral health services by 2027, while about 90 percent of the nation’s $4.5 trillion in annual health care expenditures are for people with chronic and mental health conditions. Provider alignment is critical for managing both behavioral health and chronic conditions, especially if comorbidities are involved. To deliver truly effective care, providers need to understand the patient beyond their medical record and gain a holistic view of the patient’s lived experience. This includes leveraging SDoH and demographic data, as well understanding prior care relationships, to inform where the care plan should go next. With these insights, care teams can identify preferred outreach channels, motivators, barriers, and optimal engagement times. That means behavioral health and chronic care support becomes tailored, not generic, building trust and making patients feel seen and understood. Personalized communication opens the door to stronger engagement and follow-up, enabling providers to treat the patient as a whole. This deeper level of personalization will become even more important as federal policies, including potential Medicaid eligibility shifts and funding changes, continue to reshape who has access to what services and when. 4. **2025 Prediction**: *AI Investment in Healthcare Will Continue to Experience Significant Growth \[* **2026 Outlook**: *Spot on and Still Growing!\]* The healthcare market is quickly advancing in generative AI (GenAI), agentic AI, and real-time interactions. Approximately 65 percent of U.S. hospitals report using AI-assisted predictive models, according to a study published in [Health Affairs](https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2024.00842), which means future-proofing data is no longer optional; it’s strategic. Health system leaders require data that is complete, accurate, timely, actionable, trusted, and compliant to meet the new year’s technological demands. As we previously highlighted, patient communication tools now serve as a digital front door, powered by conversational interfaces and generative AI, fundamentally reshaping the patient-provider relationship. For the evolving AI use cases, providers need to first establish a unified patient profile that includes all relevant patient data. This means detailed and up-to-date medical history, clinical and claims data, SDoH, preferences and behaviors. A real-time patient profile with clean, high-quality data is the foundation for effective AI training. When AI is fed unified data, it will produce the most relevant patient engagement and experience opportunities, whether a personalized care plan, chatbot interactions, or answering patient questions through an LLM. This reinforces why data readiness matters: 97% of healthcare data is currently wasted, and 93% of healthcare leaders say high-quality data is essential. A strong focus on data readiness resolves quality issues and makes data accessible across the enterprise, ensuring AI initiatives deliver real value. 5. **2025 Prediction**: *Health Systems Will Continue to Prioritize Data Security \[* **2026 Outlook**: *Still (Always!) a Priority Item\]* As of Oct. 3, 2025, over 33 million Americans we affected from the [364 hacking incidents that have been reported to the U.S. Department of Health and Human Services Office for Civil Rights](https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf). This means patient records are likely stolen, in part or in full, by hackers. To safeguard against data breaches, like last year, 2026 will see more healthcare systems be mindful of how they collect and use patient data, for marketing and other business purposes. Keeping patient data – particularly PHI – behind the organization’s own firewall is essential. Because of this, healthcare systems will keep their technology infrastructure on-premises or use a private cloud where patient data – whether in an EHR or a marketing platform – will remain in place using a modern data cloud. Data quality and personalization continue to be the cornerstone of success for health systems navigating the healthcare complexities of 2025. Whether adapting to VBC models, leveraging AI for patient engagement, or prioritizing data security, the ability to deliver tailored, patient-centric experiences will define the leaders of tomorrow. By embracing these trends and focusing on a holistic understanding of patient needs, providers build trust with the patients they serve. Ultimately, organizations that prioritize data readiness, ensuring clean, complete, and accessible data, will be best positioned to scale hyper-personalized experiences, drive better outcomes, and remain competitive amid regulatory and funding shifts such as ongoing Medicaid redeterminations and enrollment fluctuations. Those that invest in data readiness and personalization now won’t just adapt to change, they’ll define the future of healthcare. **Blog categories:** Healthcare --- ### [A Double-Take: Digital Twins and Customer Experience (CX)](https://www.redpointglobal.com/blog/a-double-take-digital-twins-and-customer-experience-cx/) **Published:** December 28, 2022 **Author:** Steve Zisk **Content:** Your primary care provider calls you to let you know to stop taking a recently prescribed medication because it causes a reaction when paired with another medication you’re taking. A hotel provides you with a virtual tour on a mobile app to let you choose which room to reserve. When you head to the water park, the app guides you to a vacant chaise lounge that meets your preferences for shade and proximity to a lifeguard station. At a wholesale grocery store, an associate finds an item you’re looking for by scanning the entire warehouse – all while standing by your side. Once a niche technology in engineering and manufacturing, digital twins are taking industries by storm, and the real-world examples above from healthcare, hospitality and retail demonstrate how digital twins are increasingly being used to enhance customer experience. ## **A Mirror Image: Digital Twins and IoT** Gartner [defines a digital twin](https://www.gartner.com/en/information-technology/glossary/digital-twin#:~:text=A%20digital%20twin%20is%20a,organization%2C%20person%20or%20other%20abstraction) as a digital representation of a real-world entity or system, with implementation of the entity or system as an “encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction.” As a concept, digital twinning [originated](https://venturebeat.com/technology/tech-pioneer-explains-the-evolution-of-digital-twins/) as a method to test and monitor a physical object without having close proximity to it – think rocket ships. In its infancy, use cases were mostly tied to building and testing engines and other complex physical objects. Soon enough, digital twins expanded to entire factory floors, supply chains, city grids and other processes and systems, used mainly to identity existing or future problems with the non-digital object or system, such as predicting engine failure. There is often an Internet of Things (IoT) component, with sensors connecting the physical and digital objects and providing a virtual representation. A classic example might be multiple pieces of equipment on an assembly line, where sensors on the machinery connect to a visual model that identifies potential fail points. IoT devices also represent several familiar customer experience use cases, such as a smart thermostat or a connected appliance that adjust to a customer’s preferences. Using sensors to create a virtual model of real-time inventory might enhance a personalized customer experience by helping a customer fill an online shopping cart based on availability at their favorite location. ## **Predictive Digital Twins** A predictive digital twin takes the concept a step further and allows digital twins to not just measure but also predict real-world behaviors. Once a digital representation of a real-world entity or system has collected enough sensor data, it might then be programmed to predict how the measured system will react or respond to external forces or situations. A healthcare provider advising you on medication adherence is not, for example, referencing an actual digital model of your body but a statistical model based on past experience. Similarly, a virtual downtown traffic grid, fed enough data, might predict high volume patterns which are then used by your hotel the night before your check-in to notify you of the shortest route from the airport at the anticipated check-in time. Another digital twinning use case for enhancing customer experience combines a digital model with an interactive component. One example is a virtual dressing room, where an augmented reality (AR) app allows a customer to virtually try on items before purchase. Similarly, a customer might “place” a digital representation of a piece of furniture in their living room, seeing for example whether a couch will fit in a certain corner, or how the fabric matches the paint color. Digital models can even encompass machine learning models. Using sensors in a store, a brand might collect traffic patterns for an extended period of time. Once data is gathered and the model trained, a machine learning model can run simulations with a goal of optimizing floor layout on a store-by-store basis. Similarly, a model might use a digital representation of an audience to analyze or predict a propensity to purchase, product affinity, likelihood to churn or another business metric. By feeding the results of how an actual audience behaves into the digital model, a brand closes the testing loop and improves the accuracy of the predictive digital twin. Conceptually, a digital model of a customer might represent a composite of real-world behavior with what’s happening in the virtual model, providing a brand with an even more detailed roadmap or insight into customer journeys. Still a relatively [new concept](https://www.challenge.org/insights/digital-twin-history/#:~:text=While%20its%20commonly%20thought%20to,match%20the%20systems%20in%20space.), first gaining widespread recognition about 20 years ago, the use of digital twins is quickly making inroads in customer experience across all verticals. Gartner [recently predicted](https://www.gartner.com/en/insights/gartner-business-quarterly/q2-2022/digital-twin-of-a-customer) that digital twins of customers have the potential to transform how enterprises deliver experiences by simulating and anticipating customer behavior, much like how engineers first used them for predictive maintenance. Redpoint is a firm believer that delivering a real-time, [omnichannel personalized customer experience](https://www.redpointglobal.com/blog/spread-holiday-cheer-with-a-frictionless-omnichannel-customer-experience/) requires matching the cadence of a customer as a customer journey unfolds. At its core, the use of digital twins in customer experience is all about staying close to a customer without encroaching on their experience – being relevant but not creepy. A digital representation of a customer that models close proximity adds another tool to a marketer’s arsenal, helping a brand to deliver memorable moments that resonate with customers. **Blog categories:** Journey Orchestration, Segmentation & Activation --- ### [Next-Best Actions: The Secret to Always Being One Step Ahead in Customer Engagement](https://www.redpointglobal.com/blog/next-best-actions-the-secret-to-always-being-one-step-ahead-in-customer-engagement/) **Published:** November 19, 2025 **Author:** Vin DelGuercio **Content:** A next-best action is a personalized recommendation or offer that guides a customer toward their next logical step of engagement with a brand. Done well, it anticipates the information, assistance or inspiration that a customer needs or expects in that moment, advancing the customer journey toward a mutually beneficial outcome. Instead of pushing messages based on the brand’s priorities, a next-best action focuses on the customer’s needs, wants, and desires. It meets each customer in the moment of interest with the right message, on the right channel, at the right time, helping to guide the customer toward an action such as downloading an app, scheduling an appointment, filling a shopping cart, or renewing a subscription. By adapting to each customer’s context, a next-best action breaks the mold of static, one-size-fits-all campaigns that follow a rigid cadence. Every customer moves at their own pace, with their own preferences and behaviors, and next-best actions ensure that engagements are relevant, timely, and welcome – not random and repetitive. ## **What Makes a Next-Best Action Different** A next-best action is generated based on a complete, contextual understanding of a customer, up to and including a customer’s relationships such as a member of a household or business. Content being delivered may differ based on any number of conditions and variables, such as a customer’s lifetime value, average monthly spend, purchase frequency, etc. Content may also differ based on time, channel, customer preferences or real-time behaviors, such as a browsing session or a call center interaction. Next-best actions are not synonymous with triggered actions, which refer more to the cadence of messages. A triggered action might contain next-best action content, or the triggering criteria itself may be driven by a next-best action rule, such as a shopping cart abandonment triggering a message to a particular customer at a specific time – with the message personalized for an individual customer based on the real-time journey. ## **The Next-Best Action Balancing Act** Because a next-best action is delivered in the context of an individual customer journey, the customer’s preferences for frequency of communications or channel will ideally be balanced against the message being delivered. If a customer indicates they only want to communicate via email, for instance, it might not make sense to let them know about a limited time flash sale. It is also possible that a next-best action is no action at all. For example, a customer may be placed in an audience segment that is scheduled to receive an email offer for a 10 percent discount on a product. A customer who buys the product is then removed from the segment and the email is pulled (up until the moment it’s opened). If the content is available, perhaps the customer then moves into a segment for which a next-best action is a thank you letter. ## **Optimizing Next-Best Actions: Data, Channels, and AI** Important considerations for companies building out next-best action engagements include how much content and which channels are available, as well as the type of behavior a brand is trying to encourage. It is also vital to monitor the performance of next-best actions over time, using the results as an integral part of a feedback loop and refining the strategy moving forward. Results will ideally be fed back into a unified profile, which will of course then provide a deeper customer understanding, yielding more refined personalization and next-best actions that continue to be in the precise cadence of the individual customer journey. Another consideration is how and when to use AI. Existing use cases include AI-powered segmentation, such as predictive machine learning models dynamically moving customers in and out of segments. Or using GenAI to expand and refine content. AI can help create a more data-driven, automated approach while also making it possible for companies to consider even more variables when designing a next-best action strategy. ## **How Redpoint Powers the Next-Best Action** Next-best actions can be thought of as having three layers: profile, context, and interactions. Redpoint is heavily involved in the first two layers (profile and context), with a real-time decisioning engine that takes brands to the edge of the interaction layer, where a decision will be passed off to an ESP or another communication technology. Redpoint builds a unified profile that provides the needed context and situational awareness of an individual customer that is necessary to execute a next-best action that is in the cadence of the customer journey. A customer may fit into one or several campaign flows, and Redpoint’s real-time dynamic segmentation capabilities consider an endless number of variables to determine which tactical segment a customer belongs to at a specific moment. The key to the Redpoint next-best action is that it can support an unlimited number of personalized messages, content or communications, which it will evaluate at each interaction inflection point in the context of an individual customer and channel. By giving brands a complete and current view of every customer, Redpoint ensures each next-best action feels relevant and timely – improving engagement rates, reducing churn, and deepening loyalty. Redpoint also factors in each organization’s operational and strategic considerations. For instance, one company’s messages might reflect a focus on brand building, while another company builds a next-best action strategy for a nurture campaign, or a win-back campaign. To see how leading brands are using next-best actions to transform CX in real time, watch our webinar where my colleague Beth Scagnoli and I discuss the important role next-best actions can play in a personalized customer experience, and how Redpoint helps brands optimize their approach. For access to the webinar, click [here](https://event.on24.com/wcc/r/5064636/88141C68F56F3D795A1F399F8B55CD48?partnerref=rpg-blog). --- ### [The Real-Time Edge: How Data Readiness Transforms Call Center CX](https://www.redpointglobal.com/blog/the-real-time-edge-how-data-readiness-transforms-call-center-cx/) **Published:** November 13, 2025 **Author:** Steve Zisk **Content:** One school of thought in call center interactions is that if a customer contacts the call center, the business is already losing. The implication being that most people call only when something is amiss – to resolve a billing or shipping problem, or to find an answer to a question about the website, a bank statement, or a patient portal. Avoiding these types of problems before they occur – and thus reducing call center volume – is certainly a worthwhile goal and one reason why brands seek to build a complete, contextual understanding of a customer. With a real-time, accurate, unified customer profile, brands deliver proactive, relevant, and personalized experiences that ideally optimize a customer journey, increase customer satisfaction and ultimately lessen the need to contact the call center. ## **Increase CSAT Scores with Data Readiness** But there will always be calls, and a more optimistic viewpoint is that call center interactions are a golden opportunity for a brand to create a positive experience – regardless of the reason for the call. Every call is a blank slate, and a chance to increase the overall customer satisfaction score (CSAT), a key metric in assessing call center effectiveness. An important capability for helping call center agents raise CSAT scores through relevant, personalized experiences is data readiness. Data readiness first creates and then activates a real time unified profile across the enterprise, which includes making it accessible to the call center at the moment of engagement. Data readiness provides agents with a complete, accurate, and timely unified profile that is both right and fit for purpose. It can help increase CSAT scores by letting agents know everything there is to know about a customer. With real time insight into a customer journey – details of the most recent browsing session, the status of every order, preferences, transactions, appointments, updated loyalty points, lifetime value, etc. – agents provide a relevant experience that reflects the real time, contextual understanding. One recent study shows that a personalized customer experience (CX) can lift CSAT scores by up to [15 percent](https://www.opensend.com/post/customer-satisfaction-score-ecommerce#:~:text=Personalization%20drives%20satisfaction%20upward.,break%20an%20eCommerce%20store's%20success.) vs. generic marketing. ## **The Real Time Difference** For instance, a customer contacts their healthcare insurer with questions about a co-pay. If the number is recognized and connected to a unified profile, the moment the call is connected the agent has the most recent statement and plan information and is able to answer the customer’s questions without having to ask for basic information. If the customer is overdue for a preventive screening, an agent might offer scheduling assistance. In a retail setting, an agent instantly sees the status of an open order, as well as the customer’s transaction history, average spend, and lifetime value. After providing the customer with an updated estimated delivery time, the agent makes amends for the late delivery by offering a discount on a complementary item – an item the customer viewed on the website just minutes before the call. ## **Data Readiness is More than Data Quality** In each example, the importance of real time in a call center interaction is clear. First, the ability to recognize and match the number to an existing profile in real time puts the agent on an equal footing with the customer. The agent has basic information – name, account information, etc. – that saves having to ask the rote questions that often end up with a frustrated customer on hold. Second, real time access to the updated profile – which itself is in real time – is what allows an agent to proactively resolve issues (close a preventive care gap, make a relevant offer). The [importance of a real time element](https://www.redpointglobal.com/blog/data-readiness-real-time-relevance-delivering-context-in-every-moment/) in call center interactions illustrates why data readiness is so much more than data quality. While data quality is an essential component of data readiness, data quality alone is not enough to interact with a customer in the cadence of a customer journey. A unified profile that is accurate, for instance, does not mean it’s complete. The exclusion of a recent browsing session may even be the difference between satisfying a customer with a hyper-relevant experience, or creating CX friction by perhaps offering a discount on a product the customer just purchased online – at full price. Data quality ensures accuracy. Data readiness helps ensure usability in the moment that matters. ## **Data Readiness and the AI Experience** As brands transition to an increasing use of chatbots vs. live agents for routine inquiries, routing purposes, or to even schedule a return call from a live agent, data readiness takes on an even larger role in ensuring a positive CX. Just like a live agent, a chatbot needs access to a complete, accurate unified profile, particularly for anything beyond the most routine tasks. AI is only as good as the data that fuels it, and for a chatbot to deliver a personalized CX it should have access to the same information as a live agent. In a Dynata survey, [76 percent of consumers](https://www.redpointglobal.com/press-releases/73-of-consumers-believe-ai-can-have-a-positive-impact-on-their-customer-experience/) said they would disassociate with a brand if they received a disjoined AI experience. Whether handled by an agent or AI, every call center interaction is a chance to deepen customer trust. Data readiness makes sure you’re prepared to turn those moments into lasting loyalty. To see how the Redpoint Data Readiness Hub can help you increase CSAT scores with real time engagements, click [here](https://www.redpointglobal.com/data-readiness-hub/) **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [Take Your Marketing Cloud to the Next Level with Enterprise Data Readiness](https://www.redpointglobal.com/blog/take-your-marketing-cloud-to-the-next-level-with-enterprise-data-readiness-2/) **Published:** September 10, 2025 **Author:** Steve Zisk **Content:** Data readiness should be an integral part of any marketing cloud. Having data that is clean, accurate, timely and fit-for-purpose for any marketing campaign is indispensable for interacting with the right customer at the right time and on the right channel. Cleansed, fit-for-purpose data is the lifeblood of a successful marketing cloud, leading to better decision-making, more relevant and impactful customer experiences, improved efficiency and higher revenue. If you’re using a [marketing cloud](https://www.redpointglobal.com/resources/redpoint-data-readiness-for-adobe/), you might assume it adequately covers your data quality needs. But the truth is, many top marketing clouds overlook data readiness as secondary to the tasks they excel at – executing and tracking campaigns and taking marketing automation to the next level. They offer basic data quality processes, but usually only to prepare data within the marketing cloud for a campaign. This leads to two problems: inadequate data and inaccurate data. ###### Inadequate Data In a closed ecosystem, data readiness does not extend outside of the marketing cloud. This creates problems when it’s necessary to combine the marketing cloud customer profile with any data that exists outside of the marketing cloud, such as for downstream AI/ML pipelines or any customer interaction that exists outside of the marketing cloud ecosystem. The problem, of course, is that unless the customer profile that exists within the marketing cloud is combined with all relevant data sources, the profile will be incomplete. ###### Inaccurate Data Even if all relevant data is used to create the unified profile, the basic data quality processes offered by most marketing clouds are wanting. A simple deterministic match, an inability to tune identity resolution levels for different use cases, or a lack of persistent key management fail to produce a contextual understanding of a customer ## **Enterprise Data Readiness** Enterprise data readiness is the foundation for a consistent, omnichannel CX and for a unified customer profile that provides all business users – not just marketing cloud users – with the same view of the customer, household or entity the business needs to understand at a detailed level. A data readiness hub and a marketing cloud have different approaches to making data ready and fit-for-purpose. The latter may offer basic data quality functions such as normalization, deduplication, and hygiene, but these functions are typically designed for activation-first purposes, meaning the data in the marketing cloud itself is only made ready for the purposes of the intended campaign. By contrast, [a data readiness hub](https://www.redpointglobal.com/) provides enterprise-wide, channel-agnostic data quality, where it is applied uniformly across all sources and destinations. This will of course include the marketing cloud, but also a call center, website, POS, mobile app, CRM, etc. [Identity resolution](https://www.redpointglobal.com/identity-resolution/) activities provide a stark contrast. A marketing cloud will typically use simple matching techniques tied to email or perhaps a device ID that is sufficient for the intended campaign, but where the resulting record is not matched to anything outside the system. Contrast this with a data readiness hub that brings together all data for matching, utilizes advanced rules (with a combination of deterministic and probabilistic matching), and performs [persistent key management](https://www.redpointglobal.com/blog/what-is-persistent-key-management/). In this approach, the resulting Golden Record provides an unassailable view of a customer across channels, spanning the complete customer lifecycle. From a practical standpoint, enterprise data readiness vs. data quality within a marketing cloud is the difference between a marketing campaign precisely aligning with what customers experience in other channels versus simply stitching together current data, avoiding CX missteps such as a call center agent seeing a customer vastly different than a marketing team, leading to an uneven experience. ## **Data Readiness = Data Governance** Data governance is another area where a data readiness hub shines in comparison with a marketing cloud. In a marketing cloud, there is no control or transparency over data hygiene, deduplications, or other processes to prepare data for a marketing campaign. And because what is known about a customer is not tied to a comprehensive unified profile, a marketing cloud may satisfy regulatory requirements (GDPR, CCPA, etc.) for a specific campaign, but will not link customer-specific conditions and choices to a comprehensive preference center. A data readiness hub instead offers full auditability, lineage tracking, and policy-driven compliance – vital for healthcare, financial services, and other regulated industries. ## **Data Readiness and AI-Ready Data** Finally, when it comes to having [AI-ready data](https://www.redpointglobal.com/blog/all-successful-ai-projects-start-with-ai-ready-data/), only a data readiness hub is capable of providing high-quality, unified data before it enters a downstream system or AI pipeline. For instance, predictive models (churn, lifetime value, etc.) need[ complete, accurate, timely, and trusted data across all channels](https://www.redpointglobal.com/data-the-defining-difference/). Without such upstream readiness, models are built and predictions are calculated on fragmented and inconsistent records – which lead to poor predictions and wasted AI investments. This is really true for any use case; a churn model and a decisioning engine in a marketing cloud both need the same trusted customer profile. Even if a marketing campaign appears to function well, a lack of AI-ready data may cause AI models to underperform due to fragmented or inaccurate data. ## **Data Readiness Once, Use Everywhere** A data readiness hub ensures continuous data quality once, upstream, where a unified profile is used across the enterprise vs. a marketing cloud where data quality (within the marketing cloud) benefits only marketing use cases. Using the Redpoint Data Readiness Hub alongside your marketing cloud ensures *all* customer data is clean, trusted, and unified before it reaches your marketing clouds, while also preparing the organization for AI and advanced analytics initiatives. It extends data quality beyond your marketing platform to reduce downstream work, improve campaign precision and ensure the entire enterprise can trust and act on customer data. For more on how the Redpoint Data Readiness Hub can help you transform your enterprise data strategy and optimize use of your marketing cloud, click [here](https://www.redpointglobal.com/data-readiness-hub/). **Capability****Redpoint Data Readiness Hub****Generic Marketing Cloud****Scope of Data Quality**Enterprise-wide, cross-channel, channel-agnosticLimited to data within the marketing cloud**Identity Resolution**Advanced, persistent, multi-source identity graphs; persistent key managementBasic de-duplication, often limited to email/device ID within platform**Transparency & Governance**Full visibility into rules, lineage, and audit trailsLimited transparency, often black-box processes**AI & Analytics Readiness**Prepares clean, unified data for AI/ML models across the businessData cleanup benefits marketing campaigns only**Data Activation Flexibility**Enables trusted data across all channels and systems, including non-marketing use casesConfined to activation within the marketing platform **Blog categories:** Data Quality, Data Readiness **Blog tags:** Data quality, Data readiness --- ### [3 Obstacles to Maximizing the Value of Customer Data](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/) **Published:** January 25, 2018 **Author:** John Nash **Content:** The true value of customer data lies in its ability to provide context. This can only occur when that data is connected across silos. Unfortunately, 85 percent of marketers are unable to unify online and offline customer data, according to a study by [Conversant](https://info.conversantmedia.eu/cmo-conversant-report). Further, only 15 percent of marketers in the study are confident that they know their customers well based on unified online and offline behavioral and purchase data. This lack of customer knowledge blocks marketers from reaching their full potential in terms of personalizing communications and driving revenue. Additionally, having extensive data locked inside functional and system-specific silos hampers productivity. Only 51 percent of marketers polled by [Forbes Insights](https://images.forbes.com/forbesinsights/StudyPDFs/RocketFuel_BigData_REPORT.pdf) say they frequently make sufficient use of data in their marketing decisions. Of those, 59 percent say their marketing initiatives exceed their goals. To draw the full value that customer data can provide, marketers must resolve three central challenges: data access, data analytics, and data activation. Doing so will create a connected data value chain – a direct path from connected customer data to increases in marketing performance and revenue. ## **Challenge #1 – Data Access** Businesses have an overabundance of data stored in myriad siloed systems. To unlock the value of that data, marketers need more than just access to it in its existing silos. They need connected data that will provide the context necessary to optimize customer outreach, and they need access to that connected data in real time so they can meet, and even surpass, customers’ expectations. Currently, just 24 percent of marketers use real-time customer activity to tailor their digital marketing, according to the Conversant study. Most companies need their many data siloes to support specific functional areas. So, creating one massive store for [customer](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/) data is rarely the ideal solution. Instead, data-savvy companies use technologies that bridge those siloes to create a holistic view of the customer. These technologies pull data from relevant sources based on the specific information or insight marketers need, when they need it. As helpful as those tools may potentially be, they’re only useful if marketers can access them directly for connected, real-time insights. It’s essential to find tools built not just for data scientists and data analysts, but also for business users. Sixty-three (63) percent of marketers polled in the Forbes Insights study say that access to connected data provides them with greater insight into the customer experience across channels, which, in turn, allows them to craft strategies that convert this understanding into positive results. An equally important aspect of data access is data quality. Marketers should have access to data that is complete, consistent, and current. This starts with creating systems and processes that block bad data from getting into their systems in the first place. It also includes repairing existing data by cleansing, appending, and deduping it. And, it necessitates data governance. ## **Challenge #2 – Data Analytics** Once marketers have access to connected, real-time data, they need the know-how and tools to conduct the right analyses. In some cases, these analyses should be predictive (e.g., what are customers likely to do). According to [Forrester Consulting](https://app.compendium.com/uploads/user/4f91a3ee-6ace-42a7-be93-3b21f3a1635f/7c7092b4-1fc8-48d9-8bd4-bcf49cfc6c97/File/b09103706cbc150358adc5fa67a3dd07/1394484576218.pdf), 54 percent of marketers polled have adopted predictive and real-time analytics to optimize next best actions. In other cases, the analyses are descriptive (e.g., measuring campaign performance). Currently, according to the Conversant study, only 18 percent of marketers capture “actionable measurement” to support their digital marketing goals; just 35 percent measure online *and* offline sales and 34 percent measure the impact of each channel independently, based on click data. Yes, there are times when marketers should hand the reins to a data scientist or analyst. But there are also times when marketers can get the most value from data by having the ability to analyze the relevant data themselves – especially when they need to get and act on insight in real time. Getting the most from data analytics means that marketers can use their connected data to resolve identities, and view items such as conditions, variables, and events that give context beyond basic customer profiles. It’s this context that allows marketers to improve the results of their predictive analyses, support their prescriptive analyses, and optimize their marketing performance. Marketers need access to tools that provide them with access to connected, real-time data if they are to conduct the types of contextual analyses that will enable them to squeeze the maximum value from their customer data. But, according to the Conversant study, 75 percent of marketers don’t have the technology, or don’t use it effectively enough, to deliver one-to-one digital marketing. ## **Challenge #3 – Data Activation** Consumers want information in an instant, but that doesn’t mean their next action is a purchase. With the buyer’s journey looking more like the path of a bumble bee than that of a jet liner, marketers need to be able to respond to customers’ interests and behaviors in the moment. Only access to connected, real-time data can enable this in a way that will allow marketers to deliver the optimal, contextually relevant message at the best time to individual consumers through their preferred interaction channel. Unfortunately, nearly half of marketers (46 percent) struggle to understand customers’ interactions across channels, according to Forrester Consulting. Yet, 44 percent say that responding to customer interactions with speed and relevance is critical to the future success of their marketing programs. Marketers aiming to capitalize on the consumer’s moment of need – and, in the process, get the most value from their customer data – require processes and technologies that can support a real-time approach to marketing. Marketers need to rethink their processes, including adding more automation, so they are structured to respond to customers’ actions in real time. And, they need technologies that not only provide a holistic view of customers, but also enable business users to conduct the analyses they need to act in real time. However, only 49 percent of marketers have adopted technologies that “activate customers with in-line decisioning at digital touchpoints,” the Forrester Consulting study finds. ## **The Connected Data Value Chain** Marketers who clear these three hurdles – data access, analytics, and activation – are the ones who can create a connected data value chain. Business-user-friendly technology, clean data, and agile processes are essential links in that value chain. Without them, marketers lack access to the contextual data that enables them to meet customers at their moment of need. Being where customers are with contextually relevant messaging is the optimal way to maximize the value of customer data. Yet, only 36 percent of marketers say that generating customer insights through analytics and data mining is critical to the future success of their marketing campaigns, according to the Forrester Consulting study. This presents an opportunity for data-savvy marketers who maximize the value of their connected customer data to outperform their competitors. Now is the time to get connected. **Blog categories:** Customer Data Platform, Data Management, Real-Time Personalization --- ### [Banking on Loyalty: Turn Data Readiness into Customer Retention](https://www.redpointglobal.com/blog/banking-on-loyalty-turn-data-readiness-into-customer-retention/) **Published:** September 24, 2025 **Author:** Renee Graff **Content:** Retail banks and credit unions are facing a loyalty crisis. According to Gartner, 20 percent of retail banking customers moved money from or switched primary providers in 2024. JD Power reports that [13 percent of U.S. retail bank customers](https://www.jdpower.com/business/press-releases/2024-us-retail-banking-satisfaction-study) are likely to switch institutions within a year, with an estimated average customer attrition rate at about 15 percent. Assigning a modest [$2,500 customer lifetime value](https://www.digitalgrowth.com/podcast/articles/banks-credit-unions-fintech-customer-lifetime-value-website-secret-shopping), losing 1,000 customers translates to *$2.5 million* in lost revenue. Improving bank customer loyalty isn’t just a desirable outcome; it’s critical for the business. There are a number of reasons a customer may choose to leave, but it’s clear that there’s a growing disconnect between what customers expect and what banks deliver. One common reason cited for switching is a poor customer experience (CX). In a [Financial Brand survey](https://thefinancialbrand.com/news/customer-experience-banking/how-to-integrate-digital-delivery-and-human-connections-to-boost-retention-182524), 73 percent of customers expect their bank to understand their unique needs and expectations – up from 66 percent in 2020. Across industries, customers are expecting the same relevant, personalized interactions they’re familiar with from ecommerce. Accomplishing this requires a lot of knowledge about your customers, which all boils down to data. But banks are very often a step behind, struggling to personalize experiences, anticipate needs – and retain customers – not because they lack customer data, but because the data they do have is often fragmented, outdated, or lacking context. ## **Transactional and Basic Account Data Aren’t Enough** Banks and credit unions are both data rich and often data limited. Basic customer account and transactional data paint only a partial picture of a customer, providing little in the way of the context necessary to understand a customer in order to provide a personalized, engaging experience. Transactional data shows **what** customers do, **but not why** they do it. Understanding the why requires contextual data that offers insight into their financial goals, life situation, risk tolerance, their household makeup, retirement plans, etc. Trying to deliver a personalized experience using only transactional data risks generic messages, irrelevant product recommendations and missed opportunities to engage customers, and none of those things will turn them into loyal customers. The key is to turn the raw data that every bank possesses into actionable data. While difficult, the good news is that these data issues are fixable. ## **Data Readiness: A Strategic Enabler for Loyalty** The way forward is with [data readiness](https://www.redpointglobal.com/data-readiness-hub/). Data readiness is the ability to deliver clean, contextual, continuously updated customer data across teams and systems; data that’s ready for use cases that include onboarding, cross-selling products and services, and even improving the customer support experience. It’s not just a technical capability, it’s a strategic enabler for loyalty. Data readiness makes data [*right* and *fit-for-purpose*](https://www.redpointglobal.com/data-the-defining-difference/) for any CX or AI use case. It addresses loyalty and retention in a number of ways, such as: 1. **Building** [**unified customer profiles**](https://www.redpointglobal.com/profile-unification/) that reflect a customer – or household’s – real-time behaviors, preferences, and life stages. Is a customer about to send kids to college, buy a new home, or about ready to retire? A persistently updated profile that collects customer data in real time from multiple sources, with a full list of attributes and data aggregates, provides the needed context that aligns marketing and CX teams with a real time, dynamic customer journey. *Recommending relevant services and products becomes strategic rather than guesswork.* 2. **Providing banks with the ability to launch personalized, cross-channel campaigns** with confidence. Start by improving the onboarding experience across in-person, digital and even print touchpoints to increase product and service utilization for new account holders. Then stay with them via marketing campaigns that adapt with the customer journey. Dynamic, [no-code segmentation](https://www.redpointglobal.com/segmentation-activation/) and [real-time decisioning](https://www.redpointglobal.com/real-time-interactions/) give banks the ability to build an audience segment once, process interactions rapidly, and activate everywhere – always staying in perfect cadence with the customer. 3. **Helping guide customers to relevant resources** based on updated behaviors, preferences, and life stages. A rich, contextual profile is the key for moving away from transaction-based marketing and instead delivering next-best actions in which decisions are based on a complete understanding of a customer. Kids almost off to college? Here’s information on how to optimize a 529 plan investment strategy, plus some first-time budgeting resources and student account options. 4. **Providing banks with predictive and prescriptive analytic tools** to make decisions aligned with the propensity of customers to take certain actions. By identifying disengaged customers early, or those with a high likelihood to churn, banks can deploy proactive retention strategies – increasing customer satisfaction and bringing them back into the fold. ## **The Cost of Incomplete Customer Data** Personalization is no longer “optional” for today’s customer, and inserting their first names into an email doesn’t count. A [PYMTS loyalty study](https://www.pymnts.com/tracker_posts/locking-in-loyalty-securing-top-of-wallet-status-in-a-changing-economy/) declared programs a “strategic necessity,” revealing that 60 percent of customers say they want rewards customized to their relationship with their bank – yet less than half (45 percent) are satisfied with their current options. If that’s not convincing enough, the Financial Brand study reported that a staggering 62 percent of bank customers said they would switch providers if their current bank made them feel like a number rather than a person. It’s not just that there’s a deepening expectation for customized rewards and other personalized experiences. It’s that customers have made their intentions clear – they will go elsewhere if those expectations are not met. Customers that don’t feel understood or valued are at a much higher risk of churn – which is bad for the bottom line. It’s not just churn, though. Unengaged customers lead to underutilized services and products, missed sales, lower customer satisfaction scores, and a competitive disadvantage overall. Lacking the contextual customer view can not only lead to these problems, but can also make it more challenging for teams to provide effective remediation. ## **Change Your Approach with Customer Data Readiness** Data readiness unlocks loyalty by providing banks with customer data that is always accurate, contextual, and fit for purpose. With a real-time understanding of a customer or household, banks move from older, more traditional marketing campaigns to proactive, customer-centric engagement that aligns with each customer’s journey – at scale. Continual data cleansing and contextual enrichment ensure every interaction is relevant. When unified profiles are made accessible across the enterprise – unbound by channel or business unit and integrated into relevant systems – marketing and CX teams can deliver perfectly timed interactions: the right message, in the right channel, at the right moment. Customers are guided along an appropriate path, satisfaction improves, revenue increases. In banking, loyalty is no longer won with products or rates alone. It’s won by showing customers that you see and understand them as individuals, not account numbers. Data readiness is what makes that possible – giving marketing, CX, and even support teams the confidence to deliver consistently relevant experiences that deepen trust and build lasting relationships. To see how the Redpoint Data Readiness Hub can help you unlock the full potential of your customer data and deliver consistently relevant personalized experiences – using your own data – click [here](https://www.redpointglobal.com/financial-services/). **Blog categories:** Financial Services **Blog tags:** Data quality, Data readiness --- ### [A Composability Primer: Everything You Need to Know](https://www.redpointglobal.com/blog/composable-cdp/) **Published:** July 3, 2024 **Author:** Renee Graff **Content:** You want the right data to drive personalized CX for a wide range of simple to complex use cases. You want to easily build and deploy data-rich segments to drive your data through to CX impact – delivering high ROI and low TCO without burdening IT. You want the most complete, composable CDP for any cloud environment, MarTech stack, AI approach or use case – with data-in-place to give you control and flexibility. But what is composability? You have questions. We have answers. ## What is composability? Composability refers to designing systems with interchangeable and interoperable components, allowing for greater flexibility and adaptability. Composability entails a modular approach where buyers have the freedom of choice to obtain best-of-breed components that complement existing investments. More than just assembly, composability is also an enterprise approach that prioritizes agility. According to Gartner, the core principles of a composable business are modularity, autonomy, orchestration and discovery. In deciding whether a composable architecture makes sense for your business, one question to ask is how a composable environment will help you more quickly reach untapped business value. One purpose of a composable approach is to be more nimble, ready to easily pivot to sudden business and consumer changes. Another is to achieve specific business goals and unique tasks or processes via a customized toolset. For a composable framework to reach its full potential, each component of a composable system should create value as it applies to a business function. ## What do people mean when they talk about a composable CDP? Often when people talk about a composable [CDP](/cdp/) they’re referring to what’s called a [zero-copy data or a data-in-place CDP](https://www.redpointglobal.com/blog/dont-let-architecture-terminology-get-in-the-way-of-how-cdps-can-power-your-business-outcomes/), which means that your customer data sits in a data cloud. This is a distinction from the CDP having a built-in database in a traditional “packaged” CDP which is typically offered as software-as-a-service (SaaS) where the vendor hosts and maintains your data and builds a Golden Record on your behalf. A composable CDP that runs in a data cloud and performs core CDP functionality without having to replicate data allows you to control your customer data behind your own security perimeter, while still connecting to all your MarTech touchpoints and enterprise data sources. > A composable CDP that runs in a data cloud and performs core CDP functionality without having to replicate data allows you to control your customer data behind your own security perimeter, while still connecting to all your MarTech touchpoints and enterprise data sources. A composable CDP is about choice, allowing you to bring together best-of-breed capabilities from one or more vendors to create a purpose-built, cohesive platform through API integrations to give you more flexibility and speed-to-value to achieve your unique CX and business use cases. This approach balances control with the flexibility to add or change components as your business and use cases evolve. ## Can a packaged CDP also be composable? A packaged CDP can also be composable if it still permits you to choose the functionality you need, and allows you to use predefined features and functionality to perform day-to-day tasks without a separate sign-on for each process. With a packaged CDP, the software vendor will control the database, management, security and operational components, but it will retain characteristics of a composable CDP if you the marketer or business user maintain the flexibility to select only the functionality that you need. ## What should marketers and IT expect to get out of a composable CDP? For both marketers and IT, a data-in-place composable CDP provides many benefits. For [IT](/cdp-for-it/), a composable CDP makes it easier to control costs and limit data exposure. From a practical standpoint, what this means is being able to adapt quickly to changing business requirements by assuming and reconfiguring data and the tech stack as needed. For [marketers](http://cdp-for-marketing/), a composable CDP in a data-in-place environment provides a consistent, up-to-date and accurate view of a customer without data replication, enabling faster innovation and the ability to tailor solutions to specific customer needs more effectively. You have the flexibility to customize your MarTech stack and customer data, agility to respond to a changing market and evolving regulations, and you can manage your workflow with ease without having to routinely submit tickets. For all stakeholders, a composable CDP provides vendor flexibility without having to sacrifice data security or accuracy, allowing you to: - Minimize data movement and the systems you manage, saving valuable time and effort with streamlined integrations and vendor connection architectures - Focus on your team’s projects and priorities when other teams utilize secure, no- and low-code tools that don’t require tickets - Optimize costs by leveraging pay-as-you-go pricing models - Minimize infrastructure costs - Achieve the perfect balance between performance, functionality and cos*t* ## What does assembling a composable framework look like? Just as a “one-size-fits-all” software approach isn’t ideal, there is also the possibility of having too many pieces (and too many vendors). When there are too many moving pieces to manage, the burden shifts from your software to your people. With the knowledge that custom setups should help you, not cost more money or more time and effort to manage, be wary of systems that have a separate tool for everything. That situation usually creates overlap, and when two or more of your components do the same thing, you inadvertently create an inefficient system that often results in duplicate work, more time managing vendors, additional IT oversight and increased costs. Value-driven composability refers to integrated processes with role-specific use interfaces to accomplish day-to-day tasks. Having standardized, well-documented [integrations](/integrations/) helps reduce complexity when new components are added. To avoid “over composing” or composing at too low a level, it’s important to start the design phase knowing precisely what you need a composable CDP to do. What special connector services will you need to ensure that different components “talk” to each other, for instance? Be aware that hidden costs may arise when you have to: - Use multiple tools to create and run a new campaign or orchestration - Find, troubleshoot and fix broken connections with a complex, multi-point setup - Add SQL, Python or Java headcount (or submit an IT ticket) to build new segmentthere does data quality fit into a composable architecture? ## Where does data quality fit into a composable architecture? Do not underestimate the importance of [data quality](https://www.redpointglobal.com/blog/data-quality-the-missing-ingredient-in-most-composable-cdps/). In a composable system, the interoperability of components relies on consistent and reliable data inputs and outputs. Data validation, cleansing, and normalization processes should be implemented to ensure data quality across different components. The Redpoint CDP performs all [data quality exercises](/blog/data-quality-and-role-of-cdp/) (cleansing, validation, normalization, identity resolution) as data is ingested, resulting in clean data that is ready for business use for all downstream purposes and programs. Performing data quality processes once and up front ensure that all downstream use cases are working with clean data. > In a composable system, the interoperability of components relies on consistent and reliable data inputs and outputs. Data validation, cleansing, and normalization processes should be implemented to ensure data quality across different components. It’s important to keep in mind that many vendors consider data quality as mere consented data, or as a stand-in for basic deterministic identity resolution or even basic de-duplication. A composable system that does not prioritize data quality will result in overmarketing (sending identical messages to the same person), under-marketing (thinking multiple profiles are the same customer due to poor identity resolution) and, ultimately, CX friction. ## What does operating a composable system look like? The following are important issues to consider for operating a composable system to minimize what Gartner deems “operational risks” to include loss of an easy-to-use UX, a canvas-style interface for segmentation and messaging sequence and out-of-the-box connectors to MarTech systems: - Interface Design: A user should be able to work between systems without having to jump to different interfaces. - Dependency Management: Manage dependencies between components to avoid cascading failures and minimize the impact of changes to individual components. - Documentation: Thoroughly document each component’s functionality, interfaces and dependencies to facilitate understanding and usage by developers – and to avoid feature and function duplication across your setup. - Testing: Implement comprehensive testing strategies to ensure that different components work correctly together and maintain expected behavior across various compositions. - Security: Pay close attention to security implications, especially when integrating third-party components, to prevent vulnerabilities or data breaches. - Training: Consider whether specialized training or coding will be needed. How soon will marketers be up-and-running? ## How do I make sure performance meets my use case requirements in a composable CDP? When designing a composable CDP, organizations should carefully evaluate their business objectives, data requirements and technical constraints to ensure it effectively addresses their specific needs and provides tangible business value. A common misconception about a composable CDP is that it will not power [real-time experiences](/real-time-interactions/). But that is only true for a CDP that does not store customer data, and in a data cloud environment must make call-outs to various systems to obtain information about a customer. In such a “warehouse-first” concept of what it means to be composable, workflow complexity may increase because marketers will have to work with IT to retrieve an updated dataset from a data warehouse. In contrast, a composable CDP that provides a single customer view in a data cloud environment will meet performance requirements for any CX use case. When a composable CDP runs on a data cloud and there is no data replication, API integrations with real-time data streams and event processing systems provide marketers with a consistently updated unified customer profile. Use cases that run on batch processing, near real time and real time are all supported. ## Can a composable CDP run in a private cloud/on-premise environment? A composable CDP can run in a [private cloud/on-premise, in a public cloud, or a hybrid of the two](/configurations/), and connect to all your MarTech touchpoints and enterprise data sources. Flexibility and adaptability extends to deployment options, allowing for a composable CDP to be configured to operate either in a private cloud or on-premise. Considerations for where to deploy a composable CDP include: - Security and compliance: Running a CDP in a private environment can enhance control over data security and compliance with industry regulations, an important consideration for organizations handling sensitive data. - Vendor support: A private deployment will require dedicated resources for maintenance, updates and management. Organizations should be clear about what type of support or resources a vendor will provide. - Infrastructure requirements: A private cloud or on-premise infrastructure will need to meet the necessary hardware, software and networking requirements to support the composable CDP requirements, including whether the infrastructure can handle the anticipated data volume and processing demands. **Blog categories:** Composability **Blog tags:** CDP **Blog Type:** In Nav --- ### [Driving Composable CDP Success with Data Readiness](https://www.redpointglobal.com/blog/driving-composable-cdp-success-with-data-readiness/) **Published:** September 18, 2025 **Author:** Steve Zisk **Content:** Composability is changing the landscape of customer engagement technology, promising organizations the ability to mix and match specialized components, integrate best-of-breed tools, and move quickly in response to new opportunities like generative AI. But most customer engagement technology vendors – including so-called composable CDPs – treat composability as the mere act of connecting systems and orchestrating workflows. Many composable CDPs mistakenly assume the data flowing through those pipelines is already complete, accurate, and ready for use. It rarely is. Without continuous customer data readiness, [composable CDPs](https://www.redpointglobal.com/customer-data-platform/) simply move bad data faster – multiplying errors, fragmenting customer views, and undermining AI and personalization efforts. Redpoint Global takes a different approach. We believe true composability begins with [industry-leading customer data readiness](https://www.redpointglobal.com/data-the-defining-difference/) – the ongoing process of cleansing, unifying, enriching, and activating data so it is always fit for purpose, in real time. Only then can composable architectures – and composable CDPs – deliver the agility, precision, and innovation they promise. ## Data Readiness: The Non-Negotiable Foundation True **composability begins with data** – specifically, with data that is continuously cleansed, unified, enriched, and contextually prepared for use. Data readiness is not a one-time ETL or a periodic clean-up; it is an ongoing, systemic discipline. Only data that’s been transformed into a unified, accurate, real-time customer profile – a true “golden record” – can power the speed of innovation that composable architectures promise. ###### Data Readiness Enables: - **Personalization at scale**, as business logic and AI models draw from reliable, comprehensive data to create more meaningful customer experiences. - **Trustworthy analytics and decisioning**, since only high-quality, current data can yield accurate and actionable insights. - **Real-time operational agility**, empowering teams to respond instantly and confidently across customer journeys. Crucially, customer data readiness is not static. It must keep pace with customer behaviors, emerging channels, and new models of engagement. The systems that succeed are those designed for [continuous identity resolution](https://www.redpointglobal.com/identity-resolution/), dynamic enrichment, and [real-time orchestration](https://www.redpointglobal.com/real-time-interactions/). ## Where the Composability Narrative Falls Short The composability story is compelling, but it is often incomplete. Under scrutiny, several industry myths become apparent, exposing the flaws in the majority of composable CDPs: ###### ● Composability is NOT Reverse ETL A prevalent misconception is that moving data from data warehouses back into operational tools (reverse ETL) is a [proxy for composability](https://www.redpointglobal.com/blog/composability-is-not-just-reverse-etl/) – and that is enough to call yourself a composable CDP. In reality, reverse ETL is just a tactical function. It may get data from point A to point B, but it does nothing to ensure that data is [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/), [unified](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/), or [timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/). Reverse ETL’s rise is a symptom of neglecting the preparatory work upstream – if data had already been properly modeled and made ready at entry, much of the need for complex transformations and fixes downstream would disappear. Ultimately, reverse ETL often solves for activation, not for quality. ###### ● Data Quality Must Be Central, Not Peripheral Some composable CDPs operate on the assumption that all incoming data is “good enough.” In a fragmented environment where CRMs, e-commerce platforms, and marketing tools all produce and consume different records, [that assumption is rarely true](https://www.redpointglobal.com/blog/data-quality-the-missing-ingredient-in-most-composable-cdps/). Data quality processes – cleansing, deduplication, identity resolution – cannot be a patchwork of tools or outsourced afterthoughts. They must be at the heart of the composable system – and for any composable CDP. Without this, errors are multiplied at scale: conflicting email addresses, disjointed experiences, and overall erosion of customer trust. Moreover, when data quality is scattered among multiple vendors or relegated to the edges, accountability dissolves. There is no single owner ensuring consistency, and “good enough” quickly becomes insufficient for meaningful engagement. ###### ● Real-Time and Context Matter Innovation demands that data is not just unified and accurate, but also up-to-date and relevant in the moment. A composable CDP must support not only the movement of data but also its orchestration and activation in real time. Delays – whether hours or days – compromise experiences, render analytics obsolete, and slow the pace of innovation. Modern engagement requires platforms that treat real-time customer data readiness as a default, not a luxury. ###### ● Composability Is Not (Just) About Plug-and-Play Many vendors frame composability – and composable CDPs – as [strictly the ability to plug best-of-breed products together](https://www.redpointglobal.com/blog/the-composable-cpd-faq-for-marketers/). But this overlooks the vital requirement: every “plugged-in” tool must reference and update a single, context-rich, unified profile. Orchestration and modularity are powerful, but only when each component operates from a source of truth. Simply put, composing connections among siloed data sources and calling yourself a composable CDP will never yield the agility or intelligence that today’s organizations demand. ###### ● Fully Integrated Hubs Enable Continuous Innovation What truly differentiates leading solutions is not just their modularity, but their ability to function as an intelligent, fully integrated hub. This means managing data ingestion, enrichment, and activation within a unified environment – removing silos and enabling teams (marketers, analysts, IT) to collaborate using real-time, trustworthy data. When built properly, such environments future-proof customer engagement capabilities, allowing organizations to quickly adopt new tools and adapt to trends like generative AI through open APIs and flexible orchestration. ## Rethinking the Path to Value The future of customer engagement lies not in the proliferation of interconnected apps and “composable” collections as the foundation for a composable CDP, but in the continuous readiness of the data beneath. To get there, organizations should: - **Make data quality an operational requirement.** Every data flow – entry, transformation, activation – should be governed by continuous cleansing, enrichment, and identity resolution. - **Prioritize unified, real-time profiles** that can be trusted by all operational and analytical users. - **Demand more from composable platforms** than technical integration: expect systems to own and steward the reality of business-ready data. - **Insist on architectures that enable, not impede, innovation** from real-time analytics to adaptive customer journeys, powered by the latest models and channels. ## The Right Approach in Action Composability delivers value only when anchored by rigorous, continuous customer data readiness. Without it, “integration” just extends old problems into new systems. Before buying into any composable story, ask whether a composable solution – or a composable CDP – will ensure that your customer data is always ready – **accurate, unified, enriched and available in real time** – for every channel, system and AI model. The [Redpoint Data Readiness Hub](https://www.redpointglobal.com/data-readiness-hub/) is intentionally architected to deliver on composability’s promise. Redpoint treats data as a dynamic, continuous asset – where customer data readiness is never assumed, but always assured. In this approach, composability is no longer a collection of moving parts, but a multiplier of value uniquely enabled by the trustworthiness and accessibility of business-ready data. **Blog categories:** Composability, Data Readiness **Blog tags:** Composable, customer data platform, Data readiness --- ### [Master Data vs. Ready Data: Why Data Readiness Matters for an MDM](https://www.redpointglobal.com/blog/master-data-vs-ready-data-why-data-readiness-matters-for-an-mdm/) **Published:** September 15, 2025 **Author:** Steve Zisk **Content:** While there is some overlap between customer data readiness and master data management (MDM) for “party” entities (customers, members, patients etc.), the differences in approach, data managed, and outcomes are significant. And MDM and data readiness can be complementary: When an MDM is used to optimize customer data for CX or AI use cases, data readiness is an essential component. An MDM creates an accurate customer “master” so that applications across the enterprise – data governance, supply chain, CRM, customer service and finance – are working with the same core understanding of each customer. In contrast, [data readiness](https://www.redpointglobal.com/data-readiness-hub/) provides accurate, detailed, and current data to drive business (especially customer-facing) use cases. Generally speaking, there are three key distinctions to make between data readiness – a data readiness hub – and an MDM solution. ## **Distinction #1: How Fast is Data Changing?** The first major difference between MDM and data readiness is the data itself! [MDM is concerned with “master” data](https://www.redpointglobal.com/data-readiness-hub/) that defines the entity it is mastering: people, products, machines, and so on. Master data tends to be “slow-moving.” A name, an address, and birth date change slowly – or infrequently – if at all. Data readiness on the other hand cares a lot more about “fast-changing” data, i.e. customer attributes related to time, to money, to customer behavior, etc. Buying choices, social trends, and consumer interactions all fall into this category – and the history of the frequent changes tell a story about each individual customer. This is one important piece of context – the “long tail” – which distinguishes it from the slow-moving data. Data that an MDM cares about tend to be core attributes attached to a person (or another party entity) – name, address, phone number, age. Compound attributes such as lifetime value (CLV) may be included, but an MDM does not calculate CLV, nor does it care about the transactions that contribute to CLV. For instance, a healthcare practitioner will typically have all of the master data related to a core patient record. But the MDM will not care about how that patient record relates to the EHR system, nor have any information related to a patient’s pharmacy benefit manager (PBM). Making sense of the patient journey is where a data readiness hub comes in, which will use all relevant data to manage the journey. The same holds true in retail. A pair of jeans will have attributes that are both fixed (color, size) and fluid (how many sold, how many in stock). Changing data may be tied to a product, but it may also relate to a customer’s interest in the product – time on the product page, the last visit to the website, order history, etc. Customers, patients – and their attributes – are continually changing, and the story those changes reveal about a customer or patient journey are typically outside the scope of an MDM. ## **Distinction #2: Right Data vs. Fit for Purpose Data** Think of continually changing attributes – the “fast-moving” data – as “decorations” that provide the context that a marketer or business user of data needs to fuel AI and CX. The need for a contextual understanding brings us to a second distinction; whereas an MDM and a data readiness hub both have an interest in having the *right* data, only the latter has a vested interest in ensuring that the data is *fit for purpose*, i.e., it is observable, it is actionable, and the right aggregates, models and calculations are attached. Data lineage also fits into this category. An MDM solution will store an email address and potentially the source it came from; data readiness will attach a score for its trustworthiness, know when the email was last used with respect to a customer record, when an email to that address was last opened, etc. Metadata fits into this realm to provide a layer of context to the underlying data. And while an MDM will define and own the standards for things like currencies (U.S. dollar vs. Euro) and measurement (meters vs. feet), these are also vital in the construct of data readiness. A data readiness hub will inherit the use of how an MDM defines standards, and carry over any changes. An understanding of these and other fields are important for determining how to interact with a customer where there may for instance be records for a primary address, a secondary address, etc. ## **Distinction #3: A Flexible vs. Fixed Understanding** The third key distinction between an MDM solution and a data readiness hub is that the latter must parse data in a way that an MDM might not necessarily care about. Interpersonal relationships, for instance, such as whether a unified customer profile represents an individual in the context of a household or organization, or whether data is PII or PHI and is thus subject to different rules for how to engage with the customer are both important to a data readiness hub, but not necessarily to an MDM solution. ## **An MDM is Not Data Readiness** Understanding the important differences between an MDM and a data readiness hub helps to answer the burning question of whether a company with an MDM solution *needs* a data readiness hub. The answer is – it depends. If delivering a personalized customer experience is not required as a customer data use case, or if an organization does not plan to use AI in a customer-facing way, then an MDM might suffice. The same might hold true if a “unified customer profile” only needs to include core “master” attributes like email address, name and primary phone numbers. But if a customer-focused organization wants to have a [contextual understanding to drive differentiated AI and CX use cases](https://www.redpointglobal.com/data-the-defining-difference/), an MDM solution is not enough. A company that tries to use an MDM to provide data readiness will be taking on a lot of (data) headaches. Data will not be in the cadence of the customer, will not be detailed and contextual, and will not be actionable – it will not be fit for purpose. ## **Data Readiness + an MDM** A company that has an MDM but cares about having its data both right and fit for purpose will, at a minimum, have to add all of the things that fall under the data readiness heading, i.e., the “decorations,” the customer behaviors, the customer history, permissions, interpersonal relationships, etc. That is where the data readiness hub comes in. Beyond providing a more contextual, nuanced and rich understanding of the customer, a data readiness hub will also drive new and better information into an MDM. For instance, a company may want to know how many high-value customers it has. An MDM, however, does not care about individual transactions, which are a useful metric in calculating the value of a customer. In trying to calculate a number, the company polls different departments, but each has its own metrics for determining value-related figures – number of sales, average sale, cut-off dates, etc. Conversely, a data readiness hub that continually makes data fit for purpose can provide an updated number of high-value customers as part and parcel of its underlying purpose of developing a real-time, continuously updated unified profile accessible across the enterprise. ## **MDM & Data Readiness: Complementary, Not Redundant** While MDM and a data readiness hub both work with customer data, their purposes and strengths are fundamentally different: - **MDM ensures accuracy and consistency** of core customer records across systems – a foundational need for governance, compliance, and operational integrity. - **Data readiness makes data fit for purpose**, adding the context, timeliness, and behavioral insights needed to drive personalized CX and AI-driven use cases. - **Data readiness complements MDM**, enhancing its value by continuously updating unified profiles with actionable insights, customer signals, and real-time context. If your organization’s goal extends to more than just managing data to include using data to drive experiences, data readiness is not just optional, it’s essential. **Blog categories:** Data Management, Data Readiness **Blog tags:** Data readiness --- ### [The Data Readiness Blueprint for Banking: Real-Time Unification and Always-On Quality](https://www.redpointglobal.com/blog/for-financial-services-a-superior-customer-experience-begins-with-data-unification-data-quality/) **Published:** September 5, 2025 **Author:** Mike Ferguson **Content:** Does your customer data tell a story? When a known customer or prospect appears on a digital channel, or at a physical location, do you have a complete, accurate understanding of the customer’s needs and want and use that understanding to instantaneously render a decision that matches the customer to the right product or service? For too many financial services institutions, the answer is no. They have data volume without quality. Or quality without volume. The data is either imperfect or incomplete, and either way the customer experience (CX) suffers. They have a name with slight variations in spelling, and are not sure if the records belong to the same identity. Or they are confident that the records reflect an individual customer, but the profile is either missing a feed from a key data source, or the incoming data is out of date. The result? An inferior customer experience that does not sit well with your customer, increasing the risk of churn, lost cross-sell and upsell opportunities, and weaker customer segmentation. Data unification and [always-on data quality](https://www.redpointglobal.com/automated-data-quality/) are instrumental in driving superior customer experiences that lead to enhanced customer satisfaction and loyalty, higher engagement, better retention, more cross-sell and upsell opportunities, targeted prospecting, revenue growth, and cost reduction. ## **Data Unification for the Win** In today’s financial landscape, customers access banking services through mobile apps, through the website, and through traditional retail banks. They interact with chatbots, dial the call center, search for banking products online, and use social media. With each interaction, a customer leaves important signals about their interests and intent, and banks need to pick up on those signals to drive a personalized CX that demonstrates an understanding of the customer. Data unification helps ensure that financial institutions have a complete understanding of the customer needed to drive a personalized CX. Building such an understanding by ingesting and unifying data from every source and of every type is the purpose of a data readiness hub. The Redpoint Data Readiness Hub produces and continually updates a real-time unified profile for each customer, allowing banks and financial institutions to profitably differentiate one customer from another. Redpoint provides a choice between on-premise, private cloud and public cloud deployments, providing control over how to protect customer data. Organizations that opt for a [data-in-place environment](https://www.linkedin.com/smart-links/AQGP0Mbx-syu6Q/0f2b2c63-f84b-49ba-a69a-147b448c3489), for example, are able to maintain a single source of truth for customer data behind the company’s firewall without having to copy and move it. ## **Always-On Data Quality** Data unification alone is not enough to create a data foundation that organizations can trust to power personalized experiences. Customer engagement technology that ingests data from various sources should also standardize, enrich, normalize and make data ready for business use – ideally at the moment of data ingestion to allow for that real-time, contextual customer understanding. Redpoint offers automated data quality with tunable identity resolution that includes householding to provide the most accurate, trustworthy customer profile to drive all business and CX use cases. Redpoint provides always-on data quality processes at the moment of data ingestion. Other platforms either fail to prioritize data quality, such as limiting identity resolution to a basic, deterministic match, or they rely on third-party vendors for standardization and normalization tasks. The problem in both cases is that the marketer or other enterprise business user lack visibility into the data quality processes. They do not know where data quality processes have occurred as data is readied for activation, and thus cannot trust the validity or accuracy of the resulting unified profile. For example, financial institutions have a vested interest in knowing the dynamics of a household; head of household, dependents, life stage, etc. Perhaps a customer has young children, and is interested in saving for college. Or is looking to move to a larger house. Or new empty-nesters are interested in downsizing. A detailed understanding underpins almost every decision for how to engage with someone. Is the person using the mobile app the account owner? What is the significance of a new mailing address, or a name change? Data quality, which includes [advanced identity resolution](https://www.redpointglobal.com/identity-resolution/), is the critical step that ensures that the unification of customer data is about more than just volume; it provides organizations with confidence that they are engaging with the intended customer. Many financial services companies have gaps in their data that limit the effectiveness of a unified customer profile. They are either missing data, data is old, or their MarTech stack comprises different systems that each take a different approach to data quality. Redpoint ensures trustworthiness in customer data from ingestion through activation. Redpoint brings together every source of customer data – from across the enterprise. Always-on data quality continuously cleanses, standardizes and normalizes incoming data. And data observability dashboards show users that everything is working as intended to build a consistent, accurate understanding of your customer. To learn more about how Redpoint helps financial services companies get their data both right and fit-for-purpose, click [here](https://www.redpointglobal.com/financial-services/). **Blog categories:** Data Ingestion, Data Quality, Financial Services --- ### [Banking on Change: Why a Golden Record Satisfies Customer Expectations for a Holistic Experience](https://www.redpointglobal.com/blog/banking-on-change-why-a-golden-record-satisfies-customer-expectations-for-a-holistic-experience/) **Published:** May 24, 2024 **Author:** John Nash **Content:** In a [Redpoint Global survey ](https://www.redpointglobal.com/press-releases/redpoint-survey-majority-of-consumers-say-banks-are-failing-to-meet-expectations-for-personalized-experiences/)conducted by Dynata Research, 82 percent of customers said that it is important for their bank to understand them as a customer and personalize experiences so that they are relevant and valuable. Yet only 38 percent report that their primary bank is meeting this expectation. And while 88 percent expressed a desire for banks to provide a seamless experience across all channels (brand, website, mobile app, call center), just 44 percent said their bank was “somewhat effective” at meeting this goal. The research reveals major gaps in customer expectations for a personalized and seamless banking experience across channels and a financial institution’s ability to meet those expectations. These roughly 2X gaps have the potential to widen with more consumers turning to digital channels as a primary banking option; in the same survey, 44 percent of consumers said they’ve increased digital banking habits in the past year (26 percent said that they already did most of their banking via digital channels or an ATM). What types of experience contribute to the experience gaps? While the ability to apply for a loan online is a fairly standard offering, most institutions still do not offer the capability on a mobile device. Or they let a customer start the process online, but require an in-person visit to finish it. For customers, these inconsistencies or inconveniences create friction by requiring multiple interactions for what should be a simple process. Worse yet, customers often have to deal with various business functions that do not share account information or have a shared view of the customer’s journey. ## **Closing the Experience Gap** Consumer expectations for seamless experiences across channels are not unique to banking, but financial institutions do have unique sets of challenges. First, they have less of a head start than other industries such as retail, where Amazon and others blazed a trail for customer-centricity and a digital-first approach. In the Redpoint survey, in fact, 67 percent of consumers said that retailers do a much better job of delivering personalized experiences than their bank. Second, as highly regulated entities responsible for protecting sensitive financial institutions, banks have been slow to roll out digital services or to break down channel, data and product siloes. Complex legacy IT systems, and a general resistance to change are reasons why, according to Financial Brand’s [State of Digital Banking](https://thefinancialbrand.com/92303/digital-banking-transformation-success-leadership-trends/) Transformation report, fewer than 50 percent of financial institutions believe they are prepared for customers’ digital expectations. (And just 12 percent self-identify as digital “leaders.”) Because banks work with sensitive data, they have ensured that financial data is accurate and clean. Yet consumer expectations go beyond this, as a true understanding of a customer requires banks to know their preferences, their life stages, their contact history and many other insights. Banks have fallen short of integrating all of this information across channels and product lines, and they also have difficulty responding to customers across anonymous-to-recognized and anonymous-to-known customer journeys. Another opportunity for banks is to improve their recognition of various relationships and households. Redpoint is working with several regional banks who, for various reasons, have a difficult time matching multiple accounts to a household. In most consumer industries, householding issues are usually the result of deterministic and probabilistic matching difficulties or errors, such as not linking accounts because an address doesn’t match (St. vs. street, etc.). For these regional banks, householding is more an issue with multiple members of a household living at different addresses, which becomes even more complicated when commercial accounts are introduced. Determining and servicing the centers of influence in today’s world has become increasingly difficult. ## **Meet Expectations with a Golden Record** This lack of visibility into a relationship between accounts makes it difficult for the banks to create a holistic experience for account holders engaging in different channels. Redpoint solves for this issue by creating a [single customer view](https://www.redpointglobal.com/single-customer-view/) that links a center of influence with all associated accounts, addresses and account holders. The Redpoint Golden Record is an accurate, complete, holistic picture of an individual customer that brings together customer data from every source across the enterprise. In addition to account data, the Golden Record integrates contact history, behavior data, demographic data, transaction history and any other relevant data across the enterprise. Our regional bank partners choose Redpoint, in part, because of the [advanced identity resolution](https://www.redpointglobal.com/identity-resolution/) capabilities in the [Redpoint CDP](https://www.redpointglobal.com/cdp/) that creates a 360-degree customer view that makes it possible to virtually eliminate generic or misguided messages. By eliminating siloes around the centers of influence, the Redpoint CDP facilitates targeted, hyper-personalized engagements and experiences that are relevant for an individual customer – not the account. As the Dynata Research shows, customers expect their banks to deliver value and relevance. For financial institutions large or small, creating a single customer view through a Golden Record is an important first step toward meeting demands for seamless, personalized and digital-first experiences. *More results from the Redpoint banking survey conducted by Dynata Research can be found [here](https://www.redpointglobal.com/press-releases/redpoint-survey-majority-of-consumers-say-banks-are-failing-to-meet-expectations-for-personalized-experiences/). To discover how one global financial services firm accelerated speed-to-value for its marketing programs with a unified customer view and a single point of operational control, click* [*here*](https://www.redpointglobal.com/wp-content/uploads/2024/04/Global-Financial-Services-Firm-Accelerates-Speed-to-Value-for-Marketing-Programs_AMEX.pdf)*.* **Blog categories:** Financial Services, Single Customer View --- ### [It is Time to “Act as One” for the Benefit of the Healthcare Consumer](https://www.redpointglobal.com/blog/it-is-time-to-act-as-one-for-the-benefit-of-the-healthcare-consumer/) **Published:** February 3, 2021 **Author:** John Nash **Content:** The rise of healthcare consumerism is well documented, as detailed in a [previous blog in this space](https://www.redpointglobal.com/blog/why-cx-is-the-right-rx-for-healthcare/) that examines how consumers, in possession of an ever-increasing amount of health data, expect to be in control of a holistic, personalized healthcare journey. To meet this expectation, a [2020 Gartner report](https://www.gartner.com/en/documents/3882164/healthcare-cios-will-enable-three-generations-of-consume) (subscription) explores why healthcare CIOs from payer, provider and life sciences companies need to adopt a healthcare consumer engagement generations model that solves for the challenges of engaging with empowered healthcare consumers in a digital-first environment. In short, the report breaks down why consumer engagement is a top-level initiative, and how a three-tiered generations model strategy advances an organization’s readiness to “act as one” in relationships with consumers across channels and business functions, and then finally across an entire healthcare ecosystem. ## **Consumer-Driven Changes** Before going into more detail on the consumer engagement generations model as outlined by Gartner, a [Redpoint study](https://www.redpointglobal.com/press-releases/75-of-u-s-consumers-wish-their-healthcare-experiences-were-more-personalized-redpoint-global-survey-reveals/) in conjunction with Dynata highlights why consumer engagement is such a high-level initiative. In the report, more than half of the 1,000 consumers surveyed said that they do not receive a holistic healthcare experience, with 71 percent reporting poor experiences that include impersonal visits, long wait times, confusing processes and difficulty scheduling appointments. Furthermore, 54 percent said that neither providers nor insurers have the contextual information needed to personalize a healthcare experience – such as data from wearables and other consumer-driven products. Armed with more personal data, consumers pressure healthcare organizations to help them act on that data in a way that matches a seamless experience to which they’re now accustomed. The consumerism of healthcare changes how healthcare organizations have traditionally invested in technology for consumer engagement. In the past, consumer engagement was viewed as more of a means of an end, a pathway toward siloed processes such as acquisition, retention or improving Medicare Advantage ratings. Now, engagement is more in line with the overall end-to-end healthcare experience a consumer receives. The changing mindset does not diminish the importance of retention or acquisition, rather it considers all activities holistically; siloed data and siloed processes create friction for the end consumer, making it more difficult for a healthcare organization to personalize an experience with the goal of driving better outcomes, reducing costs and improving consumer satisfaction. ## **Consumer Engagement in Three Generations** Gartner outlines its generational approach as a guideline for how healthcare organizations can invest in and use technology to aid in a new approach toward consumer engagement. Generation 1 is all about preparation; healthcare organizations that adopt a new consumer-centric approach should build a business use case, determining the two or three most important consumer journeys for the organization and outlining a vision for what an improved journey might look like. The essential starting point, the report says, is the perspective of the consumer, with a typical journey consisting of a longitudinal set of experiences. Experiences are bucketed into four categories: managing illness, accessing healthcare, managing wellness and caretaking. A focus on a specific consumer journey might be how to design an experience for a consumer who is adapting to a new chronic diagnosis (managing illness), or caring for an aging parent (caretaking). Identifying and mapping how those key consumer journeys might improve – what is the optimal course of action? – will shed light on what is needed from a technology standpoint to move into Generation 2 and Generation 3. According to the Gartner report, most healthcare organizations are currently in Generation 1 – using consumer data to optimize one or a few consumer journeys that in most cases entail one busines until or process. These are typically single channel, static journeys with very little to no visibility into the consumer’s entire healthcare profile, or the consumer’s overall interactions. ## **A Consumer-Centric Approach: Act as One** The defining characteristic of Generation 2 and Generation 3 is that first the entire organization – all business units and channels – and then the entire healthcare ecosystem “act as one,” respectively, on behalf of the healthcare consumer. If, for example, a healthcare provider focused on a consumer adapting to a new chronic diagnosis as its Generation 1 initiative, it might move from assigning a home healthcare aid (involving a single business unit) to coordinating the patient’s care across the enterprise, with all departments accessing the same data to create a more seamless experience. Scheduling, procurement, outreach, administrative all acting as one on behalf of the patient. In Generation 3, the holistic experience would also include the patient’s insurance company, the admitting hospital, a dialysis company or other third-party vendors – any stakeholder with an interest or investment in the patient’s healthcare. In Generation 3, the understanding is that consumers have access to and control of their health data from across the entire healthcare ecosystem. A consumer’s next-generation personal health record is independent of a specific healthcare organization or stakeholder. Rather, it ingests data from all permissioned sources and enables sharing with providers, insurers, caregivers, devices such as wearables, etc. There are no data, process, department or channel siloes that cloud a single view of the healthcare consumer – siloes that are largely responsible for the disjointed experiences that consumers cite in the Redpoint study, and others, as common frustrations with the healthcare system. ## **The Healthcare Consumer Engagement Hub** Gartner recommends a healthcare consumer engagement hub (HCEH) as the underlying technology to support longitudinal consumer engagement and underpin the generations model. (See Graphic 1). The HCEH moves healthcare organizations through each generation by tying multiple systems together to allow for a consumer engagement approach across channels, the ecosystem and events. The technology is the key to treating a consumer journey not as an episodic series of experiences, but rather understanding it as interactions that together compromise a lifelong health journey. ![Longitudinal Engagement of the Healthcare Consumer Engagement Hubt ](https://www.redpointglobal.com/wp-content/uploads/2021/02/Longitudinal-Engagement-of-the-Healthcare-Consumer-Engagement-Hub-300x140.jpg)Gartner: Longitudinal Engagement of the Healthcare Consumer Engagement Hub That understanding, supported by technology, allows for more personalized, contextual engagements with consumers across all channels. The “act as one” generational approach enables the new engagement approach by eliminating the barriers that have traditionally viewed sales and marketing, clinical encounters, administrative services and care management as distinct entities with different objectives. Without a comprehensive view of the healthcare consumer, it was not possible to focus on or deliver a personalized experience. ## **Consumer Engagement in Practice: Moving the Needle** Though it takes care not to make recommendations or mention technology vendors, the generational approach Gartner describes, as well as the technology capabilities that support a personalized healthcare experience, align with the Redpoint strategy and capabilities for helping healthcare organizations evolve their consumer engagement methodology. The [Redpoint rg1 platform](https://www.redpointglobal.com/one-platform/) is a consumer digital experience hub employed by healthcare organizations to accomplish many of the objectives laid out in the generational model. One [healthcare sales, marketing and engagement organization](https://www.redpointglobal.com/wp-content/uploads/2024/04/Healthcare-Pioneer-Transforms-Consumer-Experience-Case-Study_Guidewell.pdf) partnered with Redpoint to power its multi-channel marketing campaigns, scale across different business models and improve member communications. Using rg1 to achieve a comprehensive view of the healthcare consumer, the company supported several hundred multi-channel marketing programs, resulting in a 60 percent reduction in onboarding costs and 25 percent reduction in marketing operation costs. Another Redpoint customer, a healthcare organization that provides an [end-to-end operating system](https://www.redpointglobal.com/wp-content/uploads/2024/04/Redpoint-Global-Enables-Lucerna-Health-to-Transform-Healthcare-One-Patient-Engagement-at-a-Time.pdf) across payers and providers, uses rg1 as a digital experience for several end clients. The organization uses rg1 to achieve a single view of the healthcare consumer with an underlying goal of helping its clients transition to a value-based care (VBC) payment model that depends largely on providing a seamless, personalized healthcare experience to improve health outcomes. One of its clients identified closing care gaps as a customer journey experience of focus. Care gaps had built up when the pandemic hit due to reduced hours or closing of facilities and operating at limited capacity. To achieve financial bonuses based on a value-based care payment model, the provider needed to close roughly 18,000 care gaps in 90 days. With rg1 powering personalized communications and matching the right doctor with the right patient, the provider hit its goal. Specifically, it leveraged survey data from a partner provider and discovered that one healthplan’s members preferred female doctors over male doctors by a 60 percent margin. Before this realization, one marketing campaign aiming to schedule appointments to reduce care gaps reached out to every member using images of male doctors in the content. However, with insights from the survey data, marketing switched out the images of male doctors for members who expressed a preference for female doctors – resulting in a 15x increase in conversions. ## **Start the Transition Today** Healthcare organizations are increasingly embracing patient experience as a key metric because of its proven results in improving outcomes, reducing costs and improving the patient or consumer experience. Driven by consumers having more control over their health data, and their view of a healthcare journey as a lifelong connection of joined experiences rather than a series of disjointed episodes, healthcare organizations face pressure to respond in kind, delivering a holistic experience that matches consumer expectations. By adopting a generational approach that will eventually “act as one” on behalf of the consumer, and supported by the right technology, healthcare organizations will be well on their way to refining a consumer engagement strategy that puts the healthcare consumer first and achieves desired business results and health outcomes. **Blog categories:** Customer Data Platform, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Marketers, What Can a CDP with AI Do for You?](https://www.redpointglobal.com/blog/marketers-what-can-ai-do-for-you/) **Published:** May 17, 2024 **Author:** Steve Zisk **Content:** In the 2023 Gartner Marketing Technology Survey, 77 percent of MarTech leaders said that they are already deploying GenAI in some fashion (14 percent) or plan to in the next 24 months (63 percent). The ones that have already deployed GenAI report having seen increases in productivity across proven use cases including content creation and next-best action optimization. With such a wide range of GenAI use cases and many ways to measure its success, it may be worthwhile to take a step back from the front lines and look at AI from the perspective of both marketers and IT in terms of what they are hoping to gain from GenAI in the first place. When the time comes to deploy GenAI with specific use cases and objectives in place, having a base understanding of what it can do for the enterprise as a whole – for marketers and IT – might help clarify how to maximize its value. Why is it important to better articulate the purpose of bringing GenAI to the organization? Because, according to Gartner, “the field is cluttered with consultants and vendors positioning GenAI as the thing that will solve the challenges of marketing. It will not.” GenAI is not a magic bullet; its effective use depends on having a clear view into its various applications. We will attack this from the marketer’s perspective first, and turn our attention to the IT point of view in a follow-up blog post. ## **Where Will GenAI Make its Mark?** Ask an operational marketer about what they hope to get out of AI, or GenAI, and the answer will likely relate to one of two themes. Either anything that helps make their day-to-day job easier or helps deliver a better customer experience. The question helps divide out GenAI’s use from an internal perspective (making the marketer’s job easier) and an external perspective (a better CX). Externally – customer-facing – [common GenAI applications](https://www.redpointglobal.com/ai/) include better product recommendations, deeper personalization, improved journey design and doing a better job answering questions such as a chatbot or call center. The goal in each of these use cases is to improve customer experience to meet marketer goals like retention, brand awareness, basket size, etc. This is how marketers think about AI in terms of CX – how can GenAI induce change to a customer interaction that somehow improves things for the customer and the brand? Inward-looking, the focus is more on how GenAI will help simplify daily tasks. The end result – a better CX – may be the same, but in this telling GenAI has a direct impact on the marketer, i.e., GenAI in a co-pilot function such as a natural language user interface where a marketer can ask questions about customer data, an audience, or a campaign. A co-pilot might also generate insights, alerts and recommendations based on monitoring and understanding data, campaigns and customers. In other words, GenAI might speed up or even automate manual processes, or generate insights that a marketer might otherwise miss. There is certainly some overlap between the internal and external use of GenAI in the marketing realm. Using GenAI to, say, generate email subject lines both helps simplify the marketer’s job and, ultimately, creates a better CX by coming up with (hopefully) amazing content by having trained by ‘reading’ millions of other email subject lines. A marketer will want to both use GenAI to improve personalization outcomes and use a smart personalization framework with optimization, A/B testing, etc., to test and improve AI recommendations and content. ## **The GenAI Marketing Scorecard** An assessment of what a marketing department wants to get out of GenAI will help determine a starting point. Gartner helps organizations evaluate how to make the best use of GenAI with a use case scorecard for marketing. ([downloadable GenAI Use Case Prism](https://www.gartner.com/en/doc/799798-use-case-prisms-for-generative-ai-a-guide-to-emerging-opportunities-in-industries)). The scorecard ranks 20 potential use cases, from Content Copilot to Federated Collaboration, assessing each on six different metrics, three each in terms of business value (increased revenue/operational efficiency/managed risk) and feasibility (technical feasibility/organizational feasibility/external feasibility). Organizations can use the scorecard to plot one or more desired use cases and rank each one based on what’s most important – generating business value vs. the feasibility of getting an application up-and-running. There is no question about GenAI’s soon-to-be pervasive use in the enterprise. For marketers to effectively integrate GenAI into their processes, nailing down a purpose is a good start for identifying where quick wins are possible. A follow-up blog will focus on the IT approach to GenAI, and explore potential overlap. **Blog categories:** AI & Machine Learning --- ### [Key Questions to Ask When Evaluating an Enterprise CDP](https://www.redpointglobal.com/blog/key-questions-to-ask-when-evaluating-an-enterprise-cdp/) **Published:** October 17, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/10/zisk-blog-10-17-19-300x169.jpg)The Customer Data Platform Institute created the [RealCDP designation](https://www.cdpinstitute.org/) to clear up marketplace confusion about the true purpose and capabilities of a CDP. While over 100 vendors offer what they consider CDPs, the institute bestows the RealCDP label on just 44 of them, [Redpoint Global](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/) included. According to the institute, there are five qualifications for a CDP to earn the RealCDP distinction: - Ingests data from all sources - Retains full detail of all ingested data - Stores the ingested data as long as the user wants - Converts the data into unified customer profiles - Makes the profiles available to all external systems That’s a lot to unpack for companies that are evaluating CDPs as the single source of truth for customer data and as the foundation of a future-proof martech stack. To further clear up confusion, we will address five questions every company should ask during the evaluation phase. **Dynamic, Rules-Based Application of Customer Data** The five RealCDP qualifications do not explicitly state that a CDP must perform all of those functions dynamically, but that is perhaps the most important consideration for any company evaluating enterprise-grade CDPs. A solution can ingest, retain, store, and convert data and make it available to external systems, but done statically it will not keep pace with a dynamic customer journey. A rules-based solution that offers an in-the-moment, real-time, flexible decision at the moment of interaction makes all the difference between a real-time, dynamic journey and a list-driven journey that will, by definition, always be at least one step behind the customer. Attaching static information to a persistently updated [golden record](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/) devalues the ultimate purpose of a CDP, which is to guide a customer journey with segment-of-one personalization in the context and cadence of a unique, omnichannel journey. **Data Enrichment for Deeper, Richer Insights** An [enterprise-grade CDP](https://www.redpointglobal.com/blog/key-questions-to-ask-when-evaluating-an-enterprise-cdp/) should offer a broad set of data enrichment capabilities. [Data enrichment](https://www.redpointglobal.com/blog/what-is-data-enrichment/) is the merging of third-party data from an external authoritative source with existing first-party customer data. Data ingestion is a core requirement of a CDP, but data enrichment is needed to make raw data useful. It adds additional layers of detail to first-party data, generating insights that help marketers deliver a more hyper-personalized customer experience. Data enrichment is a key element of integrated identity resolution. And while it is not expressly covered by RealCDP, it is nevertheless an important topic and an important distinction between an enterprise-grade CDP and an also-ran. Brands poised to deliver omnichannel personalization recognize that well-functioning data enrichment is a key CDP capability. **Advanced Identity Resolution for an Anonymous-to-Known Journey** [Identity resolution](https://www.redpointglobal.com/challenges/identity-resolution/) is another question to ask when evaluating a CDP, because creating relevant, personalized interactions requires not just enriched records but accurate and timely understanding of the customer journey across all touchpoints. Marketers should expect the CDP to dynamically map partial sets of details from identified and anonymous records to resolve a customer identity across an unknown-to-known customer journey. Identity resolution is foundational for building an accurate customer profile. It should automatically identify customers across databases and engagement systems. Companies evaluating CDPs must be wary of solutions claiming to offer advanced identity resolution but providing little more than matching and stitching together known identities. Advanced identity resolution requires understanding, tracking, and relating the complete range of identity elements – offline and online, anonymous and identified, shared and individual – to build identities in the context of households, organizations, and changing preferences and goals. **Identity Resolution Done Right: Building a Golden Record** To provide the performance, agility and control marketers need for engagement at the cadence of the customer, a CDP must natively include all the tools required to convert raw data into a golden record. This includes handling normalization, validation, and transformation of raw data – names, addresses, numbers, business details – from both structured and unstructured sources, along with all kinds of “housekeeping” items, like handling nicknames, abbreviations, incomplete or missing fields, and data entry errors. The [Redpoint Customer Data Platform](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/) standardizes the data and uses probabilistic and deterministic matching – with more than 375 built-in functions – to decipher individuals, households, cookies, IP addresses, and IoT smart devices, among others, and handles both digital and offline identities. This process turns raw data into an identity graph that marketers use to create a personalized customer experience in any channel. Relying on data scientists to convert data into a unified customer profile is an unnecessary, time-consuming step when trying to keep pace with a customer in an omnichannel journey. **Not Just Another Solution for the Martech Stack** An evaluation should begin with an understanding that a CDP is not just another martech solution, but is an enterprise tool. As such, a major consideration is whether a CDP offers pre-built connectivity to all enterprise sources of customer data, or whether its performance relies on limited martech and channel connections. An [open garden architecture](https://www.redpointglobal.com/blog/new-redpoint-capabilities-embrace-the-open-garden-approach-to-marketing/) that supports connection to all enterprise systems is different than “open garden light” which limits connections to the martech stack. While the latter may integrate limited e-commerce and CRM data, or offer the “escape hatch” of file imports, it still requires the IT department to do the heavy lifting to bring all relevant back-office data into the CDP. A real open-garden CDP should provide high-quality, high-performance native connections to data sources above and beyond the martech world – first-party, second-party, and third-party data, batch and streaming, big data, databases, message queues – to ensure that keeping pace with an omnichannel customer journey does not require an IT project to make a new connection happen. **And for Good Measure ….** There are two additional questions to ask when evaluating a CDP that are related more to underlying performance rather than off-the-shelf capabilities. First, it is important to confirm the CDP you are evaluating meets an IT department’s security, compliance, privacy and governance requirements. In a modern, customer-centric world where data is king for creating a personalized customer experience, the data that goes into a CDP is high-value, high-risk data centered on the relationship between a brand and the customer. It is vital that this data is handled appropriately (in transit and at rest) as a high-value asset. Second, a CDP must offer performance that matches the cadence of the customer. Like the dynamic application of rules at the moment of each interaction with a customer, this requirement underpins the requirement for real-time throughout the data lifecycle, real-time at the data level, at the analytics level, and at the orchestration level. In fact, in describing the five qualities needed to be a RealCDP, the CDP institute takes pain to say that there are topics not covered under the RealCDP umbrella – and real-time data processing is first and foremost on the list. If your CDP checklist includes all five core capabilities of a RealCDP, kicking the tires of real-time ensures you’ll walk away with a solution that’s ready for the high-speed twists and turns of a dynamic, omnichannel customer journey. **Blog categories:** Customer Data Platform, Data Quality, Real-Time Personalization, Single Customer View --- ### [The Meaning of a CDP: Is Your Data Ready for Business Use?](https://www.redpointglobal.com/blog/the-meaning-of-a-cdp-is-your-data-ready-for-business-use/) **Published:** August 6, 2025 **Author:** Beth Pfefferle **Content:** As a marketing engagement technology, the customer data platform (CDP) is typically lumped together with other customer data technology – reverse ETL, CRMs, DMPs, data lakes, data clouds, etc. There are also many marketing engagement platforms that claim to be adding what have been thought of as CDP capabilities, such as creating a unified profile. The result is a lot of overlap and confusion about the meaning of a CDP. What is the meaning of a CDP? The CDP Institute says that a CDP’s meaning is a “centralized hub that collects, unifies, and analyzes all your customer data from various sources.” The CDP, it says, acts as a unified customer database that provides a 360-degree view of each individual customer – a single source of truth for all your customer information. With that single source of truth, enterprise companies ultimately design personalized experiences for their customers. ## **What is a CDP’s Meaning? To Liberate Your Enterprise Data** What’s the meaning of a CDP? The original intent of a CDP was to overcome the difficulty of unlocking the value of customer data through a single customer view. In an [Invesp survey](https://www.invespcro.com/blog/data-driven-marketing/), 87 percent of marketers say data is their organization’s most under-utilized asset, and 54 percent say that the lack of data quality and completeness is their biggest challenge to data-driven marketing. An enterprise CDP should flip the dynamic, ensuring the business that data is [ready for AI](https://www.redpointglobal.com/blog/liberated-data-and-the-art-of-the-possible-data-readiness-for-ai/), ready to [drive innovative CX use cases](https://www.redpointglobal.com/blog/real-world-examples-of-how-data-readiness-delivers-impact/), ready to feed other engagement platforms with business-ready data. But because the meaning of a CDP has been diluted, and is now largely spoken of as a marketing tool for segmentation and activation, [data quality](https://www.redpointglobal.com/blog/the-heart-of-customer-data-technology-minding-the-data/) now takes a back seat to other functionality. A [CDP that deprioritizes data quality](https://www.redpointglobal.com/blog/data-quality-the-missing-ingredient-in-most-composable-cdps/) – and makes it someone else’s problems – does not optimize value from customer data. ## **Is Your Data Right?** Maximizing value from a CDP means getting your data right (complete, accurate, timely) and making it fit-for-purpose (actionable, timely, compliant). (**See Figure 1**) ![Data Readiness Slide](https://www.redpointglobal.com/wp-content/uploads/2025/05/Data-Readiness-slide-800x370.png)**Figure 1**: *A unified profile that is ready for enterprise use is right (complete, accurate, timely) and fit-for-purpose (actionable, trusted, compliant).* But getting your data right and fit-for-purpose means different things to different vendors. For example, most CDPs promise some version of a unified profile. But where and when does that unification occur? Does it happen in real time as data is ingested? Does the creation of a unified profile use persistent keys to provide a longitudinal view of a customer? Accuracy, too, means different things to different vendors in their interpretation of the meaning of a CDP. A CDP with basic cleansing and matching may only use a simple deterministic match and lack the ability to provide a contextual understanding of a customer, i.e., [householding](https://www.redpointglobal.com/blog/what-is-identity-resolution/). Timeliness as it relates to a CDP’s meaning is understood to mean making sure that data is ready for business use up to and including [real time](https://www.redpointglobal.com/blog/yes-real-time-is-compatible-with-a-composable-cdp/). It entails real time updates to the unified profile as new data is ingested. A unified profile must always reflect a real-time understanding of a customer or household. If, for example, the intended use case is a personalized CX, a brand must recognize a customer’s identity at the moment of interaction. Real-time product recommendations and other relevant offers depend on knowing everything there is to know about a customer, and using that knowledge to deliver an experience that is in the context of an individual customer journey. ## **Is Your Data Fit for Purpose?** Having the right data accomplishes the first part of what it means to be a CDP. The second component is making sure enterprise data is fit for purpose. Of course, what is meant by “fit for purpose” is not only different for every business, it can also be different for every use case. Being fit for purpose means that your enterprise customer data is actionable, trusted, and compliant. To be actionable, customer data must be accessible. In the context of a CDP, this means that a unified profile is not just complete, accurate and updated in real time. It also means it is accessible across the enterprise to all business users. A call center, customer service, GenAI, marketing – all departments and channels have the same updated view and contextual understanding of a customer or household, guaranteeing a consistent experience across all touchpoints. Trustworthiness suggests that the accuracy and completeness of data can be validated. Tunable matching and break-aparts, for example, enable marketers and business users to set and view the rules for how a match is made, and alter those rules depending on the purpose. Compliance encompasses more than use cases. Data that is fit for purpose will stand up to scrutiny from regulators and customers (i.e., privacy compliance), with a data governance structure that can be vetted as needed. Is customer data being used in accordance with a customer’s stated preferences? Is it being used appropriately for training AI models? Is PII/PHI data being protected? ## **Data is Right & Fit for Purpose: What’s Next?** With an understanding of the meaning of a CDP and the benefits that it can bring to the enterprise, the full value of data readiness becomes clear. A CDP *should* handle data readiness, but the truth is that most do not. To determine if your CDP is capable of making your data ready and fit for purpose, one worthwhile exercise is to explore customer data readiness best practices to see if the CDP has the requisite functionality to tick off a majority of the considerations. The best practices fall into six categories: - **Determining use case priorities** – It is important to align data strategy with business goals to ensure that data efforts are focused on delivering measurable outcomes for high-value initiatives. Can your CDP support all AI and CX use cases? Is it agile enough to support future use cases without a rip and replace? - **Data Architecture & Modeling** – Flexible, scalable data models and ingestion strategies provide the technical foundation needed to unify, process, and activate data across channels and systems. - **Metadata** – Rich, standardized metadata adds context, clarity, and control – making data easier to govern, audit, and activate for AI, analytics, and customer engagement. Unless a CDP has the functionality to handle a growing volume of metadata, it is not going to provide a full, contextual understanding of a customer across all interaction touchpoints. Metadata is an important and often underrated element for understanding the meaning of customer data – the *how* and *why* behind the *what* that paints a clearer picture of a customer’s real-time, ongoing customer journey. - **Data Quality & Trust** – High-quality data is non-negotiable. Trustworthy data fuels confident decisions, reduces risk, and ensures consistency across the customer lifecycle. Organizations should know whether their CDP automates and maintains high data quality, whether data quality is monitored continuously with alerts for anomalies, makes sure that rules are in place to minimize bias, and to verify that quality thresholds are able to be tuned for different use cases. - **Unified Customer Profiles** – Identity resolution and profile unification transform fragmented data into a comprehensive, real-time view of each customer – enabling personalization, orchestration, and intelligent engagement. There are a host of considerations in this category that align with a CDP’s meaning. If your CDP does not check these boxes, that is where a data readiness hub comes in. Among the considerations: resolving identities across all systems and channels, structuring profiles to support individual, household and business views, supporting near real-time or real-time profile unification, and managing identities across the full anonymous to known lifecycle. - **Data Governance & Compliance** – Robust governance frameworks protect data while ensuring its responsible use, meeting regulatory requirements and building trust with customers and stakeholders. Questions to ask of a CDP here include whether governance policies are grounded in unified customer identities, and whether data protection and residency practices meet regulatory and internal requirements. ## **The Value of Enterprise Customer Data** So, what is the real meaning of a CDP? Beyond the vendor definitions and technical jargon, a CDP is about ensuring that enterprise customer data is not just collected, but is truly ready for the business. That means data that is complete, accurate, and updated in real time, providing a single source of truth for every customer interaction. It also means data that is fit for purpose – actionable, trusted, and compliant – so it can power AI-driven personalization, real-time engagement, and cross-channel consistency. A CDP that neglects data readiness is less likely to achieve its expected business outcomes. In the end, the value of a CDP isn’t in simply aggregating data. The real meaning of a CDP is in how it transforms enterprise customer data into a strategic asset that drives customer experiences, fuels innovation, and delivers measurable business outcomes. **Blog categories:** Customer Data Platform, Data Readiness **Blog tags:** CDP, Data readiness --- ### [What to Know About Metadata at the Data Layer](https://www.redpointglobal.com/blog/what-to-know-about-metadata-at-the-data-layer/) **Published:** June 3, 2022 **Author:** Steve Zisk **Content:** We can make a distinction between data and metadata as they relate to delivering a personalized customer experience (CX) by thinking about putting together a jigsaw puzzle. If data elements are the collective pieces that create a finished work when put together with precision, metadata are implicit or explicit attributes of the pieces. The shadings, the shapes, the connections, the number of border pieces – anything that helps form an understanding of the bigger picture. Understanding and using those individual attributes is indispensable to organizing and completing the finished product which, if the analogy holds, is a personalized CX. In a broad sense, metadata is just data about data. Understanding the various categories of metadata, particularly with the collection of customer and product data, may help explain the role of metadata in creating and delivering a personalized customer experience. The first realm of metadata pertains to the semantic meaning of an individual piece of data, attributes that determine how we use and relate the data. This category controls naming, matching, and parsing protocols, default values and/or limitations on values – anything that helps a user of the data understand the meaning of underlying data and its contribution to the automation of the data quality process. Understanding that a five-digit number is a ZIP code rather than a quantity is one example, that might allow a system to apply default rules are for collecting the number as five digits or with a ZIP-plus four delivery route code. How a currency is entered is another example, such as rules or defaults for how many digits after a decimal, what currency symbol is allowed, or even parsing or displaying thousands of separators. Each data element will have its own rules for how to handle elements individually or collectively; currencies may be added together, for instance, where ZIP codes may not. Semantic metadata supports tasks and rules for the collection and analysis of incoming data. When an individual piece of data is collected, this metadata allows software or people to analyze the individual attributes to provide an understanding of what the data means. ## **Operational Statistics** A second realm of metadata as it relates to the collection of data from various sources is an operational understanding, both of the data itself and the characteristics of the data source. The operational characteristics include statistics such as the percentage of values in a particular column that had errors. How many ZIP code entries in a nine-digit field had just five digits? Were street addresses uniform? What percentage of emails show as undeliverable? Operational characteristics of the data source itself pertain more to questions such as how often are you reading from a particular data source, when did you last query it, what is the average delay between making a call out to a system and the system responding. In general, is the system operationally capable of handling what the user wants to accomplish? An understanding of this realm of metadata contributes to the transparency of overall system performance and the quality of information being put into the system. Is the data fit for purpose, essentially, and is it usable? ## **Metadata and Trust** The first two categories of metadata speak to the level of trust that a user, a marketing department, an organization has in data being collected. Semantic attributes and operational statistics about incoming data provide core metrics that represent the meaning and quality of the data itself and the quality of the data source in terms of its availability, recency, and performance. Trust, in this context, is derived from aggregate calculation over time across multiple attributes based on a set of rules and models that determine data quality. Trust stems from knowing the sources of metadata and understanding how you arrive at a particular piece of metadata. It ranges from very simple metadata, such as a database providing column names for a table, to much more complex such as parsing through all the values of a particular column to derive a statistic about the data itself. In addition to metadata describing incoming data from various data sources and providing meaning to the data, there are additional categories of metadata more closely associated with the use of the data in building or mediating customer experience, such as a relevancy/value index and various permissions. In a follow-up blog, we’ll delve into those metadata categories and how they help not only with an understanding of the underlying data, but also their importance in creating and refining a personalized customer experience. **Blog categories:** Data Quality, Master Data Management --- ### [What’s the Deal with Customer Metadata? Why it Matters for CX](https://www.redpointglobal.com/blog/whats-the-deal-with-customer-metadata-why-it-matters-for-cx/) **Published:** March 4, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/03/Metadata_427058113-e1551473976316.jpg)Consumers are generating data at a dizzying pace, leaving bits of information about preferences, habits and activities behind at every touchpoint. Multiple devices and interaction touchpoints including web, mobile, social, email, POS and call centers provide marketers with more information about customers than ever before. Today’s savvy marketers are in turn using this data as the lifeblood to create the personalized experiences their customers expect. One concern that marketers have is how to manage increasing data volumes while boosting the accuracy and relevancy of their marketing programs. Metadata is the answer. Metadata, in a broad sense, is just “data about data.” But that extra layer of data can imbue meaning to business data, providing the relationships and rules for managing data flow, analysis, and activation in customer engagement systems. To enable this single point of control, metadata is a vital component of any Customer Data Platform (CDP) worthy of the name. At the data layer, analytics layer, and the intelligent orchestration layer, metadata provides a contextual framework for customer data in terms of how, what, where, when, and why it is being collected and used. Whether for defining, managing, and understanding a marketing program, compiling a golden record, or determining rule-based tasks, metadata powers the routing, rights, and rules to ensure quality and optimize business outcomes. ## **Building a Foundation for a CDP** Using retail customer experience as an example, a retailer with siloed data, like a POS or e-Commerce system will quickly have problems personalizing the experience at checkout because all they know is what is happening at that very moment. With a robust CDP that can offer a single point of control over data, decisions, and interactions, the same retailer can create a contextually relevant offer or next-best action recommendation. In this scenario, metadata provides the underlying structure that directs how customer data is used. At the connected data layer, metadata empowers cleansing, correcting, and validating data about a customer – first, second, and third-party data, structured, unstructured, and semi-structured data – and helps direct the merging and probabilistic matching for online and offline identity resolution. It gives heft to data modeling by providing the tagging, timing, and sourcing details to meet compliance, privacy, and security requirements. At the analytics layer, automated setup and measurement of customer models is made possible by metadata. For a model that predicts CLV, for example, a marketer will need to know which data fields represent valid inputs for a training data model. This might include demographics like zip code, or age along with calculated or aggregated data like weighted monthly average purchases. But it’s not just the constructs; metadata also informs how each relates to the other and provides important metrics for the model itself – when was the model trained, how up-to-date was the data used to train the model, how trustworthy is the data, what permissions were granted. If we think about metadata at the connected data layer as providing meaning behind the data, when layered with metadata at the analytics layer it becomes a roadmap for marketers directing them how to provide personalization at scale. Finally, at the intelligent orchestration layer, metadata can help interpret the roadmap’s twists and turns, charting a future course on behalf of the customer based on decisioning. For attribution purposes, it could simply identify what channel a marketing program is targeted for or provide metrics to guide performance analysis. Metadata provides the markers to make sense of results, knowledge which can be used to create new models, run reports, and build attribution information. ## **Behind the Scenes of Personalization** Metadata can be a double-edged sword. More is not always better, so it’s valuable for the marketer and the data analyst to focus on metadata that has specific purposes. Getting the initial metadata setup right can save tremendous effort in managing, correcting, and connecting data for better customer engagement. Not all CDPs are created equal in the treatment of metadata. In the age of the connected customer it’s more important than ever to make the right decision with that in mind. Striking the right balance between competing interests, for instance between “vanilla” segmentation requiring only basic customer data and permission-based personalized offers and actions depends on having the right metadata. A CDP that stops at merging data into a customer record will lack the nuance for providing the right experience. Customers expect a brand to offer a relevant experience, but they don’t want you to be creepy; they want you to know about their buying preferences and patterns, but they don’t want you to violate their privacy. Metadata helps a marketer know exactly how to strike the right balance, because rules and algorithms are controlled and deployed to ensure data is used according to user preferences. For example, while an e-Commerce system must collect credit card data, metadata tells us when the data coming in is a credit card number and can automatically apply a different set of rules related to privacy and encryption. Attaching privacy, quality, or auditability metrics about data ensures that a marketing organization meets not just customer expectations for personalization, but regulatory and organizational expectations. In a data-driven world, metadata is the stock in trade that provides marketers with the details to know the value of every piece of customer data, how it relates to customers and the brand, and how it can be used to master personalization at scale. **Blog categories:** Customer Data Platform, Data Management, Data Quality --- ### [Xanterra, Redpoint, and Microsoft: “A Piece of Magic”](https://www.redpointglobal.com/blog/xanterra-redpoint-and-microsoft-a-piece-of-magic/) **Published:** July 18, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/07/shutterstock_702949534-e1563375106953.jpg)Summer is a popular time for vacations, which no one knows better than Xanterra Travel Collection, a leading travel and hospitality company with a diverse portfolio of brands that offer unique experiences around the world. From cycling tours through French wine country to National Park excursions, the [Redpoint Global client](https://www.redpointglobal.com/wp-content/uploads/2017/10/CA-XAUS0917-02-Xantera-lo-res.pdf) seemingly has a getaway to suit any personal preference. With the Redpoint Customer Engagement Hub™ running on the Microsoft AI platform, Xanterra now has a far better understanding of the personal preferences for each guest and prospect – so Xanterra tailors offers and promotions accordingly, resulting in significant campaign cross-sell and ROI increases. In a [video](https://www.youtube.com/watch?v=EHNBvshgNbA) that Microsoft AI unveiled at [Microsoft Inspire](https://partner.microsoft.com/en-us/inspire), which runs through Thursday, Xanterra Chief Marketing Officer Betsy O’Rourke and Corporate Director of Marketing & CRM Andrew Heltzel discuss how Redpoint Global and Microsoft provide Xanterra brands with a single view of the guest allowing highly tailored specific offers across the portfolio. Together with Redpoint CEO and Co-Founder Dale Renner, the three explain how the platform allows Xanterra to optimize customer engagement across all touchpoints, delivering personalization that is in sync with each guest’s experience. Below is a transcript of the video, which was filmed recently at The Oasis at Death Valley, one of Xanterra’s iconic resort destinations at Death Valley National Park, California. ***O’Rourke****: Vacations create some of our most meaningful memories. Every single trip is an individual experience. We are a very unique collection of brands.* ***Heltzel****: Our data was siloed and literally dispersed all over the globe. That’s why we partnered with Redpoint Global.* ***Renner****: Redpoint helps Xanterra optimize customer engagement strategies across all touchpoints. Xanterra had over 100 different data siloes, and we were able to combine that data so that all the brands can align and work together to drive more relevant guest experiences. And working with Microsoft AI allows us to really personalize the guest experience at scale.”* ***O’Rourke****: Having that single view of the guest allows us to really modify our marketing strategy.* ***Heltzel****: When we got all of our data into a single platform we realized many of our guests are shared amongst all of our brands. We could actually put some strategy behind those relationships. There’s a lot of opportunity to further cross-promote.* ***O’Rourke****: Now, we can very quickly develop new travel opportunities and target a specific group of customers with a very specific offer.* ***Renner****: Revenue per email increased by over 800 percent while the number of guest touches actually reduced.* ***Heltzel****: Our team feels so much more confident with this personalized approach. We’ve demonstrated again and again that we’ve been able to really transform the guest experience for many of our brands.* ***O’Rourke****: The competition has yet to catch up to where we already are. We have a huge advantage because we’ve got Redpoint Global and Microsoft AI.* ***Heltzel****: Everything we do is focused on delivering a world of unforgettable experiences for our guests.* ***O’Rourke****: If we can give you what you’re looking for with one or two clicks, that’s a piece of magic.* Interested in learning more about how Xanterra and other brands work with Redpoint and Microsoft? [Contact us](mailto:contact.us@redpointglobal.com) to learn more! ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, AI & Machine Learning, Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization, Travel & Hospitality --- ### [Why Omnichannel KO’s Multichannel in the Customer Experience Arena](https://www.redpointglobal.com/blog/why-omnichannel-kos-multichannel-in-the-customer-experience-arena/) **Published:** January 6, 2022 **Author:** John Nash **Content:** One way to think about the difference between omnichannel and multichannel in the context of customer experience is to picture the classic game of telephone tag. An omnichannel customer experience is unbroken, consistent messaging marked by a complete understanding at every touchpoint. A [multichannel customer experience](https://www.redpointglobal.com/blog/omnichannel-vs-multichannel-cx), by contrast, more often than not reaches a disjointed, frustrating conclusion. The parties involved know that somewhere along the line there was one or a series of misunderstandings, but they can’t pinpoint the breaking points. Of course, delivering an omnichannel customer experience is not a game play, not when 82 percent of consumers surveyed say that they are loyal to brands that demonstrate a thorough understanding of them as a unique customer, according to results of a recent [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) commissioned by Redpoint. To further make a distinction between omnichannel and multichannel, an omnichannel customer experience goes far beyond engaging a customer across a few retail channels such as a mobile app, email and in-store. It’s one thing to understand a customer across three or more channels, it’s another to have a complete contextual understanding of a customer irrespective of how the customer engages with every part of a brand, even via a call center, service, returns, etc. With a full contextual understanding, a brand moves in the cadence of the customer throughout an entire customer journey. It’s a natural, free-flowing holistic experience where a brand is communicating to a customer with one consistent voice, unlike a multichannel CX which too often feels to a customer as if there’s an entire committee involved. ## **Stay Connected with Real Time** The reason for the all-too familiar trial by committee brand marketing experience, is that brands largely operate centered around channels instead of centering around the individual. Without a central point of truth brands lack the depth of understanding necessary to create a frictionless experience. Consider for example a brand’s website and mobile app exchanging a limited data field – say a consumer’s name and address. Each channel recognizes an engagement, but each also locks in its own knowledge of what the customer is doing. Transaction history, current behavior, intent – those are all siloed by channel, creating a loss of fidelity and an eventual fractured customer experience. Timeliness is another crucial element of an [omnichannel](https://www.redpointglobal.com/blog/the-art-of-listening-in-mastering-omnichannel-marketing/) customer experience. In addition to a failure to tie every channel and engagement touchpoint together, many brands lack a real-time capability that is a vital component for staying in the cadence of the customer. With a typical 24-hour data lag, brands are often left playing catch up to stay connected with a customer journey. Often, though, being a day late – or in some cases minutes or seconds late – means a loss of visibility into a journey, and a subsequent loss of context. The byproduct is a loss of relevance; misunderstanding seeps in, causing a brand’s actions to be inconsistent with a customer’s current state. There is an important distinction to make between having a real-time view with respect to each channel, and a real-time view that spans the entire customer journey. A brand may have a real-time view into a customer’s browsing history, capturing clicks, time on page, opens, views, movements, etc., and use that information for real-time web personalization, but that data is typically not shared with other channels. A classic example of how such a lack of transparency may introduce friction is an abandoned shopping cart. A customer may follow an abandoned cart up with a buy online, pick-up in-store (BOPIS) transaction, but lacking the real-time sharing of data across channels, a brand will miss out on valuable cross-sell or up-sell opportunities when the customer arrives in-store. ## **Relevance and Real Time** Real-time data is the lifeblood that provides brands with the depth of understanding required for delivering an omnichannel customer experience that aligns with consumer expectations, and brands seem to be on board with the concept. In the Harris Poll survey, 41 precent of marketers said that personalizing experiences with deep relevance and context that is meaningful and valued by the customer is driving advancement and innovation in their CX strategy. Furthermore, 36 percent said innovation was driven by the need to create flexible delivery systems (telehealth, curbside pickup, online banking, etc.) that meet the customer where and when they want services, and 32 percent said that creating always-on, omnipresent omnichannel experiences and support was a driving force propelling CX initiatives. To accomplish these objectives, brands cite improved data quality as an overarching imperative, with 63 percent of marketers saying it was the top area of increased investment. ## **Data Quality Underpins Omnichannel Consistency** Until recently, brands frequently conflated an omnichannel customer experience with a personalized customer experience, but it’s important to understand another key distinction. A personalized customer experience is certainly possible on a channel-by-channel basis, but surface level personalization without cross-channel awareness is not what customers mean when they say they’re loyal to brands that demonstrate a thorough understanding of them as a unique customer. In the Harris Poll, when 39 percent of customers say they will not do business with a company that fails to offer a personalized experience, they are referring to omnichannel consistency – the deep, personal and contextual understanding that transcends channel. That is only possible through having high quality customer data – an up-to-date, real time, accurate representation of a customer. The gap between high quality data that produces an omnichannel experience vs. data that produces surface-level personalization (or, worse, a fractured experience) is measurable. In recent [McKinsey research](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) on the value of getting personalization right, studies showed that companies shifting into the top-quartile performance in personalization would generate over $1 trillion in value, and that companies that excel at personalization generate 40 percent more revenue from those activities than their peers. With a real cost attached to misunderstanding the considerable differences between multichannel and omnichannel, brands can no longer afford to deliver experiences that fail to meet exalted customer expectations for consistency and relevance however and wherever they engage. ## **Related Content** [Do You (Really) Know Who I Am? Why Advanced Identity Resolution is Vital for Omnichannel Customer Experiences](https://www.redpointglobal.com/blog/do-you-really-know-who-i-am-why-advanced-identity-resolution-is-vital-for-omnichannel-customer-experiences/) [The New-Look Customer Experience in 2022: First-Party Data & Omnichannel will Drive Innovation](https://www.redpointglobal.com/blog/the-new-look-customer-experience-in-2022-first-party-data-omnichannel-will-drive-innovation/) [Multichannel vs. Omnichannel Marketing and Keeping Up with a Customer Journey](https://www.redpointglobal.com/blog/multichannel-vs-omnichannel-marketing-and-keeping-up-with-a-customer-journey/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Real-Time Personalization --- ### [Why Banks Strive for a Connected Customer Experience](https://www.redpointglobal.com/blog/why-banks-strive-for-a-connected-customer-experience/) **Published:** November 1, 2021 **Author:** Arun Rajagopal **Content:** *Editor’s Note: For an expert perspective on digital transformation in financial services, we’ve invited Arun Rajagopal, vice president of marketing solutions at Genpact to share his experiences with financial institutions shaping digital transformation efforts to deliver a connected customer experience. Rajagopal is in Genpact’s digital team in the customer engagement group, focused on driving marketing solutions and everything related to customer data.* In a recent survey Redpoint[ connected with Dynata](https://www.redpointglobal.com/press-releases/redpoint-survey-majority-of-consumers-say-banks-are-failing-to-meet-expectations-for-personalized-experiences/), 88 percent of consumers said that banks should have seamless communications and interactions across all channels (branch, website, mobile app, contact center, etc.), updated with timely, relevant and valuable information. Yet only 45 percent of consumers surveyed said that banks are effective at providing this type of experience*.* Based on these customer expectations, there is an increased focus for banks to rapidly deliver a connected experience across products and channels. Three key trends are fundamental aspects of the digital transformation of this marketplace. First, the entire bank is becoming a consumer of data and analytics. Delivering a [consumer-centric experience](https://www.redpointglobal.com/blog/why-banks-strive-for-a-connected-customer-experience/) requires any line of business or department that communicates with the customer, from marketing to contact center to collections, to come together around the customer. Second, banks are looking to eliminate limitations in their existing infrastructure that prevent the delivery of a [connected customer experience](https://www.redpointglobal.com/blog/why-banks-strive-for-a-connected-customer-experience/). A collection of unintegrated engagement solutions is ill-suited to interact with customers throughout real-time, omnichannel journeys. Cloud-native solutions that can seamlessly unify information across channels are key to the long-term growth of digital-first banking. Third, the overarching importance of data and analytics for the entire bank is leading to a significant rescaling of talent – for banks and solution providers alike. The need to put a customer at the center of experience is causing banks to shift from being driven by product and/or channel-based profit and loan (P&L) statements. Historically, each product/channel would try to sell each customer according to its own interests. A connected customer experience instead considers and makes offers in the context of an individual customer’s current situation – life stage, household dynamic, income, debts, etc. **Connected Customer Experience: An Inside-Out Approach** An acceleration of those three trends really comes down to an overarching question: Who owns, should access and should utilize [customer data](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/) at the bank? The answer to that question will shed light on how to make data actionable across the institution, what form infrastructure will take, and how people and processes will support a new approach. What we’re seeing is banks starting with a vision for what a connected customer experience will look like and working backward from there. They know that customer experience is paramount and understand the need for omnichannel orchestration. To achieve omnichannel integration, they need a data strategy that brings customer data together across the organization. And to execute, they must identify shortcomings of their existing infrastructure and determine opportunities to modernize. We’re helping banks to clarify their vision for a connected customer experience. Engagements are often prompted because of a business problem that stems from the existing product P&L structure. For instance, the marketing organization may be inundating customers with offers for a certain product – savings account, auto loan, etc. – using only basic, static audience segmentation that fails to account for the individual customer journey or life stage. In banking today, there is also still a lot of confusion about the marketplace, both in the terminology used – CDP vs. DMP vs. MDM, vs. Data Lake etc. – and in general about the ecosystem complexities. Frequently conversations gravitate toward core omnichannel orchestration, marketing automation, and real-time personalization capabilities. Clients grasp that data management and omnichannel orchestration feed off one another, and that an integrated marketing automation platform helps advance digital transformation initiatives faster than the alternative, which is to spend months trying to integrate various data sources. We’ve turned to Redpoint’s rg1 platform to help solve this issue. **Data Management and Orchestration: A Team Effort** On the data management side, there is a lot of traction around building a unified customer profile or golden record as the foundation of a connected customer experience, an important consideration for breaking free from a product P&L approach. A golden record that tells the bank everything there is to know about a customer is the key to providing relevant engagements. In a traditional P&L product approach, all customers in one segment may have been offered Product X. With a golden record, however, a bank may determine for one customer, Product X is less important than Product Y. More granular segmentation is possible when a customer’s behaviors, preferences, history, etc. are all included in a golden record – online session activity, contact center interactions, transactions – a bank will be confident that for one customer, a sequence of Products XYZ matches their needs, while another may be better served with a sequence of Products YXZ. Omnichannel orchestration is where the “connected” part of the customer experience comes into play, ensuring banks fine-tune offers, messages and content not only in the proper sequence according to individual customer needs, but also on the right channel (inbound/outbound) and at the right time. This is really the transformational change we’re seeing, where banks have traditionally concerned themselves with the “who” and the “what” (e.g., Persona A receives an offer for Product X) but with real-time personalization at play are now grasping the importance of the “when” and the “how”. When all four components are at play, the result from the customer’s perspective is a seamless, connected customer experience irrespective of how they’re engaging with the bank. **Data and Opportunity** The interplay of data management and omnichannel orchestration lays bare the need to align people and processes with the customer-first mindset. Because data and analytics drive the connected customer experience, there must be an institution-wide data strategy that will take people out of their comfort zone. Product and/or channel teams – email, website, contact center – might remain, but they will at the very least have to have access to the same unified customer profiles, and must work in concert to put a customer above provincial interest. Overall, we’ve seen a lot of excitement from clients in this area who are beginning to really see that data leads to opportunity. Each division of the banking system is answering their own questions about how data impacts interaction. Each is figuring out how does having real-time, actionable data impact sales, for example, or fraud and risk and what is the impact on operational processes? These are not marketing questions. Rather, they’re institutional and get to the heart of what it means to be a data-driven, customer-centric organization. Tailwinds indicate an ongoing acceleration of digital transformation in banking. All the conversations we’re having around prioritizing data and analytics, modernizing technology and change management show how seriously banks are taking the delivery of a connected customer experience as a cornerstone of digital transformation efforts. ## Related Content [New Customer Shockwaves Drive Need for Personalized Customer Experience (CX) in Banking](https://www.redpointglobal.com/blog/new-customer-shockwaves-drive-need-for-personalized-customer-experience-cx-in-banking/) [Banking on Change: Why a Golden Record Satisfies Customer Expectations for a Holistic Experience](https://www.redpointglobal.com/blog/banking-on-change-why-a-golden-record-satisfies-customer-expectations-for-a-holistic-experience/) [Personalization in Banking: How Banks Can Do Better by Overcoming Assumptions](https://www.redpointglobal.com/blog/personalization-in-banking-how-banks-can-do-better-by-overcoming-assumptions/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Identity Resolution, Real-Time Personalization --- ### [What is Experiential Marketing?](https://www.redpointglobal.com/blog/what-is-experiential-marketing/) **Published:** December 1, 2020 **Author:** Steve Zisk **Content:** ## **Experiential Marketing Definition In A Few Words** [What is experiential marketing](https://www.redpointglobal.com/blog/what-is-experiential-marketing/)? Also known as engagement marketing, experiential marketing is the process of creating a unique, meaningful experience with [customers](https://www.redpointglobal.com/blog/commercial-excellence-begins-with-customer-experience/) or prospects in order to change or reinforce their perceptions of a brand. The experience generally entails an invitation to participate in a physical interaction that offers the potential for fun, surprise or adventure, all with the underlying purpose of allowing customers to shape an image of a brand beyond the product it is ostensibly trying to sell. ## **What are Examples of Experiential Marketing?** - Heineken’s [“Departure Roulette”](https://www.youtube.com/watch?v=cg0oBV32ZDk) campaign hit all the right notes as a great example of what makes successful experiential marketing so powerful. At a few busy airports around the world, the Dutch beer company invited travelers to press a button on a display board, which spun letters to reveal a surprise, exotic destination anywhere in the world. Meant to mirror the “thirst for spontaneity and adventure” of Heineken drinkers, travelers who agreed to participate were rewarded with a free flight, all-expenses paid trip with $2,000 in spending money – provided they agreed to forego their existing plans and – on the spot – head off for the new destination. - Lean Cuisine’s [“WeighThis”](https://www.youtube.com/watch?v=HGYUiLxoJFA) campaign was a masterstroke in experiential marketing that empowered [customers](https://www.redpointglobal.com/blog/3-obstacles-to-maximizing-the-value-of-customer-data/) to not only shape a new perception of a brand, but to do so in a way that positively reinforced their own self-image. With a pop-up display in Grand Central Station, the brand invited women to write on a scale how they weigh themselves – exclusive of the usual concept of pounds or body image. “I’m 55 and back in college,” “Caring for 200 homeless children”, “I saved my brother’s life” were among the hundreds of messages posted on a wall. Like the Heineken campaign, apart from a Lean Cuisine logo, the brand’s products were nowhere to be seen. - JetBlue’s [“Ultimate Icebreaker”](https://inkincaps.com/2018/10/12/experiential-marketing-at-its-best/) campaign, while more directly tied to promote a specific product (direct flights from New York to Palm Springs, Calif.) still qualified as an innovative experiential marketing campaign, particularly through a unique gamification idea, a staple of . many experiential marketing campaigns. To promote the new direct flight, the airline encased summertime accessories (golf clubs, a tennis racquet, beach chair, etc.) inside an enormous block of ice that was placed on a busy New York sidewalk at the outset of winter. Passers-by were welcome to chip away at the ice with whatever they had on their person and were welcome to keep any of the items they uncovered. ## **Characteristics of Experiential Marketing Campaigns** These and other [successful examples](https://blog.bizzabo.com/experiential-marketing-examples#jetblue) of experiential marketing share some common traits. They invite customers or prospects to participate in an outside-the-box experience that is not directly tied to the selling or promotion of a product. They entail a social media campaign to spread awareness and tie back to other brand messaging around the campaign. And they encourage customers to re-think perceptions about what a brand stands for. Heineken = adventure. Lean Cuisine = powerful women. JetBlue = Escape winter doldrums. Done well, experiential marketing is about creating a memorable experience that delivers an authentic connection with a brand, apart from the product. I may be dating myself with this reference, but the [Pepsi Challenge](https://en.wikipedia.org/wiki/Pepsi_Challenge) was such a memorable experiential marketing campaign that it is still culturally relevant 45 years after its debut. The brand famously asked passers-by to participate in a blind taste test to determine what tasted better – Pepsi or Coke. Even though here the experience did involve the product, the experience itself was such a completely novel concept that it didn’t even matter. Rather than thinking they were being sold a product, participants instead felt like empowered executives – generals helping to determine an outcome in the ongoing soda wars. The challenge was – and is – a great example of how successful experiential marketing helps to change a perception of a brand – with consumers taking the lead. ## **Why Try Experiential Marketing?** There are many reasons why brands explore experiential marketing. Changing perceptions, as we’ve seen with the above examples, is one. Another reason, of course, is profit. Lean Cuisine’s WeighThis campaign was brilliant in many ways, not least of which is that the campaign – along with a broader re-branding – is credited with a [$58 million](https://www.chiefmarketer.com/lean-cuisines-package-redesign-drives-58-million-sales-increase-in-one-year/) year-over-year sales increase. After four years of declining sales totaling $400 million, 2015 started with more double-digit losses in Q1. By Q3, the re-branding – which included removing the word ‘diet’ from packaging – combined with the WeighThis campaign resulted in double-digit gains. Another reason why companies are eager to try experiential marketing is data. Misereror, a nonprofit in France dedicated to ending world hunger, is a great example of an innovative experiential campaign where consumers willingly exchange personal data in exchange for controlling the experience. In this [“social swipe” campaign](https://diousa.com/blog/experience-of-the-week-donation-billboard/), the company used interactive digital posters in airports to collect donations. When a person swiped their credit card to make a small donation, the poster showed a loaf of bread being virtually sliced and a portion going to a hungry recipient. A thank you note from the charity accompanied the donor’s bank statement, and asked if they would like to extend their initial donation into a monthly donation. Of course, because the donor had already provided their credit card information as part of the experiential marketing campaign, agreeing to the recurring charge did not entail having to collect additional information. Even experiential marketing campaigns that do not ingeniously have customers willingly provide credit card information still receive personal information – names, addresses, email, etc., JetBlue, Heineken and Lean Cuisine among them. With customer data harder and harder to come by thanks to GDPR, CCPA, the loss of third-party cookies and a deepening mistrust, experiential marketing helps bridge the widening gap. Experiential marketing counters consumer mistrust by changing the formula of a brand retrieving consumer data for the purposes of selling a product. Instead, when consumers engage with experiential marketing, they tacitly acknowledge that the experience is relevant: it encapsulates a personal trait, a goal, a desire. The Heineken campaign, for example, played on consumers’ sense of adventure and spontaneity. The personal data that is provided is a reward for the branded experience controlled by the consumer. ## **Experiential Marketing and Competing on CX** There are many similarities between experiential marketing campaigns and the growing trend of consumer packaged goods (CPG) companies opening [direct to consumer (DTC) channels](https://www.redpointglobal.com/blog/changing-customer-behaviors-open-new-dtc-doors/). Both aim to retrieve consumer data in unconventional ways, and both aim to reshape brand perception. While there are distinctions – the actual experience in experiential marketing chief among them – they’re really just different sides of the same coin, which is that brands are now competing on customer experience (CX). A [McKinsey study](https://www.mckinsey.com/business-functions/operations/our-insights/elevating-customer-experience-excellence-in-the-next-normal) published in June identified “dynamic customer insights” as one of three pillars that will define customer experience in the post-pandemic era, along with digital excellence and safe, contactless engagement. Experiential marketing accomplishes much of what’s needed to compete on CX by providing a differentiated customer experience, while also collecting personal data and insight about what’s important to a customer. Certain industries, [like travel](https://www.proven.partners/blog/experiential-hotel-marketing), are leaning heavily into experiential marketing. By tying consumers’ perception of a brand to experience rather than price and product, brands have a foundation for building on a positive image with subsequent interactions that are personalized and relevant to an individual customer’s preferences and behaviors. Experiential marketing provides brands with an important head start in competing on customer experience because, at its core, the exercise invites customers to judge the brand on the experience that’s provided. By completely changing the dynamic about what it means to engage with a brand, experiential marketing empowers consumers to begin to shape their journey with a brand. The differentiated customer experience sets the tone, putting competitors on notice that a brand will not take a back seat when it comes to competing on experience. ## Related Content [Now is the Time to Set Ambitious Marketing Goals](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/) [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) [A Personalized Customer Experience (CX) Delivers Loyalty](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Real-Time Personalization, Segmentation & Activation --- ### [What is Clienteling and How Does it Impact Personalization?](https://www.redpointglobal.com/blog/what-is-clienteling-and-how-does-it-impact-personalization/) **Published:** October 10, 2019 **Author:** Redpoint Global **Content:** ## What is Clienteling? *Clienteling is a technique used by retail sales associates to establish long-term relationships with customers based on data about their preferences, behaviors, and purchases. Using devices, sales associates deliver personalized interactions to customers to enhance the customer experience*. The retail industry is in the midst of profound change. Current and anticipated store closings have many predicting the demise of the in-store experience, but the reality is that in-store remains a core focus for retail and will be for a long time. While online-only competitors have many advantages, a physical location is not one of them – and retailers recognize that they can differentiate by transforming the in-store experience to make sure it remains compelling and convenient. ## **The Next Evolution for In-Store Customer Engagement** Clienteling is one way to transform the in-store experience. Meaning “catering to clients,” clienteling refers to the processes and technologies used to promote customer satisfaction or guided selling through the personalization of the in-store shopping experience. Clienteling technologies rely on a variety of customer datapoint such as purchase history, preferences, behavioral and response data consolidated across platform throughout the enterprise to empower in-store associates to deliver personalized interactions. Retailers rely on clienteling technology to elevate the relationship with customers on a new level, increasing the ability of a store sales associate to make shopping more personal and relevant. According to [Boston Retail Partners](https://brpconsulting.com), 63 percent of retailers today are unable to identify their customers prior to checkout and 20 percent can’t identify them until after checkout or not at all. Empowering in-store sales associates with customer data and the right customer engagement technology helps [retailers](https://www.redpointglobal.com/blog/what-is-clienteling-and-how-does-it-impact-personalization/) address the problem, increasing their effectiveness as well as revenue. Store associates can now engage with in-store customers based on an understanding of their past purchases, preferences, needs, wants, and interests. All the customer data once locked in a CRM/POS or marketing engagement system is now available to the store associate backed by a decisioning engine to determine and recommend what products will spark interest for a specific consumer. Powered by real-time data, clienteling mobile apps enable the store associate to deliver valuable insights and product recommendations that address a unique customer’s needs at point-of-sale. These apps include inventory checks to ensure the associate doesn’t recommend items that may be out of stock or sizes that aren’t available in the store. And recommendations aren’t limited to sales of new products. Clienteling can help with payment options (store card, loyalty points or possibly mobile payment like Venmo), returns and exchanges, or fulfillment options. Tailoring the entire shopping experience to meet a customer’s individual needs and wants helps ensure the customer will not purchase the desired product from a competitor. ## **Clienteling Tips** There are two main types of clienteling: directed clienteling and self-directed clienteling. Directed clienteling, which is delivered via a mobile app with a touchscreen interface, enables the in-store sales associates to quickly match products and promotions to customers based on past purchases, buying preferences, style selections, social network affinity, mobile interactions, and ecommerce behaviors. Customer information is automatically delivered to the sales associate’s in-store tablet or mobile device making it easier for them to interact in highly relevant and personalized ways with customers, such as using the latest customer data coupled with a real-time decisioning engine to deliver the next-best, most relevant offer in-person at the point of contact. While employee-facing mobile apps and devices empower associates with real-time information, self-directed clienteling refers to customer-facing apps that enable brick-and-mortar retailers to push highly relevant offers and messages to shoppers the minute they walk through the door. Customers use a brand’s mobile app in-store for a number of reasons, including redeeming in-store discounts, comparing prices, viewing product ratings/reviews, and in-store navigation. This provides brands with a unique way to engage their customers on a highly personal level. Technologies that enable a self-directed clienteling experience include: - In-store beacons that transform the in-store shopping experience by engaging with customers (both outside and inside the store) with targeted push notifications, in-app chat, and targeted SMS messages - Access to online and store inventory to ensure any off-shelf or out-of-stock situations can be capitalized on with an alternative fulfillment for the customer - In-store navigation support - Augmented reality support for retailers looking to digitize their in-person selling support by adding interactive image and video overlay to increase customer engagement ## **Benefits of Retail Clienteling Strategies For Brands** The use of a clienteling app in-store is particularly effective for brands as [85 percent of consumers](https://www.timetrade.com/about/news-events/news-item/study-85-of-consumers-prefer-to-shop-at-physical-stores-vs-online/) still prefer to shop in brick-and-mortar stores rather than online. Clienteling allows brands to engage with customers in ways that will increase the average order dollar value of each store visit, giving sales associates the power to identify the most loyal customers and target the servicing of those customers with offers based on complete understanding of that customer’s past purchases, preferences, needs, and wants. In-store is different from ecommerce. Online, retailers have access to shopping history details but in-store shoppers are effectively anonymous when they walk through the store entrance. A clienteling app can access transactional and behavior data, allowing in-store sales associates to engage a customer with offers based on their previous purchases or the customer’s value to the brand. Clienteling has the added benefit of enabling brands to capture activity in the store, building a complete omnichannel picture of customer activity. ## **The Role of Data Management in Clienteling** A seamless customer experience across all touchpoints to deliver a superior in-store clienteling experience is made possible only with a single customer view. A real-time, continuously updated profile is a foundational pillar for sales associates to engage with customers in the right context and cadence. Monetizing data through a differentiated customer experience requires more than having a single view; it requires making decisions about what to do with the data and activating those decisions in the right channel and in the right context and cadence of a customer journey. The in-store experience becomes more valuable to the brand and the consumer when it seamlessly connects with every other part of a unique buying journey. A single point of operational control over data, decisions, and interactions enables this seamless connection, and provides brands with a way to hyper-personalize a customer experience across the entire journey. Successfully done, clienteling provides a differentiated experience that ensures a personalized buying journey at every touchpoint and across every channel including anonymous in-store visits. In a [Harris Poll survey](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) commissioned by Redpoint, 37 percent of consumers said that they are more likely to purchase or use services from a brand that sends personalized offers or messages. The expectation for personalization does not stop when a customer makes an in-store visit, and neither should a brand’s personalization strategy. **RELATED CONTENT** [Clienteling and the Personalized In-Store Experience](https://www.redpointglobal.com/blog/clienteling-and-the-personalized-in-store-experience/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Customer Data Platform, Omnichannel Marketing, Retail, Single Customer View --- ### [What Does it Mean to be a Good Data Steward?](https://www.redpointglobal.com/blog/what-does-it-mean-to-be-a-good-data-steward/) **Published:** February 2, 2022 **Author:** Steve Zisk **Content:** Back in the day, “webmaster” was a prized title that bestowed respect on the holder as a knowledgeable jack of all trades for everything pertaining to website development and administration. But as the internet matured, there came a growing recognition that one person, however skilled, simply could not handle every task for building an online presence, and the title lost favor, replaced by many niche areas of expertise: web developer, architect, publisher, coordinator, content editor, etc. Considering the increasing need to deliver a transformative customer experience (CX) that includes carefully honoring a customer’s requests for the collection, use and sharing of their data, it may be time to refine how we think about the role and responsibilities of a data steward. [What does a data steward do](https://www.redpointglobal.com/blog/what-does-it-mean-to-be-a-good-data-steward/)? Traditionally, a data steward worked in IT, with a role of governance – usability, availability, and security of the data. Though an individual steward may have held some business or subject matter expertise, in that context or narrow focus, the role was understood as using technology and/or processes to meet legal and internal governance requirements. To meet those requirements, data management technology like [Master Data Management (MDM)](https://www.redpointglobal.com/customer-data-management/master-data-management/) software typically has data stewardship functionality to oversee sourcing, cleansing, mastering, publishing, and auditing data “entities” like customer/party, product, or site. The software helps the steward validate that the data representing these entities are fit for purpose, and available to the people and applications that need them. A data steward is responsible for overseeing the quality, accuracy, and completeness of records, and the processes for correcting errors and making changes. An example of this in the customer realm is handling data subject requests, such as deciding the appropriate response if a customer asks to delete a record. A data steward may also be called on to oversee match discrepancies, deciding whether a record that fails to meet a certain threshold as a solid match is or is not a valid match. Those are the traditional responsibilities of the data steward role, as they pertain to master data management initiatives: is data up to snuff, so to speak, for its intended purpose? ## **Data Stewardship & Shared Customer Values** If we look at all the modern demands for quality data – especially customer data – we find that data stewardship activities fit the “webmaster” analogy: As the set of responsibilities expand in parallel with the growing importance of delivering a transformative customer experience, the steward must wear more hats or has to specialize their role. In a 2021 Dynata survey commissioned by Redpoint, [80 percent of US consumers](https://www.redpointglobal.com/press-releases/80-of-consumers-more-likely-to-shop-with-brands-that-show-they-understand-them/) surveyed said they will only shop with brands that personally understand their needs. This personal understanding means far more than simply knowing someone’s size or color preferences, or how they like to communicate with their primary care physician. It also entails a brand being completely transparent about how it collects, shares and uses a customer’s data, honors privacy preferences like contact frequency or contact channel, and in general demonstrates a complete understanding of the customer beyond brand interactions – household information, employment, life events etc. Effective data stewardship plays a key part in fostering a deep personal understanding. From a policy perspective, data stewardship through a CX lens sets a requirement for how long to keep individual transactions in a customer database, for example. It determines how to handle personally identifiable information (PII); what information goes into a PII vault vs. non-identifiable customer data. Included in policies would be who is allowed to see or use PII data, how data usage is tracked, how to respond to data subject requests. With a transformative CX as an overarching goal, data stewardship policies may take a more liberal approach toward keeping customer records for a longer period. They may also prioritize what type of data is collected, based on how it is used for an intended purpose of enhancing a personalized CX. And they may recognize that while customer data itself is an asset, mishandling data or failing to honor customer preferences and regulatory requirements can quickly become a liability. The broader role of data steward will encompass what data is collected, understand why it is being collected, and adhere to policies for keeping, managing and protecting it on behalf of the customer. In this context, data stewardship is understood to mean what a brand is doing to make sure that customer experience transformation has the best possible impact on the customer with the fewest negative outcomes. In a world where a personalized customer experience that reflects a personal understanding is quickly becoming a base-level expectation, a data steward’s evolving role beyond meeting legal and governance requirements is to demonstrate – by action, deed and policy – that the brand shares fundamental customer values, whatever they may be. ## **Related Content** [Build a Comprehensive Customer Understanding Through Perfect Data](https://www.redpointglobal.com/blog/build-a-comprehensive-customer-understanding-through-perfect-data/) [The Elevation of Experience: Why a Deep Customer Understanding Matters Even More](https://www.redpointglobal.com/blog/the-elevation-of-experience-why-a-deep-customer-understanding-matters-even-more/) [Wave a Magic Wand: What Can You Accomplish with Perfect Data?](https://www.redpointglobal.com/blog/wave-a-magic-wand-what-can-you-accomplish-with-perfect-data/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Identity Resolution, Master Data Management --- ### [Take a Data-Driven Approach to a Consumer’s Healthcare Journey](https://www.redpointglobal.com/blog/take-a-data-driven-approach-to-a-consumers-healthcare-journey/) **Published:** May 28, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/05/Healthcare-data_1114154543-e1558721793595.jpg)Healthcare organizations – whether payers, providers, retail pharmacies or medical device manufacturers – are increasingly using first-party, second-party, and third-party data to personalize engagement with consumers. Using this data to know all that is knowable about an individual consumer, healthcare enterprises can engage in more targeted marketing to acquire new members and patients and to provide personalized communications as part of the ongoing engagement that improve outcomes. Organizations are at varying stages in collecting and using this data to drive improved performance. One example includes using a variety of third-party attributes to better understand lifestyle, behaviors, age, and location to personalize messages when acquiring new consumers. Second-party data is key to [value-based care initiatives](https://www.redpointglobal.com/blog/the-role-of-personalization-in-value-based-care/) where providers get claims data from payers, and payers get electronic health record (EHR) data from providers to treat consumers more holistically in driving to improved outcomes. This can be combined with first-party data (data originating from within the enterprise), and with other third-party data such as population health data to then recommend the next best action that may fill a gap in care. This may include outreach to set up an appointment for a baseline body mass index (BMI), a test for diabetes, a mammogram, or another screening. Those recommendations may then be delivered through a digital channel directly to the consumer, or via a primary care physician or other practitioners. There are emerging uses of first-party and third-party data as social determinants of health, which are a collection of data points that predict the propensity of a person to stay healthy. These can be gathered as first-party data directly from consumers in the form of measured behavior or self-provided information or from third-party data providers. The Centers for Disease Control lists five social determinants of health: genetics, individual behavior, social environment, physical environment, and access to health care. The Massachusetts Medical Society ranked the [impact that each of these determinants have](https://www.healthedge.com/blog-social-determinants-health-what-are-payers-doing) on a person’s overall health. Individual behavior accounts for 40 percent, ahead of genetics (30 percent), social environment (15 percent), healthcare (10 percent), and environment (5 percent). Because the healthcare a person receives influences only a small percentage of their predisposition to health, healthcare organizations are increasingly using social determinants of health to prescribe treatment and preventive measures, with goals to improve outcomes and reduce cost. The use of social determinants is a recognition that a consumer’s healthcare experience or journey isn’t isolated to interactions with providers but is instead a continuous path that reflects our always-changing behaviors and environment. It’s our daily diet, exercise patterns, and genetic footprint, yes, but it’s also how much natural light we’re exposed to throughout the day, how close we live to a park or green space, and our level of access to the internet and emerging technologies. Trailblazers have implemented many innovative programs that produce results. Health Net, a California insurer, used [geo-spacing mapping](https://newsroom.healthnet.com/press-release/health-net-earns-2017-innovation-advancing-health-equity-award) to increase childhood immunization rates by 7 percent. Since 2016, Geisinger Health Systems has provided fresh produce to diabetes patients in counties with high rates of long-term complications. Project leaders of the [Fresh Food Farmacy](https://www.geisinger.org/freshfoodfarmacy) program have reported significant improvements in clinical outcomes for enrollees of the program, which was instituted with the specific intent to address food insecurity, an important social determinant of health. Many of these types of initiatives are driven by the trend toward value-based care, which measures health outcomes against the cost of the care provided. The idea is that by better understanding an individual’s social determinants, healthcare organizations can promote better outcomes by positively influencing consumers through education and access. **Why Data Matters in Understanding the Healthcare Journey** There are several challenges for healthcare organizations to begin using third-party data such as social determinants of health to evaluate each person holistically. First, they generally do not have access to all the data they need. They may have a person’s medical history, for example, but are unaware of their occupation, where they live, or other baseline information. Without knowing all that is knowable, it is difficult to make sound decisions supported by relevant data. Data siloes are largely responsible for a pervasive lack of data access, caused by a lack of data sharing across the healthcare spectrum such as between a primary care doctor and a specialist, or between a hospital and an outpatient clinic. Incomplete data input is also a factor; healthcare organizations often do not know all that is knowable because they’re either not asking the right questions, or they cannot integrate the data with a patient’s medical history. One provider developed innovative [social determinants of health screening tool](https://www.hcinnovationgroup.com/population-health-management/article/13030685/putting-social-determinants-of-health-data-into-action), which is included in an EHR and combined with clinical data to provide caregivers with a more complete patient profile. The data is then queried against a database of local community resources, which returns a list of resources and services that meet a patient’s specific needs. Second, even with access to the right data, healthcare organizations may not be effectively using it if it has not been integrated into a single view of the customer. Matching records by patient ID within one system is relatively straight forward, but integrating third-party data, device data, mobile, and web behavior data requires advanced matching capabilities. This data also needs to persist as healthcare journeys are long and complex, and the impacts need to be measured over time. Behavior data and third-party data is particularly important as these areas account for over 50 percent of the potential to impact outcomes. Third, even professionals on the bleeding edge of using first-party, second-party, and third-party data may not have the data in the proper timeframe. Relevance is important, particularly when moving from managing populations to influencing individuals; even with social determinants of health screening tool or a similar innovation, a monthly batch file does little good when conditions change by the minute. Diet and exercise regimens provide a familiar example. Less clear but potentially equally important could be a job loss or promotion, a relocation, or becoming a caregiver to an aging parent. Real-time data access provides healthcare organizations with the capability to make a next-best action or recommendation for treatment in the right context and cadence of an individual’s healthcare journey. Machine learning algorithms that continuously update a health “score” based on data from any source measure the pulse, if you will, of the healthcare consumer at any moment in time. **Personalizing the Healthcare Experience** One reason for an uptick in treating health and healthcare as a holistic journey is that it aligns with a person’s digital experience as a consumer of most goods and services. The continuously connected consumer interacts with brands across multiple channels and devices, and there is an expectation for a brand to know the consumer as an individual across dynamic journeys. Healthcare is trending in the same direction, driven largely by the empowered consumer whose healthcare touchpoints are no longer limited to point of care interactions. Connected activity trackers such as Fitbit and Apple Watch provide the consumer with the capability to monitor and measure personal health statistics in real time. We have smartphone apps that monitor our sleep patterns, customize exercise and meal plans, and serve as personal health coaches. These data-driven, digital experiences put the consumer in charge of their own healthcare experience. An empowered consumer adds a twist to the challenges in leveraging social determinants of health. A healthcare consumer wants more than better outcomes and lower costs, two of the main benefits of using social determinants of health. They also want an overall better experience, of which improved outcomes and lower cost only play a part, however important. Much like in retail, banking, and other industries, an improved experience means personalization. Yet according to the healthcare consumer, payers, in particular, have a lot of room for improvement. According to the [2018 U.S. Health Insurers Customer Experience index](https://healthpayerintelligence.com/news/customer-service-is-primary-driver-of-health-plan-satisfaction), health insurers ranked 15th out of 19 U.S. industries for providing a positive customer experience. There are countless ways for healthcare organizations to personalize the healthcare experience, such as a rewards-based initiative for a consumer that meets monthly health goals, triggered through an integration with the consumer’s smart device data. If healthcare organizations are transparent about the use of social determinants of health, consumers could opt-in to innovative programs like the Fresh Food Farmacy where healthcare professionals become their partner in their day-to-day healthcare journey. For a consumer with a chronic condition, the use of social determinants could inform a more personalized approach to education and treatment beyond the basics such as age-based risk factor. A positive customer experience is also about engaging with the consumer in their preferred channel. Perhaps they want SMS appointment reminders but expect a phone consultation in advance of any first-time preventive screenings. **Treat Me Like a Consumer, Not a Patient** In general, a positive healthcare experience stems from healthcare organizations using data to take friction out of a consumer’s healthcare journey and becoming more proactive in treating the consumer holistically. Knowing all there is to know about the consumer to include full access and use of social determinants of health is a key step in the process, which is bolstered by real-time decisioning to offer a next best recommendation for treatment or engagement. Healthcare data management extends far beyond maintaining thorough medical records. Today’s knowledgeable and empowered consumer expects the healthcare professionals they interact with to know them as an individual beyond what’s written on a chart. By overcoming data management obstacles such as access, use, and speed and personalizing an end-to-end consumer healthcare journey outside the walls of a doctor’s office, healthcare professionals can provide the consumer what they most value: improved outcomes, personalized experiences, and reduced cost. **RELATED ARTICLE** [The Role of Personalization in Value-Based Care](https://www.redpointglobal.com/blog/the-role-of-personalization-in-value-based-care/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2018/06/CusCentricHealth-Cover-793x1024.jpg)](https://www.redpointglobal.com/wp-content/uploads/2018/04/SB-CUSEXPHCUS0318-01-CusCentricHealth-hi-res.pdf) **Blog categories:** Customer Data Platform, Data Management, Data Quality, Journey Orchestration, Real-Time Personalization --- ### [Survey Says: A Modern Architecture, Single Point of Control Drive Digital Transformation](https://www.redpointglobal.com/blog/survey-says-a-modern-architecture-single-point-of-control-drive-digital-transformation/) **Published:** October 15, 2021 **Author:** John Nash **Content:** A new McKinsey survey on business strategy for a post-pandemic era reveals the quickening pace of digital acceleration. Top-decile, tech-endowed companies – defined as those with technology capabilities, talent, leadership and resources that are linked to better economic outcomes – have moved core business practices from monthly to weekly or daily cadences. In [“The dew digital edge: Rethinking strategy for the post-pandemic era”](https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-new-digital-edge-rethinking-strategy-for-the-postpandemic-era), top-decile performers report they are now on a weekly or better cadence for using multiple sources of customer data to assess unmet needs, and also for sharing test-and-learn findings across the organization. According to the survey, what was considered best-in-class speed for these and other business practices as recently as 2018 is now slower than average, indicating that dramatic digital acceleration has touched all parts of a business. Brand new research that Redpoint conducted with Harris Poll validates that companies across all industry verticals are reimagining customer experience strategy to keep pace with digital-first customer journeys, and that those strategies – just as McKinsey probed – center around digital transformation efforts. In the [“Revisiting the Gaps in Customer Experience,”](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) a follow-up to 2019 Redpoint-commissioned research, 99 percent of all marketers surveyed said the pandemic has changed their CX strategy. The top three change indicators were an increased emphasis on flexible delivery systems (cited by 63 percent), integration of online and offline channels (51 percent) and embracing new channels for customer interactions (51 percent). ## **Digital Modernization** We can infer from the findings that the pandemic served as a wake-up call for organizations to become data driven and organize technology and business processes around a modern architecture that prioritizes a single point of control over data. In the Harris Poll, the top driver for marketers to digitally transform CX strategy was to personalize experiences to provide deep relevance and context that is “meaningful and valued by the individual customer” (cited by 41 percent as the No. 1 driver). Among the other top drivers were the need to deliver always-on, omnichannel experiences (33 percent) and moving beyond a basic understanding of customer preferences to anticipate customer needs (32 percent). To accomplish these goals, it is not surprising that marketers said that [data quality](https://www.redpointglobal.com/automated-data-quality/) is the top area of increased investment (63 percent). A prioritization of data quality is recognition that to deliver omnichannel experiences and anticipate customer needs – particularly with a need to assess unmet needs at an accelerated pace – marketers must be able to trust that customer data is accurate, complete and in real time. According to the survey, the primary culprits for introducing mistrust are having an average of 16 customer engagement systems (up from nine in 2019) and a lack of data integration between those systems. ## **Start with Data Quality** Yet, as the McKinsey study reveals, top-decile companies are now sharing test-and-learn findings across the organization on much quicker timeframes. Shared findings suggests that these data-driven leaders have solved some of the common data quality challenges and now trust their data; they’re confident data quality is such that segmentation, campaign design and campaign execution are generating a higher rate of intended outcomes for whatever the business metric that customer data is intended to drive, whether higher revenue per visit, reduced churn, lower acquisition costs, etc. As we’ve seen from the Harris Poll findings, organizations that struggle with data quality have yet to reach this enlightened state, as they are still trying to cope with unintegrated systems that breed mistrust in the entire cycle of campaign segmentation, design and execution. The number of systems makes it harder to provide a seamless CX (77 percent) or to effectively engage with customers (67 percent). ## **Better Data, Better Experiences, Increased Economic Outcomes** The way forward, as the McKinsey survey reveals, is with the modern architecture that offers a single point of control. In an analysis of how far ahead the top-decile technology-enabled companies are in front of competitors in various elements of technology endowment, the two biggest gaps were a 21-point differential between those who have moved key elements of technology to a modern marketing architecture, and a 20-point difference between those who have a common source of data that serves as the single source of truth across the organization. Digging into why a modern architecture is, as the survey suggests, a primary factor in the ability of top-decile tech-endowed companies to drive better economic outcomes, we turn again to Harris Poll results, which show that 39 percent of consumers will not do business with a company that fails to offer a personalized experience. Turning this around, 82 percent of consumers – up 5 percent since 2019 – identify themselves as loyal to companies that demonstrate a thorough understanding of them as a unique customer. Looking at both surveys through the lens of the accelerating pace of digital-first customer journeys, the drivers, challenges and solutions for providing a seamless, omnichannel customer experience are clear. An unmanageable network of fragmented systems is out, and a modern architecture offering a single point of control over data, decisions and interactions is in. Customers expect omnichannel experiences that are meaningful, and that show that a brand or organization knows the customer beyond a basic understanding. Improved economic outcome is as good an incentive as any for organizations to begin to make the effort to join the top decile and deliver a superior experience in line with the accelerating pace of business. **Blog categories:** Data Quality, Identity Resolution, Master Data Management, Real-Time Personalization, Segmentation & Activation --- ### [Supercharge Digital Advertising Measurement with Anonymous CDP Environment](https://www.redpointglobal.com/blog/supercharge-digital-advertising-measurement-with-anonymous-cdp-environment/) **Published:** December 19, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/12/dap-blog-12-19-300x200.jpg)Attribution and measurement of display advertising is a tricky business. Traditional metrics like click-through rates (CTR) and cost-per-click (CPC) are passé to a new breed of marketer who demands greater accountability for all marketing including digital advertising. This new breed of marketer is less interested if a broad audience saw an ad, or even if they went to the desired landing page. They want to know all about subsequent actions. Did the campaign drive conversions, lead to product sales, or inspire connections on social media? A desire for full visibility into how ad spend performs in the context of a customer journey aligns with a growing recognition that personalization is what turns the gears of a dynamic, omnichannel customer journey. The non-linear customer journey spans addressable and non-addressable environments, but the ability to be consistent across all and close the loop gives brands the competitive advantage they seek with personalized interactions. Consider the [Harris Poll sponsored by Redpoint](https://www2.redpointglobal.com/webinar-harris-poll-cx-2019-ondemand?_ga=2.23466618.1352694597.1576251749-1570104466.1540307570), where 63 percent of consumers said that personalization is a standard service they expect – and 43 percent said that they expect a brand to know them as the same customer across all online and offline touchpoints. This expectation clearly impacts digital ad placement, with brands expected to know how a consumer might respond to the type, frequency, and placement of a certain ad – not just where they clicked, but its influence on their buying journey. The imperative for personalization creates a need in the [CDP advertising](https://www.redpointglobal.com/blog/supercharge-digital-advertising-measurement-with-anonymous-cdp-environment/) ecosystem to move beyond traditional metrics and measure advertising effectiveness holistically in the context of customer experiences, not just on a campaign-by-campaign basis. A previous [blog in this space](https://www.redpointglobal.com/blog/advanced-personalization-complex-yes-complicated-no/) explored the personalization of a website experience for a first-time, anonymous visitor as one use case for the [Redpoint Digital Acquisition Platform](https://www.redpointglobal.com/digital-acquisition-platform) powered by LiveRamp. Measuring the effectiveness and value of digital advertising is another. Using Identity Links from LiveRamp, marketers now have an effective way to measure digital advertising campaigns across channels, and provide a greater return on ad spend (ROAS) that does not rely on ambiguous concepts of brand familiarity, exposure, awareness, or recall. **Transition to a People-Based Approach** Post-campaign reporting is often frustrating for marketers who find it less than helpful in providing a differentiated customer experience. Most often when post-campaign data is returned, it is likely channel-specific, incohesive, and inconclusive. Privacy requirements prevented connecting various data types such as transactions and revenue to ad exposure data in order paint a clear picture of digital media impact on a [buying journey](https://www.redpointglobal.com/blog/multichannel-vs-omnichannel-marketing-and-keeping-up-with-a-customer-journey/). Post-campaign reporting that is restricted to a campaign view reveals very little about how a customer moves through an omnichannel journey. The Redpoint solution transforms post-campaign reporting from cookie-based to a more people-based approach, with visibility across campaigns and creative and across addressable and non-addressable channels. It allows for a consolidated, longer-term view of digital advertising by linking data sets, capabilities and metrics typically associated with a customer data platform – namely, customer data – to advertising reporting. Anonymized customer and prospect records, in other words, can now contain advertising exposure information. **Substitute PII for Identity Links for a Complete CDP Advertising View** As the previous blog detailed, the Redpoint platform makes it possible to create a personalized customer experience in non-addressable digital channels by working with anonymized customer records. Redpoint clients onboard PII-based records to LiveRamp, which removes all PII and anonymizes the data. Clients can then activate audiences across more than 500 destinations that LiveRamp supports. Advertising measurement is enabled by LiveRamp returning post-campaign data to a Redpoint client’s auxiliary anonymous database. In this anonymized version of a CDP, clients connect the raw post-campaign data to different data types – transactions, promotional history, and impression data – to achieve a cohesive understanding of the advertising impact. Post-campaign data is sent from the ad networks, DSPs and or publishers to LiveRamp who replaces identifying information with IDLs for the advertiser. This data is collected into the anonymous CDP where it can be connected with additional customer data. Results can be viewed across campaigns, networks, publishers etc. Previously, a customer segmented view of a campaign might provide visibility of an audience’s exposure to an ad on a single platform, whereas with an built-in anonymity from IDLs, the Digital Acquisition Platform makes it possible to have a pull-down view of all audiences against all creative – and measure audience response across the open web. To ensure full compliance with privacy regulations multiple controls are in place to govern the anonymous CDP. For one, analysis can display findings on cohort groups of 25 or larger. This means marketers can better analyze, for example, which ad performed well for which group on a certain ad network. And not just by measuring clicks and opens, but actually closing the loop on how ad campaigns translate into sales for certain audiences. Utilizing an anonymized approach to connecting data sets in a new way provides marketers with a fuller, longer-term view of media spend that can, for the first time, be tied to audience response, engagement, and actions throughout the advertising ecosystem without any risk to personal information. **RELATED CONTENT** [Combat Message Fatigue with Personalization](https://www.redpointglobal.com/blog/combat-message-fatigue-with-personalization/) [Master Personalization Capabilities Across Anonymous Touchpoints](https://www.redpointglobal.com/blog/master-personalization-capabilities-across-anonymous-touchpoints/) [Getting Started with Personalization: Include “Unknown” Visitors for Website Personalization](https://www.redpointglobal.com/blog/getting-started-with-personalization-include-unknown-visitors-for-website-personalization/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2019/06/0607-SizedDownByMe-SB-RedPoint-Digital-Acquisition-Platform-239x300.jpg)](https://www.redpointglobal.com/wp-content/uploads/2019/06/SB-RedPoint-Digital-Acquisition-Platform-0519.pdf) **Blog categories:** 1:1 Personalization, AI & Machine Learning, Customer Data Platform, Data Management, Data Quality, Journey Orchestration --- ### [Redpoint Introduces Enhancements to Data Management and Security with Rollout of Version 9.0](https://www.redpointglobal.com/blog/redpoint-introduces-enhancements-to-data-management-and-security-with-rollout-of-version-9-0/) **Published:** January 15, 2020 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/01/rpdm-9.0-blog-image-2-e1579101922842.jpg)To be considered an enterprise-grade [Customer Data Platform](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/), a solution must ingest data from all sources, retain full detail of the data, store it, convert it into a unified customer profile, and make the profiles available to all external systems. Available for customers today, January 15th, [Redpoint Data Management](https://www.redpointglobal.com/solutions/redpoint-data-management/) (RPDM) v 9.0 introduces several features in the CDP that enterprises see as essential for an enterprise-grade data tool. Data privacy and integrity are a critical part of the success of any customer experience management solution, and RPDM provides the core customer data management component in the Redpoint Customer Engagement Hub. Upgrades in DM 9.0 focus on several critical areas: security, usability, manageability, reach on various platforms, and several other customer-requested updates. ![](https://www.redpointglobal.com/wp-content/uploads/2020/01/rpdm-9.0-blog.jpg) From a security perspective, RPDM 9.0 enhances encryption and access control to support both regulatory compliance and best-of-breed encryption standards, algorithms and security key storage methods. As my colleague George Corugedo wrote in a [recent blog](https://www.redpointglobal.com/blog/marketing-data-is-too-important-to-cede-control-of-the-security-perimeter/), safeguarding customer data while delivering omnichannel experiences is a choreographed dance between an enterprise and marketing cloud vendors. The global impetus behind a new level of security and privacy standards (GDPR, CCPA, HIPPA) requires CDPs to handle a broad set of permission-based personal data while providing relevant and timely customer interactions. In terms of usability, the update improves ease of use with tool containers to better organize data projects. Enhanced search capabilities allow users to search and filter on project status (status, percentage of completion, memory usage and notes), making the management of a project more intuitive. Data manageability is also enhanced in this update. Bi-directional synchronization between the repository and disk, available from both command-line and within the client, makes it easy to manage data project lifecycle. All repository objects can be synchronized: projects, macros, automations, formulas, schemas, data connections, etc. This simplifies cross-project promotion between environments, from development to test and on to production. Platform connectors have been updated to support the newest versions of common industry tools and computing environments. In Redpoint’s open garden model, extending connectivity to new data platforms is essential to ensure our clients’ ability to keep up with the changing marketplace of user endpoints and interactions. Consumers expect a brand to know who they are – preferences, behaviors, permissions – across every channel. Satisfying the always-on, connected consumer requires two-way, real-time communication across a host of new and emerging channels and the foundation of this is a strong data management solution. Interested in learning more about Redpoint Data Management v. 9.0? Contact us at for more information. ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **RELATED CONTENT** [Real-Time Data Aggregates for Today’s Dynamic Customer Journeys](https://www.redpointglobal.com/blog/real-time-data-aggregates-for-todays-dynamic-customer-journeys/) [What’s Needed to be a RealCDP?](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/) **Blog categories:** Anonymous to Known, Customer Data Platform, Data Management, Data Quality, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Monetize Your Customer Data with a Data Clean Room](https://www.redpointglobal.com/blog/monetize-your-customer-data-with-a-data-clean-room/) **Published:** February 9, 2023 **Author:** Steve Zisk **Content:** With the late November announcement by Amazon Web Services that it was launching [AWS Clean Rooms](https://press.aboutamazon.com/2022/11/aws-announces-aws-clean-rooms), an analytics service for organizations to analyze and collaborate on combined datasets without sharing or revealing underlying data, it is a good time to revisit an earlier Redpoint blog on data clean rooms. Around the same time as the AWS announcement, we [ran a blog](https://www.redpointglobal.com/blog/you-cant-have-a-data-clean-room-without-data-quality/) that focused on why data quality is a vital component in a data clean room. With the proliferation of both independent (Disney Select) and walled garden (Roku, Walmart Connect) data clean rooms, and Amazon Web Services now joining the fray with a data clean room designed specifically for analytics, the question of data clean room use cases appears to be top of mind. This blog will look at one specific use case: how an organization can monetize its own first-party data using a data clean room. ## **A Data Clean Room and High-Quality Data** First, a quick review of data clean room basics. The working definition of a data clean room – both independent and walled garden versions – is a place for companies to match and merge two or more first-party data sets without exposing any personally identifiable information (PII). All use cases entail some form of analyzing, matching and building models using anonymized data, utilizing a type of data encryption that never exposes PII. Ideally, customer data will only enter a data clean room if a customer explicitly authorizes its use for marketing or advertising purposes. When compliance and consent are followed, companies are positioned to become digital advertisers for their customers – using fully anonymized data that is compliant with all regulations. As an example of how a company can monetize its own first-party customer data as a digital advertiser, let’s use a (fictional) global specialty retailer. With millions of customers accessing its mobile app and website the company – just like a Target, a Walmart or another enterprise retailer – sits on a wealth of first-party data. Its reach makes it an attractive advertising partner for smaller companies who want to cast a wide net over a large pool of prospective customers. (This type of advertising partnership, called a [Retail Media Network](https://www.forbes.com/sites/bradadgate/2022/12/01/retail-media-networks-are-the-next-big-advertising-channel/)), gained popularity during the pandemic.) Imagine that one of those smaller companies is a regional sports outfitter. Interested in targeting some of the retailers’ customers, it provides some data about the type of customers it intends to reach, and matches it with the retailer’s data. Perhaps the outfitter, which specializes in ski equipment, wants to advertise to customers who live in either New England or the Pacific Northwest who have booked travel in February for trips to Vail, Colo. or Jackson, Wyo. It buys a list of customers who have booked travel to those ski destinations from a third-party, and combines it with some first-party data – maybe browsing sessions from customers in the target demographic who have searched for deep powder gear. With those constraints, the outfitter – without sharing PII – sends data it has on potential customers to the specialty retailer. The retailer then applies a full data cleansing, normalization, data matching and [enrichment](https://www.redpointglobal.com/blog/what-is-data-enrichment/) to a pool of first-party data to create a Golden Record for any identity that matches those constraints. Those records are then anonymized and put into a data clean room along with any cohort information. The outfitter can then access a list of [persistent IDs](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) in the data clean room and query against the list using its own data to see what kind of match rates it produces. Providing there are enough matches (contracts will stipulate a minimum threshold to protect anonymity), the outfitter will have its target audience. ## **Beyond the Cookie: Trust and a Data Clean Room** At first glance, this type of arrangement seems like little more than a simple data share. The outfitter, one might argue, is simply buying a wider audience. Skeptics might point to the efficacy of a [third-party cookie](https://www.redpointglobal.com/blog/crocodile-tears-do-not-lament-the-extinction-of-the-third-party-cookie/) for reaching a prospect on an affiliate channel and ask what’s the difference. The two main points to be made here are, first, data quality: the outfitter isn’t just tracking a device across the internet, nor is it just buying a lookalike audience, it is buying the retail company’s [*perfected* first-party data](https://www.redpointglobal.com/blog/the-secret-to-a-customer-centric-approach-is-hiding-in-plain-sight-first-party-customer-data/), and using it to target actual anonymized customers. Second, unlike a cookie, a data clean room will ideally only contain data for customers who have consented to share anonymized data for the purposes of receiving a more personalized customer experience, to include relevant advertising. Reinforcing both points is where a company like Redpoint comes in, with the [rg1 customer data platform (CDP)](https://www.redpointglobal.com/rgone/) that is instrumental in setting up the clean room with unassailable first-party data – high quality cleansing, matching and normalization in a performant, secure perimeter. Advanced identity resolution and the creation of a persistently updated [Golden Record](https://www.redpointglobal.com/single-customer-view/) are completed at the point data is ingested from every source that contributes to a unified customer profile. As for compliance, a Golden Record will also include a customer’s opt-ins/outs, right to be forgotten/erasure requests and all other conditions for how their data is allowed to be used. The level of trust partners ultimately have in the shared data environment of a data clean room begins with the trust in the CDP that delivers the anonymized data. When the use case for a data clean room is to monetize one’s own first-party data, a CDP that perfects an organization’s data at the point of data ingestion and accurately resolves the identity of an individual customer, household or other entity will ensure maximum value. All parties will reap financial rewards, either through the data clean room service or, for the buying partner, via customers who will be on the receiving end of a hyper-personalized advertising campaign. ## **Related Redpoint Orchard Blogs** [Ease Distrust in Advertising with Data Clean Rooms & PII Vaults](https://www.redpointglobal.com/blog/ease-distrust-in-advertising-with-data-clean-rooms-pii-vaults/) [There are No Third-Party Shortcuts to Understanding Customers](https://www.redpointglobal.com/blog/there-are-no-third-party-shortcuts-to-understanding-customers/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Quality, Identity Resolution --- ### [In a Heartbeat: The True Value of CDP in Healthcare](https://www.redpointglobal.com/blog/in-a-heartbeat-the-true-value-of-cdp-in-healthcare/) **Published:** October 4, 2019 **Author:** John Nash **Content:** According to [The Relevancy Group CDP Buyer’s Guide 2019](https://www.prnewswire.com/news-releases/the-relevancy-group-releases-the-relevancy-ring-2019-customer-data-platform-cdp-buyers-guide-300920143.html), real-time targeting is a key CDP use case, used by 54 percent of marketers with a CDP to execute highly relevant campaigns, with messaging that reaches an intended target at the appropriate time to drive intended behaviors. According to the Guide, “Speed of data is critical to (these) marketers and the data agility that a [CDP](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/) affords is a key toward reaching the right target, at the right time, with the right message.” For retailers and brand marketers, “speed of data” translates to successful personalization through practical use cases such as delivering a perfectly timed offer the instant a customer appears in a channel or delivering tailored content to a first-time visitor to a website tuned to their preferences and behaviors. The benefits of real-time extend far beyond the retail sector, however, and nowhere is the benefit more important than care management, where life or death decisions may depend on offering real-time support to a patient. Using real-time patient data to manage the health of an at-risk population arrives at the very essence of [CDP’s](https://www.redpointglobal.com/blog/cadence-scope-flexibility-understanding-a-cdps-data-architecture-needs/understanding-a-cdps-data-architecture-needs/) purpose. The real-time processing engine that supports real-time engagement is ultimately what drives a CDP’s value, which makes it more than just a pillar of a successful martech stack. For retailers and brand marketers, that value is measured in driving revenue. For healthcare payers and providers, value is instead measured in better health outcomes, improved patient engagement and satisfaction, and lower total costs. **Value-Based Care and Telemedicine** Value-based care is a growing performance-based compensation model that rewards physicians, hospitals, and other healthcare providers on the quality of care they deliver rather than services rendered. Under the value-based care model, providers have a financial incentive for the long-term health of a patient. As the leading cause of hospitalization for people older than 65, congestive heart failure provides a model value-based care use case. A treatment plan typically consists of regular monitoring, blood tests, medication, and lifestyle management. This makes telemedicine (defined as the distribution of health services and information electronically and with telecommunication technologies) ideal to help minimize hospital re-admittance rates and prevent disease progression – the two main treatment goals. These were key metrics for an innovative telemedicine research program piloted by a hospital, with Redpoint Global as one of its technology partners. The hospital was managing [care](https://www.redpointglobal.com/blog/redpoint-global-and-lucerna-health-join-forces-to-advance-consumerism-in-healthcare-for-payers-and-providers/) for 300 congestive heart failure patients and wished to gather real-time data from connected devices to monitor their health and recommend care. Real-time data such as vital signs could then determine a patient’s care pathway, unique for each patient based on real-time conditions. A pathway could consist of a call from a nurse, a motivational message, a visit from an ambulance, or scheduling a new treatment plan – all dependent on real-time signals from a connected device. **Optimizing Care Pathways with a CDP** Congestive heart failure patients typically have a team of providers invested in their long-term care, including primary care physicians, cardiologists, nurses, dieticians, pharmacists, exercise specialists, and social workers. For the research program to work, it had to centralize patient data into a single platform – a central requirement for real-time care management. The [Redpoint Customer Data Platform](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/) integrates data from any source or type into a central platform, providing care managers with a unified customer profile and a single point of control over data, decisions, and interactions to expertly manage a patient’s care pathway in real time based on up-to-date data. Organizations benefit from a CDP’s real-time capabilities because it consistently provides a next-best action or recommendation in any channel in the context and cadence of a unique customer journey. In care management, a next-best action is similarly tied to a customer journey – in this case a patient journey or care pathway – based on everything that is knowable about a patient. The pilot study provided invaluable patient data, but a real-time next-best action is not based solely on a real-time connected device, but rather a patient’s entire medical history. A unified customer profile, also known as a [golden record](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/), is persistently updated to include real-time data from every source. Combined with in-line analytics and automated machine learning, the golden record provides caregivers with a next-best action that is relevant to the patient’s journey at a precise moment in time. The analytics are key to scaling this to personalized engagement for thousands or millions of consumers. A connected device could, for example, monitor and transmit a patient’s heartrate, respiratory rates, and physical activity. The device data would then be integrated in real time with a patient’s entire medical history and records – interactions with dieticians, exercise specialists, and other providers, test results – as well as second-party and third-party data, and structured, unstructured, and semi-structured data to become part of the golden record. An automated machine learning model programmed to determine risk factors, a re-admittance score, or another metric will intelligently orchestrate a next-best action optimized for the patient’s care pathway. **CDP An Ally in the Healthcare Space?** In addition to the heart research program, Redpoint is partnering with other healthcare providers to help advance value-based care strategies in an ongoing basis. Last year, Redpoint partnered with Lucerna Health to advance the transformation to value-based care through personalized engagement. Patient behavior is the number one determinant of healthcare outcomes for several conditions, and the partnership was formed to help shape behavior through those personalized engagements. While the CDP was conceived as a marketing solution, and a real-time engine make it perfectly suited to personalizing a customer experience in the context of a unique customer journey, there is no more noble purpose than saving lives. With one heartbeat nearly every second, the impact of a CDP on real-time data and engagement clearly shows the linkage to tremendous value opportunities. **Blog categories:** Customer Data Platform, Data Quality, Healthcare, Identity Resolution, Real-Time Personalization, Single Customer View --- ### [How to Future-Proof Your Omnichannel Approach](https://www.redpointglobal.com/blog/how-to-future-proof-your-omnichannel-approach/) **Published:** July 8, 2019 **Author:** John Nash **Content:** “Hors catégorie” is a French bicycling term used to describe mountain climbs that are “beyond categorization,” reserved for difficult climbs with extreme twists, turns, and elevation gains. This year’s [Tour de France](https://www.cyclingstage.com/tour-de-france-2019-route/), which started Saturday, has several uncategorized climbs (along with a record 30 categorized climbs), making the 106th edition of the iconic race “tailor-made” for climbers, according to those in the know. Race spectators and support teams trying to keep track of a zig-zagging tour rider on tight climbs and steep descents is much like the challenge marketers face when trying to engage with the continuously connected omnichannel consumer. A typical non-linear, twisting and turning customer journey can also be said to be “beyond categorization,” with an unpredictability that can confound marketers trying to engage with a customer at the right moment. Because customers can appear anywhere, at any time, and on any device, seizing on the right moment of interaction requires marketers be ready with a real-time, relevant engagement wherever or whenever a customer appears. Some marketers make the mistake of thinking that another engagement system or marketing cloud will do the trick, allowing them to consolidate a fragmented martech system to deliver a true omnichannel customer experience. The truth is, there is no silver bullet, not with the martech landscape exceeding 7,000 point solutions according to the [2019 Chief Martec Marketing Technology Landscape Supergraphic](https://chiefmartec.com/2019/04/marketing-technology-landscape-supergraphic-2019/). A solution may solve an immediate engagement problem and result in modest gains, but by the time it’s deployed there may be a more urgent concern requiring new or different data or analytics to keep pace with the customer. Or the customer may have discovered and moved on to a new channel. Future-proofing an omnichannel strategy ensures that marketing is not perpetually chasing its own tail, if you will, in trying to seize the moment of interaction with a customer. There are five steps marketers can take to future-proof an omnichannel strategy. ## **An Open Garden Breaks Down Data Siloes** An [open garden](https://www.redpointglobal.com/blog/new-redpoint-capabilities-embrace-the-open-garden-approach-to-marketing/) approach to marketing is imperative to remain in lockstep with the omnichannel consumer. An open garden approach allows marketers to retain legacy systems while countermanding entrenched data siloes. Conversely, a walled garden – think Facebook or Apple – locks marketers into pre-built data models and a predefined slate of technologies. An open garden approach in a customer data platform (CDP) allows marketers to use best-of-breed individual point solutions because you can connect with any data type, any data source and create a complete view of each customer. Armed with all that is knowable about each customer, the CDP gives marketers a single point of control over customer interactions throughout individual journeys. An open garden breaks down data siloes and allows marketers to keep pace with customer engagement innovations without an expensive, time-consuming rip-and-replace. New customer engagement channels crop up continually, and staying ahead of the continuously connected consumer requires that marketers easily connect with new channels and data without worrying they’ll become obsolete soon after they expend valuable time and resources to integrate the new data source. An omnichannel approach for a marketer doesn’t have a finite destination. Because the customer is always moving, so too must the marketer. An open garden approach is marketing’s strongest ally to ensure they remain on the right path in continual lockstep with the customer. ## **A Single Customer View Ends Chasing Down Customer Data** According to a [Harris Poll Survey](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) commissioned by Redpoint Global, marketers indicated that “customer understanding” was the most important aspect of meeting consumer expectations for a positive customer experience (tied for first with privacy). Yet the consensus among consumers was that marketers were not doing a good job of meeting this expectation; consumers ranked marketers four points below where marketers ranked their own ability to understand the customer. Many marketers are stymied from keeping pace with an omnichannel customer journey for the simple reason they lack a unified customer profile. It’s one thing to have an offer ready the moment a customer appears in a channel; it’s quite another to have the right offer. This capability requires a single view of the customer, a persistently updated and instantly accessible golden record that includes customer data of every source and type – structured, unstructured, and semi-structured. A golden record is the foundation for creating hyper-personalized experiences relevant to the entire customer experience. With a single customer view, marketing knows everything there is to know about a customer, allowing them to infuse context into engagement and show they recognize a customer’s preferences and behavior. An enterprise-grade [CDP](https://www.redpointglobal.com/blog/myths-debunked/) solves identity resolution by ingesting and processing all types and sources of customer data to produce a full representation of an individual customer. This real-time capability increases customer understanding across online and offline touchpoints across unknown-to-known stages of a customer proxy, or record. ## **Real-Time Decisioning Keeps Pace with the Customer** Omnichannel is, quite simply, impossible without the real-time capability of activating a golden record with a next-best-action. In-line analytics, machine learning, and self-training models breathe life into a golden record by turning customer data into insights in the timeframe needed to take action and engage with a customer in the perfect cadence. Because models re-train themselves based on current data – as opposed to a simple data refresh – real-time predictive models prevent model decay and guarantee that there is no latency between ingestion and activation. An [intelligent orchestration](https://www.redpointglobal.com/knowledge-center/intelligent-orchestration/) layer of a CDP packages a next-best-action for the marketer, based on the real-time decision of the intent model. The next-best-action is instantly accessible, giving marketing a single point of control over data, decisions, and interactions from which to navigate the omnichannel customer journey. The key to real time in this context future-proofing an omnichannel approach is that models do not have to be re-built or re-coded to avoid decay; a hands-off approach ensures that a next-best-action is always tuned to the exact moment in the customer’s journey that will produce an optimal outcome. ## **Capital ‘M’ Marketing is a Revenue-Generating Line of Business** According to a [McKinsey study](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/marketings-moment-is-now-the-c-suite-partnership-to-deliver-on-growth?cid=other-eml-alt-mip-mck&hlkid=ac7d7a2f3c04404da4217a094a674d85&hctky=10026786&hdpid=b8318c6b-72a4-4134-b370-4e877bb5c1f6), 83 percent of global CEOs say that marketing can be a major driver of growth. These organizations have what the study says is the “marketing with a capital M” approach, where diverse areas of the business – sales, finance, product innovation, HR, and technology – are vested in marketing’s success and recognize that “marketing and technology are inextricable partners” in developing capabilities to unlock value in new ways. The impetus for this approach is the value to a company of personalizing the customer experience. According to research from [Boston Consulting Group](https://www.bcg.com/publications/2017/retail-marketing-sales-profiting-personalization.aspx), there will be $800 billion transfer of revenue to companies that get personalization right in just three industries – retail, healthcare, and financial services. Likewise, according to a recent [Accenture study](https://www.bcg.com/publications/2017/retail-marketing-sales-profiting-personalization.aspx), personalization failures annually cost US firms $756 billion, or a whopping $2.5 trillion globally. The capture of greater wallet share through personalization requires that marketing breakthrough data and departmental siloes that stand in the way of customer understanding to deliver a personalized, omnichannel customer experience. Cross-functional alignment that puts marketing at the helm of an enterprise growth strategy future-proofs an omnichannel approach by knocking down operational barriers and eliminating fragmentation. ## **Programmatic Advertising Unbound** Because digital advertising operates in the non-addressable realm, programmatic media buying can be costly, ineffective and difficult to track. But new solutions are bringing the targeting and measurability of addressable media to the adtech ecosystem. The Redpoint Digital Acquisition Platform powered by LiveRamp solves many of the problems associated with poorly targeted digital advertising by providing clients the ability to onboard data directly to any of LiveRamp’s more than 500 destination partners, activate audiences and measure performance in an individualized way. Fully compliant with privacy standards, clients can access and control granular data across both known and anonymous audiences. This ensures targeting of programmatic advertising to a defined audience with optimized interaction, re-targeting, and increased contextual relevancy. This capability is a game-changer in media targeting and programmatic buying. The DAPfuture-proofs an omnichannel approach by giving marketers an in-roads, if you will, into the anonymous ecosystem and linking a digital advertising strategy for all anonymous, known, addressable and non-addressable identities. Display advertising on the open web can deliver the personalized customer experiences of addressable channels, which in turn provides a holistic end-to-end omnichannel strategy. Future-proofing your omnichannel approach can seem a contraction in terms. Because the customer journey is “beyond categorization”, there is no state where a marketer crosses a finish line, having finally caught up to the always-on consumer. Unlike the Tour de France, it’s a never-ending journey for the customer and marketer alike. Marketers who future-proof their omnichannel approach, however, can ensure that they stay with the customer across today’s channels as well as tomorrow’s touchpoints and are not bringing up the rear. --- ### [For the True Value of a CDP, Look Past its Capabilities](https://www.redpointglobal.com/blog/for-the-true-value-of-a-cdp-look-past-its-capabilities/) **Published:** January 13, 2022 **Author:** Steve Zisk **Content:** There is an old parable about preparedness that offers a parallel to how some marketers view technology’s role in reaching outcomes. In the parable, a person takes cover under a tree during a downpour. Asked what they plan to do when the canopy becomes drenched and no longer offers protection, the person replies, “I suppose I’ll just move to the next dry tree,” with the lesson being that a short-term fix often blinds people to the bigger picture. I’m reminded of this lesson when I see the approach many companies take toward the purchase of a [customer data platform (CDP)](https://www.redpointglobal.com/customer-data-platform). Often, the current desired outcome carries undue weight at the expense of broader objectives. Marketers then tend to focus on a singular capability, rather than assess a CDP more for its ability to deliver value-based outcomes that persist over time. ## **Weighing a CDP’s Capabilities, Features, & Functions** Even stipulating that value – as it pertains to a business outcome tied to a specific use case – differs by company and by end user, it is still important to understand that a justification for a CDP program should account for shifting KPIs. It is also important to think of value as a cumulative output – particularly when an improved customer experience (CX) is the CDPs intended core function – rather than being caught up in short-term goals. A [real time capability](https://www.redpointglobal.com/real-time-interactions/) presents an illustrative example of value’s pliancy in the context of a CDP’s capabilities and features. For most analysts and data scientists who see a CDP as a valuable tool for throwing data into machine learning models, a real-time capability will have minimal value. But for a marketer interested in real-time cross-channel journeys with use cases such as switching channels mid-stream (among web, mobile, IVR, call center and other “live” channels) without losing context or slowing down interactions, the requirement that data be made available with low latency, and that data updates and decisions be accomplished in the same timeframe, become very important. ## **Uncover a CDP’s Hidden Value** It is also true that certain outcomes may not be readily apparent to a constituency laser focused on a specific capability. A paid search/paid ad team, for example, may be enticed by a CDP’s ability to aggregate customer data, helpful for finding look-alike audiences. Yet the team may place little value on a CDP’s ability to produce consistent, accurate probabilistic and deterministic matching, sloughing off that function to the DMPs and DSPs of the world that match up a selected ad with an audience. Taking a wider view helps reveal value that may not be readily apparent. The same paid search team may find probabilistic and deterministic matching to be of tremendous value when shown a significant improvement in suppression rates. By knowing an audience at a granular level through [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/), the ad team suppresses a school backpack ad for empty nesters. Also, what’s of value today may not hold the same value tomorrow, and vice-versa. In the case of our ad team that shrugs off identity resolution, the loss of third-party cookies will throw a wrench in contextual advertising strategies; a customer’s current behavior may become much more important, and accurate matching that ties together a host of behaviors will suddenly be of great interest. Real time and advanced identity resolution are just two of many examples that show why a narrow focus on a specific capability or feature of a CDP is short-sighted. A focus on outcomes helps ensure that a CDP’s value remains constant, even with objectives constantly in flux. ## **Build Value that Persists Over Time** A CDP’s unique role in the martech stack as a platform for delivering an [omnichannel customer experience](https://www.redpointglobal.com/rgone/) makes it especially important to focus on long-lasting value. When the outcome in question is a personalized, highly relevant customer experience irrespective of channel or engagement touchpoint, value deepens over time. A marketer may of course prioritize results of a single campaign, or what they’re doing on a particular channel, but if the overarching goal is to transform CX, a marketing team will be better served strategizing, realizing, and measuring value over a wide range of interactions that span time and channels. Tying value to a particular capability makes it difficult to embrace a broader perspective. By measuring value through this larger prism, features are viewed more for how one or more act in concert to put the customer at the center of a holistic customer journey. This view will then shift priorities from day-to-day marketing tasks to look at things such as customer intent, lifetime value and other customer-centric metrics that are not tied to a specific capability. ## **A Cumulative View Accounts for Shifting Priorities** In a customer journey context, a CDP enables a brand to visualize an entire journey, to look at past journeys by an individual customer and by customers like them, and to understand the likely outcomes and probabilities of moving along the customer journey in particular paths. This, in turn, allows marketers to prepare highly relevant offers for an individual customer in a precise moment of a journey, which will then improve the customer-centric metrics. Every CDP will of course have some particular contribution to this type of a value journey, ranking higher in some features and capabilities than others. While it’s not wise to discount any one particular feature, companies will always make prioritizations based on the type of customer journey they care most about. But thinking long-term is paramount. Even in the case of presenting a customer with a relevant offer at a precise moment of a journey, is that truly the end goal? Or is it, instead, part of treating the customer as one who will have multiple brand interactions, where it’s also important to receive additional first-party or zero-party data and making sure that permissions and privacy preferences are honored? That long-term view will depend on a host of a CDP’s core capabilities. The depth of the customer experience platform, and the stickiness of outcomes are important considerations for a company that’s interested in building a value chain that persists over time. Customers change. Priorities change. Business goals change. What shouldn’t change is a customer experience platform’s ability to produce value-based outcomes encompassing any use case, irrespective of fluctuating priorities or goals. **Blog categories:** Data Quality, Identity Resolution, Segmentation & Activation --- ### [Five Ways Retailers Can Drive Customer Loyalty](https://www.redpointglobal.com/blog/five-ways-retailers-can-drive-customer-loyalty/) **Published:** April 25, 2022 **Author:** Thomas Kaczmarek **Content:** A less-than-stellar customer experience (CX) reminded me that with April being [Customer Loyalty Month](https://www.forbes.com/sites/shephyken/2022/02/27/customer-loyalty-month-is-in-april-are-you-ready/?sh=cfa38cf6c038), retailers have their work cut out for them to demonstrate to loyal customers that they value them beyond a transactional basis. Some time ago, I used a rewards card at a national retailer while buying a few items, and on the way back to the car I noticed the receipt included a coupon for the same brand of shampoo I just purchased. My first thought at that time was that maybe I forgot to buy the shampoo, and it wouldn’t be the first time I got an earful returning home without something on the list. Or perhaps the clerk looked me up and down and decided that whatever hair product I was using, I needed more of it? On a more serious note, I wondered why the discount hadn’t automatically been applied to the bottle I bought. I briefly thought about going back in and making an exchange, but it would have been too much of a hassle. I ended up leaving on somewhat of a sour note. Is it enough for me to never buy from the retailer again? Most likely not, but as a card-carrying loyal member I expected to be treated better. Like most customers, I expected the brand to demonstrate some level of personal understanding. A legitimate challenge for retailers is: when does a customer-centric approach instead of “product-centric” truly become the #1 marketing objective? With apologies to David Letterman, here’s a Top Five list of things retailers can do to show their appreciation for loyal customers. 1. **Ask me:** Rather than provide a one-size-fits-all rewards tier, or auto-generate rewards, take the time to ask the customer about their preferences. First-party data is the core of a personalized CX, and a brand that provides a consistently relevant experience that aligns with the customer’s preferences will be rewarded with even more personal data. It’s a win-win. 2. **Follow up**: Brands fixated on transactions, rewards, and points often lose sight of the customer at the other end. Redeemed points, though, means that a customer is using a product or service. Or maybe they’ve used an upgrade. Did they like it? Did it meet their expectations? Did they consider it fair value for the money spent? Consider a [McKinsey survey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying#:~:text=Personalization%20is%20especially%20effective%20at,customer%20lifetime%20value%20and%20loyalty.), where 58 percent of customers said they expect brands to follow up with them post-purchase. A failure to do so may be construed by customers as a “take it or leave it” approach. 3. **Be consistent**: In a [2021 Dynata survey](https://www.redpointglobal.com/press-releases/74-percent-of-consumers-believe-brand-loyalty-is-about-feeling-understood-and-valued-not-discounts-and-loyalty-perks/), 17 percent of consumers said the No. 1 way for a brand to earn loyalty was to provide a high-quality experience *across all channels*. Like the shampoo example, a customer who buys a product online shouldn’t receive an offer for the same product during the next in-store visit. An offer for the perfect complementary item, however, demonstrates the type of personal understanding that drives loyalty, especially if it’s generated on a different channel. Delivering an omnichannel customer experience shows that a brand is in synch with a customer journey. Customers respond to this level of personalization. 4. **Provide updates:** Here’s a sobering stat: In a [Capgemini survey](https://www.capgemini.com/resources/loyalty-deciphered/), 28 percent of customers surveyed said they’ve abandoned a loyalty program without redeeming any points. I’d be curious to know how many do so because they’re unaware of how many points they’ve accumulated. Sending a friendly email reminder is a simple way for a retailer to show that they value the relationship with a loyal customer. Better yet, personalize a reminder. Let a customer who has used points in the past for a fly-fishing rod know that they’ve accumulated enough rewards for a pair of waders. Or, you have enough points for those waders, and by the way there’s a fly-tying clinic next month near you. 5. **Share my values**: If a personal understanding is a more important driver of loyalty than discounts and offers, so too is a brand’s position on issues that are important to a customer, be it social responsibility, political, environmental activism, etc. In a [2021 Harris Poll/Redpoint survey](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236), half of the consumers (49 percent) said they find it frustrating when a brand doesn’t align with their personal values, and 28 percent said it would make them less likely to use the brand’s products or services. The list may seem self-evident, but too many brands seem to not be getting the message. In the same Capgemini report mentioned above, 80 percent of executives surveyed claimed their brand understands the needs and desires of their customers, but just 15 percent of customers agree. No one said that closing the gap would be easy. Brands must account for increasingly complex omnichannel customer journeys, customers using multiple devices, heightened privacy restrictions and other hurdles. Data-driven retailers that recognize the value of providing a transformative customer experience are overcoming the common hurdles with a [single view of the customer](https://www.redpointglobal.com/single-customer-view/), a key component for demonstrating a personal understanding of an individual customer. Curious how to reduce costs and drive customer loyalty with rg1 as an omnichannel CX platform that leverages a single view of the customer to deliver a next-best action in the cadence of a customer journey, all from a single point of control? [Click here to learn](https://www.redpointglobal.com/request-demo/?utm_source=website&utm_medium=footer) how Redpoint can help your organization put its goals to retail loyal customers within reach. ## **Related Content** [A Personalized Customer Experience (CX) Delivers Loyalty](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty) [What is Customer Lifetime Value (CLV)?](https://www.redpointglobal.com/blog/what-is-customer-lifetime-value-clv/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Identity Resolution, Journey Orchestration, Real-Time Personalization, Retail --- ### [Data and the Empowered Customer](https://www.redpointglobal.com/blog/data-and-the-empowered-customer/) **Published:** December 6, 2018 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2018/12/Empowered-Customer_525001696-e1544042335325.jpg)It’s abundantly clear that customers’ expectations are greater than ever and climbing higher with every disruptive startup and technology. What’s not so clear to many marketers is how they can meet those expectations without busting their budget—or losing their sanity. Although it’s unlikely that you can meet all your customers’ high expectations all the time, you’re far more likely to meet them by getting the most from the customer data available to you. With the right processes and technologies, you can access far more data, much more quickly, than you may think is possible. No need to rip and replace what works or look far beyond the data already in your sphere. By using a customer experience hub (CEH) to create a holistic view of the customer and serve as a single-point-of-control over customer data and interactions you can overcome many of the challenges you currently have in delivering the experiences your customers expect. Providing and maintaining a superior customer experience—that is, one that’s relevant and personalized—not only delights the customer, it also pays dividends for your organization. Customers will feel recognized and valued, and as a result, will spend more, more often recommend and advocate, and, over time, cost less to retain. A CEH is the rock-solid foundation of your profitable, future-proof CX strategy. **Stark Realities** According to the “[Wantedness](https://www.wantedness.com/)” report by marketing agency Wunderman, 63 percent of U.S. customers consider great brands as the ones that exceed their expectations across the entire customer journey. Loyalty apps and programs; comparison and review web sites; social media for sharing and showing interactions and products — these advances have put the consumer squarely in charge of the buying experience. As a result, many customers expect purchase experiences that are tailored uniquely to them. About [three quarters of consumers](https://www.loyalty.com/home/insights/article-details/the-importance-of-prioritizing-high(er)-value-customers) say receiving personalized discount offers based on their purchase history is important and a study from [Marketing Insider Group](https://marketinginsidergroup.com/content-marketing/content-marketing-personalization-imperative/) states that 78 percent of consumers say personally relevant content increases their purchase intent. As these empowered consumers’ demands grow and their expectations increase, marketers need the ability to respond — in real time. Today, more than 60 percent of consumers are always-on and readily addressable. Using a CEH to support a real-time synchronous interaction model is the only way to speak to customers with the relevancy (content, timing, etc.) they want in the channels they prefer. By providing a single-point-of-control over data and customer journeys, a CEH helps marketers overcome silos of data, business rules, and processes and enables them to deliver seamless, [hyper-personalized](https://www.redpointglobal.com/challenges/personalization/) interactions at the speed of the customer. That single-point-of-control allows marketers to implement a real-time synchronous interaction model to orchestrate customer interactions across an entire lifecycle. A real-time interaction model allows marketers to respond effectively despite the proliferation of channels, touchpoints, and data. Channels and touchpoints include far more than the obvious mobile, social, on-premise, email and website. Beacons, IoT, and sensors are examples of touchpoints that marketers can use to improve the customer experience. And it’s essential that they do: Research from consultancy McKinsey & Company found that [more than 50 percent of customer journeys](https://www.slideshare.net/McK_CMSOForum/customer-experience-journey-webinar-v10-091713) are now multievent and multichannel. Those touchpoints aren’t just vital customer interaction points, they also exemplify the ever-expansive sources of data available to marketers. These include anonymous-to-known customer, structured and unstructured, batch and streaming, and first-, second-, and third-party data — sources of invaluable information that includes customer behavior, transactions, preferences and ultimately intent. **More is more** Using a CEH, it’s possible to access all the data in real time, so marketers can know all that is knowable about their customers with little latency — and without breaking the bank or ripping and replacing their tech stack. The more information marketers have on customers, the more valuable it is. For example, having behavioral data may reveal the need to respond within a given timeframe and in a specific channel. Additionally, when a company’s systems are connected it allows marketers to use automation to make a next-best offer and enable customers to act on it in whatever channel they’re in. One challenge marketers face in implementing this vital real-time synchronous interaction model is the fragmentation brought about by the accelerated pace of technology innovation. The more specialized technologies that are introduced to the market, the more potential there is for marketers to add silos to their already disjointed marketing technology stacks. A CEH provides marketers with an open-garden approach that links disparate data sources to create a holistic, always-updating view of the customer. This is a must to overcome the obstacles of siloed data and technologies and support and enable the single-point-of-control that allows marketers to reach their customers in real-time in the customers’ chosen touchpoints. Plus, it helps to extend the value of existing marketing technology investments and future-proof the enterprise to take advantage of any technology innovations to come. **Holistic Customer View** Three quarters of marketing executives polled believe that it’s possible to get that holistic customer view, according to research from Redpoint Global conducted with the [CMO Council.](https://www.cmocouncil.org/thought-leadership/reports/the-state-of-engagement) The challenge, respondents say, include having a single customer record that is embraced by all functions (65 percent), the right technologies (53 percent), and cross-functional data (52 percent). A single-point-of-control over data and real-time interactions is a foundational element that marketers can use to gain that holistic view of the customer and build and maintain a superior customer experience. With a CEH you can delight your customers with personalized experiences that show you know and value them. In return, they’ll buy more and buy more often, recommend and advocate for your brand, and costs less to serve—increasing their lifetime value. Research firm Gartner [predicts](https://www.gartner.com/doc/3698955?ref=SiteSearch&sthkw=customer%20experience&fnl=search&srcId=1-3478922254) that, by 2020, more than 40 percent of all data analytics projects will relate to an aspect of CX. Don’t wait. Use your customer data to create a competitive advantage today. **RELATED ARTICLES** [How to Personalize Retail Interactions Without Being Creepy](https://www.redpointglobal.com/blog/how-to-personalize-retail-interactions-without-being-creepy/) ###### *Be in-the-know with all the latest customer engagement, data management and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Anonymous to Known, Customer Data Platform, Omnichannel Marketing, Real-Time Personalization --- ### [Cooking with Fire: Perfect Data, Perfect Outcomes](https://www.redpointglobal.com/blog/cooking-with-fire-perfect-data-perfect-outcomes/) **Published:** August 30, 2021 **Author:** Redpoint Global **Content:** The famous “for want of a nail” allegory describes how a cascading series of seemingly insignificant events eventually causes the loss of a kingdom, showing that small details matter. This is true for calculating the business cost of bad data, where small mistakes may easily lead to the loss of a sale or loyal customer. An incorrect email address. An offer for a product the customer recently purchased. A misunderstanding of household dynamics. A customer in a wrong segment. A mis-spelled or incorrect name or address. A late or an irrelevant response to an abandoned shopping cart. The list goes on. When bad data creeps in, what happens is that marketers and executives are conditioned to accept loss, in one form or another. Fewer conversions. More attrition and/or less retention. Is 2 percent acceptable? Five percent? Whatever it is, there’s a certain number that will be tolerated for one of two reasons. It either comes down to a decision that the annual cost of bad data is less than the cost of fixing the problem, i.e. perfecting data, or it’s more general despair over not knowing which holes to plug, a determination that the problem can never truly be isolated. ## **Nailing Down the Cost of Bad Data** The problem with both lines of thinking, however, is that whatever the agreed-upon number – 2 percent, 5 percent, etc. – is not the true cost of bad data. If, for example, 2 percent of your emails are wrong, you also need to factor in opportunity cost; who didn’t receive that email that should have? How many of that 2 percent would have taken the next step in the customer journey? Now say you’re running five campaigns a week. Over the course of a year, that initial 2 percent has suddenly ballooned to a significant number of missed opportunities. Consider the ramifications for a static email offer for, say, 10 percent off a gas grill to an audience of homeowners ages 35-50. Brands without an updated unified customer profile that includes behaviors on all channels run the risk of the offer being irrelevant the moment it’s opened. Any member of the intended audience who happened to visit a store and paid full price for the grill before opening the email are now annoyed when they do open it. Is the offer still available on the product they’ve already purchased? Now instead of being proud of their new purchase and excited to host a backyard barbeque, the product reminds them of the bad experience. How many of those customers never return? Multiply that times the average customer yearly spend. Also factor in the numerous missed upsell or cross-sell opportunities if the customer profile doesn’t really tell you everything there is to know about a customer. Maybe it has transaction history, some PII, an account number, etc. but it doesn’t contain all relevant data – affinity, preferences, demographics. Conversely, with a unified customer profile that is updated in real-time that tells a brand everything there is to know about a customer, a brand can update content on-the-fly until the moment of interaction. If you know the customer bought the grill at the store, perhaps you change up the email with a 10 percent offer for a stainless steel set of grilling tools with next-day delivery and a brochure on how to sear the perfect ribeye. Or perhaps a customer browses the website, and because your [golden record](https://www.redpointglobal.com/single-customer-view/) – the unified customer profile – includes household dynamics you know the visitor is the father, not the son, and so you display images and content for a more expensive line of grills with higher profit margins. Or you know the visitor is a caterer or a contractor browsing the website for a portable commercial grill. Maybe instead of an offer for grilling tools, that customer receives a discount offer for an annual bulk propane subscription. By knowing everything there is to know about a customer, brands can move away from random audience segments that limit a success pool, instead creating far more granular segments that match offers, content and actions to what truly matters to a customer at the moment of interaction. ## **Leave Irrelevance up in Smoke** The above examples start to get at the real cost of bad data and the repercussions of trying to market to a customer without an accurate, up-to-date unified customer profile that is instantly accessible so that real-time decisions are executed as the customer journey is ongoing. It’s not just a static 1 percent or 2 percent – it’s compounded through each phase of the customer journey, with opportunity cost factored in at every step. By knowing everything there is to know about a customer – every transaction, online session, preferences, interests, etc. – a brand reduces or eliminates wasted communications, irrelevant messaging and lost opportunities. When data quality processes are completed at data ingestion, including advanced identity resolution with probabilistic and heuristic matching, accepting a certain loss percentage because of bad data becomes a thing of the past. [Perfect data](https://www.redpointglobal.com/blog/cooking-with-fire-perfect-data-perfect-outcomes/) is possible. And with it the cascading series of compounded mistakes that cause the true cost of bad data to pile up are stopped in their tracks. **Blog categories:** Data Quality, Identity Resolution, Real-Time Personalization --- ### [Automated Machine Learning: One Size Does Not Fit All](https://www.redpointglobal.com/blog/automated-machine-learning-one-size-does-not-fit-all/) **Published:** December 17, 2019 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/12/ML-compaines-blog-12-17-300x200.jpg)Gartner defines automated machine learning – which it calls AutoML – as the capability of automating the process of building, deploying, and managing machine learning models. It sits at the peak of [Gartner’s 2019 Hype Cycle for Artificial Intelligence](https://www.forbes.com/sites/louiscolumbus/2019/09/25/whats-new-in-gartners-hype-cycle-for-ai-2019/#1b5ce4f1547b), one of eight new “AI-based technologies” that reflect enterprise clients’ plans to scale AI across DevOps and IT while supporting new business models. Reflecting its increased prominence, many companies now provide stand-alone automated machine learning platforms, among them Amazon SageMaker, Big Squid, and Google Cloud Platform. These vendor offerings give marketers a lot to think about as far as what automated machine learning can do for their organizations. First, though, marketers must have a clear purpose in mind. Machine learning tailored for marketing is, at its core, about taking large sets of data that represent customer information and behaviors and turning it into predictive or prescriptive models for driving customer interactions. In other words, to understand how past customer behaviors drive marketers’ understanding of future expectations. **Machine Learning for Marketers** With this in mind, there are three important things for marketers to know before bringing automated machine learning to bear. One is that the understanding of customer behaviors presupposes a subject matter expertise, and this knowledge is crucial to ensure that machine learning models are optimized for a specific business purpose. This affects which variables correlate or aggregate to represent the modeling goal, for instance reducing churn or selecting high-value customers. Out-of-the-box machine learning, broadly suited for use cases not specific for marketers, will lack this important in-house knowledge. Second, and in a similar vein, most machine learning platforms are geared toward data scientists rather than marketers. The platforms thus leave many high-value tasks outside the realm of automation, entrusting data scientists with data quality tasks such as record matching, cleansing, and normalization. This presents a roadblock for marketers who need instant access to a persistently updated unified customer profile, or [golden record](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/what-is-a-golden-record/), that is needed to deliver a differentiated customer experience with personalization, a key to driving revenue. Third, in addition to data preparation, standalone machine learning platforms that are often built as a web services model will often require manual intervention or programming tasks before integrating the platform into an existing martech stack. Core tasks around building and improving models are automated, but model validation, deployment to a marketing platform, and model assessment will generally not be automated – all of which are important to feed customer data to the models and tie models into specific campaigns. To summarize, for automated machine learning to optimize a specific marketing-related business case requires a subject matter expertise about customers, a knowledge about the underlying mechanisms to build a model, and an understanding about the relationships between various components of the martech stack. **The More the Merrier** Despite the built-in challenges, marketers should welcome having more vendors enter the competitive machine learning fray, if only to help them focus their ambitions and nail down a use case. Because the question is not whether automated machine learning will transform marketers’ ability to offer a hyper-personalized customer experience, but which platform will best lead to that result. Even more good news is that vendors do not rest on their laurels. They’re always refining the platform’s intelligence, simplifying the mechanisms for making models available, and continuing to build on automating the mechanisms for building effective models. That leaves a deep understanding of the customer, and an understanding of how models operate in the context of intelligent orchestration – the automated data quality tasks – as the two major roadblocks for marketing organizations that are kicking the tires on automated machine learning platforms. The bottom line for marketers considering an automated machine learning platform is how much work they’re willing to take on to complete those tasks. Many customer data platforms on the market today are proficient with solving the front-end problem of bringing in enough customer data, even though the depth of customer understanding will vary depending on how well the CDP solves for advanced identity resolution and other capabilities. Some CDPs are even adept at providing an orchestration layer and the right mechanisms to incorporate machine learning models in them. **In-Line Analytics Makes a** **Difference** CDPs that solve for both the front and back-end, with an automated machine learning platform that solves for the data quality tasks, identity resolution, and intelligent orchestration are few and far between, however. Purpose-built to deliver a hyper-personalized customer experience, the Redpoint Customer Engagement Hub automates every task related to data ingestion, cleansing, matching, and normalization. Redpoint Automated Machine Learning (AML) – a core component of the hub – provides in-line analytics and machine learning for marketers and easily deploys models to add machine-learning insights to martech orchestration. Tuned for a specific business metric, AML provides continuous automated optimization of customer interactions, delivering a real-time next-best action or offer to a customer that is always in the context and cadence of a dynamic customer journey. AML uses evolutionary AI, testing, selecting, and rerunning variant models to find the best approach to solve the marketer’s specific business problem without continual attention from data scientists and engineers. Marketing organizations are rightly looking to automated machine learning to deliver personalized customer experiences at scale that are increasingly proving to be a fast path to revenue. While it is encouraging that vendors are racing to enter the automated machine learning platform market, marketers must recognize that one size does not fit all. Before researching options, marketers must have a firm understanding of their intentions for how automated machine learning will enhance customer engagement strategy. **RELATED CONTENT** [Key Questions to Ask When Evaluating an Enterprise CDP](https://www.redpointglobal.com/blog/key-questions-to-ask-when-evaluating-an-enterprise-cdp/) [The Sky is the Limit with AI and Machine Learning: Capitalize on the Moment of Interaction](https://www.redpointglobal.com/blog/the-sky-is-the-limit-with-ai-and-machine-learning-capitalize-on-the-moment-of-interaction/) [Evolutionary Programming: Survival of the “Fittest” Data Models](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, AI & Machine Learning, Customer Data Platform, Data Management, Real-Time Personalization, Single Customer View --- ### [2020 Predictions: Expect Personalization to Gain a Distinctive Edge for the Next Decade](https://www.redpointglobal.com/blog/2020-predictions-expect-personalization-to-gain-a-distinctive-edge-for-the-next-decade/) **Published:** November 25, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/11/2020-blog-predictions-300x200.jpg)The customer experience gap measures the difference between how marketers and consumers rate marketers’ ability to provide a personalized customer experience. According to a [Harris Poll survey](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) commissioned by Redpoint earlier this year, marketers generally rate themselves 2.5 times higher than consumers on their ability to provide an excellent customer experience. Marketers aim to close the gap, recognizing that a personalized customer experience is an imperative and that there is no time to dither. According to the Harris Poll, 37 percent of consumers will stop doing business with a brand that fails to offer a personalized experience. Whether marketers make headway on closing the gap has a lot to do with the consumer – if expectations for a deeper, more relevant personalized experience continue to *increase,* marketers will have to adapt with the moving goalposts before they can spike the ball. With the clock about to start on 2020, I anticipate that narrowing the customer experience gap will be top of mind for marketers for the new year and beyond. Because a hyper-relevant, personalized customer journey drives revenue, we will see a heightened sense of urgency from marketing organizations to modernize customer engagement technology to deliver on deepening customer expectations for [personalization](https://www.redpointglobal.com/omnichannel-personalization/). With personalization as the underlying, driving force as we get set to close out the “teens”, here are additional predictions for 2020 and beyond: **Moving Beyond Compliance: An Equilibrium Between Data Privacy & Personalization** Marketers must strike the right balance between delivering a personalized experience while still respecting data privacy regulations and honoring customer preferences for how their personally identifiable information (PII) is stored, shared, and used. As data privacy legislation including GDPR and CCPA either mature or [go into effect](https://oag.ca.gov/privacy/ccpa), we see 2020 as a tipping point between the balancing of personalization and privacy. Marketers will find the equilibrium between these seemingly conflicting objectives, finally resolving the typical pendulum shifts. One reason for this is because consumers are clear that they will accept the trade-off of providing personal date in exchange for a more personalized experience. In the Harris Poll survey, 54 percent of consumers said they would make this trade, with the percentage skyrocketing for younger consumers (72 percent of Gen Z, and 70 percent of millennials). The trend of consumers being willing to exchange data for [personalization](https://www.redpointglobal.com/omnichannel-personalization/) provides a strong incentive for marketers to safeguard consumer data beyond mere compliance and prove to consumers that opting in and sharing personal data will result in a differentiated experience. The value exchange benefits consumers with a more personalized experience, less friction, added convenience, and special offers that are available only to them. **The Marketing Role Will Continue to Evolve** As data unlocks personalization and grows revenue streams, 2020 will see the marketing role continue to evolve. The marketer will increasingly serve as the key cog in marshaling customer data to create innovative customer experiences. There will be a consensus that marketing is a mission-critical business function, with a growing expectation that it will lead the enterprise in driving revenue. In pursuit of this goal, collaboration between marketers and data scientists will continue to deepen, forming a cohesive team that is empowered to innovate customer journeys. The role of the “citizen data scientist” will also be key – marketers who increasingly have the skills to implement advanced analytic models, empowering them to easily test, tune and optimize customer experiences As organizations strive for a comprehensive, [single customer view](https://www.redpointglobal.com/blog/single-view-customer-essential-success/), marketers will more broadly embrace their role as being chiefly responsible for creating and using the single customer view to deliver individualized next-best actions and to orchestrate engagement across all touchpoints. The trend has long been percolating, as companies have high expectations of their CMO’s taking advantage of new technology to drive growth. This [added ](https://www.wsj.com/articles/average-tenure-of-cmo-slips-to-43-months-11559767605)[pressure and complexity](https://www.wsj.com/articles/average-tenure-of-cmo-slips-to-43-months-11559767605) has led to volatility in CMO position, with both an opportunity to thrive (e.g. CMO’s are increasingly becoming board members), and an opportunity to be replaced (CMO’s have the shortest tenure of any C-level executive). A detailed [Accenture report](https://www.accenture.com/_acnmedia/PDF-87/Accenture-Rethink-the-role-of-the-CMO.pdf#zoom=50) from 2018 posits that a shift from brand to customer experience has changed the CMO remit, with the CMO evolving into a chief collaborator steering the organization toward delivering “world-class customer experiences”. This top-down mandate marks a shift in strategy in response to customer experience overtaking price and product. Interestingly, a much-referenced research report from [Frost & Sullivan](https://inform.tmforum.org/data-analytics-and-ai/2016/08/customer-experience-overtake-price-product-differentiator-2020/) from 2016 actually pegs 2020 as the year that customer experience overtakes price and product as a key brand differentiator. **Advertisements Will Decline in Influence, Putting a Spotlight on Innovation** With customer experience becoming paramount, it stands to follow that the influence of impersonal digital advertising will wane. Consumers will no longer tolerate an influx of irrelevant, intrusive digital ads, preferring instead ads tailored to their interests, preferences, and recent behaviors as a consumer engaged with a brand through a multitude of channels. In response, marketers must focus on innovating the customer experience for both anonymous and known customers, such as delivering personalization for the [first-time visitor](https://www.redpointglobal.com/blog/advanced-personalization-complex-yes-complicated-no/) to a website. Relevant messages must be consistently delivered in the context and cadence of individual consumers, across the entire customer journey. This type of innovation requires real-time access to deep customer data, [automated machine learning](https://www.redpointglobal.com/blog/data-modeling-in-a-lights-out-environment/), and an open garden approach to connect all the different last-mile touchpoints to consumers. While an [open garden approach](https://www.redpointglobal.com/blog/new-redpoint-capabilities-embrace-the-open-garden-approach-to-marketing/) is key to innovation, it needs to be done in a way with a strong security perimeter around the enterprise’s data assets to ensure privacy compliance. **Say Goodbye to the Hype Cycle** Finally, we see 2020 squashing the notion that AI and machine learning are merely fashionable trends with a short shelf life. The flashy ad-hoc use cases such as facial recognition, chatbots, and robots will yield to a consensus that automated machine learning has the power to fully transform an enterprise by positively impacting revenue. In 2020 and beyond, successful organizations will no longer rely on AI and machine learning for one-time, one-dimensional projects. Instead, it will always be running behind the scenes – with several in-line analytics models embedded and driving real-time decisioning for personalized engagement with consumers. To power these models, marketing organizations will place a far greater emphasis on having more quality data. With all of the above on their to-do lists, another easy prediction is that marketers will have their hands full in 2020 with delivering on the promise of a personalized customer experience and narrowing the gap between what customers expect and what marketers are able to deliver. It might be too soon for another “Roaring ‘20s” nickname for the decade, but we can guarantee that marketers will be busy. ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Management, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Here’s Why You May Need a CDP – Even if You Have an MDM](https://www.redpointglobal.com/blog/heres-why-you-may-need-a-cdp-even-if-you-have-an-mdm/) **Published:** October 24, 2022 **Author:** Steve Zisk **Content:** On May 3, in celebration of the newly coined International MarTech Day, Scott Brinker released his [MarTech Technology Landscape](https://cdp.com/articles/martech-day-2022-marketing-technology-landscape/) supergraphic with 9,932 vendors in 49 categories, representing a 22 percent growth since the release of the previous graphic in 2020. In an attempt to cut down on the infamous complexity, some organizations make the mistake of thinking that differently named applications are the same thing. Case in point is a master data management solution (MDM) and a customer data platform (CDP). We have the first, so why would we need the second is a common question from companies looking to streamline their martech stack. While there is some overlap, the short answer to the question is that an MDM and CDP have substantively different goals, target audiences and core use cases. We’ll explore the key similarities and differences and explain why MDM vs. CDP is not a straightforward either-or decision. ## **MDM and CDP View of Data** An MDM, typically an IT initiative, is a solution whose goals encompass mastering the data realms for all the entities that make up an enterprise; not just the customer or party entity, but a range of domains that include product, site, contracts and service. These domains are inherently related to what a CDP does, with a key difference that the CDP’s focus is on customer data. For instance, a CDP records transaction information which by definition includes product information, but a CDP is not trying to perfect the product, it is only trying to accurately relate the product to the customer. The same distinction holds true with the other domains; a CDP may require accurate site information to drive an offer when a customer breaks a geofence, but mastery of the site data is not within its purview. We can also think of the key distinctions in reverse. That is, an MDM has an interest in perfect customer data so that IT applications related to governance, supply chain, CRM and service and cost management have accurate customer data, while a CDP’s interest in in perfect customer data is to create a perfect customer experience. The audience for a CDP, then, is not IT but marketing, which means they are using a CDP a source of truth about a customer to execute against use cases such as acquisition, engagement, retention and upsell. The different focus or goal of the product creates interesting consequences. Even in the customer domain, an MDM will care about core customer attributes such as having the correct name and address, and having that accurate information as it relates to other customers. But an MDM is not going to care about the customer’s behavior or other attributes that will help analyze customer intent. ## **MDM and CDP Areas of Overlap** While both a CDP an MDM will have an interest in creating a golden record, the former will use it for the sole purpose of improving customer experience, while the latter will use the unified customer record to standardize service engagements or enterprise governance applications, for instance. Looking at specific data areas of interest, order and shipping details is one area that highlights the distinction. A CDP will be interested in the information to resolve a customer inquiry perhaps, but for an MDM the information is critical for enterprise resource planning (ERP) and supply chain management. Similarly, semantic definitions and constraints, such as defining a master key for various entities, standardizing currencies and measurements, etc., are typically owned by an MDM while a CDP will inherit those definitions and constraints. Likewise, because MDM owns master data, any time there is a change to a description of a product, any system – a CDP included – that carries a description of the product will need to inherit the change using the MDM as the source of truth. To summarize the overlap, the primary areas of comparison are: - Building a unified profile, but with different data and goals (MDM for domain app standardization, CDP for improving CX) - Identity resolution for relevance and compliance (MDM for details of semantics and governance, CDP for CX in marketing) - Data quality for reducing errors and ambiguity (MDM for enterprise control and unity, CDM for details of interactions/behaviors) **CDP****MDM****Data scope****• Operational Systems (primarily CRM)****• Transactions (online & offline)** **• Martech (behavior, offers, responses)** **• Predictions & Aggregations** **• Operational Systems (ERP, CRM, etc.)****• Transactions (including PIM)** **• Operational “best value” selections** **Data Governance & Stewardship****• Basic assessment of accuracy****• Can include stewardship for privacy (GDPR, CCPA, etc.)** **• Data Modeling****• Metadata repository** **• Entity definition & resolution** **• Data constraints and relations** **• Stewardship for detailed control** **• Enterprise distribution or availability with access management** **Data Cleansing and Quality****• Varies… can be very simple parse & cleanse or rich normalization and cleanse of name, address, phone with profiling and trending of quality****• Generally, follows enterprise-wide rules for cleanse, normalize, enrich, with profiling and constraint / anomaly rules****Identity Resolution****• “Profile Unification”****• Usually, simple deterministic rules** **• Can use probabilistic or ML-based rules** **• Relation to “household” or “organization”** **• Specific to use case / purpose** **• “Entity Resolution” – Party, Product, etc.****• Complex rules with human oversight** **• Rules specific to industry and domain** **• Relation to all covered entities** **• Enterprise-wide fixed rules** **Segmentation****• Core capability of CDPs to support marketing use cases and activation****• Analytics for assessment, optimization** **• Generally absent****• Analytics for metadata and stewardship** **Activation****Publish segments into martech and ad tech “channels”****Generally absent, but see Stewardship***Figure 1* A closer look at the comparison details, specifically data stewardship and governance, reveals why an organization with an MDM may also need a CDP. If for instance data stewardship capabilities are needed for entity definitions and resolution, data constraints and other reasons listed in Figure 1, a company should examine whether its current tech stack has those capabilities and, if not, determine if an enterprise-wide approach using MDM is worth the investment or whether a domain-specific approach will suffice. Because if the purpose of data governance is solely to handle data subject requests under GDPR, perhaps a specific privacy and consent solution integrated to a CDP will meet these goals better than an MDM would. Applying that same calculation for data scope, identity resolution, segmentation and other key purposes will help an organization make the right determination. As we’ve seen, there is room for both an MDM and CDP depending on an organization’s reasons for collecting, storing and using enterprise data, and more specifically customer data. Broad enterprise goals will tilt toward MDM, where a strict focus on improving CX will tilt more heavily to a CDP. **Blog categories:** Data Quality, Master Data Management --- ### [Eliminating Ghost Networks with a Data-First Approach](https://www.redpointglobal.com/blog/eliminating-ghost-networks-with-a-data-first-approach/) **Published:** July 23, 2025 **Author:** Renee Graff **Content:** Ghost networks have become a costly and persistent challenge for health plans. These inaccurate healthcare provider directories include clinicians who are no longer in-network, not accepting new patients, or are listed with outdated locations, names or specialties, leading to a disjointed member experience, operational inefficiencies and heightened regulatory scrutiny. Maintaining up-to-date provider data is not just a compliance issue, it’s a strategic move that impacts Consumer Assessment of Healthcare Providers & Systems (CAHPS) scores, Medicare Star Ratings and health plan member retention. With increasing oversite from state and federal regulatory bodies, the stakes for health plans have never been higher. To eliminate ghost networks effectively, health plans must move beyond inefficient methods for updating their directory to a proactive approach to data readiness. ## **What’s The Root Cause of Ghost Networks?** Many factors contribute to health plan ghost networks, including: 1. **Inconsistent Market Requirements**: States and health plans each have their own data accuracy requirements. Different policies impact how often provider directories are updated. These variations can complicate attempts to maintain consistent data, as information for a specific state may not be lined up with that of the overall health plan. For example, if a state updates their database every 90 days, but the health plan does not update its provider directory accordingly, a member attempting to schedule care using the plan directory may run into issues confirming if a provider is or is not actively accepting new patients. 2. **Fragmented Ownership and Knowledge Gaps:** If a health plan’s team is not working in a shared technology ecosystem, data may not be entered consistently. New job responsibilities, shifts in teams, and knowledge gaps can increase data inconsistencies resulting in delays or omissions in provider directories. 3. **Complex Cross-System Integration**: Data for provider directories lives across many platforms and often is not in the same format. There are many systems at play to integrate provider data into a central repository. Bringing data together, controlling the formatting and synchronization, and version control can be challenging factors for health plans trying to maintain an updated database. ## **Pain Points & Government Action** The frustration associated with ghost networks is threefold. Health plan members experience the largest frustration, as they are left with an inaccurate list of providers and struggle to find someone in-network for their needs. [Findings published](https://jamanetwork.com/journals/jama/fullarticle/2802329) in the Journal of the American Medical Association, reviewed physician directories from five large health insurers and found that 81% of entries had inconsistencies, such as address errors or the wrong specialty’s being listed for a physician. This can result in patients calling multiple offices trying to schedule care or mistakenly scheduling appointments with a provider who is not covered by their insurance. Providers themselves experience hindrances to their workflow, with phone lines tied up by members calling to try to schedule appointments, only to learn that they are working from an out-of-date list. For health plans, ghost networks can result in regulatory and financial stressors, as non-updated lists can lead to a decrease in quality scores. A [government review](https://www.cms.gov/Medicare/Health-Plans/ManagedCareMarketing/Downloads/Provider_Directory_Review_Industry_Report_Round_3_11-28-2018.pdf) of Medicare Advantage plans found that the share of inaccurately listed provider locations ranged from nearly 5% to 93%, depending on the directory. The cost associated with cleaning up ghost networks, the lost opportunity to effectively engage members in in-network care, heightened member dissatisfaction and member churn can lead to penalties, leaving health plans. Under the [No Surprises Act](https://www.cms.gov/nosurprises), members are protected against surprise medical bills and determining out-of-network provider payments. This provides an incentive for health plans to keep their provider directories up to date, as the lost revenue from not only having a member go out-of-network for care but also having to cover the difference between in- and out-of-network care can quickly add up. Additionally, plans are expected to verify and update their provider directories at least every 90 days, establish clear procedures for removing providers that they are unable to verify, and make updates to provider information within two business days of receiving it from a provider. ## **A Data Readiness Approach to Provider Accuracy** Addressing ghost networks requires more than cleaning up existing databases, it requires a comprehensive data strategy. Leading health plans are investing in technology platforms and processes that unify, verify, and maintain an accurate provider database through: - **Real-Time Identity Resolution**: Advanced tools help payers merge existing data from disparate systems, resolving identity mismatches to create a single, accurate record for a provider. - **Stewardship and Automation**: Ongoing data quality checks and new data integration are built in as a continuous process aligned with regulatory cadence (e.g. every 90 days). - **Flexible Deployment and Governance**: Modern solutions offer flexible deployment (on-prem, private cloud, or hybrid), to help health plans meet HIPAA and HITRUST standards while maintaining customized infrastructure. - **Support for Merger & Acquisition and System Consolidation**: As health plans grow or modernize, scalable platforms can consolidate provider information, maintain organizational hierarchies, and preserve data accuracy during transitions. Ghost networks are a symptom of outdated, siloed infrastructure, and regulators are catching up. By adopting a [holistic data readiness approach](https://www.redpointglobal.com/data-readiness-hub/), health plans can strengthen member trust, reduce regulatory risk, and improve access to accurate provider information. **Blog categories:** Healthcare --- ### [All Successful AI Projects Start with AI-Ready Data](https://www.redpointglobal.com/blog/all-successful-ai-projects-start-with-ai-ready-data/) **Published:** July 14, 2025 **Author:** John Nash **Content:** Determining the effectiveness of AI models at any given moment can be challenging due to the rapid changes in situations and context, which complicate the assessment of actual outcomes. However, when AI is applied to robotics, it becomes somewhat easier to identify errors visually. That is why this comment from Troy Demmer, Co-Founder of Gecko Robotics is so spot on: “Even the best AI applications are only as good as the data they are trained on,” Demmer said. “Trustworthy AI requires trustworthy data inputs – data inputs that are auditable and interrogatable.” Demmer’s point is clear: No matter how extensive your AI ambitions, a strong data foundation is essential for success. Because as powerful as AI is, it is not meant to fix bad data. A comprehensive [data strategy](https://www.redpointglobal.com/) must precede an AI strategy. ## **Is Your Data AI-Ready?** The concept of AI-ready data emphasizes the importance of having [complete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/), [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/), [timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/), [actionable](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/), [trusted](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/), and [compliant](https://www.redpointglobal.com/blog/ready-data-is-compliant-data-does-your-data-hold-up-to-scrutiny/) data as the foundation for all successful AI projects. AI-ready data initiates a closed-loop cycle where high-quality data leads to better insights and improved results. This data is tailored for the specific task at hand and is in the appropriate form for the intended AI use case. A platform that generates AI-ready data validates and connects three crucial types of data for AI use cases: the source data itself, the data produced by the AI process (such as recommendations and predictions), and the response/outcomes data, which is then fed back into the platform for further tuning. Producing AI-ready data is a key primary function of a [data readiness platform](https://www.redpointglobal.com/blog/the-data-readiness-advantage-turning-trusted-data-into-business-value/). Making data right and fit for purpose as data is ingested validates data before it is used in downstream processes, or accessed via APIs and native integrations. Your enterprise’s custom models, rules, third-party analytics frameworks, embedded visualizations, LLM & NLP prompt-based workflows and reporting all depend on high-quality data to produce accurate, trusted results. Whatever a business’s use case for AI, high-quality data improves the accuracy and reliability of AI models, reduces bias, increases trust in outcomes, reduces inefficiencies, and saves cost by reducing errors and unnecessary workflows. ## **AI Accountability Starts Upstream** Research shows that failing to establish and follow strong data quality and governance practices is a recipe for AI failure. A recent [Forrester study](https://myplanb.ai/why-85-of-ai-projects-fail/#:~:text=According%20to%20a%20Gartner%20report,requirements%20(Gartner%2C%202024) found that 68 percent of organizations face significant data quality and integration challenges that directly impact their AI success. And [Gartner predicts](https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk) that by next year, organizations will abandon more than half (60 percent) of AI projects that are unsupported by AI-ready data. One reason why companies are discovering (the hard way!) that their enterprise data isn’t healthy enough to support AI is because they mistakenly assume the investments they’ve made in master data management (MDM) systems, data clouds, and other technology – even CDPs – checks the box for cleansing and normalizing data. The problem is an [overall lack of accountability](https://www.redpointglobal.com/blog/a-new-approach-to-choosing-customer-data-technology/) from any one system as the ultimate truth for clean, accurate, and timely data. A lack of standardization creates downstream data quality issues that ultimately produce AI results that are untrustworthy. For instance, it is common for organizations to fuel GenAI applications with enterprise data believing that because the systems involve direct interactions with customers that the data produced are self-validating. They believe the health of the data upstream is less of a concern. Organizations suffer from a clean data mirage. They may have the illusion of data readiness, but in reality what they possess is typically not contextually usable, at least not to support AI use cases that depend on having a precise, updated and in-depth understanding of a customer. ## **AI in Action: Great Data = Great Results** A modern data readiness platform corrects for the problems associated with bad data and AI by ingesting all customer data (behavioral, identity, permission, transaction, etc.) and ensuring both accuracy and timeliness, the former by cleansing and matching all data into a single view (including householding), and the latter through continually updating the profile, in real time, as data is ingested. Those processes ensure that data is right, while also ensuring that data is fit for its intended purpose, e.g., for high value AI use cases. Being fit-for-purpose means three things. One, data is [actionable](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/), meaning it’s in the right form for consumption. Two, it’s [trusted](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/), meaning that it is observable – marketers and business users can tune identity resolution for a specific use case and see that the rules are producing the intended break-aparts (as with householding). Three, it is compliant – it is secure, with managed access to permissions and PII data. For an example of how AI-ready data helps negate downstream issues, consider a GenAI-powered chatbot meant to provide personalized support. If the customer’s identity is poorly matched due to outdated information or poorly cleansed data, the chatbot might reference the wrong account, make irrelevant product recommendations, or fail to recognize the customer has recently completed a purchase or logged a complaint. Conversely, with properly matched, real-time, trusted data, the chatbot engages in a relevant, seamless interaction that reflects the full, current context of the customer’s relationship with the brand. ## **Amplify AI Possibilities with Strong Data** AI built on a solid data foundation with data that is right and fit-for-purpose can unlock transformative value. But reliable and trusted AI results are only possible with data that is clean, complete, current and contextually usable. Investing in a strong data-readiness hub isn’t just a technical requirement, it’s a strategic imperative. Organizations that prioritize data readiness are better positioned to scale AI responsibly, improve decision-making, and deliver more meaningful customer experiences. Before launching AI projects, ask yourself if your data is AI-ready. If not, you’re not ready for AI. **Blog categories:** AI & Machine Learning, Data Management, Data Readiness **Blog tags:** Data readiness --- ### [Data Readiness for AI: Fueling Data, Insights and Action](https://www.redpointglobal.com/blog/data-readiness-for-ai-fueling-data-insights-and-action/) **Published:** July 11, 2025 **Author:** John Nash **Content:** In touting AI capabilities of customer engagement technology, vendors typically point out how their systems use AI to enhance or optimize a business process. Their system uses AI to make better predictions about customers, for example, or to improve customer experience (CX) with more human-like interactions using natural language processing (NLP) and other generative AI (GenAI) capabilities. While the use of AI in customer engagement technology is undeniably important to extract the maximum value from customer data, the view here is that models and algorithms behind AI are just one component of an overall AI approach. Data Readiness for AI is a comprehensive approach that ensures systems are not only infused with AI but are also equipped to supply, manage, and act on AI with the right data, insights, and execution. Data readiness for AI posits that customer engagement technology should leverage AI to power a superior CX. But it should also ensure the best data is fueling the AI – that the data is right and fit for purpose – and that it is ready to support high value AI use cases. These AI uses may be linked to dynamic segmentation, better matching and other specific functions, or specific external systems – a call center, an ESP, online chatbot, etc. A recent Gartner survey of marketers shows a broad use of three kinds of AI projects (see **Figure 1**), with AI-ready data required for all three. In addition, modern customer data technology should also facilitate agentic AI, empowering AI agents that design and execute personalized customer journeys – whether those agents were created by the enterprise itself, by a platform the enterprise uses, or independent providers. ![AI is Coming From Everywhere](https://www.redpointglobal.com/wp-content/uploads/2025/07/AI-coming-from-everywhere-800x428.jpg)Figure 1: Vendors, in-house teams and business departments make up the three main categories of AI projects. 2004 Gartner survey. There are multiple ways to create value in the market by enhancing AI, in other words. The most powerful and far-reaching way it is provide unified and high-quality data as input to AI – which improves the AI output. A second way is to take advantage of third-party AI innovations and integrate them into software products and solutions. And yet a third way to create market value is to make agentic AI actions more effective through proven, interconnected automations that agents can leverage to produce better results. ### **AI-Ready Data** What is AI-ready data? Quite simply, it is data that is made ready for any AI use case immediately upon data ingestion, through a robust process of transformation, cleaning, and enriching to continually provide accurate data and contextual metadata. AI-ready data powers better predictions because the data is both right ([complete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/), [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/) and [timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/)) and fit for purpose ([actionable](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/), [trusted](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/) and [compliant](https://www.redpointglobal.com/blog/ready-data-is-compliant-data-does-your-data-hold-up-to-scrutiny/)) across the enterprise. Autonomous data quality at data ingestion ensures high quality data from data ingress to egress – from nearly any source, to any third-party application. It is data that has been cleansed, enhanced, standardized, and matched upstream to optimize any AI system that requires high quality data to produce trusted outcomes. AI-ready data helps control costs by eliminating the need for additional workflows and resources to standardize data before it can be used to train AI models or power segmentation. Companies avoid the many inefficiencies associated with complex AI plumbing, ensuring high performance, accuracy and more predictable results. Downstream analytical systems are able to handle more data, generating improved analytics. Well-managed data allows AI models to function more effectively to generate more meaningful, trustworthy insights. ### **AI Inside** From simple to complex, there are an endless number of ways to use AI to improve CX through the use of software products and solutions. As a starting point, perhaps the initial objective is to simply integrate with a third-party innovation to produce a more human-like chatbot. Advancing on the AI maturity curve, it is also possible to infuse AI into products to bring out-of-the-box predictive models and machine learning into workflows to create next-level personalized journeys and campaigns – with optimal journeys and campaigns those where out-of-the-box models and machine learning are fed with AI-ready data. Embedding AI innovation from a third-party predictive analytics platform into software unleashes a full spectrum of AI-driven decisioning, from descriptive analytics and evaluation to recommendations and next-best actions. Infusing customer engagement technology with AI is a closed-loop process, where integrating AI into processes generates a better lift in performance – from detecting anomalies in data to improving identity resolution. When those results are fed back into a system that makes the data right and ready for business use at ingestion, the methodology behind a process like identity resolution is continually fine-tuned. It’s a form of captured intelligence, an AI sidecar that continuously improves both inputs and outputs. Finally, to fully capitalize on AI innovation customer engagement technology should also support built-out integrations with key AI platforms such as Databricks and Snowflake Cortex AI. ### **Agentic AI** According to Mordor Intelligence, the market for agentic AI is expected to be $7.28 billion this year, reaching more than $41 billion by 2030 for a compound annual growth rate of 41 percent during the forecast period. The rapid growth is driven by the increasing adoption of AI technologies, combined with the development of more sophisticated AI Agents capable of performing increasingly complex tasks autonomously. To capitalize on this expanding market, modern customer engagement technology should have engines capable of producing and empowering AI agents to facilitate the creation of customer journeys. By autonomously creating triggers, messages, and next-best actions, AI agents act as intelligent surrogates for human marketers, executing real-time decisions and ensuring seamless delivery of personalized experiences across channels. However, true agentic AI requires more than just task automation. It depends on access to high-quality data, rich metadata, and deeply integrated APIs, so that AI agents can evaluate options, make decisions, and take context-aware actions. Without this foundation, AI agents become blunt instruments – tools in search of a purpose rather than intelligent actors creating value. ## **A Three-Pronged Strategy to Optimize AI** A comprehensive approach to infusing AI into customer data technology and its associated outcomes is a three-pronged strategy that can be summed up as data (AI-ready data), insights (AI inside) and action (agentic AI). Fully capitalizing on the promise of AI requires all three. More than simply leveraging cutting-edge technology, providing agents and third-party innovations with data that is right and fit for purpose is the key to using AI to transform how a business operates and engages with customers. This series on Data Readiness for AI will continue with posts on each of the three pillars, starting with AI-ready data. For more on the Redpoint approach to AI, click [here](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/). **Blog tags:** Agentic AI, Data readiness, GenAI --- ### [In Data we Trust: Data Integrity & Data Readiness](https://www.redpointglobal.com/blog/in-data-we-trust-data-integrity-data-readiness/) **Published:** July 7, 2025 **Author:** John Nash **Content:** According to a study by [HFS Research](https://www.hfsresearch.com/news/75-of-executives-dont-trust-their-data/), 75 percent of business executives do not have a high level of trust in their data. This lack of trust comes despite 89 percent of executives surveyed saying a high level of data quality is critical for success. This disconnect goes to the heart of **trusted data readiness**. Trusted data isn’t just [complete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/), [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/) or [timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/) – it’s data you can explain, validate, and stand behind. Whether powering AI, personalization, or strategic decisions, **trust** is essential – the second of the three data readiness pillars related to making data fit-for-purpose. We previously covered data being [actionable](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/), and will close out the series with a focus on the importance of data being compliant. ## **Trusted Data & Actionable Data** We discussed how trust is related to data being actionable – a separate pillar of data readiness. To recap, some data that is made accessible – such as PII or PHI data – has certain rules attached for how it may be accessed and used. Those accessing it must have faith that the appropriate APIs govern its use. The same holds true for synthesized data and other more obscure data types. ## **Trust and Validation** While having the right APIs in place covers one element of trust, data readiness also requires the validation component. Full data readiness provides users with the ability to verify the dataset and not only see what the quality, completeness and timeliness are but to also see how those components change over time. [Data observability](https://www.redpointglobal.com/data-observability/) describes the ability to monitor data quality and data pipelines to ensure that data is reliable, trustworthy and compliant. It fast-tracks how marketers, CX professionals and business users of data are able to vet the data they’re working with, without having to rely on IT or data scientists. ![Data Readiness Trusted Graphic](https://www.redpointglobal.com/wp-content/uploads/2025/07/Data-Readiness-Trusted-graphic-800x367.jpeg)The Six Pillars of Data Readiness: Trusted Data observability yields confidence; whatever the business use case for customer data, having ready access to dashboards that provide a way to interrogate the data allows the user to identify any issues, or determine the source of a bottleneck or other problem. It’s the difference between moving forward confidently vs. having to accept on faith that data is fit-for-purpose. ## **Maintain Trust Over Time** To ensure completeness and timeliness are monitored over time, data observability dashboards must also provide instance history as another layer of trust, a backstop to track data evolution over time and verify if an anomaly is really an anomaly. For example, if there are an unusual number of customer records in a particular feed, instance history provides an easy way to double-check the accuracy. Users have the ability to seek further confirmation, enhancing trust. ## **Trust & Tunable Identity Resolution** The ability to monitor changes is also beneficial when changes are intentional, such as with [tunable matching for identity resolution](https://www.redpointglobal.com/blog/tunable-transparent-identity-resolution-propels-personalized-cx/). It’s one thing to monitor an anomaly such as an unusual number of customer records, but when a marketer regularly sets different rules for how tight or loose a match should be, the ability to monitor whether the appropriate set of metadata is being used is critical for trusting the results. For example, a marketer using a household flag should be able to easily identify the household flag across the customer and prospect base, be able to question why a household flag was used for specific groupings, and to fine tune the flag as needed per the desired use case. When identity resolution is delivered as a black box function, the entire match, merge and identity stitching process is inflexible. Marketers and business users of a unified customer profile have no insight into why records were or were not matched, merged or split – degrading trust. True data readiness accounts for the enormous volume of metadata that is generated amid constant customer change, e.g., names, household dynamics, addresses, emails, other identifiers and behaviors. Tunability empowers marketers to make sense of constant customer change — and to trust that every CX or AI use case runs on the right data. Data trust isn’t optional — it’s foundational. When users can verify, monitor, and explain the data they’re working with, they gain the confidence to act boldly and deliver better outcomes. Want to see what trusted data readiness looks like in action? [Visit the Redpoint Data Readiness Hub](https://www.redpointglobal.com/) to connect with an expert or schedule a personalized demo. **Blog tags:** Data Observability, Data readiness --- ### [3 Reasons Customer Data Silos Inevitably Fail](https://www.redpointglobal.com/blog/3-reasons-customer-data-silos-inevitably-fail/) **Published:** July 5, 2018 **Author:** Steve Zisk **Content:** ![Customer data silos inevitably fail](https://www.redpointglobal.com/wp-content/uploads/2018/07/customer-data-silos-failure.jpg)You may be thinking: *My data is in purpose-built silos. There’s no way to dismantle them.* Fortunately, you don’t need to. Those silos were built so functions such as accounting, marketing, sales, and shipping could work with only the customer data they need in the format they need it. Doing so increases efficiency and improves data management for individual teams. And, specifically in marketing, DMP, ESP, social channels, and display all typically represent silos just within the marketing team. But in the age of the omnichannel consumer, you absolutely must bridge those silos. Half of customer interactions now happen during a multi-event, multichannel journey, [according to McKinsey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/from-touchpoints-to-journeys-seeing-the-world-as-customers-do). Consumers expect brands to understand their entire history regardless of channel, which is something most brands aren’t able to do — primarily because of data silos. When customer data is present in disconnected silos, it becomes a barrier to effectiveness and success. Marketers are unable to deliver the kinds of relevant experiences that consumers demand and desire in the modern age *and* that will help cement loyalty and increase revenue. Data scientists struggle to get the proper data in the proper context to build models that effectively monetize the data. Consider that, according to Forrester Research, when consumers have “excellent” experiences with a brand… - 83 percent will stay with, - 82 percent will spend more with, and - 94 percent will recommend that brand. There are three primary reasons why marketers who have access to only siloed data are unable to act on customers’ desires: lack of visibility into the customer lifecycle, inability to understand signals necessary to personalize in real time, and no way to deliver messaging through the correct engagement touchpoint. Fortunately, a data hub such as a [customer data platform](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/) (CDP) can bridge data silos and help marketers overcome those three obstacles. Basically, a data hub is a central location to store customer data such as personally identifiable information (PII) and behavioral data from interactions across offline and online channels including second- and third-party data. A robust data hub such as a CDP allows marketers to integrate anonymous and known customer data into a single platform to create a persistent, real-time, holistic view of each customer. This enables marketers to orchestrate more relevant and personalized omnichannel campaigns, as well as respond to customers in their moment of need or action. Let’s take a closer look at each reason data silos cause a customer experience fail, as well as how implementing a CDP can help overcome them. ### **Lack of Visibility into the Customer Lifecycle** The buyer’s journey today is more like the erratic flight of a bumble bee than the linear path of a jet. Customers’ interactions throughout their lifecycle aren’t much different. Customers traverse online and offline channels as they research, shop, and buy, *and*, as they upgrade, renew, seek service, and the like. Marketers stymied by siloed customer data have little insight into potential opportunities or problems that customers’ seemingly disconnected interactions may signal. For example, you may have a warranty from a customer who bought a refrigerator 10 years ago from a retail partner. You may know that the customer subscribed on your website to purchase a refrigerator filter every month from your automatic replacement service. You also may know that he phoned customer service. And, you may know that someone who “looks like” that customer has been researching your latest models online. But without connecting the dots, you may not know that your customer called to cancel his filter subscription because he’s in the market for a new fridge. A CDP brings together data from across the functional and channel-specific silos in your organization to know all that is knowable about your customers, allowing you to build progressive profiles as your customers interact with you over time. Additionally, a CDP helps you recognize individual customers across multiple channels and interaction touchpoints, so you can deliver relevant, personalized offers whether customers are in your physical location, online, or on their mobile device. As a result, you can capture that in-market refrigerator customer’s attention and sell him your latest model instead of losing him to a competitor. ### **Inability to Understand Signals Necessary to Personalize in Real Time** It may seem crazy, but 79 percent of U.S. consumers say they expect brands to show they “understand and care about me” *before* those consumers will consider making a purchase, according to a study by marketing agency Wunderman. When marketers lack the access to a continuously updated, holistic view of the customer that a CDP provides, they can’t move at the speed of the customer. Nor can they deliver contextually relevant, personalized communications at the moments it matters most: when customers are making a purchase decision. In fact, 74 percent of marketers say they can’t recognize customers in real time, [an Acxiom study found](https://www.acxiom.com/resources/). A CDP’s ability to bridge silos and create an always-on, always-processing unified view of a customer’s online and offline interactions allows marketers to track and respond to buying signals in real time. That’s why access to customer data is so vital for the modern marketer. Customer data should be like electricity, allowing access to data in the moment of need, so marketers can deliver the contextually relevant interactions that engage customers, increase loyalty, and improve both short- and long-term results. The benefits you can gain from personalizing interactions in real time are too valuable to keep data locked in functional or channel-specific silos. A [customer data platform](https://www.redpointglobal.com/customer-data-platform) can bridge those silos and empower you with the most up-to-date insights and analytics, so you can understand your customers and their needs and motivations, and then act at the speed of the customer, delivering contextually relevant interactions that build loyalty and sales. ### **No Way to Deliver Messaging Through the Correct Engagement Touchpoint** Nearly three-quarters of consumers (73 percent) use multiple channels during a shopping journey, according to a [recent study](https://hbr.org/2017/01/a-study-of-46000-shoppers-shows-that-omnichannel-retailing-works). And, on average, marketers are using 16 different channels to reach and interact with customers, according to [Forrester Research](https://go.forrester.com/blogs/consumer-marketing-2018-could-dos-vs-must-dos/). The question is: How often do those channels match? The answer: Not often enough. A [study by Acxiom and Digiday ](https://www.acxiom.com/resources/)found that 70 percent of marketers have suboptimal or no ability to integrate customer data between online and offline sources. Plus, point solutions implemented for different functions may use different identifiers for customers. That siloed customer data makes it all but impossible to know which channels will be most effective with specific customers. Marketers need visibility into a unified view of customers’ interactions in offline and online channels to make informed decisions on how to optimize their communications. A CDP brings together disparate information about each customer, such as associate ID, account ID, and email. It also connects with internal and external systems through APIs and SQL queries. This allows for chaining and appending external data. A CDP supports identity management and cross-device and cross-record matching. All of this enables both omnichannel marketing and customer journey management. In other words, you’re far more likely to deliver messages through touchpoints that will have the greatest impact. ### **What’s Next** Using a robust data hub such as a customer data platform, you’ll gain visibility into the customer lifecycle, improve your ability to understand signals necessary to personalize in real time, and have a way to deliver messaging through the correct engagement touchpoint. As a result, you’ll have more engaged customers who are more likely to stay and spend more. More than half of the consumers polled in [Wunderman’s “Wantedness” study](https://wantedness.com) (56 percent) say they’re more loyal to brands that “get me” as a segment of one; in other words, businesses that show a deep understanding of their customers’ preferences, needs, wants, and past purchases. Only marketers who bridge silos through a CDP can help their brands achieve this. Don’t let data silos lead to a customer experience fail for your organization. Bridge your silos using a CDP and use that unified customer data to build loyalty and salesl **Blog categories:** Customer Data Platform, Data Quality --- ### [For CPG Companies, DTC Inroads Start with Brand Promise](https://www.redpointglobal.com/blog/for-cpg-companies-dtc-inroads-start-with-brand-promise/) **Published:** April 29, 2021 **Author:** Steve Zisk **Content:** Traditional consumer packaged goods (CPG) and fast-moving consumer goods (FMCG) brands see the writing on the wall. With department stores going out of business, malls in decline, foot traffic down and customers moving online, opportunities narrow for using third-party retailers, partners and wholesalers to expose products to consumers. What’s equally troubling for CPG brands is that their traditional go-to-market model may be permanently out of fashion. Even with an expected [retail boom](https://www.bostonglobe.com/2021/04/18/business/there-will-be-extraordinary-spending-retailers-anticipate-post-pandemic-consumption-boom/) on the way, customers are unlikely to revert to their old buying patterns. Expectations have changed to where consumers today *expect* to be able to have a direct relationship with a brand. In a (pre-pandemic) [eCommerce survey](https://magento.com/sites/default/files8/2020-06/direct-to-consumer-ecommerce.pdf), 59 percent of consumers said they prefer to do research directly on a manufacturer’s website, with 55 percent saying they prefer to make purchases that way. The expectation for a direct relationship presents a Catch-22 for CPG brands: they can’t establish a direct relationship without customer data, and they can’t secure customer data without fostering a direct relationship. To break through the logjam, many brands are taking incremental steps to [transition to a direct-to-consumer (DTC) model](https://theloadstar.com/major-global-brands-setting-out-their-stall-for-more-direct-to-consumer-sales/). Under Armour, for instance, plans to remove its products from nearly 3,000 stores by next year. Levi Strauss, with roughly 40 percent of revenue in direct sales last year, plans to up that to 60 percent this year. FMCG companies including Kraft-Heinz, PepsiCo, General Mills and Kellogg have all made efforts to boost online, direct sales during the pandemic. ## **Brand Promise & Trust** Brands making a concerted effort to increase DTC sales know that customer experience, more than the largely commoditized price and product, is now what drives consumers. Consider a recent Dynata survey commissioned by Redpoint, where [70 percent of consumers](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/) said they will exclusively shop with brands that personally understand them. Without a trove of first-party customer data, though, many traditional CPG brands breaking into a DTC model are making headway by focusing on brand promise. Brands establish what they stand for on sustainability, social or political issues to attract customers who share their values. In a [Marketing Dive survey](https://www.marketingdive.com/news/environment-and-brands-approach-to-it-matters-more-than-ever-to-consum/585803/), 41 percent of millennials said that the environment plays a role in their purchase decisions all or most of the time. A 2019 study from the [NYU Stern Center for Sustainable Business](https://hbr.org/2019/06/research-actually-consumers-do-buy-sustainable-products#:~:text=NYU%20Stern's%20Center%20for%20Sustainable,came%20from%20sustainability%2Dmarketed%20products.) found that sustainability-marketed products were responsible for 50 percent of market growth among CPG brands between 2013-2018. Patagonia is the often cited as the gold standard of sustainable-friendly companies, with its recycled fabrics, aversion to fast fashion and even a “Don’t Buy This Jacket” marketing campaign to tackle the issue of consumerism run amok (launching on Black Friday, no less). Establishing and honoring issue-based brand promise is one way CPG and FMCG companies are making DTC inroads. Another is centering brand promise around trust. Kraft-Heinz, for example, launched a [“Heinz to Home”](https://www.marketingweek.com/the-best-marketing-campaigns-of-2020-part-1/) campaign to transform its well-known products into “lifestyle brands” that evoke familiarity, comfort, and reliability. Online experiences include themed gift packages for celebrations and events like Father’s Day or heading back to college. The brand’s intention is that a customer who shares the same values and identifies with the brand promise will seek out experiences on a branded website. These brands, and many others like them, approach customer experience not as a set of interactions necessary to deliver a product, but as a [full value exchange](https://www.redpointglobal.com/blog/the-elevation-of-experience-why-a-deep-customer-understanding-matters-even-more/) between a customer and brand. By understanding and acting on what matters to a customer, CPG companies making DTC inroads start to form a personal understanding that breaks the logjam; by attracting customers with like-minded values, they begin to gather more first-party customer data, which may then be used to deliver a more personalized and relevant experience. What these brands are doing, in essence, is forming a bond with a customer by exhibiting shared values, trust and respect, and then working backward to provide relevant experiences based on the shared values. ## **Extend the Value Exchange** It’s easy to see the motivation for CPG brands to re-invent themselves by selling experience over product. According to the [2020 Consumer Culture Report](https://smallbiztrends.com/2020/02/brand-values-alignment.html), 71 percent of consumers (83 percent of millennials) prefer buying from companies aligned with their values. With a foothold in a DTC model, companies are able to strengthen the bond with a customer – and receive more first-party data – by building on the value exchange. An eco-friendly company might, for example, encourage a customer to sign up for a monthly sustainability newsletter, or receive (paper-less!) notifications on new zero-carbon footprint products. Likewise, a FMCG company may offer weekly themed menu ideas, or run a recipe contest. Customers who perceive these experiences as value are generally willing to share more personally identifiable information (PII) to generate an improved customer experience. In a [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/), 54 percent of consumers said they are willing to share personal data for a more personalized experience. And in a Sitel CX Index report, more than 60 percent of consumers surveyed said that receiving personalized communications over email, chat and social media is important. Moving to a DTC model will entail new considerations for CPG companies suddenly awash with first-person customer data, such as the imperative to [safeguard data privacy](https://www.redpointglobal.com/blog/what-you-need-to-know-about-consumer-data-privacy-compliance/), essential to further the value exchange and maintain trust. There are also major supply chain and last-mile implications, but the trade-off – personalized, relevant experiences that drive customer loyalty and new revenue – are worth it. That’s the value of a deep understanding of a customer built on trust. It is, in a way, priceless. **Blog categories:** 1:1 Personalization, Retail --- ### [The Role of Personalization in Value-Based Care](https://www.redpointglobal.com/blog/the-role-of-personalization-in-value-based-care/) **Published:** February 19, 2019 **Author:** John Nash **Content:** The topic of value-based care in health and wellness has been making waves for some time. The system, which awards providers based on prevention and health outcomes instead of a traditional fee-for-service model, brings personalization to the forefront of the healthcare relationship. The value-based care approach received an enormous tailwind with the[ January 2018 announcement](https://qz.com/1192693/amazon-jp-morgan-and-berkshire-hathaway-are-starting-a-healthcare-company/) by Amazon, Berkshire Hathaway, and JP Morgan Chase of a partnership to form an independent health care company for their combined 1.2 million employees. The intent is to explore a system focused on creating value free from profit-making incentives and constraints. Value-based care adopts a [“Triple Aim”](https://www.verywellhealth.com/triple-aim-4174961) approach of providing better care and better overall health outcomes at lower cost. One of the biggest opportunities is the roughly 70 percent of healthcare costs that are influenced by consumer behaviors, whereby payers and providers are incentivized to help consumers address those behaviors. Helping a patient with a chronic condition such as Type 2 diabetes manage a diet and exercise regimen, for example, can reduce medication intake and long-term costs while also improving the overall wellness for that patient. [Change Healthcare estimates](https://www.prnewswire.com/news-releases/change-healthcare-study-finds-value-based-care-bending-the-cost-curve-300667419.html) that by 2021, 75 percent of payments in healthcare will have a value-based care component. Making in-roads to a value-based care approach requires providers and payers to establish deeper relationships with the consumer, which is a large part of the reason why the recent announcement triggered such enthusiasm. Amazon has long been a disruptor in personalizing customer experiences using data and analytics, and there’s genuine excitement to see what transpires when it applies this expertise to the healthcare space. The new partnership will have plenty of company in the race to disrupt the industry with value-based care innovations. Oliver Wyman Health, for instance, anticipates that there is [$500 billion opportunity](https://www.oliverwyman.com/content/dam/oliver-wyman/v2/publications/2016/mar/The%20new%20front%20door%20to%20healthcare%20is%20here.pdf) for disruptors to create the best customer experience and become a new “front door” in healthcare. **The Personalization Factor** Mounting interest in value-based care intersects perfectly with both consumers’ rising expectation for personalized experiences and an abundance of smart devices and other technologies that help an always-connected consumer be stewards of their own health. Many smartwatches, for example, provide users with a mobile app dashboard with real-time and aggregate health metrics that we used to only get at a yearly physical, including resting and active heart rate, an activity log, exertion statistics, calorie intake, and sleep patterns. A payer or provider with metrics from a Type 2 diabetic’s smartwatch could use it to customize an exercise plan, send timely reminders, congratulatory notes, or other engagements to help change behavior. Data drives the consumer expectation for personalization; we provide data with every purchase we make and every online search we conduct, and we expect the companies and brands we do business with to use it to provide us with relevant, timely experiences. Personalization also works in changing behaviors. In one [smoking cessation study](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881995/pdf/lgt021.pdf), subjects who received tailored health content were more than twice as likely to have abstained from smoking at a 12-week follow-up than subjects who received generalized content. With so much data available through mobile, social, web, email, call centers, and connected devices, the consumer expects payers and providers to know their health history and patterns. Furthermore, consumers have more choices in healthcare than ever before, and the consumerization of healthcare makes it critical to meet these increasing customer expectations for personalization and exceptional service. An [IntelliResponse survey](https://www.linkedin.com/pulse/new-survey-americans-expect-highest-level-customer-tim/) measuring the state of customer service revealed that health insurance tops the list of industries where Americans expect the highest level of customer service accuracy, with 54 percent of respondents selecting health insurance as the most important for accuracy – nearly four times higher than utilities, which ranked second at 14 percent. Additionally, 58 percent of respondents said they would consider switching health insurance if they had a poor online customer service experience. **A Single View of the Healthcare Consumer** The winners in the race to value-based care will be payers and providers who establish the deepest level of understanding and engagement with individual consumers. This approach recognizes claims data and clinical data as just a fraction of data useful to drive insights. In prioritizing behavioral and third-party data, payers and providers must re-imagine every aspect of how they’re engaging with the consumer. The business processes, technology, and delivery models need to all support putting the customer first. Often, data and engagement silos stand in the way of this goal. Most organizations have multiple engagement systems, and few say that they possess a complete view of the customer from all their available data sources. A solution that links all first-party, second-party, and third-party data sources to create a unified view of the customer is the first step to engage with the customer. With a [single customer view](https://www.redpointglobal.com/blog/single-view-customer-essential-success/), Redpoint Global clients in the healthcare space are making the transition to value-based care by developing analytic-driven processes and programs. These include tracking patients outside of provider or clinic visits, rolling out multichannel marketing programs, and supporting care selection from a network of personalized services such as home health, social care, transportation, and telemedicine offerings. Innovations are made possible when you have a single point of control over data, decisions, and interactions. Behavioral data is not only different for every consumer, it’s also defined by constant change. Advanced capabilities that include in-line analytics and machine learning (ML) make it possible to respond to an individual consumer with relevance in the context of those changing behavior patterns. A single point of control ensures that these next-best action recommendations will always be performed in the cadence of the customer. Payers and providers know that they need to move beyond general population health, and value-based care now provides an extra incentive to develop a deep relationship with each consumer through a personalized experience. Fee-for-services systems and the associated simple segmentation by age, gender, and risk factor fall short of producing the types of outcomes the market now demands. Value-based care recognizes that we need to get more out of our investments in healthcare given the rising costs and uncertain outcomes. Personalized consumer engagement that accounts for changing behaviors is one of the more effective ways to accomplish the Triple Aim goals of better care, better outcomes, and lower cost. **Blog categories:** Customer Data Platform, Healthcare --- ### [How Does Your CDP Stack Up? The Real-Time Difference](https://www.redpointglobal.com/blog/how-does-your-cdp-stack-up-the-real-time-difference/) **Published:** November 18, 2019 **Author:** Steve Zisk **Content:** The CDP Institute recently released its new [CDP Vendor Comparison](https://www.cdpinstitute.org/), a side-by-side look at CDP features and functionality offered by 33 vendors, including Redpoint. From assessing base features to analytics, engagement, and offline capabilities, Redpoint is the only vendor to receive a checkmark for all 27 capabilities across 10 distinct categories. The CDP Institute cautions prospective CDP buyers that the comparison is not intended to rank solutions, or that more features are necessarily better. Rather, it suggests the comparison is meant to showcase features that support various use cases. Because real-time functionality generates a great deal of interest as well as misconceptions among buyers, it’s worth exploring what two capabilities the CDP Institute lists in its comparison guide – “[real time interactions](https://www.redpointglobal.com/real-time-interactions/)” and “multi-step campaigns” – mean from a use cases standpoint. ## **“Real Time” Uncovered** It is possible, of course, that a prospective CDP buyer has no use for real time. If the intention is for the CDP to create and use records of customer information for a traditional drip, outbound, or offline campaign, and all the business needs is basic transactional and behavioral information and the ability to make it available in a specific channel, there are plenty of vendors out there who don’t check the “real-time interaction” box. In addition, a prospective buyer must be aware that there are different levels of “real-time interaction”. Even a base-level CDP will make customer information available directly in response to an API call from an external system, and it might even make the information available in what most would consider “real time”. The pertinent question, though, is whether the information it makes available is itself real time, or is it just producing a nightly flat file feed that’s 24 hours old? That might pass for a “real-time” record of a specific customer, but of course it’s not real time if the customer has purchased something or otherwise engaged with the brand in the last day – a visit to a store, an online search, or any other interaction. Real-time access interface through an API, in other words, is not the same thing as a [real-time update to an underlying record](https://www.redpointglobal.com/blog/real-time-data-aggregates-for-todays-dynamic-customer-journeys/) – which requires that information is pushed out, matched, merged, and made available in short order. ## **Start with Data Ingestion** If the business needs to interact at the cadence of the customer across digital touchpoints, then real-time constraints become a factor and bring into play another set of requirements, including gathering data in real time and responding with relevant content and offers in real time online. If the business also needs to recognize a customer across a broad range of devices, situations, and systems, then identity management becomes important. The business, in other words, may need to correlate and interpret an entire range of information about the customer – both online and offline information – from every enterprise system, including CRM, POS, and service systems, as well as every interaction or transaction within those systems. A typical use case for digital interactivity and identity management would be for a company to recognize a customer and provide a consistent experience across various brands. One Redpoint customer, for example, uses the Redpoint platform to ingest, clean, sort, merge, and match customer data across seven different systems of record, each representing a unique brand. When a customer books a service online through one brand, the company knows not to offer the same service when the same customer makes an in-store visit to another brand, opening the door to a hyper-personalized customer experience that would not be possible if they were unable to recognize a customer’s journey across their brands, channels, and touchpoints. ## **The Real-Time Difference** A CDP that can keep pace with a non-sequential, non-linear customer journey helps an organization differentiate from its competition by demonstrating they understand and respond to the customers’ needs and expectations. Basic personalization is still possible if all a CDP does is push lists of customers into a channel – email, for example – to receive a relevant offer. But differentiation arises when the CDP can orchestrate individual offers based on a customer’s up-to-date history across every channel, online or offline, and has the power to suppress, extend, or change the offer based on a customer’s action in the moment irrespective of channel. The multiple layers of real time surface in data acquisition, in the matching, merging, and updating of a customer record into a golden record, and in decisioning – being able to see and engage with the customer in real time across channels, and make a determination of a next-best offer or action accordingly. A broad range of real-time capabilities infuses a CDP with its ultimate value as the single source of truth for customer data and as a mission-critical enterprise system that provides a fast path to revenue with a hyper-personalized customer experience. An investment in a CDP is a major decision. Like researching car models, a buyer must weigh a host of features in accordance with their purpose and goals. When real-time is under the hood of a CDP, a buyer will know that performance is guaranteed. **Blog categories:** Real-Time Personalization --- ### [Health Plan Predictions for 2025: The Roadmap to Member-Centric Engagement and Operational Agility](https://www.redpointglobal.com/blog/health-plan-predictions-for-2025-the-roadmap-to-member-centric-engagement-and-operational-agility/) **Published:** January 3, 2025 **Author:** John Nash **Content:** Health plans face some pressing challenges in 2025. Rising healthcare costs driven by inflationary pressure, prescription drug spending and behavioral health utilization will make operational efficiency a key theme in the coming year. Member engagement, with the goal of boosting retention, will also be a key focus, fueled by increased competition, particularly among Medicare Advantage (MA) plans. To address these and other key challenges, health plans will make the strategic use of member data a priority. By leveraging advanced data and AI strategies, health plans will aim to become more member-centric, achieving a competitive advantage and sustainable growth. Here are specific areas of focus on the horizon for 2025: ## **1. Operational Agility and Efficiency Become Essential** According to McKinsey, there are [several factors at play](https://www.mckinsey.com/industries/healthcare/our-insights/how-the-healthcare-industry-can-weather-ongoing-challenges) that drive health plans to become more efficient. These include increased utilization of covered services, inflationary pressure, and a tightening of government reimbursements – an especially challenging proposition for managing and growing Medicare Advantage and Managed Medicaid plans. Tangible steps that will help health plans navigate these challenges include: - The standardization and simplification of workflows to reduce redundancies and administrative waste. - Investment in scalable technologies to ensure data quality and availability, improving care coordination and compliance. - Utilization of analytics to identify high-risk populations and allocate resources effectively to optimize care pathways and reduce downstream costs. These steps share a common foundation: payers need to take a hard look at how they are using member data and how they are segmenting their member base. Workflows, for instance, that revolve around individual member journeys not only reduce redundancies and waste, they also lead to better member engagement. Similarly, ensuring data quality helps improve health outcomes by being more relevant for individual members. Using analytics and AI tools is crucial for learning about specific member cohorts, yielding more refined segments and enabling more personalized member experiences. ## **2. Member Retention Takes Center Stage** Due to its vast and expanding beneficiary base, particularly with the aging baby boomer population, Medicare Advantage plans offer significant opportunities for payers in 2025, combining federal funding with flexibility to innovate in care delivery and supplemental benefits. This predictable revenue stream supports financial stability, while Medicare’s emphasis on value-based care aligns with industry trends, encouraging cost-effective and outcome-driven care models. It is for these reasons that retention is a key focus, especially as member churn increases. A [2024 Commonwealth Fund study](https://www.commonwealthfund.org) showed that Medicare Advantage members are more likely to switch coverage than those in traditional Medicare (21 percent, vs. 6 percent), compared with 15 percent of beneficiaries in private health plans. Members cited various reasons for seeking new plans, including a lack of personalized experiences and poor customer service. Meeting member expectations for personalization, then, has a direct impact on increased retention. According to Boston Consulting Group, within 6 to 12 months of implementing personalization, some payers see [improvements in CX by 10 percent](https://web-assets-pdf.bcg.com/prod/how-to-develop-healthcare-personalization-capabilities.pdf) – correlating to higher member satisfaction, which addresses a key reason members explore different options. Member-centric engagement through personalization strengthens retention for several reasons. A health plan offering personalized experiences demonstrates its commitment to the member relationship beyond collecting premiums and processing claims. Examples of personalization include: - Matching members with available providers in their area. - Recognizing social determinants of health (SDoH) and addressing barriers to scheduling preventive care. - Clearly communicating changes to benefits or coverage that impact members or their dependents. In contrast, irrelevant screening reminders, outdated educational materials, or static mass-marketing emails frustrate members and introduce friction into their experience. Payers must understand all there is to know about each individual and make that knowledge accessible in the right ways to drive superior member experiences. This requires continually listening for member signals, proactively addressing issues, and delivering relevant experiences at the right time and through the right channels. A single member view containing all relevant data – including claims information, medical history, prescriptions, transactions but also consumer behavior and preferences, SDoH and other attributes – is pivotal. This unified view supports member-centric engagement, improving satisfaction and loyalty. ## **3. Secure Data and Tech Stack Modernization** To manage costs, safeguard customer data, and advance member-centric engagement, health plans will increasingly emphasize technology modernization and consolidation. They will invest in solutions that enhance their ability to leverage and protect data effectively. As a result, payers will maintain a strong focus on on-premises and private cloud deployments, ensuring a secure and controlled environment for strategic data management and utilization. One prominent health plan – a Redpoint customer – said that a technology consolidation significantly improved its ability to deliver relevant experiences. During a technology audit, the plan discovered that it had three different marketing campaign platforms as well as six different ESP and SMS providers. The lack of full visibility into the consumer journey created a disjointed member experience. Additionally, the siloed architecture introduced risks, as data had to be copied and shared among platforms. Consolidating the tech stack not only improved member experiences but also reduced operational costs and minimized security risks by lowering the number of potential attack points. Health plans will continue to refine their practices for collecting, storing, and utilizing member data, particularly as it concerns PHI. For this reason, many health plans will keep all member data – not just PHI – behind their own firewall. Keeping data together, such as within the confines of a private data cloud warehouse or on-prem, minimizes data movement and thus reduces the risk of a data breach. ## **4. Rise in Self-Service Tools and Smart AI** According to Deloitte, roughly one-third of healthcare executives [identified technology investments](https://www2.deloitte.com/us/en/insights/industry/health-care/life-sciences-and-health-care-industry-outlooks/2025-us-health-care-executive-outlook.html) as a priority for 2025. That includes investment in AI-enabled solutions. As health plans continue to explore how to best integrate AI tools, there will be a larger focus on making sure that data is fit for its intended purpose, with AI use cases of particular concern such as an increased demand from members for self-service tools. An effective use of AI however, depends on training AI with clean, accurate data. Using AI to mediate customer complaints and predict a next best action relies on building a real time, unified member profile with data that is cleansed, standardized, enriched and made ready for business use as data enters the ecosystem. Any digital self-service tool that is built for member use, for instance, must have a complete, up-to-date understanding of the member to ensure that every interaction is seamless and reflects the member’s individual needs at the moment of interaction. According to McKinsey, [AI adoption in healthcare](https://www.mckinsey.com/industries/healthcare/our-insights/harnessing-ai-to-reshape-consumer-experiences-in-healthcare) has been lagging behind other industries, partly because of a lack of alignment on where to start as well as heightened risk. The study solidifies a focus on data readiness, zeroing in on member experience priorities, and the optimization of real-time insights as critical steps in optimizing the use of AI for healthcare organizations. ## **Moving Toward a Data-Driven Future in 2025** Sustained success for health plans in 2025 will hinge on their ability to address rising costs, enhance member retention, and deliver more personalized and more secure digital experiences. The following three pillars emerge as critical to navigating these challenges and achieving long-term growth: 1. **Pristine Unified Member Profiles for Data Availability and Quality** A single, unified member view that combines claims, medical history, prescriptions, and consumer behavior will be the cornerstone of operational agility and member satisfaction. High-quality, real-time data will not only empower health plans to personalize engagement but also ensure that AI and analytics tools deliver actionable insights to optimize care pathways, predict needs, and reduce costs. 2. **Member-Centric Engagement Through Personalization** Personalization is no longer optional. Health plans that proactively address member barriers, recognize SDoH, and deliver tailored interactions will strengthen loyalty and retention. A consistent focus on listening to member signals and delivering relevant, timely experiences across channels will demonstrate a commitment to member well-being, driving satisfaction and improved outcomes. 3. **Tech Stack Optimization and Data Security** With member data becoming increasingly valuable, safeguarding it is paramount. Consolidated, on-prem tech stacks offer a secure environment for member data while enabling seamless and relevant experiences. Keeping sensitive information closer to the vest streamlines operations, minimizes risk, and ensures compliance with privacy regulations. By prioritizing these strategies, health plans will overcome the immediate pressures of 2025 while building a foundation for sustainable growth. Unified, clean data, personalized engagement, and robust data security will enable payers to enhance member experiences, maintain loyalty, and achieve a competitive edge in a dynamic and demanding healthcare landscape. **Blog categories:** Healthcare --- ### [Meet the Moment of Complexity with a Change in Culture](https://www.redpointglobal.com/blog/meet-the-moment-of-complexity-with-a-change-in-culture/) **Published:** December 16, 2021 **Author:** Redpoint Global **Content:** Author, management consultant and educator Peter Drucker is credited with the famous saying “culture eats strategy for breakfast.” The widely acknowledged father of modern management, who died in 2005 (at 95), enjoyed the bulk of his career well before ubiquitous data ruled the business world, but the aphorism holds true in the digital age. The implication is that human beings – businesspeople – are ultimately responsible for executing business strategy, but often row in different directions. The reason for the aphorism’s staying power is that then, as now, strategy execution is ultimately dependent on people, but it is difficult for a fixed business culture to keep pace with the rapid influx of big data, automation and complex technology in era of digital transformation. [NewVantage Partners](https://www.businesswire.com/news/home/20210104005022/en/NewVantage-Partners-Releases-2021-Big-Data-and-AI-Executive-Survey) 2021 Big Data and AI Executive Survey sheds light on how data complicates the marrying of culture and strategy. Consider that 92 percent of companies continue to identify culture (people/process/change mgmt.) as the biggest impediment to becoming data-driven organizations, with the predictable result that just 24 percent of companies have forged a data culture or otherwise created a data-driven organization. The reason culture is such an impediment is because businesspeople (vs. their IT counterparts) aren’t conditioned to relate data – more specifically, the quality of data – to business outcomes. When the business side engages with data and sees first-hand the transformational business outcomes produced by the virtuous cycle of data, insight and action, it will spark the needed cultural change. Businesspeople will understand their pivotal role in managing data as a business asset, which in turn will help forge a data culture with universal support for a well-articulated data strategy. ## **Why a New Mindset is Needed** What is a data-driven strategy, and how does it differ from the prevailing cultural mindset around business strategy? A look at the evolution of data-driven marketing reveals the magnitude of change in recent years, and the need for a different mindset. For years – decades even – creating a customer experience by matching people to content was a one-way push. Direct mail and email blasts are opposite sides of the same coin, and marketing technology was developed along this paradigm. The problem, of course, is that audiences are not lists, they are comprised of unique individuals with various interests, behaviors and preferences. Lists do not capture the dynamic nature of how an individual customer engages with a company or a brand. Content, too, is no longer a static piece of mail, or an email. Like the dynamic audience it is created to serve, bi-directional content must be displayed on different devices in different moments and cadences, with various resolution, structure, size, etc. The bottom line is that a data-driven culture and strategy must be in place to meet customer expectations for personalized, omnichannel experiences that better captures the dynamic nature of a customer journey, particularly with an increase in digital-first engagements and a digital acceleration. Consider a new [McKinsey study](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) that suggests that companies shifting into the top-quartile performance in personalization would generate over $1 trillion in value across US industries, and that companies excelling at personalization generate 40 percent more revenue from those activities than their peers. To shift into the top-quartile in performance, it’s important to understand that a true personalized experience – the kind that drives new revenue – must be omnichannel. It’s a key distinction. A brand might personalize an experience on one channel, but not another. An omnichannel customer experience, by contrast, is as if the brand is conversing with the customer with one consistent voice across every interaction over the complete customer lifecycle. An omnichannel framework presupposes having a deep, personal customer understanding, and is measured across three dimensions. The first is a recognition that the customer must be at the center of everything. Second is ensuring that a holistic experience is equal on every channel. The third dimension is a cross-channel awareness. If a customer abandons a shopping cart on one device, will that instantly be taken into the context of the customer journey if the customer contacts service on a different device minutes or seconds later? ## **Capitalize on the Magical Moments** Accepting that data is a strategic asset is the first step toward building a new structure to allow for personalized experiences at any moment, on any channel, and consistent with a singular brand voice irrespective of how a customer engages. A data-driven culture maps a strategy for matching a dynamic audience to dynamic content or assets. With a firmer understanding of what omnichannel entails – including the rules-based, bi-directional, real-time construct – we can start to think differently about audiences and assets, and how they manifest themselves to create moments in an omnichannel experience (**See Figure 1**). Because that is really the end goal – creating a magical moment of marketing where the asset (a real-time decision, a next-best action, an engagement with a person) meets the customer at exactly the right moment in their particular journey. ***Figure 1: The Omnichannel Customer Experience Framework*** If we think of a magical moment as being made possible by a release of energy, the trigger causing the reaction has three parts. One is through the sequencing of a campaign that may have a flow, a sequence, timing, branch logic. Another is real time, a crucial element that recognizes moments are just that – brief moments in time that appear and disappear in a flash. They are not happenstance, but rather flow from marketers being prepared for any set of circumstances, recognition, context or cadence, and having the absolute latest data accessible. A third context is the human-to-human element. This is often overlooked in a digital construct, but it’s important to remember that a consistent brand voice also includes how actual employees engage with customers, whether through service, a call center, a front desk, etc. It’s an important part of a holistic omnichannel experience. Chemical reactions, of course, must have a source of energy. When dynamic audiences and assets combine to create a magic moment, the potential or stored energy that is ultimately released comes from the built-in rules. There are rules for audiences (logic, timing, structure, cadences and rules for who/when/why) and rules for assets (types/sizes of images/content, sequencing, exposure or inventory limits, etc.). From a marketing perspective, these pre-built, structured, scalable assets and audiences that are filled with business rules are now ready to go – ready for use anytime and anywhere. At the precise time when their release will create a magical marketing moment, they take on the principles of kinetic energy in motion, coming together to delight a customer with a superior omnichannel experience that is consistent with every action and decision that has come before, and that will persist over time. ## **A Single Focus: Data as a Strategic Asset** In a previous blog, I wrote about how Redpoint rg1 is architected as an [omnichannel customer experience platform-as-a-service](https://www.redpointglobal.com/blog/future-proof-customer-experience-with-an-omnichannel-cx-platform/), and detailed what that means for creating consistent experiences, particularly through having a comprehensive, single view of the customer and pristine customer data. Technology can certainly help solve some of the biggest challenges enterprises face in knowing everything there is to know about their customers and delivering meaningful – and yes, magical – experiences, but I thought this addendum important to point out that technology alone is generally only as effective as the users who interact with it. That’s why a culture change is so important, which is why [rg](https://www.redpointglobal.com/rgone/)1 is unique as a customer experience platform in that it gives users unprecedented visibility into, and control of, their customer data. With exposure to a new structure for dynamic audiences and assets, and the context for how this structure brings omnichannel experiences to life, marketers will have a new appreciation for data as a strategic asset. With culture and strategy in harmony, a major roadblock will be removed for creating innovative and transformational business outcomes. ## **Related Content** [Customer Journeys are Dynamic: Your Engagement Technology Should Be as Well](https://www.redpointglobal.com/blog/customer-journeys-are-dynamic-your-engagement-technology-should-be-as-well/) [The Sky is the Limit with AI and Machine Learning: Capitalize on the Moment of Interaction](https://www.redpointglobal.com/blog/the-sky-is-the-limit-with-ai-and-machine-learning-capitalize-on-the-moment-of-interaction/) [Data as a Revenue Engine: How to Monetize Customer Data with a Single Point of Control](https://www.redpointglobal.com/blog/data-as-a-revenue-engine/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Data Quality, Real-Time Personalization, Segmentation & Activation --- ### [Data Readiness and Actionable: Is Your Data Ready for Takeoff?](https://www.redpointglobal.com/blog/data-readiness-and-actionable-is-your-data-ready-for-takeoff/) **Published:** June 30, 2025 **Author:** John Nash **Content:** A preflight checklist is a vital tool for ensuring a safe flight. Pilots routinely use a written checklist to confirm fuel and hydraulic fluid levels, to inspect for damage or obstructions, manage trim settings and flight controls and perform other safety checks. Invaluable for avoiding preventable accidents, the practice clears a flight for takeoff. The concept similarly applies to [data readiness](https://www.redpointglobal.com/). To avoid downstream problems with inferior data or data that has not been validated for its intended purpose, IT professionals, marketers and business users of customer data need to have faith that data has been put through its paces so that it will get them where they want to go. In this series on the six pillars of data readiness, we’ve discussed what it takes to make sure that data is right, i.e., that it is [complete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/), [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/) and [timely](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/). When those criteria are met, the next step is to make sure that customer data is **actionable**. The engines are firing and you’re ready to start rolling down the runway. Data is actionable when it is meaningful, accessible, and available at the right time for the right purpose. ## **Actionable and Metadata** Actionable means a few different things. One is that customer data is formatted and semantically tied in such a way that it’s meaningful in the context in which you will use it. This is important for AI. For instance, if AI is given a field that says “Name” what does that mean? Is it a product name? A person? If the latter, is it a full name, just the last name, etc. “Name” as a standalone word does not reveal much. The same holds true for other entries, such as denominations. If someone paid 50.00 for a product, is that in U.S. dollars, Euros or another value? (As an aside, a mistaken fuel conversion using pounds/liter instead of kilograms/liter caused Air Canada Flight 142 – aka the [“Gimli Glider”](https://actionable%20means%20a%20few%20different%20things.xn--%20one%20is%20that%20you%20have%20the%20data%20formatted%20and%20semantically%20tied%20in%20such%20a%20way%20that%20its%20meaningful%20in%20the%20context%20in%20which%20you%20will%20use%20it-hl57j.%20this%20is%20really%20important%20for%20ai.%20for%20example,%20if%20you%20give%20ai%20a%20field%20that%20says%20/) – to run out of fuel halfway to Edmonton and crash land on a racetrack.) ![Data Readiness Actionable Graphic](https://www.redpointglobal.com/wp-content/uploads/2025/06/Data-Readiness-Actionable-graphic-800x370.jpeg)The Six Pillars of Data Readiness: Actionable The need for a common understanding highlights the importance of metadata in making customer data actionable. Metadata provides the relationships and rules for managing data flow, analysis and activation in a data readiness hub. It provides a contextual framework for customer data in terms of the how, what, when, where, and why the data is being collected and used, e.g., creating a golden record, GenAI, etc. ## **Actionable and Accessibility** Data being actionable also refers to its accessibility. The act of collecting data into a database without appropriate metadata or APIs just pushes the problem downstream. Being complete, accurate, and timely are separate goals from making data properly available to any system that needs it. There are a few ways to look at accessibility in this context. One is to have APIs that are meaningful and useful so if someone wants to pull the data they can. Another is to have an appropriate set of metadata documentation to allow someone to perform queries directly on the database that will give them what they need. That is typically what AI will want to do. A third is to make sure that data pushed out to an external source is meant for that specific source. This ties into the concept of data being trusted, a topic we will explore in greater depth in the next blog in this series. The reason data being actionable and being trusted are related is because some of the data being made accessible may be PII or PHI data. In this case, accessibility must be both specific and limited – with rules in place for who has a right to access and use the data, make a copy, etc. The recipient – the user – needs to understand (and abide by) any limitations in place. These limitations may both be regulatory in nature – GDPR, CCPA, HIPAA, etc. – or set by a customer through permissions. In short, data provided through an API must be shared in such a way that it is used for the right purpose. ## **Actionable and Timeliness** A final point on making data actionable is the role of timeliness. Any access points and/or API’s need to work in the cadence of the customer flow, which may mean only passing through parts of the profile record to speed up its transit, or in connecting technologies in a way that is scalable and cost effective for that cadence. Timeliness in this sense is distinct from its role in building the unified customer profile, which we [covered as one of the six pillars](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/). Essentially, the role of timeliness as it relates to data being actionable refers to putting data to work at exactly the right moment. This means timeliness of access and activation, making sure the right data is available to the right system or person, in the right context and at the right time. A real-time, up-to-date unified profile has little value if the systems that need it – AI engines, personalization tools, marketing platforms – can’t access or act on it in real time. Making data actionable means ensuring that timeliness extends all the way through to activation, not just ingestion or transformation. Learn how Redpoint helps ensure your customer data is not just available—but actionable across every channel in real time: [Data Readiness for AI, CX and more](https://www.redpointglobal.com/). **Blog categories:** Data Readiness, Omnichannel Marketing **Blog tags:** Data readiness --- ### [Timeliness in Data Readiness: The Hidden Driver of Relevance](https://www.redpointglobal.com/blog/timeliness-in-data-readiness-the-hidden-driver-of-relevance/) **Published:** June 24, 2025 **Author:** John Nash **Content:** In a Gartner report on how to [evaluate AI data readiness](https://onedata.ai/gartner-research-note-how-to-evaluate-ai-data-readiness/), timeliness is one of five data governance components listed as essential for approving data for use – especially when data is captured across multiple systems or when multiple datasets are combined. Previous posts in this series on data readiness focused on what it means for data to be [complete](https://www.redpointglobal.com/blog/is-your-data-really-complete-understanding-the-first-step-in-data-readiness/) and [accurate](https://www.redpointglobal.com/blog/accurate-data-confident-decisions-building-trust-through-data-readiness/) to support existing or emerging business, CX or AI use cases. Here, the attention turns to **timeliness** as the third pillar of getting data right – with more to follow on the fit-for-purpose pillars of being actionable, trusted and compliant. Timeliness as a key component of data readiness is distinct from real time actionability – the ability to deliver a next-best action across an omnichannel customer journey. Rather, it refers to timeliness in the building of the unified customer profile that provides a [real-time, contextual understanding](https://www.redpointglobal.com/profile-unification/) of a customer, household or business. It is timely if it is updated and available in the cadence of a customer journey, no matter what cadence any one customer has. Just as the completeness and accuracy of data are not mutually exclusive, timeliness is an integral part of a data readiness *process*. That is, data can be complete and accurate without being timely, as well as being timely without being complete or accurate. ## **Updated Attributes and Model Scores** Timeliness in building and maintaining a unified profile means that the profile is continually updated, and that attributes and model scores are continually updated. These updates are essential to guarantee that a profile reflects a customer’s most current behavior, preferences and likely actions. ![Data Readiness Timely Graphic](https://www.redpointglobal.com/wp-content/uploads/2025/06/Data-Readiness-Timely-graphic-800x377.jpeg)The Six Pillars of Data Readiness: Timely The continuous updating of attributes means that identifiers and every data point associated with a customer – name, email, and contact details, last purchase data, preferred channel, browsing history, loyalty tier, etc. – are continually updated. To make these real-time or near real-time updates to a unified profile, a data readiness hub must continuously ingest new data from all channels and sources to ensure that the profile reflects the latest understanding of a customer. In addition, the sources themselves – POS, CRM, EHR, etc. – must also be updated in real time, ensuring for instance that a transaction record reflects an updated view. The same holds true for real-time updates to model scores such as propensity to buy, churn risk, customer lifetime value (CLV) and any other predictive indicator used by the business to engage with a customer. Recalculating model scores as new behaviors and events occur vs. on a batch basis is a foundational requirement for engaging a customer with a relevant experience. ## **Why Timeliness Matters** Together, real-time updates to the profile attributes and model scores are what allow marketers to react in the moment, not after the moment has passed. But more importantly, the combination ensures that personalization is contextual and timely, not based on stale data. This is where the difference between real-time decisioning and timely updates becomes apparent. Because when a brand reacts in real time – such as real-time website recommendations – with outdated attributes or models, the interaction is likely to be irrelevant. Conversely, a real-time decision based on real-time updates to a unified profile (complete, accurate *and* timely) results in triggered communications that strike with pitch-perfect relevance. A partial list includes cart abandonment emails or SMS notifications, offers or content that align with a customer journey. Updated model scores also enable a brand to prioritize outreach, such as targeting an audience more likely to purchase. A timely profile even influences suppressions, e.g., withholding an offer for a customer with a recent purchase. Making sure that your customer profiles are complete, accurate and timely are essential for getting data *right*. That is, for making sure that a profile represents the customer, household or business that a brand is trying to understand. But having the right data does not guarantee that it will drive decisions. That’s where the next pillar of data readiness comes in: actionability. In the next post, we’ll explore what it means for data to be truly actionable – and why even the best data falls short if it isn’t used effectively. **Blog categories:** Data Readiness, Real-Time Personalization **Blog tags:** Data readiness, Golden Record, identity resolution --- ### [A Quick-Hitting Guide to Customer Experience Gaps](https://www.redpointglobal.com/blog/a-quick-hitting-guide-to-customer-experience-gaps/) **Published:** November 4, 2021 **Author:** Redpoint Global **Content:** Across four dimensions of customer experience – customer understanding, personalization, privacy and omnichannel consistency – marketers believe they are delivering a better experience than consumers give them credit for. That disconnect, with a roughly 2X gap between marketers’ and consumers’ assessment of the ability of marketers to deliver an “excellent” experience across those four dimensions, is one of the key findings of new research from Harris Poll in a survey commissioned by Redpoint Global. Published last month, [“Revisiting the Gaps in Customer Experience”](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) explores the state of CX today, probing marketing strategies, consumer expectations, challenges, technologies, and the shifting landscape. Among some facets of CX, comparisons were drawn between how marketers are doing now vs. in 2019 when initial research was conducted on the topic. The report also sheds light on new trends, such as how marketers are coping with the fallout from Covid-19 and their plans for adapting to the loss of third-party cookies. The Customer Experience Infographic provides a one-page summary of more key findings. Download the infographic [here](https://www.redpointglobal.com/resources/infographic-the-customer-experience-gap/) for a quick glance at some of the important findings, including: - Marketers are roughly twice as likely as consumers to say they are delivering an “excellent” customer experience - Consumers rank privacy as the biggest CX challenge; marketers rate data quality as the top challenge - Data quality also scores as the top area of increased MarTech investment, cited by 63 percent of marketers as the No. 1 focus (AI, personalization, and real-time engagement followed) - A majority of marketers agree that it has become increasingly difficult to manage the number of customer touchpoints they have - While only 49 percent of marketers are “very confident” in the quality of their customer data, just 18 percent of consumers share that sentiment - A greater focus on delivering an omnichannel CX, and a need to integrate on-line and off-line channels rank as the top two ways CX strategy will evolve over the next 12 months - A majority (66 percent) of consumers say that they are willing to trade more information about themselves in exchange for a more valuable CX - Majorities of both consumers and marketers agree that brands struggle to meet consumer expectations for personalized experiences Related Content [The State of the CX Gap: Harris Poll, Redpoint Revisit Benchmark Survey](https://www.redpointglobal.com/blog/the-state-of-the-cx-gap-harris-poll-redpoint-revisit-benchmark-survey/) [Addressing the Gaps in Customer Experience: Redpoint Global/Harris Poll Benchmark Survey](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) [Hit a Personalization Home Run with a Five-Tool Solution](https://www.redpointglobal.com/blog/hit-a-personalization-home-run-with-a-five-tool-solution/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* --- ### [Redpoint Global Product Updates: Reduced Complexity, Ease of Use Highlight New Release’s Intelligent Orchestration & AML Capabilities](https://www.redpointglobal.com/blog/redpoint-global-product-updates-reduced-complexity-ease-of-use-highlight-new-releases-intelligent-orchestration-aml-capabilities/) **Published:** July 28, 2021 **Author:** Steve Zisk **Content:** Ease of use and reduced complexity through fewer hand-offs highlight updates to the Intelligent Orchestration component of the [Redpoint rg1 platform](https://www.redpointglobal.com/one-platform/) released today. Just as with the [previous release](https://www.redpointglobal.com/blog/redpoint-global-product-updates-simplicity-ease-of-use-highlight-new-releases-intelligent-orchestration-capabilities/), enhancements intend to make the platform easier, cleaner and faster for both novice and power Redpoint users. Ease of use in the latest release primarily manifests itself with visual representations that facilitate real-time website personalization processes. With visual representations, marketers and other end users are able to map dynamic content – images and text – to the eventual corresponding live web page without having to use a web designer or developer intermediary. All rules, details and other assets that are used to produce a personalized region of a web page are now located in one place, easily accessible and visually understood by the marketer who is producing the personalized content. ## **Realtime Layout** A new feature in the [Intelligent Orchestration](https://www.redpointglobal.com/orchestration/interaction-connections) component of rg1, Realtime Layout organizes information in rules that visually match the final webpage format. A visual layout of the web page **(see Figure 1)** is organized by rectangles drawn directly inside the real-time layout guide. Each rectangle (homepage carousel, homepage hero, homepage merchandising) is associated to a rule, represented in the Layout Hierarchy **(see Figure 2)**. **Figure 1:** *A visual representation of a web page, with a rules-based organization, highlight the new Realtime Layout feature in rg1.* With real-time website personalization gaining traction, fewer static images and more dynamic content, Realtime Layout addresses the need for marketers to easily map personalized content to a rule and quickly have a visual representation of real-time changes – all in one place, without having to interact with developers or other tools. As an example, rather than publish rules into the real-time engine that will eventually correspond to sections of the web page, directing a web designer which rules correspond to which dynamic section of the page, and then confirming a preview, Realtime Layout empowers marketers to control the entire process. ***Figure 2:** In the Realtime Layout Hierarchy, each area of dynamic content corresponds with a rule designating personalized content.* The previous method is still an option, but the new process addresses user feedback that marketers are more in tune with personalization needs than designers or developers and thus should have more control. With Realtime Layout, personalization optimization, attribution and measurement can all be done in one place, absent handing off some of the process to a designer. As an added bonus, this visual metaphor can be applied to any visually oriented personalization, be it the website, mobile app, call center and even email real time “on open” content. ## **AML Product Recommender Models** AML Product Recommender Models is similar to the Realtime Layouts feature in that it gives more control to marketers with less required interaction from web developers, putting all the needed assets in a product recommendation engine in one place. It also represents an important evolving trend within rg1, empowering the intelligent edge. Redpoint is increasingly integrating machine learning capabilities directly into the marketer’s workflow, delivering true, inline analytics, with just a few clicks from the marketer. Through a deeper integration with the [Automated Machine Learning](https://www.redpointglobal.com/machine-learning) (AML) component of rg1, marketers will have direct insight into product recommendations that are based on product and customer data. Previously, support for product recommendations in AML was through web developers packaging assets to assemble a product recommendation section. Because marketers no longer must hand off the plumbing of connecting all the pieces needed to build a recommendations page, they can more easily personalize content and offers through a recommendation smart asset. It’s both easier to program and easier to manage, with more pervasive support due to fewer moving parts. As with Realtime Layouts, the rationale for the enhancement aligns with the increase in dynamic content, and the desire to give marketers more control of real-time website personalization, here through more personalized, relevant product recommendations. In summary, AML Product Recommender Models gives marketers an easy way to create a recommender model inside the intelligent orchestration component of rg1, link it to the AML component to build out a product recommendation section, and then create a recommendation smart asset to make connections between the assets in the recommender model and the actual web page or mobile app. It is faster, easier, more direct and with more support for product recommendation models, directly inside the intelligent orchestration workflow. ## **Standards-based Identity Validation** A minor change improves the rg1 platform’s standards-based identity validation, dovetailing with the desire for most enterprises to simplify and consolidate the procedure. With OpenID facilitating user identity verification using a single authorization server (Keycloak, AuthO, Okta), platform users and administrators can manage verification in an enterprise repository. A consolidation of identity verification through any of the three commonly supported authorization server providers matches increasing enterprise requirements for standards-based single sign-on, permissions and otherwise meeting IT requirements for security, compliance and other operational requirements. ## **Additional Data Connectors and Channels** Channel support extension is another enhancement of the new release. In line with the platform’s open garden architecture, the new release offers simpler CRM, data onboarding and email data connectors as well as simplified one-click segment export – including HIPAA and HITRUST compliant Paubox as a new email service provider. ## **Related Content** [Redpoint Global Leaps Forward with Usability and Machine Learning Enhancements](https://www.redpointglobal.com/redpoint-global-leaps-forward-with-usability-and-machine-learning-enhancements/) [Redpoint’s rg1 Platform Empowers Ambitious Business Leaders to Drive Real-Time Customer Engagement](https://www.redpointglobal.com/blog/rgone-is-how-ambitious-marketers-lead-markets/) [Satisfy the Customer Expectation for a Personalized Experience with Intelligent Orchestration](https://www.redpointglobal.com/satisfy-the-customer-expectation-for-a-personalized-experience-with-intelligent-orchestration/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Real-Time Personalization --- ### [The New “Birds of a Feather”: Real-Time, Dynamic Audience Selection with Automated Machine Learning](https://www.redpointglobal.com/blog/the-new-birds-of-a-feather-real-time-dynamic-audience-selection-with-automated-machine-learning/) **Published:** May 20, 2020 **Author:** Redpoint Global **Content:** In a recent article in MIT Technology Review, [“How AI is changing the customer experience”](https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2020/04/28/1000675/how-ai-is-changing-the-customer-experience/amp/) a survey of more than 1,000 business leaders reveals that sales and marketing is expected to lead the increase in AI deployments over the next two years, from 33 percent using it actively today to an expected 59 percent by 2022. While the survey did not directly address AI adoption in response to the coronavirus crisis, a McKinsey survey did. In its [B2B Decision Maker Pulse survey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/rapid-revenue-recovery-a-road-map-for-post-covid-19-growth), 25 percent of companies surveyed said that the pandemic is leading them to redirect and increase spend toward emerging opportunities. The MIT article posits that personalization will largely be the driver for sales and marketing to take the leap, in a transition from more efficiency-based use cases, because AI has been shown to “transform the way companies interact with their customers … bring a deeper level of customer understanding, drive customization and create personalized journeys.” One result of using AI to interact with customers on a more personalized basis is that it devalues the use of personas. With a deeper understanding of customers at an individual level, the traditional persona attributes – income, age, household size, location – become less effective in predicting how a customer might react to an offer or message. AI takes away the need to hazard a guess. “The idea that you can create personas, and then use them to target or serve someone, is over in my opinion,” says one CEO in the MIT article – an opinion I share. **Old School vs. New School** Marketers who rely on personas to segment customers may then rightly ask what will replace them? Will there simply be more granular groupings? Here, AI closes the loop by automating the end-to-end process; if a personalized message or offer is the result of using AI to become closer to the customer, another AI use case is dynamic [audience selection](https://www.redpointglobal.com/blog/new-birds-of-a-feather-real-time-dynamic-audience-selection/). There are numerous techniques that can be applied here such as tradition “K-means, medians, mediods”; Collaborative Filtering; or more technical approaches like Real-Time Attention Based Lookalike Model (RALM). The key in selecting an algorithm is to be able to deploy analytically based clusters as fast and easily as you would deploy your personas. In fact, audience generation using real-time algorithms that select an audience on the fly is the second most frequent use case of Redpoint Automated Machine Learning (AML). The first is the real-time decision over what to present – the next-best action or offer. This is a completely new way for marketers to think of clustering models vs. personas, with groups determined based on certain characteristics. Intelligent grouping of discrete data, where the data tells you what the clusters need to be, means that an audience is created only at the point in time where a next-best action will be rendered. This is because customer data – preferences, behaviors, transactions – loads in real time and at an individual level, not a group level. Multi-dimensional clustering with an algorithm groups on dimensions that are otherwise impossible to select manually – you could never figure out which or how many of 20, 50, 100 characteristics warranted inclusion in one persona over another. Rather than setting and adjusting discrete parameters of a persona, model clustering with AML provides a far more nuanced way to bring customers together, tap into similarities and then test on the fly. It’s the difference between choosing a known algorithm vs. asking which algorithm to use based on the real-time data presented to the model; the task of having to define the model, in other words, is done by the algorithm – not the marketer. **Making Data Science Accessible** There’s a common misconception that an army of data scientists is required to build models, but one of the unique aspects to AML is that in addition to off-loading the task of defining models, the solution provides marketers with access to the modeling done in the AML tool. Traditionally, analytics has been essentially trapped inside of a handful of data scientists who build segments and models off-line. This is a big hurdle for widespread AI adoption for sales and marketing use cases. AML removes this barrier for success because it’s built for marketers. The everyday marketer can go out and build a model without having to write a line of code, and the algorithms will dynamically select the optimal audience at the time of selection, on demand. Since the advent of marketing, ambitious marketers have been innovating with ideas to group “birds of a feather” to more effectively engage with customers. With AML, dynamic audience selection using multi-dimensional modeling puts the feathers under a microscope. A customer is only included in an audience if the customer’s up-to-date behaviors and preferences at the very moment of the audience selection warrant inclusion. If there’s to be a significant rise in AI sales and marketing use cases over the next few years as the MIT article predicts, I can think of few better ones than phasing out old school personas with next-level audience selection as the basis for delivering a superior [personalized](https://www.redpointglobal.com/blog/get-at-the-why-for-true-personalization-with-automated-machine-learning/) customer experience to a segment of one. **RELATED CONTENT** [Evolutionary Programming: The Survival of the “Fittest” Data Models](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/) [The Sky is the Limit with AI and Machine Learning: Capitalize on the Moment of Interaction](https://www.redpointglobal.com/blog/the-sky-is-the-limit-with-ai-and-machine-learning-capitalize-on-the-moment-of-interaction/) [What Data Scientist Shortage? Empower Marketers to Use Machine Learning](https://www.redpointglobal.com/blog/what-data-scientist-shortage-empower-marketers-to-use-machine-learning/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Management, Omnichannel Marketing, Real-Time Personalization, Segmentation & Activation, Single Customer View --- ### [Modern Data Quality Starts at the Door: Rethinking Ingestion](https://www.redpointglobal.com/blog/modern-data-quality-starts-at-the-door-rethinking-ingestion/) **Published:** June 13, 2025 **Author:** Renee Graff **Content:** If you’re investing in AI, [real-time personalization](https://www.redpointglobal.com/real-time-interactions/), or [omnichannel engagement](https://www.redpointglobal.com/orchestration/), your success hinges on the quality of your customer data. That data quality can’t be an afterthought. It must start at the source – when it’s first ingested into your system. Adopting a [modern approach to data quality](https://www.redpointglobal.com/automated-data-quality/) automates the structuring, formatting, and cleansing of incoming data before it ever enters your systems. You can be confident that it’s complete, accurate, timely, and fit to use anywhere, from day one. ## **Clean Data at the Start, Every Time** [Automated data quality](https://www.redpointglobal.com/resources/automated-data-ingestion-and-data-quality/) at ingestion isn’t a one-time fix. It needs to be a built-in, ongoing process, working continuously behind the scenes in your data pipelines, making sure that every piece of data – whether it’s headed to a marketing cloud, a CDP, or an ETL tool – is clean and reliable. It’s an essential element that helps you avoid negative downstream effects of bad data. Your customers don’t stand still. They move, they marry, they change emails and phone numbers, and they even occasionally make a typo when entering information. Keeping up with them, especially from a data point of view, is a constant challenge. For companies looking to eliminate inefficient legacy processes, reduce manual work, and build a strong, trustworthy data foundation across the tech stack, automated data quality at ingestion can be a game-changer. ## **Demand More from Data Ingestion** While automated data quality at ingestion sounds intuitive, many companies still rely on outdated technology and manual processes. Some apply different standards for data quality depending on where it came from or its intended use. Data is often pulled into a central processing hub, with formatting, cleaning and quality processes typically put off until the data is needed. Even then, [customer data is often walled off](https://www.redpointglobal.com/resources/redpoint-data-readiness-for-salesforce/) from data inside marketing clouds like Salesforce or Adobe. The result? Multiple versions of the same customer, scattered across systems: one customer profile that exists in Adobe or Salesforce, and another that exists in a data warehouse or data lake, with a lot of time, effort and manual work required to meld the two. Today’s customers expect real-time, personalized experiences, and time-consuming workarounds, heavy coding and other DIY stopgaps won’t cut it. Taking it one step further, customer profiles manually adjusted and built on inconsistent data simply can’t be trusted to guide your decisions surrounding those customers. ## **Your Ingestion Tool Should Do More** A good ingestion tool doesn’t just bring data IN to your stack. It should also format and export clean, processed data out to every other system you have integrated. We tend to think of pipelines as one-way or bidirectional conduits, but to support a modern tech stack there should be as many off-ramps as there are systems that plug into and utilize or activate your customer data. ## **Pre-Built Pipelines = Less Work, More Trust** When data quality is automated from the start, you get consistent, secure integrations that meet your organization’s security, governance and compliance standards. Reducing manual prep work with streamlined data preparation in a no-code environment also saves money and time. With pre-built, flexible pipelines that match your workflows and connect via APIs, you can bring in and send out data without needing to cut a ticket for IT. That means fewer delays and more agility. Ideally, your marketing stack and enterprise data strategy should work together seamlessly. A platform that offers automated data quality at ingestion and that connects to nearly any data source – whether a cloud or a legacy platform – means the end of manual interventions and inconsistent data quality standards. ## **Primed for Takeoff: Clean Data for Any Use Case** Data ingestion *done right,* as the front end of an automated data quality pipeline, sets the stage for a strong enterprise data strategy. Your customer data is clean, accurate, consistent, and ready to use wherever and whenever you need it. No coding. No extra work. Marketers should be able to trust their work without having to worry about the downstream impact of inconsistent or incorrect data that was previously fed into the system. That trust should come standard, whether they’re using it to drive acquisition, train AI models, [build dynamic segments](https://www.redpointglobal.com/resources/segmentation-for-the-modern-marketer/), or anything else. Companies that modernize ingestion processes and embed automated data quality into the foundation of a data strategy will be best equipped to scale AI and unlock the full value of their customer data across the enterprise. **Blog categories:** Data Ingestion --- ### [The Express Lane to a Satisfied Customer: Personalized Travel Experiences](https://www.redpointglobal.com/blog/the-express-lane-to-a-satisfied-customer-personalized-travel-experiences/) **Published:** May 10, 2022 **Author:** Redpoint Global **Content:** Do you have travel plans this year? If so, you have plenty of company. Hopefully, though, you can leave the crowds behind at the airport or highways. According to a survey from Dynata commissioned by Redpoint, 77 percent of respondents have booked or plan to book travel this year – which includes the 15 percent of respondents who have already taken a trip. The irony to vacation planning is that for many Americans, an ideal getaway consists of a visit to a deserted beach or a secluded lake where they can unplug from email and devices, yet they are increasingly turning to digital channels to make their travel plans. In the survey, 42 percent of respondents said they use a travel brand’s mobile app to book or receive notifications about travel, and 39 percent consult travel websites (Yelp, TripAdvisor, etc.) before making plans. When it comes to actual travel, about one-third of survey respondents expect digital conveniences such as digital check-in/check-out processes (28 percent), digital flight check-in (27 percent), and self check-in stations for checking bags (27 percent). ## **A Digital-First Travel Concierge** The survey also reveals that travelers have high expectations for how travel brands themselves communicate through digital channels. Nearly half of respondents (47 percent) indicated some level of dissatisfaction with a travel brand. Poorly communicated logistical changes led the way (14 percent), followed by frustrations around a brand’s communication of expired points/miles or refund policies (10 percent), irrelevant offers (10 percent), and inconsistent communications across channels (8 percent). Consumers, it appears, are turned off when a brand sends a travel offer when they are unable or unwilling to travel. To alleviate some of the existing frustrations, travelers said that brands can do a better job personalizing the customer experience. Respondents cited personalized safety recommendations as one tactic brands should adopt (28 percent), followed by personalized offers that enhance travel (27 percent), personalized follow-up (20 percent), and consistent personalization across all channels to include kiosks, website, mobile app, and call center (20 percent). Interestingly, expectations for a personalized experience did not stop with booking travel plans. Asked specifically about the overall travel experience, 24 percent of respondents said they would like travel companies to send real-time personalized messages and notifications during/about the context of the journey (pre-travel, travel, post-travel). A canceled or delayed flight, a road closure, an opportunity for a room upgrade – essentially respondents expect a travel company to be a digital concierge, keeping them abreast of any and all changes. Of course, that level of service comes with an understanding that a travel provider must have an understanding of each customer. Respondents said they would expect travel companies to know their individual preferences, such as where and how they plan to travel (20 percent), and also have an understanding of travel objectives and personal priorities – in addition to transactional information (20 percent). For more on how to proactively engage travel customers across every touchpoint, remove all friction from the customer experience, and ultimately grow revenue, check out the Redpoint [Travel & Hospitality homepage](https://www.redpointglobal.com/travel-hospitality/) that includes testimonials from organizations using Redpoint to deliver unforgettable travel experiences. **Blog categories:** Real-Time Personalization, Travel & Hospitality --- ### [Multiple Travel Portfolios, One View of the Customer](https://www.redpointglobal.com/blog/multiple-travel-portfolios-one-view-of-the-customer/) **Published:** May 5, 2023 **Author:** John Nash **Content:** There are obvious clues that help travel brands discern whether someone is traveling for business or pleasure. A traveler’s attire, a set of golf clubs and the presence of family members are key giveaways. But for data-driven travel and hospitality brands with a financial incentive to provide personalized, omnichannel experiences throughout each customer journey and across the customer lifecycle, educated guesses are not the same as a deep, personal understanding that drives revenue. According to a recent [Dynata survey](https://www.redpointglobal.com/press-releases/up-up-away-77-percent-of-americans-are-planning-getaways-this-year/) commissioned by Redpoint, 39 percent of customers said that personalization has not been achieved by most travel brands. Communication challenges and logistical issues were cited as the main sources of dissatisfaction, resulting in disengaged customers. According to [SmarterHQ](https://c.smarterhq.com/resources/Retail-Personalization-Playbook.pdf), 72 percent of customers will only engage with messages tailored to their interests. To meet the expectation for personalization, many travel brands are exploring customer engagement technology, specifically for which solutions can maximize the value of customer data. With revenue closely tied to loyalty, retention and [customer lifetime value (CLV)](https://www.redpointglobal.com/blog/what-is-customer-lifetime-value-clv/), hyper-personalizing every customer engagement demonstrates to customers that a travel brand respects and honors their preferences, and is using [first-party data](https://www.redpointglobal.com/blog/use-cases-for-best-in-class-first-party-identity-resolution/) to enhance the omnichannel experience. This article will explore a CDP use case for a travel brand, and detail how [Redpoint ](https://www.redpointglobal.com/)differs from other vendors in how it perfects first-party data, a key requirement for providing omnichannel personalization in the cadence of the customer. ## **Stay Relevant Amid a Data Influx** In correctly identifying a type of traveler, business vs. pleasure barely scratches the surface for the level of granularity a travel brand must reach to deliver a personalized CX. Is the customer an employee of a business partner? How many bookings has the customer made in the last six months? Do they have a rewards account? What is their loyalty status? Is the customer in the high-net worth segment? What is their vacation travel propensity score, and what recent actions have they taken to influence this score? How many active campaigns or journeys is the customer in that are marked with a priority score of more than 1.5? Or, in an emerging trend, is the customer a “set-jetter,” taking off for exotic destinations made popular by TV shows such as “White Lotus” and “Emily in Paris.” A customer’s profile changes by the second, in other words, and delivering relevance in the cadence of a customer’s journey requires a brand to make decisions based on a real-time, persistently updated, [unified customer profile](https://www.redpointglobal.com/single-customer-view/). Consider for example a customer who books a Los Angeles hotel room on an employee travel portal for travel the following day. She is placed in a partner business traveler segment. After she places a room service phone order late that night, she is selected to take a survey as a high lifetime value (CLV) customer, even though most of that rating is through leisure travel with her family. Because of the order and her CLV, there is a trigger to send an SMS with an offer for 400 loyalty points if she rates her ordering experience – with a condition to suppress any other message in the business traveler segment for that evening. The rating, and any text natural language processing analysis, are immediately appended to her unified profile. One week later, she uses a personal computer to book a room for four at a property in New Orleans. Although this trip is labeled “leisure,” all data from the previous travel is applied to help guide personalized interactions. ## **Comprehensive Data Ingest, Processing and Unification** Reconciling multiple travel portfolios into a unified profile or [Golden Record](https://www.redpointglobal.com/blog/the-role-of-a-golden-record-in-providing-a-consistently-relevant-personalized-cx/) with perfected first-party data to create a seamless omnichannel experience is a Redpoint strength. Redpoint completes all data transformations and identity resolution at ingestion, ensuring that a Golden Record is updated and actionable the moment of each customer interaction. Redpoint provides comprehensive raw data ingest, processing and unification on a continuous basis to create a 360-degree view of prospects and customers that, like customers themselves, is a living, breathing representation of the data from every possible source. With this dynamic view, marketers create sophisticated dynamic segments that (like the room service example) involve real-time interactions, interaction combinations over time, customer modeling and persistent customer attributes. Diverse segments support triggered activity and are also available and accessible to all front-line engagement platforms in an open garden architecture. Redpoint supports travel and hospitality companies executing next-best actions in milliseconds at scale, integrating seamlessly with any combination of inbound and outbound customer engagement environments. ## **Golden Record vs. Unified View** A key distinction between the Redpoint Golden Record and what many vendors label a unified profile is that the former – by completing [data quality processes at the moment data is ingested](https://www.redpointglobal.com/cdp/data-ingestion-and-quality/) – is far more trustworthy and accurate than customer engagemen technology that puts off transformations downstream. If a unified profile refers to integrating data from multiple sources, with the assumption that this integrated view already possesses relevant keys, the view will only be as up-to-date as the recency of the oldest data. The Redpoint Golden Record guarantees accuracy. A true unified customer profile, the Golden Record provides brands and marketers with a customer identity graph that encapsulates a persistently updated view of a unique customer or household’s behaviors, preferences, transactions, devices and IDs. The Golden Record is a single source of the truth for the customer because it is continually updated as a journey unfolds. And because it uses persistent keys, it provides a contextual understanding of an individual customer that deepens over time, layering meaning with every new customer behavior. ## **An Accurate, Complete and Up-to-Date View** The use of [persistent keys](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) is a key component in creating a master customer record, but that in turn requires that every customer signal is correctly matched, merged and attached to the proper record, as the data is sourced. That is where Redpoint’s advanced identity resolution capabilities come into play. Using probabilistic and deterministic matching, Redpoint finds, cleans, matches, merges and relates every disparate signal about a customer to produce an accurate, complete and up-to-date view of a customer. Identity resolution also effectively links individuals into a house­hold or other entity (such as a business) that a brand wishes to engage with. Thus, even with a continual influx of new data or new or different customer identifiers such as a new address, email, marriage or other lifestyle change, the Redpoint Golden Record is always in the context and cadence of a customer journey and is the foundation for delivering real-time, omnichannel personalized experiences. Consider our Los Angeles business traveler who orders room service. Perhaps she registers a complaint that evening because her steak wasn’t as rare as she had specified. A day later, after checking out, she contacts the call center about a lost item. Based on the caller ID, the agent knows immediately that the caller is a high-value customer who is 5,000 points shy of the next loyalty tier, and that she has logged a recent complaint. The agent proactively apologizes for the dinner mishap, and rewards the customer by moving her to the higher loyalty tier. When she accepts the offer, the agent is instructed to pull a different promotion that had been queued for the end of that conversation. [Real-time decisioning](https://www.redpointglobal.com/real-time-interactions/) in Redpoint enable this type of seamless interaction based on the totality of the customer’s experiences with the brand, where a next-best action is in the context of the journey and relevant for a specific travel portfolio. With the Redpoint Golden Record, a brand is never caught off guard. A hotel or airline desk clerk, a concierge, a marketer – everyone who interacts directly or indirectly with a customer is in tune with the customer across an omnichannel journey. For more on how Redpoint helps travel marketers navigate even the most complex customer journeys with highly personalized experiences across every stage and touchpoint, [click here](https://www.redpointglobal.com/travel-hospitality/). **Blog categories:** Identity Resolution, Omnichannel Marketing, Real-Time Personalization, Single Customer View, Travel & Hospitality **Blog tags:** CDP --- ### [Beyond Fast Data Ingestion: Take Your CDP to the Limit](https://www.redpointglobal.com/blog/beyond-fast-data-ingestion-take-your-cdp-to-the-limit/) **Published:** December 7, 2020 **Author:** Redpoint Global **Content:** One performance metric commonly touted in ads for sports cars is the acceleration speed from 0-60 mph. The ads, however, fail to mention that the horsepower comes with trade-offs. If you’re flooring the vehicle every time you leave the driveway, you’ll pay more in fuel and put a lot of unnecessary wear and tear on the tires and brake pads. It’s analogous to some customer data platform (CDP) vendors who tout data ingestion speed as a definitive performance metric without telling the complete story about what’s under the hood, as it were. If one performance metric – ingestion speed – shortchanges other features, the result could compromise the intended use case, namely using customer data to provide a real-time, personalized customer experience (CX) across an [omnichannel journey](https://www.redpointglobal.com/omnichannel-personalization/). ### **Know a CDP’s Features – Inside and Out** To avoid finding out about limitations at an inopportune time down the road, organizations should read the fine print. A proof of concept (POC) is the time to put data through its paces, looking beyond ingestion speed as a defining metric to understand the intricacies of a [CDP as an engine for digital transformation](https://www.redpointglobal.com/one-platform/). One hidden cost of many CDPs that promise fast ingestion speeds is that customer data often remains unstructured until query, at which point a data model is applied. At first glance, quick data ingestion might seem to be an attractive feature but the downside, of course, is that by withholding structure until query the data model will be different for different users based on their security access, user access, or intended use. This will not only create inconsistencies in the data by returning different audiences, it also creates a runtime issue for the obvious reason that having to structure customer data at runtime means a longer runtime, adding to the overhead of resolving the query. ### **No Data Structure, No Single View** Apart from the longer runtime, returning different audiences is antithetical to everything a marketer is trying to accomplish in terms of engaging a customer with relevant, personalized experiences. The lack of audience consistency throws a wrench into this effort because you can’t be sure that the audience being extracted is truly representative of the latest data. Many CDPs promise some version of a “single customer view.” It may be called by different names, but a core tenet is the ingestion of all customer data – every type and source. But a failure to match data until point of query makes a true [single customer view](https://www.redpointglobal.com/single-customer-view/) impossible. When a momentary data structure collapses after a query, there is no precision. Some CDP’s may use up all their horsepower to quickly go from 0-60, but the trade-off is that performance suffers because they are then not able to match the data upon ingestion at scale and speed. To compensate, they let customer data accumulate and only take the latest data to run through the data model. Using the latest data may present ‘a’ view, but certainly not a true single customer view, the 360° unified profile that is updated in real time to provide a complete customer record. Another consequence of failing to apply a data model until query is a lack of accountability. It creates an enormous data lineage problem, particularly worrisome in industries such as healthcare and financial services where data lineage is under constant audit. An organization unable to verify how it has used its data over time will face compliance barriers on many fronts; in addition to potential financial penalties for a failure to comply with GDPR, CCPA and other regulations, the company may also face a loss of customers whose preferences have not been met, among other trust violations. ### **Persistent Keys and Data Match Consistency** A second hidden cost from many CDP’s that will also create inconsistencies is with regard to data matching without persistent keys. When keys change with every match, a data match accomplishes little more than a slice-in-time view of a customer, household or audience. There is simply no connection between a match one day and one the next, or minute-to-minute, hour-to-hour or week-to-week. This makes it impossible to track key structure changes over time. Operationally, it means not knowing, for instance, whether a household composition has changed over time. It also forces a company to do a complete re-mapping of accounts to the new keyset every time they match the data. One major drawback to this approach, aside from the time and effort having to re-map accounts with every data match, is that it handcuffs marketers from providing a next-best action for a customer that is contextually relevant to a customer’s journey. If a customer is marketed to as head of household based on a data match one week, but has since finalized a divorce and established a new residence, any engagement is likely to be irrelevant to the customer’s up-to-date profile. To ensure consistency and accountability, Redpoint immediately structures data at ingest – providing precision with matching results for a particular query. Accountability is ensured with persistent keys that augment and change over time, providing a clear picture of what is happening to a population over time, including the changing dynamic of relationships and contextual history. Not all CDPs are the same. If your company is exploring how a CDP can power a digital transformation and engage with customers with relevant, personalized omnichannel experiences, it’s important to cut through any confusion or uncertainty about flashy features that may look good right out of the gate, but will soon leave you on the side of the road. **Blog categories:** Data Management, Data Quality, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Without Real Time, Next-Best Actions May be “Second Best”](https://www.redpointglobal.com/blog/without-real-time-next-best-actions-may-be-second-best/) **Published:** October 27, 2021 **Author:** Steve Zisk **Content:** A favorite Boy Scouts mantra about the importance of being prepared is that it’s better to have and not need than need and not have. In the same vein as it relates to customer experience (CX) is the importance of having real-time capabilities for delivering a next-best action. While not every next-best action needs to be in real time, the decision that generates a next-best action must be based on accurate, real-time data. In other words, a real-time view of the customer during an ongoing, fluid [customer journey](https://www.redpointglobal.com/resources/real-time-decisioning-puts-the-customer-in-charge-of-the-journey/) is critical in deciding if a real-time response is – or is not – the next-best action for a specific moment during that journey. ## **What is a Next-Best Action?** To better understand the importance of a real-time engine in delivering a next-best action, let us first examine what we mean by both next best action and real time. A next-best action is, at its core, meeting the customer where the customer is with an action, an offer or a piece of information that precisely matches the customer’s desires and inclinations. The concept inverts the traditional “marketing-out” approach which is to guide the customer along a path to purchase. It does so by striving to innately understand the customer. Selling a product becomes secondary to a brand or organization making the customer recognize that the brand understands them, which is accomplished by a recognition that the customer is in control of a dynamic, customer-driven journey. A next-best action, then, is an action that is perfectly aligned with a customer’s aspirations in the moment of the journey that it is delivered. A next-best action may or may not directly drive a brand’s measurable goals – whether those are to sell a product, enhance [CLV,](https://www.redpointglobal.com/blog/to-optimize-revenue-growth-tune-in-to-customer-lifetime-value/) reduce churn, etc. A brand that knows a customer is interested in hiking, for instance, might display an image of a mountaintop sunrise during a web visit by a customer who just bought hiking boots, and that aspirational message could be the next-best action for that customer in that moment. But if the reason for the visit is to start a return, a mountaintop image might introduce friction. A next-best action must recognize this context, meaning that it should be at the nexus between what the brand thinks is the next action to take for the customer – move along to purchase, loyalty, retention – and what the customer wants. Part of the context for a next-best action is to respond in the channel where the customer is operating. If a customer is on the mobile app, a next-best action will likely be in the mobile app as the customer journey unfolds on that channel. A next-best-action during an active engagement must also feel conversational. From the customer’s perspective, it is a fluid, unforced and relevant interaction that feels natural within the context of the engagement. ## **The Meaning of Real Time** Making a next-best action part of a natural conversation sometimes means the real time arrow must stay in the quiver. In an abandoned shopping cart situation, a customer who receives an instantaneous message about an abandoned cart with an offer to come back and complete the transaction may be annoyed, feeling the brand respects neither their desires nor proper boundaries. Conceptually, a real-time framework is easy to understand. It may be as instantaneous as the brand can possibly make it: The customer arrives on a homepage, and the mountaintop sunrise image loads within milliseconds. Or, for an abandoned shopping cart, perhaps the next-best action is to wait 10 minutes or even longer depending on a customer’s next signal. In between those extremes, another real-time construct is a human-to-human conversation, such as a customer dialing up the call center, or interacting with a store associate or a front desk clerk. The overarching consideration in any interaction with the customer is that real time is whatever time is required to ensure that the next-best action is frictionless, relevant and organic to the dynamic, customer-driven journey. With this goal in mind, it becomes clear why having pristine, real-time data is vital for orchestrating real-time decisions into the right touchpoints – and why every next-best action depends on having this real-time understanding. Consider, for example, the constraints imposed with a one-minute data refresh. For an online session, the images, text, banners or pop-ups that a brand shows a customer that are even 10 seconds or so behind the dynamic journey may be irrelevant. Perhaps they’re close to the customer’s aspirations at that moment, but even being slightly off indicates to the customer that that the brand does not fully understand them. Or consider the shopping cart example, where real time may mean waiting for another signal from the customer before the brand responds. If the brand is one minute behind the customer, that response may not be optimized for the customer’s follow-up signal, whatever it may be. If placing another item in the cart warrants an instantaneous response, one that is a minute or two later may not be relevant or may seem unnatural. And in a human conversation, it’s easy to see that friction is a likely result if the brand’s agent lacks a real-time understanding of the customer journey. ## **Real-Time Intelligence** To ensure decisions are organic, smooth and in a cadence that matches the journey, a real-time capability must be infused with intelligence to make next-best action decisions. That intelligence is formed through a combination of human-curated rules and machine learning models. Both are required because a next-best action is typically the product of many things happening at once. A simple example is the brand should not show an image of a sweater that is not in stock in the customer’s size or color. Human-curated rules can encompass those types of inventory or supply chain constraints, but also handle other situational details like matching a message to a customer’s location: If a customer enters this geo-fence, they are a candidate for that offer. [Machine learning models](https://www.redpointglobal.com/blog/theres-a-model-for-that-how-automated-machine-learning-aml-tackles-any-business-use-case/) are the predictive and prescriptive decisions made about a customer. Is she in danger of churning? Is he ripe for an upsell? Machine learning models ensure that the brand is probing every possible signal to arrive at the most detailed, accurate and precise view of the customer. The human-curated rules may then serve to narrow a potentially long list of possible actions to take based on that complete understanding. Finally, the intersection of what the machine learning models tell a brand, combined with the human-curated rules, is itself not a static process. As dynamic as the journey it is probing, a real-time engine is continuously arbitrating among a set of decisions. Real time, then, is not just about when a next-best action is presented, it is also about how long an action is valid – how long it remains relevant. Any customer action has the potential to swap out one potential action for another that is more relevant and more natural to the customer journey. In other words, a static list and ordering of decisions – if the customer does A, offer B first, C second – may seem arbitrary or chaotic if the list is based on predictions and/or rules that are not in the context of the journey. One way to think of real time in the context of a next-best action is to envision the flawless execution of a championship ballroom dance team. One action dictates the next in a seamless combination of maneuvers that appear natural, almost magically so. The ballroom orchestra is the real-time engine that carries a continual beat and underpins the entire performance. The routine is perfectly timed to the crescendos and bar changes, so fluid it is hard to discern which plays off the other. The thunderous applause at the end is a recognition of something special; a series of next-best actions that delight the customer because it makes them feel like they’re with a brand that understands who they are, not just what they want. **Blog categories:** Data Quality, Identity Resolution, Real-Time Personalization --- ### [Advance on the Personalization Maturity Curve](https://www.redpointglobal.com/blog/advance-on-the-personalization-maturity-curve/) **Published:** May 1, 2025 **Author:** John Nash **Content:** When your data is ready across the enterprise for any use case that drives business value – AI, personalized CX, customer acquisition, paid media – what will you do? Is there a “perfect world” vision for what your business can accomplish when you have full faith and confidence in the accuracy, completeness, and timeliness of your enterprise data? Even if a finish line is not yet in sight, the potential rewards are alluring enough to have likely brought your business to the starting block. Research from McKinsey shows that data-driven brands are [19x more likely](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights) to be profitable and successful, stating that improved analysis through technology is the key to gaining deeper insights into customer behaviors and preferences, improving personalized experiences, and incorporating tactics that feed into a long-term personalization strategy for growth. And Gartner says that organizations with the most mature marketing technology functions will achieve [75 percent greater marketing ROI](https://www.gartner.com/en/doc/799798-use-case-prisms-for-generative-ai-a-guide-to-emerging-opportunities-in-industries). Mature technology that provides deeper insights into customers, households, and other entities of importance to the business is centered on **getting data ready for business use**. Cleansed, accurate, and fit-for-purpose data is the common denominator that underlines enterprise use cases for extracting value from data to drive business outcomes. ## **Of All Stripes: Data Readiness at a Use Case Level** But unlocking data-driven personalization through having data continually ready for business use is not a uniform exercise; a business’s use cases will largely determine the depth of understanding needed from the customer data and the capabilities required to actualize value. Whatever the initial use cases may be, **technology centered on data readiness enables companies to progress on a personalization maturity roadmap – and expand their use cases – because it takes care of the hard part first**. It clears a path for advancing with personalization use cases by continually improving upon the veracity of enterprise data – making no assumptions that data will be cleansed and made ready for business use somewhere downstream, or by a third-party vendor. ## **Success Through Data Readiness Builds Off Itself** One Redpoint customer – a travel and leisure company – started its personalization journey with a goal to create a single data repository for 45 federated clubs constituting 55+ million North American customers for a straightforward use case. “We asked ourselves ‘what’s the first business problem we can solve?’ It was: ‘How can we help our clubs get accurate, up-to-date member lists for mailing, without waiting for their IT teams to do it?’ ” said the company’s director of marketing. Yet with Redpoint customer data technology solving that problem by cleansing, normalizing, matching and otherwise making the company’s data ready for business use, the company found that accurate member lists for mailing was just the tip of the iceberg. “We can only do effective analyses and create successful offers because we have strong confidence in our data. Redpoint gives us that confidence,” the director said. “We can take practically any incoming data, understand its health and align it to a member, past member, even someone who’s not a member yet. … We used to have teams that did nothing but manage data all day, using multiple platforms. Redpoint gave us a platform we could leverage across all our people and clubs.” ## **Advancing on the Personalization Maturity Curve** Boston Consulting Group maps the five stages of data-drive personalization capabilities (**see Figure 1**) to potential revenue gains, starting with audience suppression for paid media (Level 1: Starting) through omnichannel and real-time personalized engagement (Level 5: Excelling). The vertical axis shows potential revenue gains as organizations advance in their personalization capabilities. ![Personalization Maturity Roadmap](https://www.redpointglobal.com/wp-content/uploads/2025/04/Personalization-Maturity-Roadmap-800x450.jpg)**Figure 1**: *The five stages of the data-driven personalization capability maturity model* Examining the horizontal line progression more closely, it becomes clear that moving from “starting” to “excelling” involves both data maturity and activation of a unified customer profile across all enterprise touchpoints. Next-level personalization starts with fully cleansed, accurate data, which opens the door to more impactful use cases and results in brands being able to fully operationalize that data effectively across the entire martech ecosystem – a single brand voice, a single set of decisions, and a single set of experiences optimized for individual customers at scale *and* the company’s goals. Modern customer engagement technology makes simple to complex use cases possible by creating cascading value (**See Figure 2**). A unified profile that is accurate, up-to-date, and provides a contextual understanding of a customer (household, business, etc.) across an ongoing customer journey, resulting in more accurate and finely tuned segments, which then results in an ability to orchestrate next best actions (up to and including in real time) across the customer journey – any channel, any touchpoint. ![Data The Defining Difference Part 9 4 28](https://www.redpointglobal.com/wp-content/uploads/2025/05/Data-the-Defining-Difference-part-9-4-28-1-800x391.png)**Figure 2**: *The creation and building out of value in the context of personalized CX as data readiness capabilities expand*. ## **Value Creation – a Breakdown** Companies can plot their personalization maturity by what their current engagement technology can support, i.e., what use cases they are capable of. - *Identity Resolution/Profile Unification* For organizations just starting a personalization build-out, it’s likely that customer data is siloed across the enterprise without data quality standards in place. Profile unification and identity resolution are on an ad-hoc basis, with one-time matching and a lack of persistent keys. As they build out their capabilities, data quality becomes automated on an episodic basis. Perhaps they begin deterministic matching on an identifier, or start with black box probabilistic matching. For companies that excel at personalization, data quality is automated on a consistent basis during data ingestion. They never have to question whether or where it’s happening downstream. Identity resolution uses a combination of deterministic and probabilistic matching, and it is also tunable, transparent and flexible – optimized for any use case. - *Segmentation* A company’s status in building out a unified customer profile affects its ability to create dynamic segments that synchronize to a real time profile. At level one, siloed data and ad-hoc matching result in product-centric or organization-centric segmentation, a limitation that is forced on a company because brands at this level do not yet have a deep, contextual understanding of a customer. As companies progress on the personalization maturity roadmap, they may start to create granular segments with some omnichannel activation. As segmentation capabilities advance, dynamic segmentation becomes possible where segments are automatically updated according to reflect and capture a customer within the context of a customer journey (segment of one). More sophisticated personalization becomes possible when dynamic segmentation becomes reflective of response data, meaning that real time results are fed back into the system, opening the door for real time iterative improvements. - *Orchestration* The state of data readiness bleeds into orchestration. If a unified profile and dynamic segmentation are the foundational fuel for a smarter CX, orchestration lights the match, the transition from having the right data to making use of the right data. Just as with the steps required to getting data in the right place, orchestration follows a similar playbook. Basic personalization is limited to product-centric segmentation and audience building, which means static experiences when designing triggers and journeys themselves. (A static segment created off a list results in a static offer.) Instead of omnichannel personalization and execution, brands are limited to orchestrating experiences on a channel-by-channel basis. > Advancing on the personalization maturity curve is not an overnight transformation, it’s a strategic evolution that begins with data readiness. … With a unified customer profile, dynamic segmentation, and real-time decisioning in place, organizations can shift from fragmented, static experiences to truly orchestrated, omnichannel personalization. The ability to act on fresh, accurate data in real time means businesses can anticipate customer needs, optimize engagement across all touchpoints, and drive measurable growth. At the mid-level of orchestration capabilities, more granular segments begin to unlock omnichannel activation. Dynamic segments open up the possibility of orchestrating next-best actions using rules based decisioning with selective outbound and inbound coordination. Personalization maturity, as it relates to orchestration, entails intent-based decisions and customer-centric omnichannel execution, propelled by dynamic segments that are reflective of response data. This means that the enterprise is perpetually in synch with a customer (household, etc.) in real time as a customer journey unfolds. Aggregations are calculated on the fly, models scores are used on the fly, and customer intent is anticipated through data – all because the enterprise is in possession of an accurate, updated customer profile. - *Real Time* At the far end of the value chain, real-time interactions progress in a similar fashion. At a basic level, real time consists of simple, in-channel reactions based off basic PII personalization, such as welcoming a customer by name. At a more advanced level, a better understanding of a customer makes more contextual personalization possible, where personalization is responsive in the channel and there can even be a hierarchy of ranked actions, where a next best action is determined by what a customer does in real time. In healthcare, for instance, this might entail basic contextual suggestions related to searches and clicks. The state of full data readiness, applied to real time interactions, means that an organization is capable of cross-channel, next-best actions across the enterprise. This means that decisions are responsible – or arbitrated – across all channels and enterprise functions, with context and decisions based on predictive journey details (e.g., aspirational journeys, holistic wellness, etc.). ## **The Path Forward: From Readiness to Real-Time Personalization** Advancing on the personalization maturity curve is not an overnight transformation, it’s a strategic evolution that begins with data readiness. As the example of the travel and leisure company demonstrates, solving one foundational data challenge can set the stage for broader personalization capabilities. With a unified customer profile, dynamic segmentation, and real-time decisioning in place, organizations can shift from fragmented, static experiences to truly orchestrated, omnichannel personalization. The ability to act on fresh, accurate data in real time means businesses can anticipate customer needs, optimize engagement across all touchpoints, and drive measurable growth. The question is no longer *if* your organization should invest in personalization maturity, it’s *how far* you can take it. With the right technology in place, the finish line isn’t a destination, but a continuously expanding horizon of possibility. **Blog categories:** Data Readiness --- ### [Real-World Examples of How Data Readiness Delivers Impact](https://www.redpointglobal.com/blog/real-world-examples-of-how-data-readiness-delivers-impact/) **Published:** April 24, 2025 **Author:** John Nash **Content:** Data readiness isn’t just a technical challenge, it’s a competitive advantage. Businesses that harness real-time, high-quality data drive faster decision-making, better personalization, and measurable revenue growth. In the latest installment of “Data: The Defining Difference,” we explore how leading brands across industries have transformed their data into an asset with Redpoint technology. Data that is ready for business use – at the time that it is needed – supports unlimited use cases at the enterprise level across in any industry. (**See Figure 1**). It starts with building a unified profile for whatever asset the business needs to understand, whether that’s a customer or household (retail, hospitality, travel, financial services, etc.) a member of a health plan (healthcare payer), a patient (healthcare provider), acreage (agribusiness), a vehicle fleet (transportation), a product, a supply chain, or another entity. ![The Impact V.02](https://www.redpointglobal.com/wp-content/uploads/2025/03/The-impact-v.02-800x446.png)**Figure 1**: *Digital advertising, email marketing, journey orchestration and AI are among the business use cases supported by data readiness.* Activating a complete and contextual understanding of a customer through a next best action, and managing the processes around it – all in real time – are the keys to extracting optimal value from enterprise data. No-code software with pre-built functions for all data quality processes allow enterprise users to build their own data pipelines for their business’s specific use cases. ## **Diminished Data Prep Time** Using Redpoint technology, a Fortune 100 global financial services company reduced the time to build and execute marketing campaigns by 80 percent. It had struggled to create a relevant customer experience largely because of siloed data, with separate databases for international and specialty customers. This led to timely and cumbersome data extracts that resulted in a limited data model that lacked the speed and efficiency needed to deliver new segmented campaigns and offers, or to guide multichannel customer journeys. With Redpoint, the financial services organization now collects and stores customer data from multiple sources to create a unified customer profile. Data quality processes and advanced identity resolution steps are completed in real time as data is ingested. And with Redpoint software providing dynamic segmentation capabilities in a no-code environment, segments can be created once and used everywhere, giving the company the ability to provide contextually relevant experiences aligned with individual customer journeys. It is truly customer centric: customers move in and out of segments in real time based on the pre-set metrics, e.g., reduced churn, lifetime value, etc. ## **Improving the Member Experience** Beyond financial services, healthcare payers have also leveraged data readiness to streamline operations and enhance customer experiences. One payer was challenged with providing relevant experiences for members largely because each member’s multi-phased journey (shopping, buying, onboarding, risk assessment, renewal, etc.) was handled by different departments (marketing, sales, clinical vendors), each with its own member data and its own view of the member. > “Having all customer data available to us in one place, with the confidence that it is accurate, timely and comprehensive, has been the biggest asset of partnering with Redpoint” > > – CEO, family-owned hardware store. Using Redpoint technology, the payer lowered costs by 20 percent and improved engagement rates by 20 percent, driven in large part by vastly improving the member experience through consolidating the experience around a single member view. The payer attributed Redpoint with helping it reduce their tech stack expenses, establish more transparent relationships, and optimize channels by member preference. By creating a single member view, the payer established a multi-channel integrated approach, with personalized and staged messages delivered in the context of each member’s individual multi-phased journey. ## **A Revenue Boost Through Personalization** The benefits of data readiness also extend to retail. An independent, family-owned hardware store achieved a 20 percent lift in market basket size with a personalized buy online, pick-up in-store (BOPIS) initiative using Redpoint technology. Opting to transition from marketing flyers, the company leveraged Redpoint to create a Golden Record for each individual customer, using it to develop segments around the wants and needs of a customer vs. based on products. It now personalizes replenishment program emails timed to coincide with a customer’s known depletion rate (e.g., dog food, AC filters, etc.) leading to increased open rates and higher sales. Customers who take advantage of the BOPIS program now see relevant product recommendations and offers based on their purchases, leading to the lift in market basket size as well as a 68 percent higher average spend than customers who strictly shop in-store. “Having all customer data available to us in one place, with the confidence that it is accurate, timely and comprehensive, has been the biggest asset of partnering with Redpoint,” said the CEO of the family-owned hardware store. ## **Tackling Data Management Complexity** A Golden Record, as we’ve seen throughout this series, is not just to create a single view of a customer. DTN insights help fuel decisions for fast-moving industries worldwide, and a DTN business line uses Redpoint technology to provide market intelligence to the US agriculture industry. Redpoint has been indispensable with developing a single view of millions of acres of farmland – more than 800 million acres of fields mapped with geospatial coordinates and linked to the farmers, the soils, weather, satellite imagery, water tables, irrigation, and yield data. To integrate and process this data, DTN needed a platform that had the integration, automation, and processing power to manage big data at scale. Redpoint’s data management capabilities were proven to be up to 20X faster than competitors with processing structured and unstructured data for address hygiene, giving the company the ability to integrate all types of data to deliver new insights to customers, such as determining crop identity based on infrared signatures from satellites. The depth of information it now provides its precision agriculture customers includes tornado tracking and the integration of elevation data, all with unparalleled processing speed and scalability that includes an 80 percent reduction in data prep time. “When you have many different files of this magnitude to bring together, process and harness, you are talking numbers in the quintillions and beyond. There really was no other platform that had both the capability, processing and scale to handle what we need to do,” said the business unit’s president. ## **Liberating Existing Customer Data** A travel and hospitality company that owns and operates an enormous portfolio of properties, resorts, national park concessions and specialized adventure tours (cruises, bicycle tours, etc.) leveraged Redpoint to develop a single customer view across its many different brands. Redpoint’s data management processes handle all cleaning, standardization and enrichment with more than 300 third-party appended attributes, providing the company for the first time with actionable unified customer profiles – without having to rip and replace its existing technology stack. “Redpoint engineered a data intake solution that allowed us to keep basically all our existing IT infrastructure 100 percent intact,” said the company’s marketing director. “We didn’t have to standardize systems or data entry processes across all of our businesses, or address inconsistencies, or overcome a lack of connections across our enterprise. Instead, by leveraging Redpoint’s data management strengths, we quickly got a 360-degree view of our customers.” The 360-degree view was critical for generating insights across the complete customer lifecycle, that the company then leveraged to automate multi-wave trigger campaigns that connect at key lifecycle stages, and capture data to refine personalization. Redpoint integrates data, decisions and interactions – including content, digital assets, creative assignments, rules, A/B testing, and outcome monitoring – in real time, acting as the single point of control to orchestrate engagement across all touchpoints. This is the key to omnichannel personalization, including a 97 percent growth in revenue from email marketing using personalization techniques such as unique and targeted imagery, content and subject lines hyper-relevant to an individual customer’s journey. ## **Coming Next: The Data-Driven Personalization Maturity Roadmap** Is your data ready to drive personalized CX? Join us in the next series installment as we look at how companies are progressing along the personalization maturity roadmap, starting small with isolated use cases before building out their personalization capabilities. As this series continues, we will also periodically explore some of the innovative ways our customers are using Redpoint technology to stand out from the crowd, as the customers mentioned here by no means belong to an exclusive club. Want to see how your organization can accelerate data readiness and unlock new growth? Contact us today for a demo: **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [Enterprise Data Readiness – the Key to Filling the Gaps](https://www.redpointglobal.com/blog/enterprise-data-readiness-the-key-to-filling-the-gaps/) **Published:** April 17, 2025 **Author:** John Nash **Content:** The question of who holds the ultimate responsibility for data quality was largely left unanswered when the customer data platform (CDP) came along to fill in the gap between mastering data in the IT department and relating master data to customer signals. Without a clear mandate as the source of truth for data quality, the CDP market was quickly saturated with many different solutions each claiming the CDP throne, yet each with a different idea about a CDP’s core capabilities. For most, data quality was not included in the vision for a CDP as responsible for preparing data to create personalized experiences. Enter the concept of data readiness to finally addresses the data quality problem. As the volume of customer data increased, and customer journeys became more complex and expectations for real-time personalization hardened, a Band-Aid approach to solving the data quality problem started to fray. This is evident with a precursory look at most organizations’ technology stacks, where more systems do not translate to better CX outcomes. In a [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/), despite 63 percent of businesses claiming to have made investments in data quality, 70 percent say the number of systems they have make it harder to provide a seamless CX. A big reason for this discrepancy, and the reason a CDP has not really caught on as the one stop shop for data quality, is that in the decade plus since its introduction, a CDP has had two buyers. On one side is the IT/data engineer contingency, which is focused first and foremost on data being up-to-snuff from the point of being accurate, governed and up to date. On the other side are marketers and business users, whose primary concern is that the data is ready to support a business or CX use case. Will the data help them improve ROI, reduce costs, drive acquisition, increase lifetime value, create better segments, or accomplish any one of dozens of other use cases? Organizations have made data quality investments, but because they’re trying to serve two masters, what gets lost in the shuffle is the customer experience. The ability to deliver real-time personalization, as an example, becomes nearly impossible when organizations are unsure if their data is ready. Is the master record up-to-date, does it include all the relevant customer signals – up to and including what the customer is doing at the moment of engagement? ## **Data Readiness and a New Approach to an Old Problem** Data readiness splits the difference between the two cohorts, filling the gaps between mastering data and attaching customer signals that until now have allowed data quality problems to persist. From the IT perspective, data readiness addresses the steps and items needed to refine raw customer data, to tie together mastering the data to the relevant signals about a customer, household, product or asset that a user is trying to understand. It’s the tools and steps pertaining to data infrastructure – refining data so that marketers and business users can accomplish what they need to accomplish. For the marketer, data readiness provides the assurance that data is ready for business and CX use cases; data readiness for AI, data readiness for paid media, data readiness for a retail media network, etc. Data readiness puts them on the express lane for executing all their campaigns, creating finely tuned segments, and interacting with conversational AI to answer all the questions they have about their data. > The foundation of data readiness is data quality, making data ready for every possible CX or business use case. Data readiness is a new take on a traditional method of refining data by writing code. But the acceleration of multi-channel, omnichannel customer journeys, the proliferation of channels and devices, and the expectation for real-time personalized experiences do not give most organizations the luxury of time and resources to develop the code that will satisfy marketing’s needs for CX data in the needed timeframe. Writing code – or contracting it out – leaves too much of a gap between learning about a customer (household, entity, etc.) at a demographic level and knowing everything there is to know about a customer, i.e., what the marketer or business user needs to differentiate on CX. To provide the fuel needed for CX, with CX to include all possible interactions or touchpoints a customer may have with a brand, data readiness processes include everything in the category of master data (identities, addresses, etc.) as well as all the contact information – behavioral data, transactions, predictions, aggregations, calculations, best point of contact. The output of data readiness becomes the Golden Record, which is distinct from what is traditionally thought of as master data management, where a master record is detached from a customer’s live activity. Rather, it’s a persistent, unified record with all the information that is needed at the scope and pace that it’s needed. ## **The Mission of Data Readiness: A Unified Profile** The foundation of data readiness is data quality, making data ready for every possible CX or business use case. Data readiness continually accesses real-time data from every conceivable source, which will include the results of marketing campaigns in a closed feedback loop. Internal enterprise data may be combined with second- or third-party data, and all of it will be cleansed (address and email validations, normalization, standardization, enrichment, etc.) and matched to a new or existing record. Identity resolution must be specific and suitable for the intended CX or business use case, whether it’s to differentiate a customer or household, or to determine a contextual relationship within a business unit or asset such as a vehicle fleet or farm acreage. Finally, with advanced identity resolution completed, data readiness involves enhancing the Golden Record by attaching every detail needed for CX – aggregate calculations such as last visit, screen time, lifetime value, predictions, and any customer attribute that will deepen a marketer or business user’s understanding of the customer or asset they’re trying to understand. Data readiness involves making data complete, accurate, and timely: data ingestion/processing, hygiene/transformation, and identity resolution/profile building, with the creation of the unified profile the central activity. ## **Data Readiness is Not a Synonym for a CDP** Data readiness, in essence, strips out the data quality work from what is thought of as traditional CDP functionality such as visualizations, dynamic segmentation, metadata and operational data management, campaign and journey management, and interaction management for both inbound and outbound interactions – all of which fall into the territory of data activation. Data readiness ensures that IT and marketers both have what they need in terms of data infrastructure being up-to-snuff; governance for IT, campaign ready for marketers, and – for both – the agility needed for modern technology requirements, i.e., cloud, private clouds, AI and GenAI and to quickly pivot toward any new trend on the horizon. ## **Looking Ahead: The Future of CX Depends on Data Readiness** Customer expectations for hyper-personalized experiences will only continue to rise. Brands that rely on fragmented, outdated data infrastructure will struggle to keep pace —not because they lack data, but because they lack the right data at the right time. Data readiness isn’t just an enhancement to your existing data strategy; it’s a fundamental shift in how organizations prepare customer data for activation. It ensures that IT and marketing rely on a single source of truth for customer data, one that adapts to new channels, new AI-driven innovations, and the next evolution of customer engagement. The brands that win on customer experience will be those that invest in data readiness today so they can execute at speed tomorrow. The next blog in this series will focus on what actual brands are doing today to differentiate on customer experience using Redpoint customer data technology. **Blog categories:** Data Readiness **Blog tags:** Data readiness --- ### [A New Approach to Choosing Customer Data Technology](https://www.redpointglobal.com/blog/a-new-approach-to-choosing-customer-data-technology/) **Published:** April 11, 2025 **Author:** John Nash **Content:** For as long as there has been customer data, there’s been a need for data quality. Yet even though revenue often depends on having deep insight about a customer, a household, a product, a business, or another entity, data quality challenges persist across industries. Pain points like low customer satisfaction, churn, overmarketing, and taking on unnecessary risk all stem from poor data quality. With so many investments in customer data technology intended to solve for the problem of low-quality data, it’s hard to fathom why the problem is still so pervasive. It’s certainly not for a lack of trying. Rather, many organizations simply aim at the wrong target. They try to solve the data problem in the IT department, such as with a master data management (MDM) solution. One pitfall with this approach, however, is that IT is generally not incented to tie the master data to the customer signal infrastructure. That is, IT will be a data steward, but is not concerned with the reasons why a master record is important for a marketer or a business user. The actions a customer takes (transactions, behaviors) and the actions a customer *may* take (intent, predicted behaviors) are outside the scope of IT. Clean, accurate data is not recognized as an enterprise principle – that there is an actual business or CX purpose behind the need for master data management. ## **Data Quality and a CDP** Bridging the gap between mastering data and attaching behavioral, transactional and predictions was the original intent behind a customer data platform (CDP). By overlapping with an MDM and layering in customer signals to a master record, a CDP would ideally present users with data ready for their CX use cases. Instead, with a few notable exceptions, what transpired was that CDPs took upstream data, unified it somehow, and presented it to marketers without fixing the data quality issue. In most organizations, there is still a wide gap between master data and data that is ready for business use, with the output of an MDM and a CDP siloed to one another. Efforts to bridge the gap, to overlay the understanding of what a customer is doing with a master data record, have fallen short. ## **A Stopgap Approach to Data Quality** One approach is to use AI to improve the output of identity resolution. The issue, though, is that while this and similar efforts (e.g., basic deterministic matching) may fulfill the promise of better matching, they still leave raw data in the system without any means to refine that raw data into meaningful, accurate, up-to-date and detailed customer experience data. Another attempt at making data ready for business use has been to deploy a composable CDP on top of an existing cloud database, bringing the data closer to various endpoints. But this too has a few shortcomings. First, this method still runs on the assumption that data quality is being taken care of somewhere upstream. This has been the promise of the “marketing clouds,” which for the most part do not even claim to be responsible for data quality. > Businesses need to recognize (data readiness) as a foundational enterprise principle. A CDP alone cannot fix poor data quality, and traditional stopgap measures do not ensure the accuracy, completeness, or relevance that is needed to support simple to complex business or CX use cases. Yet another attempt is that, with a composable CDP in place, there is a default reliance that there will be a bridging strategy between the customer data in the cloud and the operational data needed for the CDP, e.g., segment definitions, campaigns, output, etc., which might even be operating in a different cloud. By copying a subset of data and sharing it, this approach attempts to federate a system that is not naturally federated. What inevitably suffers is the accuracy and completeness of the view of the customer or asset that you’re trying to understand. ## **A New Approach to Data Quality** What these stopgap approaches all share in common is that they all lose sight of data quality as being mission critical for the enterprise. The goal becomes more about moving data around so that a CDP can do some analytics and segmentation before activating the data, but the importance of upfront data quality is lost in the shuffle. This becomes evident when looking at how marketers typically interact with a CDP, such as using GenAI capabilities where questions or tasks are skewed to make things easier for marketers. Questions are usually in the vein of asking the CDP to define natural segments, or to provide answers on how to market to a certain segment, or to build a segment so that the marketer does not have to use SQL. These are all enormously beneficial, but rarely do marketers ask about, say, the validity of a certain data source, or what data is providing the latest understanding of a household’s dynamics. These and other questions that get to the essence of data quality are typically not being asked – or answered – in most CDPs, clear evidence that data quality is, if not an afterthought, generally thought of by marketers as being someone else’s responsibility. ## **Data Quality, an Enterprise Initiative** If organizations want a complete and reliable understanding of their customers or other enterprise assets, they must shift their approach to customer data technology. Rather than treating data readiness as an afterthought or placing the responsibility solely on IT, businesses need to recognize it as a foundational enterprise principle. A CDP alone cannot fix poor data quality, and traditional stopgap measures do not ensure the accuracy, completeness, or relevance that is needed to support simple to complex business or CX use cases. The key to solving this persistent challenge is to prioritize data readiness – where high-quality, well-structured data serves as the foundation for analytics, segmentation, and activation. By doing so, companies can ensure that both IT and marketing work from a shared, trustworthy data source that drives better customer experiences and business outcomes. As this series continues, we’ll explore how data readiness as an enterprise principle bridges the gap between raw data and actionable insights, and why it should be the foundation of a modern customer data strategy for the enterprise. **Blog categories:** Data Quality, Data Readiness **Blog tags:** Data readiness --- ### [Direct Marketing in the Digital Age: Why Data Readiness is Key to Success](https://www.redpointglobal.com/blog/direct-marketing-in-the-digital-age-why-data-readiness-is-key-to-success/) **Published:** April 14, 2025 **Author:** Renee Graff **Content:** Direct mail and catalogs have long been a marketing staple for the obvious reason that they resonate with customers and drive sales. For instance, [87 percent of consumers](https://www.enru.io/why-catalogs-still-matter-in-a-digital-world/) say that catalogs make them more interested in a retailer’s products, with 84 percent of catalog recipients claiming that they either share it with someone else, make a purchase, or visit the brand’s website or store. Because direct mail has proven so effective, retailers are hesitant to improve what’s not broken, particularly because it is so closely associated with a brand’s image. But with the rising quantity of customer data, the increasing complexities of dynamic customer journeys, and the proliferation of channels, retailers struggle with creating a relevant experience. This applies to all of direct marketing; direct mail, catalogs, and personalization on digital channels. In the direct mail space, customers identify in a myriad of ways with various names, prefixes, addresses, etc., which makes it more difficult for list processors to trust that their campaigns are targeting the right audience. The same holds true in the digital space, where 24-hour list processing runtimes are unacceptable. Furthermore, the ability to deliver a personalized experience – online and offline – depends on how well a retailer determines a customer’s identity in the context of various relationships, such as within a household or a business. ## **Flip the Script with a Focus on Data Readiness** Retailers can increase the efficiency and effectiveness of direct marketing with a focus on data readiness. Data readiness ensures that retailers have the most complete and accurate customer profiles available for business use the moment they’re needed. Data quality and advanced identity resolution are foundational elements of data readiness. When data quality is completed in real time as data is ingested, retailers have the contextual customer understanding that is needed for direct marketing success, whether for direct mail campaigns or for instantly recognizing a customer online and providing a hyper-relevant experience. ![Successful cataloging and data readiness](https://www.redpointglobal.com/wp-content/uploads/2025/04/Catalog-blog-physical-catalog-674x450.jpg)Eighty-seven percent of consumers say that catalogs make them more interested in a retailer’s products, with 84 percent of catalog recipients claiming that they either share it with someone else, make a purchase, or visit the brand’s website or store. The more relevant the online experience, the more a customer is engaged. A personalized digital catalog experience, for example, might show a loyalty member how many points she has accrued, an itemization of what she’s eligible for, and how many points she needs to move to a higher tier. It might display a hero image where a model that resembles the customer (age range, demographic, etc.) styles an outfit that complements something the customer has already purchased – or that a predictive model indicates will be of interest for the target audience. In short, the depth of customer understanding greatly influences the level of personalization. The need for an accurate, updated customer profile is just as essential for outbound marketing. The same personalization tactics apply for a direct mail campaign, but retailers must also be confident that the content is going to the right customer. Address hygiene, householding and other components of identity resolution are essential for reducing waste and inefficiency. ## **Customer Journeys are Dynamic, Can Your Segmentation Keep up?** With a real time, continuously updated customer profile that lets a brand know everything there is to know about a customer – a Golden Record – the next step is to ensure that dynamic segmentation produces targeted audiences that can be activated across every interaction touchpoint, both digital and physical. Dynamic segmentation accounts for the complexities of the customer, e.g., life changes, evolving interests, preferences, transactions, and other factors that influence the Golden Record. If a customer makes an online purchase that makes them ineligible for an offer – or eligible for a new offer – dynamic segmentation automatically moves the customer into the right segment. The same holds true for other changes or updates such as a new address. Furthermore, dynamic segmentation means that models do not have to be taken offline for updates, either because the business decides it wants to chase a new metric or because a segment has to be built for a new channel. The ability to build a segment once and use it across all channels is a cornerstone of consistent personalization, providing marketers with the ability to engage with a customer in real time as the customer journey unfolds. For instance, a customer who starts their journey by opening a catalog, then accesses product details through the mobile app, through a link in an email, or through a browsing session using a laptop should expect the same experience irrespective of the interaction touchpoint. ## **Maximize Your Data Value with Minimum Code and Development** Direct marketing is at a crossroads, where it is becoming counterproductive to accept diminishing returns by following the same script. Customer expectations for personalization are intensifying just as the volume of customer data is increasing, giving retail marketers the formidable task of cost-effectively processing an unprecedented amount of data with newfound speed and agility. A game-changer for direct marketing is being able to build and access unified customer profiles without having to write code. Retail specific data models provide access to dozens of pre-built tools to ingest, parse, transform, and cleanse data from any source – giving marketers data that is always ready for use. The ability to easily configure, test, and save data pipelines is a huge factor in driving down direct marketing costs while increasing efficiency with a readily available menu of templates that make it easy to change content on the fly. > A game-changer for direct marketing is being able to build and access unified customer profiles without having to write code. Retail specific data models provide access to dozens of pre-built tools to ingest, parse, transform, and cleanse data from any source – giving marketers data that is always ready for use. Without a need for outside experts to configure data, direct marketers have more control over how to optimize campaigns based on a current understanding of a customer. And with a system that connects to all sources and destination systems, they are able to support omnichannel personalization from a single platform. ## **Improve Direct Marketing with an Investment in Data** Direct marketing is a powerful tool in a marketer’s arsenal, but in today’s complex retail landscape, success hinges on data readiness. Whether optimizing traditional direct mail campaigns or delivering highly personalized digital experiences, retailers must ensure that their customer data is accurate, accessible, and actionable in real time. Investing in data quality and dynamic segmentation reduces inefficiencies and unlocks new opportunities for engagement and revenue growth. As customer expectations evolve, the brands that harness data effectively will be the ones that thrive. **Blog categories:** Data Readiness, Retail --- ### [The Heart of Customer Data Technology: Minding the Data](https://www.redpointglobal.com/blog/the-heart-of-customer-data-technology-minding-the-data/) **Published:** April 2, 2025 **Author:** John Nash **Content:** Every so often there’s some blowback about the value of customer data technology from analysts who question the economic benefit. The “less is more” calculation holds that developing a unified customer profile to ultimately deliver personalized experiences is a flight of fancy, based on some marketers claiming that the more data they collect, the less benefit they see. A [Gartner Marketing Symposium](https://martech.org/marketers-under-pressure-to-cut-martech-spend/) helped perpetuate the narrative by pushing back on the value of “customer obsession,” hinting that a Customer 360 is unattainable. But that cost to benefit analysis minimizes the overall financial benefits of data-driven personalization. In addition, the ROI calculation is based on the activity of collecting data, not on the customer data technology itself. That is, there is no accounting for what the customer data technology *does* with the data. Enterprise-grade customer data technology that makes customer data ready for business use – a solution that puts the “D” in a customer data platform (CDP) or any other related application – makes the collection of customer data and creation of a unified profile a competitive differentiator. A high ROI is measured through more loyal customers, greater lifetime value, higher average spend, less churn and other success metrics calculated from having more satisfied customers. All while decreasing costs through improved productivity of CX, marketing, IT and data staff, improving customer outcomes, lowering overall customer interaction costs and reducing technology stack costs. > A differentiated customer experience has never been about merely collecting data and connecting data points. It is about data readiness, fully preparing customer data for any CX or business use case. Everyone can agree that collecting data with the thought that volume alone will provide more insight about your customers is neither smart nor efficient. A differentiated customer experience has never been about merely collecting data and connecting data points. It is about data readiness, fully preparing customer data for any CX or business use case. ## **Data Readiness and Trust** Clearly data is not ready for use in many cases, as evidenced by 87 percent of marketers making the claim that data is their most underutilized asset (**See Figure 1**). One way to tell if customer data technology prioritizes data readiness is whether marketers and business users trust the data. When a CDP focuses on data quality from ingestion through activation, a marketer or business user is confident that the customer, household or business entity they interact with is the *right* customer. ![Invesp Statistics](https://www.redpointglobal.com/wp-content/uploads/2025/04/Invesp-statistics-800x450.jpg)**Figure 1:** A lack of data quality and completeness is the biggest challenge to data-driven marketing, according to an Invesp survey. Moreover, a hyper-focus on data quality does more than let a marketer recognize a specific customer, it provides a deep, contextual understanding of a customer and a customer’s omnichannel journey. This knowledge translates to consistently relevant experiences that align with a customer’s journey as it progresses, across all channels inbound and outbound. This data quality issue is key, as 54 percent of marketers said the lack of data quality and completeness is the biggest challenge to data-driven CX. Giving data quality the utmost care and attention as soon as data is ingested is vital for interacting with a customer in the cadence of a customer journey, up to and including real time. Data readiness unlocks the full potential of a unified customer profile; it is the key to recognition, to relevance and real time, the main ingredients in being able to profitably differentiate one customer from another. Data readiness is a core customer data technology capability. The line between winning and losing in the experience economy will come down to the skillful application of data, and the better the customer understanding, the better the experience. ## **Reverse ETL is NOT Data Readiness** There are many reasons why data may not be ready for business use. Some ways in which customer data technology falls short include not updating data sources. Perhaps data is ingested in real time, but some of the primary data sources are on a nightly batch feed. Data readiness implies that all data reflects the absolute latest understanding of a customer, household or business entity. Inferior identity resolution, i.e., basic matching, is another key roadblock preventing data from being ready for business use. Some technology that touts reverse ETL capabilities may claim to perform identity resolution, but really only perform a basic match of first-party data against a reference file that is gathered by a third-party organization and enhanced in some way. But this is incongruent with developing a deep customer understanding. Infrequent updates to a reference file mean that an ensuing match does not reflect the dynamic, real-time nature of a customer journey, resulting in a loss of trust in the accuracy and validity of a customer record. Inferior or incomplete data quality processes impact everything that depends on having a complete understanding of the customer, including segmentation. Automated segmentation using a rules-based approach will update an audience based on any data change, i.e., a new purchase, a browsing session, a new email address, etc. To generate an audience that accurately reflects the latest customer understanding, it is critical that data quality processes are completed upstream. ## **Data Readiness Unlocks Innovation** Data quality is a foundational requirement to scale CX use cases, giving marketers and business users the ability to create and execute innovative experiences at scale without having to worry about the trustworthiness of customer data. When it comes to providing a superlative customer experience, there are no small problems. The wrong email address, a mis-spelled name, a late response to an abandoned shopping cart, a misunderstanding of household dynamics – all are avoidable, and all have the potential to create friction in a customer journey. By prioritizing data readiness, brands avoid the cost of bad data, e.g., fewer conversions and cross-sell opportunities, more attrition, etc. In addition to avoiding bad outcomes, having data ready for business use also ensures that the customer receiving a next best action is more likely to progress on the customer journey. The customer buys the product, signs up for the loyalty program, schedules the appointment. It’s one thing to send the right email to the right customer which obviously avoids creating friction, but consistently getting that right also leaves open the possibility of the customer taking the desired follow-up action, advancing the customer journey in a way that benefits both the customer and the brand. ## **Data Readiness: The Cornerstone of Success with Customer Data Technology** In a recent Gartner [marketing technology survey](https://www.adexchanger.com/data-exchanges/cdps-are-in-the-gartner-hype-cycles-trough-of-disillusionment/), 67 percent of respondents said they had onboarded a CDP, yet of those only 17 percent reported “high utilization.” Some of this disparity can be chalked up to a lack of data readiness; if the CDP does not make data ready for business use, then users must rely on other applications to cleanse the data and make it ready for segmentation and activation. But this is untenable when customers expect a brand to instantly recognize who they are and deliver a personalized, omnichannel CX. In contrast to most CDPs, enterprise customer data technology embraces data readiness as a core tenet, going beyond what a CDP typically does to liberate customer data across the enterprise. Composed of a customer data refinery, customer data activation, and customer data management throughout with built-in AI, a customer data hub turns raw, fragmented data into actionable insights that drive personalized, impactful customer experiences. Redpoint is built on this principle, delivering enterprise-grade data quality and real-time updates that provide a unified customer profile marketers and business users can trust. As Gartner’s survey highlights, many organizations struggle to realize the full value of their CDP investment due to incomplete or untrustworthy data. Redpoint’s focus on data readiness ensures that every piece of customer data is prepared for immediate business use, eliminating the need for additional applications or manual interventions. This unlocks innovation at scale, enabling marketers to deliver seamless, relevant, and timely experiences across every touchpoint. By prioritizing data readiness, [Redpoint](https://www.redpointglobal.com/) empowers brands to achieve higher ROI, improve customer loyalty, and differentiate themselves in competitive markets. The result? Not just better data, but better business outcomes – proof data readiness isn’t just a feature; it’s the foundation of exceptional customer engagement. **Blog categories:** Data Quality **Blog tags:** Data quality --- ### [The Cost vs. Control Trade-Off in Liberating Your Customer Data](https://www.redpointglobal.com/blog/the-cost-vs-control-trade-off-in-liberating-your-customer-data/) **Published:** March 26, 2025 **Author:** John Nash **Content:** Because personalization is now a competitive differentiator, there is widespread agreement in the value of customer data, that it holds the secret to generating hyper-personalized customer experiences that reflect a deep understanding of the customer. Yet despite the shared belief that becoming more data-driven with a focus on customer-centricity is the way forward, there is less accord over the proper role of enterprise technology in orienting the business around the customer. There are competing visions for how to best extract insight from customer data, and then how to use a deeper understanding to achieve better outcomes through more personalized experiences. Most agree that it is necessary to bring all customer data together in the form of a single customer view, and to then activate the data by sending it to various end channels. Within this framework, however, there are different viewpoints for how to go about it. Some consider the collection of data in a data warehouse or a data lake as sufficient for the creation of a single view. Some tout reverse ETL as the main requirement for moving data through the system to activation. It seems that every vendor has a different idea for how best to bring data together better, faster, and easier. What tends to get lost in all the noise about which tool can do what, however, is what marketers and business users care about most, which is using technology to deliver the best possible customer experience. Most solutions fall short of this ideal because they focus on doing one thing well (e.g., reverse ETL) rather than make sure that customer data is always ready for business use, from ingestion through activation. ## **Customer Data Technology: The Right Powerhouse to Drive Outcomes** What is clear is that enterprise-grade customer data technology is key to delivering personalized customer experiences at scale based on a deep understanding of a customer by focusing first and foremost on data. What is less clear is what form that technology takes, whether as a CDP, a customer data management software, data quality & hygiene technology, identity resolution technology, or a verticalized solution. There are dozens of customer data technology vendors, and each may highlight a different strength, but the good ones recognize that, in the end, the purpose is to provide the best data to create the best segments, audiences and personalized downstream actions. > Enterprise-grade customer data technology is key to delivering personalized customer experiences at scale based on a deep understanding of a customer by focusing first and foremost on data. To accomplish this, certain core capabilities are required. Those are to [ingest and fix messy customer data](https://www.redpointglobal.com/data-quality-and-data-ingestion/), to [resolve identities at individual and relationship levels](https://www.redpointglobal.com/data-quality-and-data-ingestion/), to create an accurate and real-time unified customer profile, to [build reusable segments](https://www.redpointglobal.com/data-quality-and-data-ingestion/) (ideally without code) and to [activate those segments](https://www.redpointglobal.com/resources/segmentation-and-activation/) against any marketing or CX use case. In this context, enterprise-grade customer data technology is more than a marketing tool. It may primarily be used by marketers, but making data ready for business use as it flows through the enterprise is ultimately mission critical for the business. Trust in data results in better marketing campaigns, but also better programs, initiatives and results for any use case that rests on having a better understanding of the customer. The core capabilities of enterprise-grade customer data technology are distinguishable from other systems that work primarily with their own data, store limited details for limited periods, do not resolve underlying data quality issues and/or do not maintain a persistent profile useable in real-time for direct customer engagement. ## **Customer Data Basics: Build a Unified Customer Profile** Some of the market confusion about how to understand customer data technology stems from vendors offering definitions tailored to include their systems – and to exclude competitors. This creates a wide gap, where any system that assembles customer data might call itself customer data technology, while others claim that the title is reserved for a system with specific activation capabilities such as campaign definition, journey orchestration, and message delivery. The unfortunate result of competing claims about what’s most important about customer data technology is an inability to understand what a particular product actually does, which leads some to devalue customer data technology altogether. But there should be no confusion about the basic function of any underlying customer data technology foundation – to assemble data from all sources to build a single customer view (also known as a golden record or unified customer profile) that is available to any system that needs it. Accepting that this is the ultimate purpose, any company considering customer data technology to deliver personalized experiences should determine how it will meet the company’s specific business and technology requirements. Adopting a use-case driven approach to selecting this technology, i.e., determining an ROI for hyper-personalized experiences, will help ensure that the technology can grow with the company as use cases evolve. A use-case driven approach also helps an organization pare down unwanted or unnecessary features and functionality, and avoid duplications. It is also critical to map use cases to where the data resides. Many innovations have been made with SaaS solutions, but many use cases require the data to remain in place, within an organization’s security perimeter particularly where the use of customer data is tightly regulated as it is in healthcare and financial services. The good news is that some technology innovations are empowering enterprises to break the cost vs. control tradeoff that has been difficult to navigate to-date with SaaS solutions. That tradeoff generally held that a SaaS offered a more cost-friendly option with less control, whereas an on-premises environment offered more control but with a higher cost to operate and maintain the software. There are now a variety of self-hosted options available that break the traditional cost vs. control trade-off between a SaaS or a self-hosted (on-premises/private cloud) environment. In a self-hosted environment using modern data cloud technology like Snowflake, organizations maintain control over their data within their own security perimeter, and software companies can manage the software within that perimeter. The same holds true for on-premises or private cloud deployments. A cost calculation considers data ops and system ops. In a SaaS the software vendor will run both, in a self-hosted/data cloud environment the client will typically run the data ops and the software vendor the system ops, and in a private cloud or on-premises, the client will run both. (**See Figure 1**) ![Part 4 (ch 3) Redpoint Cdp Deployment Options](https://www.redpointglobal.com/wp-content/uploads/2025/03/part-4-ch-3-redpoint-CDP-deployment-options-800x360.png)**Figure 1**: Tailor the Redpoint CDP to your data and operational requirements ## **Composability and Customer Data Technology** In the CDP space vs. customer data technology at large, one trending topic is the composable CDP. A composable CDP aligns with a use-case approach by offering customers greater choice in terms of features and functions based on the specific needs of the business. Composability refers to designing a CDP with interchangeable and interoperable components, allowing for greater flexibility and adaptability. Composability entails a modular approach where buyers have the freedom of choice to obtain best-of-breed components that complement existing investments. More than just assembly, composability is also an enterprise approach that prioritizes agility. One factor to consider whether a composable CDP is right for your business is how important it is to quickly reach untapped business value. A composable approach allows a business to be more nimble, easily pivoting to rapid business and consumer changes. A customized toolset also is more dialed in to help the business achieve specific business goals and unique processes. The full value of a composable CDP is reached when each component creates value as it applies to a business function. The right customer data technology will be composable in the sense that each part can be used to create value independent of the other parts (**See Figure 2**). In this sense, customer data technology requires two essential pieces: - Componentized software that can be used to create, improve and continually update customer profiles - Organizations can create their own end-to-end data pipelines as required using customer data management software. ![Part 4 (ch 3) Fully Liberating Data](https://www.redpointglobal.com/wp-content/uploads/2025/03/part-4-ch-3-fully-liberating-data.jpg)**Figure 2**: Each part of the Redpoint CDP creates value. Composable customer data technology with a prime focus on data quality will ensure that all components of the composable stack will work better with the highest quality data and more accurate profiles used across the value chain. Embedded analytics and AI across every component help unlock deep insights from first-party customer data that produce data-driven personalization. Consider as an example a company that has a reverse ETL application that moves data from a data warehouse or data lake and activates it to customer-facing end channels. In some cases, this reverse ETL tool might even call itself a CDP. With a composable approach, this functionality may be augmented by adding an application that focuses on data management, addressing data quality, identity resolution and other data management tasks that a reverse ETL tool either ignores or downplays. It is also important to note that there will be different users for different pieces of customer data technology, but to be effective the inputs and outputs need to be integrated in a way that streamlines workflow for any one function, and ultimately turns raw customer data into tangible business outcomes. (**See Figure 3**) ![Part 4 (ch 3) Personas](https://www.redpointglobal.com/wp-content/uploads/2025/03/part-4-ch-3-personas-800x450.jpg)**Figure 3**: Different components and pieces of customer data technology will have different users. Integrated inputs and outputs will streamline the workflow for any one function, helping users move toward their intended business outcome. ## **Coming Next: The Importance of Data Quality in Customer Data** The growing complexity of customer data and the increasing demand for hyper-personalization make it clear that robust, enterprise-grade customer data technology is essential for delivering exceptional customer experiences. While we’ve explored the foundational capabilities and the importance of aligning them with business use cases, there’s one critical factor that underpins the success of all customer data: data quality. Without clean, accurate, and actionable data, even the most advanced technology will fall short of its potential. In our next part of this series, we’ll delve deeper into the role of data quality in powering your use cases, exploring why it’s the cornerstone of creating unified customer profiles and driving meaningful personalization at scale. --- ### [Retailers: Put a Dent in Shopping Cart Abandonment with a Customer Data Platform (CDP)](https://www.redpointglobal.com/blog/retailers-put-a-dent-in-shopping-cart-abandonment-with-a-customer-data-platform-cdp/) **Published:** March 24, 2025 **Author:** Renee Graff **Content:** [Baymard Institute](https://baymard.com/lists/cart-abandonment-rate) research shows that the average documented online shopping cart abandonment rate for 2023 was 70.19 percent, representing the first time it has climbed above 70 percent in 10 years. The more than 7 in 10 consumers who leave a website without making the transaction translates to about **$18 billion in lost sales revenue** annually. Common reasons for cart abandonment include extra (read: hidden) costs, the need to create an account, and a complicated checkout process. Retailers spend a lot of money to increase fulfillment through an improved UX, but cost transparency and a more seamless end-to-end process only go so far in mitigating cart abandonment. Retailers should also consider the impact of a personalized customer experience (CX). In one [McKinsey study](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying), **71 percent** of consumers said that they expect personalized interactions. Probing deeper into what consumers expect from a modern, personalized CX, **67 percent** said relevant product recommendations, **66 percent** said tailored messaging and **53 percent** said that a brand should send triggers based on the consumer’s behavior. ## **Which Approach Should Marketers Use?** Knowing these expectations, which of the three approaches yields the best results? Is it the product recommendations, tailored messaging, or [behavioral-based triggers](https://www.redpointglobal.com/resources/enhance-customer-experience-with-timely-welcomed-triggered-actions/)? All three methods can be effective in reducing abandoned shopping carts versus a generic approach that runs the same cart abandonment playbook for every customer, e.g., the same email or offer every time any customer abandons a cart. An impersonal, static offer or trigger fails to meet the expectation of the modern consumer that a brand provides a consistent, personalized CX. It’s also cost inefficient, as it doesn’t take into consideration customer intent, including the customer’s cart abandonment patterns. For example, will a static email be relevant? Will the customer try to game the system and abandon the cart just to receive the free shipping offer she knows is coming? ## **Put the Brakes on Abandoned Carts with a Golden Record** In an effort to reduce cart abandonment and recoup some of that $18 billion in lost revenue, many retailers are focusing on the [customer data platform (CDP)](https://www.redpointglobal.com/cdp/) and its ability to shine a light on customer intent through construction of a unified customer profile. With a deep, contextual and real-time understanding of the customer, retailers respond to cart abandonment with hyper-relevant interactions that align with the context of the customer journey – all triggered by that abandoned cart. A retargeting ad that reflects an in-depth understanding of the customer’s behaviors. A real-time offer on an item that complements what’s sitting in the cart. A personalized message that plays to the customer’s brand affinity, based on recognizing them as a loyal customer. > What was once disparate pieces of customer data stored across business units come together to form a complete picture that can be used to understand and influence customer behavior down to the individual level, but at scale. All of this becomes possible with a unified customer profile, which is also known as a Golden Record. A supercharged single customer view, a Golden Record includes a full identity graph as well as a full contact graph. It includes customer data from every possible source and of every type, and all of a customer’s behaviors, preferences, transactions and sentiments. It also includes all known devices and IDs, email and physical addresses. What was once disparate pieces of customer data stored across business units come together to form a complete picture that can be used to understand and influence customer behavior down to the individual level, but at scale. Other important characteristics of a Golden Record that make it the single source of truth for a customer are real time data ingestion, persistent database key management and householding. - [Real time data ingestion](https://www.redpointglobal.com/blog/boost-your-marketing-automation-platform-with-clean-data/) ensures that a Golden Record provides marketers and business users with the ability to engage with a customer in the moment, within the context of an ongoing customer journey. This is key in curbing shopping cart abandonment. Consider a customer who leaves items in a shopping cart and then engages with a chatbot. A chatbot that accesses data from the current browsing session through a Golden Record that is updated in real time is better able to guide a customer journey to an optimal outcome. Not many CDPs can truly manage real time data well, so it’s important to include this when evaluating CDP solutions. - [Persistent key management](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) provides marketers with an all-important contextual understanding of a customer over time. Persistent key management relies on a mechanism for creating a unique ID – a master customer record – and attaching the unique ID to a signal, with rules for keeping it or replacing it over time as the customer journey evolves. This consistency ensures that users do not unexpectedly lose or gain information about a customer over time because of differences in how the customer identifies themselves or how the identity resolution process unfolds. As this capability pertains to shopping cart abandonment, consider a customer who abandons a cart who perhaps last engaged with the brand several months ago using a different device, and is now newly married and using a different name. With a Golden Record that uses persistent database key management, the brand knows it’s the same customer and can personalize the abandonment response based on the complete customer record. - [Householding](https://www.redpointglobal.com/blog/heres-why-householding-matters-for-a-seamless-customer-experience-cx/) provides a contextual understanding of a customer within a particular unit. This may be a true “household,” but may also be set up to reflect a customer’s role within a business or as an account holder in a situation with multiple accounts. A feature of advanced identity resolution in the creation of the Golden Record, householding is a valuable tool for mitigating shopping cart abandonment by providing essential insight into exactly *who* is filling the cart. In B2B sales, householding reveals if the person filling the cart is the final decision maker in a situation where perhaps a dozen or so employees have access to a shared account. In the more traditional household setting, consider the mother that fills the shopping cart with bedding for her daughter’s dorm room, but holds off on the purchase until the daughter’s approval. The brand that knows if the mother and daughter both use the device is better able to formulate a targeted response. Like real time data ingestion, not many CDPs include householding as part of their identity resolution capabilities. Most only perform basic deduplication, so it can be an important consideration depending on planned analytics, CX and marketing campaigns. ## **Stop Abandoned Carts in Their Tracks** With the Redpoint CDP, retailers have everything they need to put a serious dent in shopping cart abandonment by being able to respond in real time with a hyper-personalized CX. Redpoint cleans your data as it is ingested, and built-in data quality with tunable identity resolution provides the most accurate, up-to-date Golden Record to power every retail use case, shopping cart abandonment mitigation among them. For more on how Redpoint helps retailers ignite their customer data to tackle shopping cart abandonment and deliver hyper-relevant experiences that increase loyalty, lifetime value and revenue, click [here](https://www.redpointglobal.com/retail/). **Blog categories:** Retail **Blog tags:** Golden Record --- ### [The Evolution of Customer Data Technology](https://www.redpointglobal.com/blog/the-evolution-of-customer-data-technology/) **Published:** March 19, 2025 **Author:** John Nash **Content:** The need for brands to deliver personalized experiences is not a new reality, it is just vastly different from what’s come before. At the start of this series, we depicted personalization using the analogy of the friendly corner store proprietor, whose chief concern is providing each and every customer with value. While such an experience might seem as archaic as a door-to-door encyclopedia salesman, it’s not as outdated as one might think. One [Redpoint customer](https://www.aubuchon.company/about) famously did not install its first telephone in any of its stores until 1992, just four years before it launched its website. What is different is the complexity involved with duplicating the corner store experience at scale across a mix of digital and physical channels that encompass the modern customer journey. It’s about trying to personalize the customer experience to an almost unlimited number of customers, and doing so at precisely the right time and in the right context as those customers engage with the brand across a growing number of channels. ## **Early Attempts at Personalization at Scale** As consumers and brands transitioned to digital-first experiences, technology such as a CRM system or a [Data Management Platform (DMP)](https://www.redpointglobal.com/blog/demise-of-the-dmp-long-live-the-cdp/) became early attempts to gain insights about customers through data. A CRM is a valuable technology for handling human-sourced data, creating a record-keeping destination so that every time a contact is made at any point via any channel, an agent or salesperson can effectively track every interaction. And a DMP held early promise as a marketing engagement tool because of its powerful capabilities for look-alike modeling, onboarding and audience buying, which all hold value for select business use cases – particularly when a need arises to build or buy a particular audience. A DMP also can handle first, second and third-party anonymous data as well as interface with demand-side and supply-side platforms. Because of this, there was general agreement that a DMP could do anything from a data perspective. But as those technologies matured, and as customer journeys became more complex, it became clear that both had limitations in terms of [orchestrating omnichannel personalization](https://www.redpointglobal.com/orchestration/). Neither was designed nor intended to meet elevated customer expectations for consistent personalization across a mix of physical and digital channels. A CRM, for instance, is not intended to orchestrate next best actions across channels in the context of a customer journey. Nor is it designed to clean or match data beyond simple account level details. The same is true for a DMP; despite its early promise, it never caught on as providing a single source of truth for the customer. As personalization became a competitive differentiator, its lack of first-party data (PII in particular) and lack of reporting and measurement capabilities were also limiting factors. And, as with a CRM, its main purpose (in its case finding a lookalike audience) did not require the depth of data ingestion, standardization and matching that are necessary to deliver the level of personalization that today’s always-on customers have come to expect. Customer Data Platforms (CDP’s) are a promising technology for pulling customer data together for marketers, but they too have shortcomings. The early promise of CDPs was to unify customer profiles for marketers’ use, solving for the issues of data warehouses not having the right level of data in them, or requiring IT to compile data in some form for Marketers. As the CDP market evolved, most of the vendors in this space made the assumption that ‘someone else’ resolves the inherent quality issues in customer data, a poor assumption to make. Most CDP’s in turn have failed to deliver sufficient ROI for organizations, having failed to wrestle with the underlying data complexity and messiness. In other instances, [CDPs have created value](https://www.redpointglobal.com/cdp/) for simple use cases like onboarding audiences for paid media, but fail to scale to the rich uses cases that drive value, like multi-stage, omnichannel and/or real-time customer journeys. ## **Data, Nuance & A Deep Customer Understanding** Over time, as companies began to amass more and more first-party data from a wealth of new sources such as IoT and social media, it became apparent that a data aggregation tool and surface level personalization would not meet the standard for omnichannel personalization. Organizations may have been data rich, but they were still insight poor. The need for a deeper understanding of the customer has become more pressing, particularly to meet the demand for [real-time personalization](https://www.redpointglobal.com/real-time-interactions/) and, of late, the need to incorporate AI tools. The increasing complexity of customer journeys has outpaced the ability of basic personalization technology to satisfy customer expectations for a seamless, omnichannel CX. A typical customer or member journey – whether in retail, travel and hospitality, financial services, healthcare, etc. – is nonlinear. What was once a traditional buying journey of awareness, evaluation, and decision may now involve a dozen or more channels, with no easily predictable sequence. For instance, consider how many different reasons there might be for a customer to abandon a shopping cart. The customer may have moved on (perhaps due to a poor experience), may be using the cart as a staging area while they comparison shop, or perhaps is simply trying to trigger an automated offer – knowing that every abandoned cart usually results in a juicy offer. > The increasing complexity of customer journeys has outpaced the ability of basic personalization technology to satisfy customer expectations for a seamless, omnichannel CX. How a brand decides to respond is often the difference between a good customer experience and a bad one. The former is based on a detailed understanding of a customer and the customer’s intentions, and it is delivered at the optimal time (up to and including real time) and on the optimal channel. A next-best action considers the entirety of a customer’s interactions with the brand – of which the abandoned shopping cart is one factor. Recency, social sentiment, and other behaviors are all factors, along with any other customer signal that tailors the response to the individual customer. Optimizing the experience for the customer in the context of the journey helps guide the customer to the desired outcome, benefiting both the brand and the customer. It also strengthens the relationship with the customer, building trust that leads to higher satisfaction, greater loyalty and a higher lifetime value. This type of response is fundamentally different than a static one-size-fits-all, kneejerk reaction that all too often is counterproductive, introducing friction into the customer journey. A deeply nuanced customer understanding is likewise crucial for delivering a personalized CX when it comes to interaction with [conversational AI and other GenAI tools](https://www.redpointglobal.com/ai/). When a member of a health plan engages with a chatbot, for instance, it will ideally know the reason for the engagement and be prepared to help guide the member to a resolution. Similar examples that demonstrate a need for a deep customer understanding abound, particularly as customers now associate real time and AI with their definition of a modern personalized CX. Consider a healthcare organization that wants to provide the optimal care path for someone with a chronic condition. When a patient visits the website, a good experience depends on how quickly the organization can recognize the visitor. This can even be when the patient is on an anonymous-to-known journey, such as a browsing session using a new device. A healthcare organization will ideally have the right customer data technology in place to be able to determine in as close to real time as possible whether a visitor to the website is a patient, or perhaps a family member, and be able to optimize the patient journey. ## **Technology, Mindset, Data: Recipe for the Right CX** The evolution of customer journeys – from the simplicity of the corner store experience to today’s omnichannel complexity – has made one thing clear: delivering a truly personalized experience at scale requires the right combination of technology, mindset, and data. It’s not just about responding to customer signals but doing so in real time, across channels, and with a deep, nuanced understanding of each individual. Brands that succeed in modern personalization embrace purpose-built technologies that enable them to orchestrate next-best actions, leverage AI to enhance decision-making, and, most critically, put data at the center of their strategy. Real-time, high-quality data is no longer a “nice to have” – it is the fuel for AI, the key to understanding customers, and the foundation for seamless, contextual, and trust-building experiences. Getting personalization right doesn’t just drive outcomes; it creates competitive advantage, fostering stronger relationships, greater customer loyalty, and long-term value. In our next part of this series, we’ll take a closer look at what customer data technology works, and how it prepares brands for this future, transforming raw data into readiness for the modern customer experience. **Blog categories:** Data Quality, Data Readiness --- ### [Customer Centricity Begins with the Data](https://www.redpointglobal.com/blog/customer-centricity-begins-with-the-data/) **Published:** March 13, 2025 **Author:** John Nash **Content:** The customer experience gap refers to the difference between the level of personalization that today’s always-on, connected customers expect vs. the experience that brands actually deliver. Recognizing that customer experience (CX) is a competitive differentiator, brands across retail, travel, financial services, healthcare, hospitality and other industries are focused on narrowing the gap, although there is work to be done. Marketers rated themselves roughly 2X higher than consumers on their ability to deliver a satisfactory CX in a [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/) that measured the gap across four dimensions: customer understanding, personalization, consistency, and privacy. What consumers indicate that most brands lack is the ability to recognize them as the same person across channels, with predictably adverse effects on each of the four CX dimensions. The primary reason for this failure is that despite recognizing CX as a competitive differentiator, brands are still siloed in some way, shape or form – set up operationally around products or services, channels, and/or functions. The underlying customer data, technology, processes and teams are organized independently of what is best for the customer. Despite sitting on vast amounts of first-party customer data, companies struggle to deliver relevant, individualized CX because data are accessed and actioned unevenly across those various siloes, preventing the single customer view that is necessary for consistent personalization. Many brands have a single view at some level, but it is lacking in the accuracy, completeness and/or timeliness required to drive the types of experiences consumers expect. This gap is reflected in the declining CX Index, Forrester’s measure of the quality of the customer experience, connecting quality to loyalty and revenue. (**See Figure 1**) ![Forrester Declining Cx Index](https://www.redpointglobal.com/wp-content/uploads/2025/03/Forrester-declining-CX-index-800x450.png)Figure 1: The Forrester CX Index – the ROI of CX Transformation It is an overall measure of how well brands deliver customer experience – which has been declining for three years in a row, reaching a nine-year low in 2024. Forrester cited two key drivers for the decline: - An inability of brands to provide a seamless customer experience, and - An underwhelming digital experience. The root in both of these cases is customer data. Brands simply do not understand their customers at the depth these customers expect. The good news is this does create opportunities for brands that can capitalize on this opportunity to extract more value from their customer data. Brands can earn 10’s or even 100’s of millions in incremental revenue for every 1 percent gain in the CX index. (**See Figure 2**) ![Forrester Value Of Improving Cx Index](https://www.redpointglobal.com/wp-content/uploads/2025/03/Forrester-value-of-improving-CX-index-800x450.png)Figure 2: Potential monetary gains across industry verticals for every 1 percent improvement in the CX Index. This series will highlight these types of gains from customers using Redpoint customer data technology. ## **Data-Centricity Leads to Customer-Centricity** Creating an exceptional customer experience requires treating data as a living, breathing asset, one that needs to be continually updated, improved for accuracy, added on to, and accessed in the customer’s cadence. This approach does not entail a digital transformation; rather, it starts with applying customer data that is centered on the customer vs. applying data around a channel, a process or a functional team. For instance, a marketing team responsible for creating and executing an email campaign will likely only prioritize customer data relevant for a specific campaign or that specific channel; an email invitation to join a loyalty program will broadly segment out an audience of eligible customers, and the campaign will run with built-in incentives for opens and conversions. All too often, the campaign and the results exist in a vacuum, independent of an individual customer’s journey with a brand. Eligibility for the campaign may have been determined by creating a static list of customers, chosen because they had yet to join a loyalty program or they met an average monthly spend criterion. But a static email fails to account for a contextual understanding of a customer journey – the customer’s interests, other interactions with the brand across different channels, intent or another marker. A true customer-centric approach begins with a data-centric approach. With a focus on data as an enterprise asset, organizations break through the technology, process, and channel siloes that prevent them from effectively monetizing their customer data. If the same email campaign is organized around a single customer view, a dynamic segment will produce a smaller group of customers for whom the invitation is hyper-relevant at the time the customer opens the email. It becomes a natural extension of the customer’s journey up to that moment, a next best action. Opens and conversions are then only success metrics in furtherance of a larger enterprise goal such as an increase in lifetime value vs. basing incentives on a team’s performance or the success of any one channel. ## **Commit to Organizing Your Data Around the Customer** Becoming customer-centric starts with a mindset change. By accepting that bringing together the first-party data that already exists inside your organization is essential to competing on CX, companies start to organize their data and processes around a single customer view. Once companies have a clear vision and a sustained commitment to putting customer data at the center of business operations, they can gradually introduce more advanced personalization capabilities. With even minor changes in a company’s culture in terms of its approach to data, teams begin to collaborate differently, decisions become more data-driven and customer focus begins to become more instinctive. > A true customer-centric approach begins with a data-centric approach. With a focus on data as an enterprise asset, organizations break through the technology, process, and channel siloes that prevent them from effectively monetizing their customer data. This culture shift is essentially an enterprise recognition that superior customer experiences transcend marketing. Operationally, a single view of the customer should be viewed as a capability that empowers sales, service delivery, customer service, product teams, and is supported by data science and IT teams. Everyone has a stake in strengthening the brand-customer relationship. This mindset shift is possible without needing to restructure an organization, shortening the time to value. According to a landmark [McKinsey study](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) on personalization, the strength of a relationship a brand builds with a customer and revenue gains are directly related to how a company approaches the collection and use of first-party data. Brands that traditionally operate without a direct customer relationship (e.g., CPG) and thus do not prioritize the collection of first-party data might achieve revenue gains of up to 10 percent through personalization. By contrast, digitally native brands and those for whom first-party data is the heart of decision-making generally have a very strong customer relationship and achieve revenue gains of up to 25 percent. More advanced personalization correlates to higher revenue gains because experiences are oriented around the customer. In the McKinsey study, 72 percent of customers said they expect the businesses they buy from to recognize them as individuals and know their interests. Asked to define personalization, those same consumers said that it is when brands demonstrate an investment in the relationship, not the transaction, as manifested by positive experiences that makes them feel special. The stakes are set. As customer engagement technology matures and as capabilities expand, customer expectations continue to rise, and competition to win individual customers intensifies, the ability to consistently deliver personalized experiences at scale will widen the advantage of market leaders over the rest of the pack. The winners will be those who can build the capabilities to constantly transform their customer data into meaningful experiences that add real value to customers’ lives. --- ### [5 Ways to Know if a CDP Solution is Really a CDP](https://www.redpointglobal.com/blog/5-ways-to-know-if-a-cdp-solution-is-really-a-cdp/) **Published:** January 22, 2025 **Author:** Redpoint Global **Content:** Customer data platforms (CDPs) are still emerging as a solution class, which means that there is still some lingering confusion in the marketplace about the proper definition. Some of the confusion stems from vendors making the claim that the capabilities of a [CDP](https://www.redpointglobal.com/cdp/) are already fulfilled by other solutions, which leads to many solutions branded as CDPs being limited in scope and functionality. ## **What is a Customer Data Platform?** The [CDP Institute](https://www.cdpinstitute.org/learning-center/what-is-a-cdp/) defines a CDP as “packaged software that creates a persistent, unified customer database that is accessible to other systems.” That definition leaves some room for interpretation. What is meant by persistence? At what cadence is the unified customer database accessible to other systems? What type of data is required to create a unified customer record? ## **How Can You Tell if a Solution is *Really* a CDP?** There are generally five key characteristics that separate a CDP from other solutions that, for one reason or another, cannot accurately lay claim to the CDP mantle. To be considered a CDP, a solution must: 1. **Accept data of any structure or cadence** – A real CDP functions as a single point of access and visibility for data silos throughout the organization. The high variance in structure and cadence of data throughout most companies – streaming unstructured data from social media, structured batch data from the CRM, etc. – means that any CDP worth the name needs to accept any structure or cadence of customer data. As volumes of data increase, CDPs need to have the ability to ingest broader varieties of information and still leverage it. A CDP solution is a single point of truth for customer data across the enterprise. 2. **Resolve customer identities across the anonymous-to-known lifecycle** – A CDP solution needs to possess robust [identity resolution](https://www.redpointglobal.com/identity-resolution/) capabilities. From a functional perspective, this means blending anonymous behavioral data with known information about customers into a coherent customer identity. With identity resolution capabilities, a CDP should be able to build and maintain a golden record from everything that is knowable about the average consumer. 3. **Manage customer data in real time** – CDPs are designed to build and maintain a golden record, which is also known as a unified customer profile (or single customer view). Any CDP worth the name must be able to perform the necessary tasks to accomplish that goal at the [speed of the customer](https://www.redpointglobal.com/real-time-interactions/). While the tasks are often done at a real-time pace, the definition of “real time” is based on business needs. Real time for one business (or one use case) might be a few milliseconds, or a few seconds for another business. Despite this, the fact remains that any CDP needs to be able to update the unified customer profile at a moment’s notice. 4. **Empower business users with access to unified customer data** – The unified customer profile created by a CDP solution means very little if no one has access to that data. CDPs need to allow marketers and other business users to access customer records with minimal IT assistance; this is partly what makes CDPs so different from traditional data management technologies and other customer engagement solutions. Business users who can access customer data at their moment of need can do their jobs quicker, which leads to greater responsiveness to customer signals. 5. **Function on any deployment model and database technology** – A CDP needs to be flexible in its deployment model. Some companies prefer on-premises deployments, others are fine with cloud-based software, and still others tend toward a hybrid deployment. The type of deployment shouldn’t matter. In terms of database technology, many brands have legacy databases in place that they don’t want to transition away from. A CDP shouldn’t force any organization to deploy a new database – the solution is designed to maximize investments in data technologies, not replace them. ## **How to Compare CDP Solutions** Customer data platforms are a powerful solution class with a potent ability to ensure brands can deeply understand their customers. With a fuzzy definition in the marketplace, however, comparing CDP solutions can be a challenge. It’s crucial that companies know what to look for when evaluating solutions. If the solution being evaluated doesn’t accept all forms of data, handle real-time updating, make data accessible, have a flexible deployment model, or resolve identities across the anonymous-to-known customer lifecycle, then chances are the solution isn’t really a CDP. **Blog tags:** CDP --- ### [Securing Enterprise Data: The Shift to Behind the Firewall, On-Prem Deployment in Regulated Industries](https://www.redpointglobal.com/blog/securing-enterprise-data-the-shift-to-behind-the-firewall-on-prem-deployment-in-regulated-industries/) **Published:** March 3, 2025 **Author:** Steve Zisk **Content:** Enterprise companies in financial services, healthcare and other regulated industries are increasingly “self-hosting” critical applications and data due to security and privacy requirements, opting to keep customer or patient data behind the firewall in on-prem or private cloud deployments to reduce the risk of a data breach. A shift to private environments is a growing trend. In a 2024 [survey from Citrix](https://www.techopedia.com/cloud-exit-as-companies-move-data-on-premises), 42 percent of organizations reported that they are considering or already have moved at least half of their cloud-based workloads back to on-premises infrastructures with 94 percent involved in some kind of “cloud reparation” project. Protection against a data breach is a major reason for the trend. According to a report in [Harvard Business Review](https://hbr.org/2024/02/why-data-breaches-spiked-in-2023), more than 80 percent of data breaches in 2023 involved data stored in the cloud. Attacks are unfortunately becoming all too common, as threat actors target enterprise companies with unsecured data, in any industry. A healthcare communications platform that connects providers with patients recently announced a [major data breach](https://www.hipaajournal.com/connectoncall-data-breach/) that exposed the PHI of nearly 1 million patients, resolved only when the platform was restored in a more secure environment. In the [largest data breach](https://www.americanbanker.com/list/the-biggest-data-breaches-of-2024-in-financial-services) in the financial services industry in 2024, approximately 17 million customers of a top-ranked U.S. mortgage lender had their PII exposed, including names, addresses, dates of birth and financial account information. Enterprise companies handle massive amounts of consumer data, much of it sensitive and governed by stringent regulations such as GDPR, CCPA, HIPAA, and other global or local privacy frameworks. SaaS solutions, which store data on third-party servers, raise concerns about compliance and security. Keeping data behind the firewall, marketing and compliance teams maintain tighter control over data storage and processing, reducing the attack surface for potential breaches. This approach not only mitigates risks but also ensures adherence to compliance standards. With consumer trust becoming a cornerstone of brand equity, safeguarding data becomes essential. ## **Not Your Father’s On-Premises** A modern on-prem solution where an organization keeps its customer and patient data behind the firewall no longer refers strictly to an on-site data center, but rather an organization’s private environment that doesn’t sacrifice cloud-like agility and scalability. In such an environment, tools like hyper-converged infrastructure, private clouds, and containerization technologies (e.g., Kubernetes) empower organizations to deploy and scale applications quickly while retaining full control over their environments. These advancements make on-premises solutions an attractive alternative to traditional SaaS platforms, combining the best of both worlds and allowing for the highest levels of data security. Regulated organizations realize that enterprise control over a valuable asset – customer data – often outweighs the traditional cited benefits of a SaaS solution, e.g. scalability, cost efficiency and rapid deployment, particularly when an organization’s reputation and financial liability are at stake. ## **Customization, Control & (Less) Complexity** One drawback of a SaaS solution is that it requires the use of built-in features, which may or may not be compatible with components of an organization’s existing technology stack, such as an analytics platform. This drawback becomes a greater hindrance in an era of rapid change and innovation. Companies increasingly require the flexibility to meet new and evolving use cases, such as quickly rolling out GenAI tools, and a SaaS solution limits flexibility. An on-prem or private cloud environment provides enterprise companies with the flexibility to install and upgrade software as needed, with the freedom and flexibility to configure the software needed for any business use case. Reduced complexity is another feature of an on-prem system vs. the increased fragmentation that is a byproduct of SaaS proliferation. In a SaaS environment, organizations commonly have to manage multiple platforms, ensure seamless integrations across various applications, and maintain consistent data flows across systems. In contrast, an on-premises system allows for the consolidation of operations and data within a controlled environment, reducing the complexity of managing disparate systems and minimizing the risk of data silos. ## **Cost Reduction** While a SaaS application may offer cost savings in the short term, keeping data behind the firewall has several potential cost benefits. This can include ingress/egress and storage charges for cloud data, along with indirect costs for monitoring and managing data compliance and usage. By retaining control over privacy exposure, security, and usage patterns (i.e., who has the right to look at the data, use it, export it, etc.) an organization ensures more direct and often simpler data usage and management costs. Subscription fees with unforeseen overages and yearly price increases as organizations scale operations also tilt the scale in favor of an on-premises deployment. Companies with stable and predictable workloads often find that owning and managing their infrastructure is more cost-effective in the long run, allowing them to reduce recurring expenses while maintaining operational control. ## **Play it Safe: Maintain Control over Your Enterprise Data** In summary, the trend of enterprise companies shifting away from SaaS solutions reflects a strategic response to the unique pressures and opportunities within regulated industries, which increasingly require that data never leave the control and oversight of the organization. More and more companies are making the trade-off of potential higher upfront investment to reap the benefits of increased security, control, customization and long-term cost savings that are associated with an on-prem deployment. By prioritizing data security, operational control, and cost efficiency in an on-premises environment, companies can better align their technology strategies with their business objectives. **Blog categories:** Financial Services, Healthcare --- ### [Ten Things You Can Do with a CDP](https://www.redpointglobal.com/blog/ten-things-you-can-do-with-a-cdp/) **Published:** February 6, 2024 **Author:** Redpoint Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Blog categories:** Customer Data Platform --- ### [Graphs, not Guts: Engender Trust in Your Customer Data](https://www.redpointglobal.com/blog/engender-trust-in-your-customer-data/) **Published:** March 28, 2024 **Author:** John Nash **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Blog categories:** Data Observability --- ### [Don’t Be Hit by the Bad Data Snowball Effect](https://www.redpointglobal.com/blog/bad-data-snowball-effect/) **Published:** April 8, 2024 **Author:** Steve Zisk **Content:** The snowball effect, as it pertains to the escalating consequences of bad data, is often understood from the standpoint of a starting and end point. Just as a tiny snowball quickly grows when given a push downhill, starting out with a small data problem becomes a big problem by the end of its run which, in the customer experience realm, translates to negative outcomes. While true, another way to think about the snowball effect is more analogous to the work of an avalanche forecaster who studies a snowpack, determining weak layers and assessing a propensity for failure. Bad data, in other words, can occur at every layer – the data layer, analytics layer and the orchestration layer. In this context, the snowball effect does not necessarily have a set starting point; failure at any layer puts the entire structure at risk. Stopping bad data in its tracks, then, requires superior data quality at every layer. A closed loop of perfected data makes the entire cycle resilient in the face of data problems. Rather than bad data feeding off itself, the opposite occurs; the consistent introduction of higher quality data leads to continually improving outcomes, taking away the runway for inferior data to progress. ## **Data Layer** Bad data at the data layer generally means that data is either missing, inaccurate, out of date, or tied to the wrong customer. The root of these problems is human imperfections: poor data entry, misunderstandings in defining or assembling data, and inconsistency or inattention in human interactions. Missing data happens for a variety of reasons. A brand may ask for data that seems irrelevant or too personal, or ask customers to fill out a form that is too long or doesn’t have the right input fields, resulting in customers entering incorrect information or leaving a field blank. This can include birthdates, zip codes, household income, etc. Similarly, a customer service rep or other agent can omit or forget to enter customer data during a hurried or noisy interaction. Inaccurate data can be the result of similar imperfect processes, with people mistyping, filling in false or inaccurate information, or accepting default values in forms. It can also be the result of mishearing or misunderstanding in a face-to-face or phone interaction. Inaccuracies are often compounded by use of external reference data that are low quality or out of date. Data that is not timely or not attached to the right customer via poor matching speaks to the negative consequences of failing to develop a Golden Record, a persistently updated unified customer record that a brand trusts will include everything there is to know about a customer. Depending on the use case, updates and aggregates may be needed in milliseconds. A lack of real-time updates or advanced identity resolution capabilities is just as consequential as missing or inaccurate data; the business can’t trust it, and its ability to make bold marketing decisions that depend on perfect data suffers. There are a host of things brands can do to ameliorate bad data issues at the data layer. Perfecting the [Golden Record](https://www.redpointglobal.com/single-customer-view/) is one, which would include steps to eliminate missing and inaccurate data. It might also include turning the traditional notion of data collection on its head by introducing [zero party data](https://www.redpointglobal.com/learn/zero-party-data) into the equation – data a customer willingly provides. Rather than observe a customer and deduce what’s important, a brand might instead have a direct conversation with a customer, making transparently clear why certain data is being requested, how it will be used, and what the customer might expect in return. This conversation can be an explicit value exchange, where a customer volunteers data about themselves in return for a personalized experience that improves over time. When a customer trusts that a brand will use personal data in accordance with the customer’s stated preference as well as provide a superior customer experience, the customer provides information that a brand might otherwise get wrong. Any interaction point – transactions, returns, social footprint, website behaviors, abandoned cart activities, etc. – has the potential to introduce missing or inaccurate data. By having a direct conversation, a brand takes steps to close the door to bad data. ## **Decisioning Layer** On the surface, it’s a little harder to recognize the cause and effect of bad data at the decisioning layer – where [analytics and machine learning](https://www.redpointglobal.com/machine-learning) play. That’s because even with some incorrect data, it may still be possible to build reasonably accurate predictive models. The predictive model’s intended use case will determine to a large degree the level of incorrect data that is tolerable. At the same time, if data is skewed because of how you’re collecting it – customers who balk at a retail associate asking for a zip code, and providing random digits – then a model built off skewed data will not fix the underlying issue. This is where the old “garbage in, garbage out” saying applies; customer ages, income brackets, recency of last transaction – any grouping where assumptions are made will introduce the possibility of an inaccurate understanding of the data and models that produce similarly inaccurate results. The other part of the equation as it concerns the decisioning layer is that if a model is dependent on a particular piece of data from a specific customer, or decisions are dependent on the same, then even an “accurate” predictive model does not necessarily make the best decision for an individual customer. A simple example is that collecting birthdates to understand the age ranges of your customers may be suitable for broad demographic models and segmentation purposes, but if 20 percent of your customers entered the wrong data, those customers are at risk of a poor customer experience if a brand is sending a birthday greeting on the wrong day. ## **Orchestration Layer** Already we see the beginning of the snowball effect from the first layer to the second layer, where a model may be incorrect because it’s based on data that has not been accurately collected and/or correlated with the right customer. The orchestration layer, though, is really where the figurative snowball either gains steam or is stopped in its tracks depending largely on how well a brand understands the customer. This layer is where the value exchange is proposed regarding offers and actions that either demonstrate a [deep, personal understanding](https://www.redpointglobal.com/next-best-action/) or, conversely, indicate an unfamiliarity as expressed by irrelevance. The latter, of course, is when customers who expect a personalized experience instead choose to opt-out, unsubscribe, or give their business to a brand that understands them. Irrelevance in interactions can occur because of bad data at either the data or decisioning layers, but also during orchestrated interactions. Consider, for example, a brand that truly knows everything there is to know about a customer and queues up a perfect offer for a product that precisely matches a customer’s size, color, and style preferences. If inventory is locked in a separate channel, the brand will introduce friction into the customer journey if the product that’s offered isn’t available. The same dynamic applies to product and/or service data, such as a financial services organization making an email offer for a credit card and then denying the card when a prospect fills out the application. ## **A Closed Data Feedback Loop** Beyond the orchestration layer’s impact on an individual customer journey, this layer is also where metrics such as customer lifetime value, churn, or predictions of customer sentiment may also go off track depending on the accuracy of the data layer. The offshoot is that campaigns will not yield the expected results. To avoid this pitfall, and also to avoid incorrect attribution and reporting (e.g., mistakenly attribute an uptick in sales to a specific campaign), it is important to accurately measure interaction results. A/B testing, correlation analysis, testing and optimization, basic attribution and other instrumentation tools are vital for an accurate understanding of interactions. Correlating results back to the data collected in earlier layers will then measure and close the loop for understanding which champaigns or interactions are succeeding, and which are not. Bi-directional feedback loops exist from each layer to the next. Setting up attribution within the orchestration layer feeds data back to the data layer. A/B testing is a feedback loop into the decisioning layer for understanding a model’s accuracy, and the decisioning layer can be used as feedback into the data layer for understanding how quality, trust, completeness, and timeliness vary over time. By thinking of the snowball effect as it impacts data at the data, decision and orchestration layers rather than in a linear sense, we see how the impacts of bad data do not merely affect the customer, but also marketers who are asked to trust the data. By knowing the traditional points of failure at each layer, marketers can more easily [gauge data’s trustworthiness](https://www.redpointglobal.com/data-observability/) at each stage and over time, playing the figurative role of the avalanche forecaster by continually assessing data and shoring up any points of weakness. A recognition of where and how bad data appears is a marketer’s best defense in preventing bad data from careening out of control. By stopping the snowball effect, marketers can trust and rely on perfect data, which is necessary to provide customers with the personalized experiences that they have come to expect. For more on the role of data quality in the Redpoint CDP, click [here](https://event.on24.com/wcc/r/4500079/D080F774F5F15DC1A8944F3D605DAD61/5265642?partnerref=rpblog) to join Redpoint VP of Product Management and Redpoint VP of Engineering Kris Tomes in the **“CDP Back to Basics” webinar series**. **Blog categories:** Data Quality, Master Data Management --- ### [Tunable, Transparent Identity Resolution Propels Personalized Customer Experiences](https://www.redpointglobal.com/blog/tunable-transparent-identity-resolution-propels-personalized-cx/) **Published:** April 15, 2024 **Author:** Steve Zisk **Content:** In *Song of Myself 51* Walt Whitman famously wrote “I am large, I contain multitudes.” In the nearly 200 years since, people have been analyzing its meaning. What multitudes did Whitman contain? Some say the phrase is a statement on human complexities and contradictions, that each of us is defined by a vast collection of experiences. We are not the same person from one day to the next. We meet new people. We like new and different things. We form new relationships, sever old ones. We move, change jobs and go through different life stages. Whitman of course lived before the age of the internet, mass media and the smartphone, so by his 19th century standards we carry an even greater number of multitudes, and have far more outlets for expressing ourselves. Trying to unravel a person’s complexities is the job of brands intent on delivering a personalized customer experience (CX), which is possible only if the brand knows something about a customer. It is the reason brands deploy customer data platforms (CDPs) to create a unified customer profile that is the foundation for profitability differentiating one customer from another through personalization. CDPs, like people, also contain multitudes in that one is different from the next. How a CDP handles identity resolution is a key difference that will ultimately determine the strength of the unified profile, i.e., the better the process of searching, analyzing and linking customer signals across disparate data sources, the better – and more trustworthy – the profile. ## **Types of Identity Resolution** Some CDP vendors offer identity resolution as a black box function, meaning the entire match, merge and identity stitching process is inflexible. Even if the process uses both deterministic and probabilistic matching, the issue for marketers and business users of the unified customer profile is that have no insight into why records – various signals – were or were not matched, merged or split. Still other CDP vendors might claim to offer identity resolution, but a close look reveals that they are referring to a simple deterministic matching process – using a common identifier such as email to link various devices to the same customer. And still other CDP vendors outsource identity resolution altogether, claiming their primary job is to collate customer data across various sources. In their telling, identity resolution is done by bouncing customer data off a third-party reference file, offering little more than a match in time. A major limitation with this method is that reference files change keys with every iteration, making it virtually impossible to gain an understanding of a customer over time, or to see how a customer proceeds through a customer journey. ## **Robust Identity Resolution: Transparency & Tunability** Accurate, dependable and trustworthy identity resolution, by contrast, should be completely transparent as to why a match, merge or split was made or not made. And rather than identity resolution as a black box service, tunable identity resolution allows marketers and users to adjust resolution at the individual and household levels across multiple cases, accurately mapping a customer’s personal and/or business relationships. As an example of how a marketer may want to control the details, consider a scenario in which multiple employees of a company share access to a purchasing portal. For B2B marketing, it may be necessary to interact with an employee at an individual level in some circumstances and as a shared account holder in others. Similarly, tighter identity resolution may be a requirement of a healthcare provider sending PHI, where a looser standard may suffice for sending general information about flu shot availability. Or, as resolution levels pertain to householding, consider a regional bank or financial services firm with an interest in determining the centers of influence when an account holder has multiple accounts, with multiple linked physical addresses. Understanding the dynamics of a household becomes important for many reasons, particularly with the potential sharing of sensitive financial information and knowing which products to offer. A retailer, too, will want to know which family member is responsible for a browsing session when using a shared home device. In a household with a mother, father and two college kids, is it the mom purchasing bedding and linens, and why is there a new shipping address? Which family member was on the website the day before looking at blenders? The use of probabilistic matching techniques will help determine household dynamics, but transparency and tunability are key components for providing marketers with confidence that their campaigns are targeting the right audience. If probabilistic matching determines that the mom is buying bedding for her daughter’s college dorm room to be shipped to that new address, a marketer will likely not be using that address to send offers, but may want to link it to the daughter’s unified profile for use in a different campaign. The intertwining of transparency and tunability are key to ensure that identity resolution levels may be adjusted to best optimize the intended business outcome, to protect PII/PHI or to ensure that – for any customer or household engagement – a marketer has the proper contextual understanding of the entity. For more on how the Redpoint CDP handles identity resolution, click [here](https://event.on24.com/wcc/r/4500079/D080F774F5F15DC1A8944F3D605DAD61/5265642?partnerref=rpblog) to join Redpoint VP of Product Management Beth Scagnoli and Redpoint VP of Engineering Kris Tomes in the **“CDP Back to Basics”** webinar series. **Blog categories:** Identity Resolution **Blog tags:** CDP --- ### [Dynamic, Rules-Based Audience Segmentation for the Win](https://www.redpointglobal.com/blog/dynamic-rules-based-audience-segmentation/) **Published:** April 22, 2024 **Author:** Steve Zisk **Content:** A previous blog on the evolution of [audience segmentation](https://www.redpointglobal.com/blog/segmentation-is-evolving-but-data-driven-insights-are-here-to-stay/) focused on the benefits of automated segmentation through machine learning, specifically how unsupervised models discover data correlations that reveal something important about an audience, beyond the capability of an operational marketer. Dynamic segmentation through AI and machine learning solves another challenge that marketers have when it comes to audience selection, which is that audiences – i.e., customers – change faster than marketers can keep up. Digital-first customer journeys are becoming more complex by the day, with customers randomly moving between digital and physical channels, yet still maintaining an expectation that the brands they engage with demonstrate a personal understanding. The challenge stems from the fact that a list-based approach to audience segmentation, a standard practice for many marketing organizations, does not keep pace with a dynamic, omnichannel customer journey. Selecting an audience through a static list may suffice for a “batch and blast” outbound campaign restricted to a single channel, but the list does not account for the fact that customers freely move between channels. It will also become static the moment a change occurs that would otherwise add or remove a customer from the list – the customer makes a purchase, signs up for a loyalty program, downloads a manual, etc. ## **Staying In Step with a Customer** A rules-based, dynamic approach to audience segmentation in the [Redpoint CDP](https://www.redpointglobal.com/cdp/), by contrast, by accounting for real-time pivots, guarantees that a brand keeps pace with a customer as the customer moves through a customer journey. Unbound by channel, rules-based machine learning finds audience similarities and tests segments on the fly. The algorithm trains on real-time data that is presented to the model, and the algorithm – not the marketer – dynamically selects the optimal audience at the time of selection, on demand, up to the last possible moment before campaign execution. It’s like a living, breathing model that changes in step with the customer. Because the model analyzes the composition of an audience on the fly, it becomes re-usable. Unlike static lists that decay as soon as they are created, a rules-based approach enables organizations to create a segment that can be universally applied across any engagement channel. Information is collected in real time, and new data about a customer might move the customer to a different channel. The customer who signs up for the loyalty program, for example, may no longer be qualified for the segment queued for an email offer, and is instead moved to a segment of high-value customers who receive a biweekly newsletter with a VIP redemption code. ## **Real-Time Relevance** Dynamic audience segmentation is a key feature that allows a company to look at customers in their entirety across the breadth of a customer journey vs. a view constrained by interactions on a single channel. In Redpoint, customers fall into and out of a segment based on their latest activities as they interact with the company, across all channels. It is a key distinction with a list-based approach, because it allows a brand to engage customers with up-to-the-moment relevance and deliver hyper-personalized experiences that reflect a customer’s precise customer journey status. Basing an audience segment on static customer profiles, by contrast, fails to provide the cross-channel insight required to keep pace with a customer on a typical omnichannel journey. Knowing how a customer behaves only on a single channel is of little help when a brand is reaching out on another channel. By segmenting audiences based on customer insights across many channels and touchpoints in real time allows a brand to use a customer’s behaviors in one channel to inform how the brand will engage with the customer on another channel. In this fashion, segments are created not by using mostly static parameters such as a customer’s age bracket, location or income, but far more nuanced (read: relevant) attributes as determined by a machine learning model. Think of your own experiences as a customer; when a brand reaches out to you only because you fall into a certain age demographic, does it feel impersonal? Your age may have little bearing on movement through an omnichannel customer journey. Dynamic audience segmentation in the Redpont CDP gets at the heart of what’s important to a customer at any given moment in time, allowing a brand to consistently serve up relevant interactions in real time and on any channel. For more on how the Redpoint CDP handles segmentation, click [here](https://event.on24.com/wcc/r/4500079/D080F774F5F15DC1A8944F3D605DAD61/5265642?partnerref=rpblog) to join Redpoint VP of Product Management Beth Scagnoli and Redpoint VP of Engineering Kris Tomes in the **“CDP Back to Basics”** webinar series. **Blog categories:** Segmentation & Activation --- ### [Why CX is the Right Rx for Healthcare](https://www.redpointglobal.com/blog/why-cx-is-the-right-rx-for-healthcare/) **Published:** January 15, 2021 **Author:** Sarah Lull **Content:** Healthcare consumerism is defined as the healthcare consumer increasingly being in control of the experiences that constitute a healthcare journey, empowered as an informed, knowledgeable stakeholder throughout a coordinated care path. The trend is largely the result of consumers being in control of their own health data through wearables, apps, telehealth visits and a general expectation that the consumer owns the relationship with other stakeholders. It’s a similar customer-centric mindset as in other industries, which recognize that the customer is in control of the journey throughout digital and physical channels. The elevation of the customer, or the healthcare consumer, is driven by an acceleration of digital-first experiences, as well as an expectation for a seamless, personalized experience at every touchpoint. In the Deloitte 2020 [Survey of Health Consumers](https://www2.deloitte.com/us/en/insights/industry/health-care/consumer-health-trends.html), research shows that consumers are becoming increasingly active and engaged in their healthcare, measured by such metrics as an increasing use of technology and apps to measure and maintain health, and a growing belief that using technology such as wearables helps drive positive behavior change. Research even saw a sharp increase in patients comfortable telling their doctors that they disagree with them – so they are now asserting their control. ## **A Differentiated Experience Starts with Data** Findings from the Deloitte survey are consistent with the healthcare consumerism movement. One offshoot of consumers taking more control of an individual healthcare journey is expectations for new types of engagement; now in control, consumers expect seamless, personalized experiences. In the Deloitte survey, asked to define the “ideal healthcare experience,” 44 percent of consumers said it was when a doctor or provider “listens to me and shows they care about me.” Other top responses were when a doctor spends time with them and doesn’t rush through a visit (42 percent), when a doctor explains every step during an exam and clearly explains follow-up care (39 percent) and when doctors *and other caregivers* communicate with one another and coordinate treatment (25 percent), showing that expectations for their doctor extend to the entire healthcare organization. Providing a personalized healthcare experience, and coordination between stakeholders requires having the right data. A provider must know everything there is to know about a patient. To become an invested partner in a consumer’s healthcare journey, a provider should have more than clinical data. In a pandemic, what is the patient’s tolerance for risk? What are the patient’s preferences as far as channels of engagement? What are the applicable social determinants of health? What does IoT data reveal about a chronic condition? To provide the personalized experience that a healthcare consumer expects, providers must have a consistent line of sight into everything that constitutes the healthcare journey; claims and clinical data, IoT and sensor data; preferences and behaviors. ## **Data in Exchange for a Personalized Experience** For the healthcare consumer, the expectation for a personalized experience comes with an understanding that they must share the data that makes personalization possible. In a Deloitte [Healthcare Consumer Response to COVID-19 Survey](https://www2.deloitte.com/us/en/insights/industry/health-care/consumer-health-trends.html), conducted in April, there was a sharp increase in the percentage of consumers willing to share personal health data with caregivers. The research showed that 60 percent of patients are willing to share their data with a personal provider, versus 53 percent in 2018. Likewise, there was a 6 percent increase in consumers willing to share health data with payers, and a 6 percent increase in those willing to share data with leading national healthcare providers. By sharing data, the healthcare consumer becomes even more in control of a healthcare journey, leading to an even more seamless, personalized experience – a win/win for the consumer and caregivers. ## **Prepare for Consumerism: Three Generations** Delivering personalized healthcare experiences is not an end in and of itself, but rather a tactic to ultimately drive superior outcomes – whether health outcomes or revenue outcomes (acquisition, retention). Leading healthcare organizations, to prepare for the growing trend toward healthcare consumerism, recognize that personalization of the healthcare experience is the default starting point toward achieving the desired outcomes. [Gartner frames this preparation](https://www.gartner.com/en/documents/3981326/the-evolution-of-healthcare-consumer-engagement-hub-arch) as three different generational capabilities of healthcare engagement. Each of the generations describe the levels of a healthcare organization’s preparedness for helping guide an active, engaged consumer through the healthcare journey. Generation One – where most healthcare organizations are today – is receiving consumer data and optimizing an engagement on a single business unit or process. Generation Two is providing the experience across the entire enterprise, such as an insurer leveraging the same data to optimize for acquiring Medicare members and closing care gaps – which may be two business units acting as one on behalf of the healthcare consumer. Generation Three refers to the entire healthcare ecosystem, breaking down traditional siloes between payers and providers that cloud a single view into the healthcare consumer and create a fragmented experience. To move from Generation One to Three lies in technology, specifically, as Gartner suggests, a Healthcare Consumer Engagement Hub that ties data and systems together, allows personalized, contextual engagement across all channels and between all business units and processes. In a follow-up blog, we will explore the three generations of personalized engagement in more detail, and show why data-driven healthcare organizations are choosing Redpoint as their healthcare consumer engagement hub of record to drive superior health and revenue outcomes. **Blog categories:** 1:1 Personalization, Data Management, Data Quality, Real-Time Personalization, Single Customer View --- ### [Go Deep: Why a First-Party Data Strategy Needs to Incorporate Depth](https://www.redpointglobal.com/blog/go-deep-why-a-first-party-data-strategy-needs-to-incorporate-depth/) **Published:** June 28, 2022 **Author:** John Nash **Content:** Whether it’s [diamonds from the deepest ocean](https://qz.com/1110789/debeers-and-namibias-government-are-mining-diamonds-buried-at-the-bottom-of-the-atlantic-ocean/) or a famous [Ansel Adams photograph](https://www.theguardian.com/artanddesign/gallery/2022/feb/17/ansel-adams-rare-photographs-in-stunning-hi-definition), depth provides a richness of context and value, real or perceived. As it concerns a first-party data strategy, depth is the difference between providing a hyper-personalized, relevant, and meaningful customer experience that is consistently in the cadence of a customer journey, or a fleeting, inconsequential experience that is quickly forgotten. Consider a recent [Dynata survey](https://www.redpointglobal.com/press-releases/74-percent-of-consumers-believe-brand-loyalty-is-about-feeling-understood-and-valued-not-discounts-and-loyalty-perks/) where 52 percent of consumers surveyed said that a brand makes them “feel understood” when they provide relevant product/service recommendations. Furthermore, 41 percent indicated that a brand expresses an understanding of them as a unique individual through sharing the right amount of information, and 37 percent said it is when a brand communicates on the channels a customer prefers. That depth of experience which resonates with customers is made possible only through a deep, rich contextual knitting of first-party data, which requires precise levels of data quality and identity resolution. Surface-level personalization is possible without it, but customers are keenly aware of the difference. ## **Perils of Surface-Level Personalization** With the use of [third-party cookies](https://www.mobilemarketingmagazine.com/its-the-end-of-an-era-for-third-party-cookies-heres-what-happens-next) as a proxy for customer understanding going by the wayside, there has been a renewed interest and focus on first-party data as the foundation of a CX strategy. Real-time website personalization illustrates why depth matters in a first-party data strategy. With basic demographic data and perhaps some recent behavioral data – recent as in that exact website session – a brand might provide what passes for personalization, showing one image or text field out of perhaps a choice of a half dozen or so. For many brands, such an action might constitute “personalization,” but showing a customer a certain image only because the customer clicked on a link to a specific product page can just as easily be done through the use of a third-party cookie, with the only real difference that the customer just happens to be on the brand’s own website. But, as the Dynata survey and other research shows, that surface-level personalization does not drive revenue. A short-tail record of a customer’s transactions and/or behaviors lacks the needed context that is derived from knowing the entirety of a customer’s behaviors not just over time, but across channels, and across all devices and identification proxies, known and unknown. ## **A Record’s Depth and Breath Provide Opportunity** A brand’s approach to an abandoned shopping cart illustrates the power of depth through a long-tail customer record that captures all behavioral and response data and hundreds of attributes across every channel, and having this record fully accessible in real time. With a full, contextual understanding of a customer, a brand will know with a far greater certainty a reason for the abandoned cart and will be able to craft a real-time response in the context of an individual customer’s buying journey. The brand will know, for example, what channel to engage the customer on, the appropriate waiting time, and the optimal frequency of contact. Is an immediate, real-time response the most effective way to re-engage every customer, for instance? Perhaps a next-best action might be to instead wait two days before sending an SMS, not with a reminder of the cart abandonment, but with an offer for a similar product the customer expressed interest in during a call with a call center agent. With this granularity, brands can also act on partially abandoned carts. By contrast, with only a short-tail record to help guide a customer journey, a brand’s response to an abandoned shopping cart might be limited to an automated, instantaneous “Don’t go yet!” email reminder. Again, a brand might consider this to be personalized – and in real-time, no less – but the approach has no connection to an individual customer journey or insight into a customer’s purchase intent. ## **Embrace the Art of the Possible** Depth may also be understood in the context of the history of a customer record – how far back a long-tail record goes. A retailer needs flexibility here: they might want a complete record to include every lifetime transaction or website behavior to improve the customer’s online experience, but may act on aggregate data and/or only more recent data for a call center agent. The point is, an unwavering focus on first-party data creates unlimited possibilities for enriching a unified customer record to help a brand guide an individual customer journey to its logical, optimal conclusion. In whichever direction a customer proceeds, the deeper the record the more a brand is positioned to move with the customer. Orchestrating real-time decisions and an omnichannel customer experience around a long-tail record exponentially increases the art of the possible, and all with first-party data. The Redpoint golden record is the pulse of Redpoint customer data technology that our data-driven clients use to animate omnichannel experiences tailored to an individual customer. For more on how Redpoint can help your organization capitalize on the “bottomless” opportunity afforded by rg1 in the orchestration of real-time experiences at scale using only first-party customer data, [click here](https://www.redpointglobal.com/request-demo/?utm_source=website&utm_medium=footer). **Blog categories:** Identity Resolution --- ### [Top 5 Questions a Multi-Brand Retailer Needs to Ask about a CDP](https://www.redpointglobal.com/blog/top-5-questions-a-multi-brand-multi-region-retailer-needs-to-ask-about-a-cdp/) **Published:** April 22, 2024 **Author:** Thomas Kaczmarek **Content:** Multi-brand retailers have unique data and marketing complexities that not every customer data platform (CDP) can handle. Factors such as overlapping customers, multiple regulatory requirements and complex MarTech stacks complicate the chances for a successful enterprise CDP. Here, then, are the Top 5 questions a multi-brand retailer should ask when contemplating an Enterprise Customer Data Platform to ignite their customer data and deliver a transformative customer experience (CX). ### *1. Does the CDP support the unification of data from multiple brands?* Another way to phrase this question is to ask if a CDP is enterprise-ready? A multi-brand company will typically have multiple profiles for a single individual or entity, with data for that entity residing in multiple databases, marketing clouds and other marketing technology. An enterprise-ready CDP will integrate data from every source into a unified profile, or [Golden Record](https://www.redpointglobal.com/single-customer-view/), that includes all customer attributes and IDs combined with transactions, behaviors, preferences, permissions and data aggregates that provide an accurate, real-time representation of a customer. As a result, marketing teams truly can deploy data best practices such as evaluating customer lifetime value (CLV) of a given customer by brand, or across the enterprise all from a single point of control in real time. This is a key factor for a multi-brand to deliver cross-sell or upsell experiences at scale, often a key objective for a conglomerate with customers interacting with several brands in a portfolio. Some CDP’s are restricted in that they either do not integrate with various components of a MarTech stack, such as multiple marketing clouds for instance, or what they call a unified profile is little more than a basic match of customer data. Advanced [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) and robust [data quality](https://www.redpointglobal.com/customer-data-management/data-quality/) are required to form an accurate unified profile that will unlock cross-sell opportunities by letting a brand know everything there is to know about a customer, across multiple brands and multiple channels. ### *2. Does the CDP support the parent company’s strategic vision?* One of the big advantages of an enterprise-ready CDP that unifies all disparate customer data is that when used with [Orchestration](https://www.redpointglobal.com/orchestration/) and [Real-Time Interactions](https://www.redpointglobal.com/real-time-interactions/) it provides organizations with a single point of operational control over all customer data. That is, with real-time decisioning and the intelligent orchestration of next-best actions that are unbound by channel, every application and user works with the same unified profile. For a multi-brand retailer, a single point of control is invaluable because it helps align various brands that may have competing interests for how a customer proceeds through a customer journey. A value retail brand, for example, might target a customer with a high likelihood to churn with a certain offer, where a luxury brand may identify an opportunity for acquisition based on the customer’s search behavior. A single point of control allows a company to align metrics around a single overarching objective. This helps mitigate one of the bigger fears or challenges of merging data, the cannibalization of customers, an all-too common occurrence when multiple brands under one banner remain locked in competing strategies between multiple marketing departments. ### *3. How does the CDP handle data privacy and permission-based marketing?* Customers expect the brands they interact with to be transparent about how they collect and use the data a customer provides. The expectation includes using data to enhance the overall customer experience (CX), and not in a way that violates trust, i.e. selling it to a third-party without consent. Different regulations governing data privacy in the U.S. and Europe, for instance, increase complexity for a global company. Honoring customer preferences to the letter, both for how their data is used and their permissions dictating opt-ins and other preferences, requires that a CDP incorporate [preference management](https://www.redpointglobal.com/for-a-superior-cx-elevate-preference-consent-management/) into a Golden Record. By integrating a preference center into a CDP and incorporating preferences and consent into a Golden Record, a multi-brand retailer is able to interact with a customer across brands according to an individual customer’s preferences across the brand portfolio. ### *4. How does the CDP handle segmentation?* The simple calculation is that more brands – and more countries – translates to more customer data, and hence more testing. If your Golden Record contains potentially upward of 1,500 customer attributes for example (as one Redpoint retail customer’s does), discovering commonalities across your customer base spanning multiple brands necessarily becomes the job of machine learning. One of the key benefits of the [Redpoint CDP](https://www.redpointglobal.com/cdp/) is rules-based, dynamic [audience segmentation](https://www.redpointglobal.com/segmentation-activation/) that tests models on the fly, using real-time customer data that accounts for changing customer behaviors. This is an important feature for global multi-brand companies with the incentive to discover the potential synergies between customers who transact with multiple brands. What do customers of Brand A have in common with customers from Brand B, and what is a next-best action for those customers who also have shown an indication to churn, as an example. ### *5. What is the CDP’s approach to data quality and identity resolution?* We mentioned identity resolution and data quality at the outset as important steps in data unification, but it’s worth expanding on the Redpoint approach, where data quality steps are completed immediately upon data ingestion. This is a continuous process, where cleansing, matching, validation and data governance are taken care of in real time, eliminating the latency that too often derails attempts to deliver a relevant CX in the cadence of a customer journey. Conversely, many CDPs outsource data quality to a third party, bouncing customer data off a reference file that may be days, weeks or months old and where a new key is created for every match. The Redpoint CDP solves for this problem with the use of [persistent keys](https://www.redpointglobal.com/customer-data-management/key-management/). As data quality processes are completed, persistent keys enable matched data to be assigned to a unique ID, providing a multi-brand organization with a longitudinal view of a customer over time. This, in turn, provides a Golden Record with depth and context, of particular importance when a multi-brand company wants to analyze a customer’s behaviors and interactions with its brand portfolio over time. To summarize, not every CDP offers the same features and capabilities. For a multi-brand, global enterprise, some capabilities outweigh others in terms of being able to manage customer data across an extensive portfolio of brands and intelligently orchestrate personalized next-best actions, at scale, across various brands. For more on how Redpoint helps global retailers meet every customer with highly personalized experiences, [click here](https://www.redpointglobal.com/retail/). **Blog categories:** Data Quality, Identity Resolution, Retail --- ### [2025 Healthcare Trends: How Personalization is Reshaping the Patient Experience](https://www.redpointglobal.com/blog/2025-healthcare-trends-how-personalization-is-reshaping-the-patient-experience/) **Published:** January 15, 2025 **Author:** John Nash **Content:** As healthcare continues to evolve, providers face a critical crossroads in how they deliver care, engage patients, and navigate shifting industry dynamics. Trends such as value-based care (VBC), increased outpatient services, and advances in AI technology are reshaping patient expectations and the patient-provider relationship. At the same time, the rise in behavioral health and chronic care management needs present new challenges – and opportunities – for health systems. What does this mean for healthcare providers in 2025? The common denominator and key to success is based on delivering personalized, patient-centric experiences that address evolving demands while balancing cost, quality, and security. This article explores five key trends that will shape healthcare providers’ strategies in the coming year and outlines how they can prepare to thrive in a rapidly changing landscape. ## **1. With an Increase in VBC Models, Providers will Need a More Holistic, Comprehensive View of Patients** Over the past few years there has been a big shift in the patient-provider relationship due to an increasing number of patients enrolled in value-based care (VBC) models that reward physicians on quality of care and improved long-term outcomes, vs. a traditional fee-for-service compensation model. According to McKinsey, by 2027 there will be [90 million patients in VBC models, ](https://www.mckinsey.com/industries/healthcare/our-insights/what-to-expect-in-us-healthcare-in-2024-and-beyond?stcr=FEFACC03D40F492EBD8401B5DCEEB6C3&cid=other-eml-alt-mip-mck&hlkid=2d8461c861f644cdb13cae55b6af9283&hctky=14669808&hdpid=edd3998f-a0c6-45fa-86c6-b0cd71574e4d)more than double the number in 2022. When healthcare systems make the gradual shift to a VBC model, providers have more of a vested interest in a patient’s overall health, which changes the dynamic of the patient-provider relationship. The goal of VBC is to promote integrated care, delivering high-quality care services and optimizing costs using a patient-centered approach. It involves care teams working together to address all health needs, including mental health and social determinants, to tailor an individualized care plan and provide continuous monitoring, education, and support, which entails open lines of personalized and consistent communication. To accomplish this goal, providers need to have a deep understanding of a patient and a patient’s ongoing needs. For instance, in a VBC model, health systems can adopt a holistic approach to managing Type 2 diabetes, focusing on patient outcomes and cost reduction. A multidisciplinary team, including physicians, dietitians, and diabetes educators, would collaborate to create personalized care plans. These plans feature continuous glucose monitoring for real-time data, comprehensive patient education to empower self-management, and coordinated care to address comorbidities. Behavioral health support would also be provided to manage the psychological impact of diabetes. Additionally, providers can connect patients with community resources, such as local gyms and nutrition programs, and address social determinants of health (SDoH) by ensuring access to affordable medications and healthy food. This integrated approach not only improves diabetes management but also enhances overall quality of life, reduces emergency care needs, and lowers long-term healthcare costs. To provide such comprehensive care and support requires a deep patient understanding. As the trend toward value-based care grows, more health systems will prioritize the collection of patient data to deliver a personalized healthcare experience that is a hallmark of the value-based care movement. ## **2. A Shift to Outpatient Care will Heighten the Need for Dynamic Patient Journey Personalization** There are currently more than 14,000 urgent care centers in the United States, with the growth rate for new centers hovering at about 7 percent, according to the [Urgent Care Association](https://urgentcareassociation.org/wp-content/uploads/2023-Urgent-Care-Industry-White-Paper.pdf), which studied their growing influence on the post-Covid healthcare ecosystem. Furthermore, the association estimates that urgent care facilities prevent approximately [24.5 million](https://urgentcareassociation.org/wp-content/uploads/2023-Urgent-Care-Industry-White-Paper.pdf) emergency room visits annually. Additionally, outpatient care centers provide an opportunity, through comprehensive patient understanding and personalized follow-up, to drive acquisition for the health system they are affiliated with. Once a patient accesses care – an insect bite, a sinus infection, a UTI, etc. – an urgent care center will only know it has prevented the patient from an eventual visit to the ER through follow-up. A call, an email, or a survey let a clinic know how the patient responded to the care, the patient’s level of satisfaction and intent to visit the facility for future urgent care needs. As with VBC, the more a health system knows about the patient, the better care it can provide, both at point of care and long-term. Did the patient visit urgent care because of convenience? Was it a scheduling issue with a PCP? Does the patient have a PCP? Does the patient have any underlying and potentially unaddressed health conditions? The decentralization of care is driving health systems to focus on gaining a deeper understanding of their patients, not only to ensure continuity of care but also to maintain financial sustainability in an increasingly competitive landscape. Unified and actionable medical and behavioral data enable urgent care centers to execute targeted marketing campaigns and serve as vital entry points for engaging new patients. Simultaneously, hospitals can leverage affiliated specialized ambulatory centers for cross-promotion and referrals, reducing the risk of patient leakage. By adopting this integrated approach, health systems can deliver more personalized, closed-loop care journeys, setting themselves apart from competitors and fostering long-term patient loyalty. ## **3. Behavioral Health and Chronic Care Management Will Change the Patient-Provider Dynamic** One trend shaping the healthcare economy is the upswing in patients seeking behavioral health and chronic care services. Roughly [25 percent](https://www.trillianthealth.com/hubfs/TH_Annual%20Report_2023.10.25%20(2).pdf?hsCtaTracking=5d012254-6902-4aa3-9ff8-a01abc8d5738%7Cab894e50-dd03-428f-84b2-27361c687a84) of the U.S. population is expected to utilize behavioral health services by 2027, while about 90 percent of the nation’s $4.5 trillion in annual health care expenditures are for people with chronic and mental health conditions. Consistent long-term patient engagement is vital to managing both behavioral health and chronic conditions, especially if they involve one or more comorbidities. To make a positive impact on patient outcomes, providers need to understand the patient beyond their medical record. Only then are they able to build personalized open lines of communication to improve engagement and follow-up. In the case of behavioral health, for example, a medication regimen might undergo continual change based on many different factors including side effects, lifestyle changes, life stage, job change, sleep patterns, etc. Knowing everything there is to know about a patient – how engaged the patient is with their care, health risk, preferred methods of communication, motivations, SDoH, etc. – helps providers personalize the healthcare experience, leading to increased engagement, better adherence and improved outcomes. ## **4. AI Investment in Healthcare Will Continue to Experience Significant Growth** According to [Management Group Medical Associates](https://www.mgma.com/mgma-stat/ambient-technologys-role-in-the-ai-revolution) (MGMA), AI investments in healthcare are expected to surge to $150 billion over the next five years (from $20 billion in 2024). Enhanced “patient and member engagement and experience” ranked second in anticipated generative AI (GenAI) healthcare use cases, just behind clinical productivity and just ahead of administrative efficiency. Patient communication tools – digital front door, conversational AI – highlighted the member engagement use case as examples GenAI having the potential to re-shape the patient-provider relationship. Other use cases for AI in healthcare include using machine learning to create personalized care plans as well as personalized patient outreach and follow-up. For each use case, a provider must have a unified patient profile that includes all relevant patient data. A detailed and up-to-date medical history, yes, but also patient preferences and behaviors, clinical and claims data, SDoH, etc. A real time patient profile with clean, high-quality data will ensure that AI is training on the right dataset and will then produce the most relevant patient experience, whether a personalized plan, a chatbot interaction, or answering a patient’s questions through a LLM. ## **5. Health Systems Will Continue to Prioritize Data Security** According to the 2024 [HIPAA Journal Healthcare Data Breach Report](https://www.hipaajournal.com/h1-2024-healthcare-data-breach-report/), healthcare ransomware attacks went up 278 percent between 2018-2023, with hacking-related incidents up 239 percent in the same timeframe, causing massive disruption for the affected providers. In one major breach, the amount of data stolen in one attack on a healthcare organization potentially put the PHI of 110 million Americans at risk. To safeguard against data breaches, 2025 will see more healthcare systems be mindful of how they collect and use patient data, for marketing and other business purposes. For an increasing number of healthcare organizations, keeping patient data – particularly PHI – behind the organization’s own firewall is a prerequisite. Because of this, more healthcare systems will keep their technology infrastructure on-premises or use a private cloud where patient data – whether in an EHR or a marketing platform – will remain in place using a modern data cloud. Data quality and personalization will be the cornerstone of success for health systems navigating the healthcare complexities of 2025. Whether adapting to value-based care models, leveraging AI for patient engagement, or prioritizing data security, the ability to deliver tailored, patient-centric experiences will define the leaders of tomorrow. By embracing these trends and focusing on a holistic understanding of patient needs, providers can not only improve outcomes but also build stronger, more trusted relationships with the patients they serve. The organizations that invest in data readiness and personalization today will be the ones shaping the future of healthcare. **Blog categories:** Healthcare --- ### [What is Data Observability?](https://www.redpointglobal.com/blog/what-is-data-observability/) **Published:** May 22, 2024 **Author:** Beth Scagnoli **Content:** What is Data Observability? Gartner, Forrester and the CDP Institute all describe [Data Observability](https://www.redpointglobal.com/data-observability/) in some shape or form as the continuous monitoring of data quality and data pipelines to ensure data is reliable, trustworthy and compliant with regulations. Marketers and business users should care about Data Observability for several reasons. Greater transparency through the continual monitoring of [data quality](https://www.redpointglobal.com/data-quality-and-data-ingestion/) and data pipelines improves trust in customer data, helps reduce the negative impact of data issues and allows for proactive data management. The validity of data has always been of interest to marketers. One reason Data Observability is gaining traction is that it is easier for marketers to monitor the health of data than it used to be. Traditionally, marketers sought the help of IT or data analysts in vetting customer data, a process that took time away from being able to execute campaigns with the confidence in the underlying data. Even today, the default protocol for many operational marketers is to summon IT. In doing so, they are making the calculation that having the utmost confidence in the health of their data is worth the delay. Time, though, is a luxury marketers no longer have when it comes to keeping pace with the always-on, connected consumer. A dynamic customer journey across multiple online and offline channels is fast and unpredictable. Meeting the right customer with the right message on the right channel and at the right time is a delicate operation that requires accurate, timely data that is fit for its intended business purpose. Marketers who accept a delay, however short, in running a campaign risk alienating customers with irrelevant or untimely messages, offers or content. ## **Data Observability: Key to Confidence** Data Observability allows marketers to confidently make decisions about how to proceed in their day-to-day activities. Data issues – as any marketer can attest – can and often do happen; Data Observability is what allows marketers to head those issues off at the pass before they become bigger issues, or before they negatively impact customer experience. Data Observability is like being able to see the flow of water from source to destination. If the only time you notice a problem is when you see murky, brown water coming out of your tap, you will likely put a plumber on speed dial and shut everything down until however long it takes a plumber to diagnose and fix the problem. But if you could continually check the pipes and the water flowing through it, perhaps you detect the issue before it flows to the tap, make a quick repair yourself and not skip a beat. By avoiding having to call the plumber – the homeowner version of calling on a data analyst team or building out SQL code – marketers not only save valuable time, they also begin to learn about data – where and why are the common issues occurring, is there one feed in particular that’s causing a problem? Is another feed always late? Does another return wildly inconsistent numbers? By providing access to data pipelines, Data Observability not only vets the validity of data, it allows marketers to gain trust and confidence in the actual data feeds; this one has never had an issue, I see this one is always on time, etc. ## **Data Observability in the Redpoint CDP** With an understanding of what Data Observability is, how exactly does transparency and visibility manifest itself? In the case of the [Redpoint CDP,](https://www.redpointglobal.com/cdp/) a single UI provides graphical visualizations that allow marketers to detect patterns, discover issues and validate the health of customer data as it moves through the CDP. Coding is never needed. Redpoint provides a simple visual representation of different aspects of data quality. (**See Figure 1)** ![Data Observability (5 8)](https://www.redpointglobal.com/wp-content/uploads/2024/05/Data-Observability-5-8-800x397.png) **FIGURE 1** – *A Data Observability dashboard in the Redpoint CDP showing feed state and status and the quality of a data match of a CRM system.* In another Data Observability dashboard **(See Figure 2**) marketers can dive into the data feeds, checking the status, the number of records, whether there are duplicates, and easily gauge the validity, timeliness and completeness of each feed. If there is an issue, marketers can then drill down further to investigate the cause. These are simple visual representations that would normally require marketers and business users to have access to the database, pull data, write SQL. ![Data Observability (2) 5 8](https://www.redpointglobal.com/wp-content/uploads/2024/05/Data-Observability-2-5-8-800x396.png) **FIGURE 2** – A Data Observability dashboard in the Redpoint CDP showing the validity, timeliness and completeness of data feeds. Having continuous daily access to data in a Data Observability dashboard also provides an instance history. If there is, say, an unusual number of customer records in a particular feed, it’s simple to check whether or not there is an actual anomaly. In the types of instances that might not ring an alarm bell, it’s doubtful that a marketer would ever ask IT to look into the matter. But with easy access to the daily ebb and flow of a particular feed and how it flows into the rest of the CDP, Data Observability dashboards in the Redpoint CDP give marketers the ability to ask questions of data that might otherwise be ignored. ## **Does Your CDP Offer Data Observability?** Data Observability is not a replacement for performing data quality, data enrichment and identity resolution as soon as data enters the system, all of which the Redpoint CDP takes care of as core functionality. Because many CDPs outsource those key functions, they are unable to provide details about the health of data that flows through the system. How can they provide an updated data hygiene report (without writing a line of code) if data quality steps are offloaded to a third party, or completed somewhere downstream? The answer is – they can’t. Completing all data quality processes and creating and updating a Golden Record as data is ingested is a differentiating feature of the Redpoint CDP. Data Observability provides marketers with a simple way to validate that data are performing as expected as these processes occur. Great marketing campaigns start with great data. With Redpoint, marketers know in advance that all systems are go. **Blog categories:** Data Observability **Blog tags:** Data Observability --- ### [Boost Your Marketing Automation Platform with Clean Data](https://www.redpointglobal.com/blog/boost-your-marketing-automation-platform-with-clean-data/) **Published:** June 10, 2024 **Author:** Renee Graff **Content:** Streamlining your customer engagement process across multiple touchpoints often includes a heavy investment in one (or more) marketing automation platforms (MAPs). Once the platform is installed, your next steps are to get your engagement plans off the ground and running and show value for the MAP investment. Many teams view the MAP as the solution to their marketing communication and engagement needs. Yet while organizations should see some gains soon after a solid implementation, there’s so much more you *could unlock* by addressing your data quality before it flows into your MAP for activation. To maximize the value of a marketing automation platform and produce optimal results while controlling costs, you must give data quality its due. High-quality, trusted data is essential to ensure your automations are targeting the right customer at the right time with the right message, offer or communication. Streamlining and expanding your customer engagement processes and capabilities is vitally important, but the amplification powers of your new MAP could amplify your problems if you end up delivering an irrelevant message or communication to *even* *more* customers than you were previously. Whether you’re using Eloqua, HubSpot, Adobe, Salesforce or another platform, including data quality as core functionality of upstream tech solutions can guarantee you make the most of your MarTech stack. That’s where the CDP comes in. ## **Better Data, Better Results** A [good CDP](https://www.redpointglobal.com/cdp/) should clean your data as it comes in, as well as when it gets activated. Yet many CDPs will ingest data from all your connected sources only to put off data “cleansing” until it’s being extracted for use in a campaign. Others simply consider it the responsibility of a third-party somewhere else in your tech stack, essentially assuming it will “all go to plan.” The latter approach – performing data hygiene at end-point extraction and leaving it to another provider – often leads to additive costs and inefficiencies down the entire line of activation, including in your MAP. If you’ve invested in a robust marketing automation platform, but you can’t fully trust the data and segments that you’re feeding into it, you’re not getting the full value out of your MAP purchase. If you have a nagging suspicion that your results aren’t quite what they could be, or if you’re not seeing much of a material improvement over your previous manual campaigns, you need to check your [data quality.](https://www.redpointglobal.com/resources/automated-data-ingestion-and-data-quality/) That translates to wasted budget and resources, “okay” campaign results, and potentially increased executive scrutiny on future procurement asks. It’s imperative to make sure your automation efforts produce tangible results while reducing costs, whether your organization is focused on B2B, B2C, or a blend of both. ## **The Positive Impact of Good Data** By leveraging advanced data quality, [identity resolution](https://www.redpointglobal.com/resources/unification-and-identity-resolution/), and segmentation capabilities, your organization can: - Eliminate redundancies and inefficiencies in email, text and other campaigns, leading to cost savings. For example, if you’re pushing customer records into your MAP that include individuals that haven’t given you marketing permissions, you might be eating up far more of your MAP’s data storage allowance than is necessary, since those ultimately wouldn’t be used. - Deliver personalized experiences that drive higher engagement and satisfaction. Consider the up-sell potential when your differentiated messages reflect one person’s interest in running compared to their spouse’s preference for mountain biking. - Achieve better campaign results through targeted, contextually relevant and timely communications. If your bounce and click-through rates haven’t improved compared to when your process was more manual, better data coming into the MAP makes your messages more likely to land on target, both in terms of the correct inboxes and the message content. ## **A “Clean Data” Checklist** Pristine data that is reliable and always accessible is fundamental for the performance of your marketing campaigns. Look for the following capabilities to make sure your data is optimized for the best outputs across all your MAP-related use cases (and beyond). The Redpoint CDP includes them, but no matter which vendor you ultimately choose, these are “must-haves” in terms of your data quality. Precise Identity Resolution: Blending deterministic and probabilistic matching yields the most precise identity resolution. Deterministic matching is the more basic of the two, identifying a user across devices using a common identifier such as an email address or device ID. Probabilistic matching goes a step further and uses an algorithm to analyze two similar but not identical records, such as recognizing 123 Main St. and 123 Main Street are the same address and parsing out the spouses that both use shared devices at that location. You’ll be able to understand not just the individual customer but also their context, such as whether they’re engaging with your brand as a member of an organization, as an individual, or within a household in a B2C scenario. Such contextual understanding is pivotal for accurately crafting personalized interactions and targeted campaigns within your MAP. Privacy and Security: Upholding customer privacy and complying with regulatory standards such as HIPAA, GDPR and CCPA is non-negotiable. Seek out a CDP that integrates robust privacy and security measures, including data encryption, PII protection and accurate identity resolution. By respecting customer preferences and regulatory requirements, you build trust and credibility while mitigating risks. Additionally, consider a data-in-place deployment option in which the CDP runs directly in your data cloud environment without data replication, enabling your organization to maintain control of your customer data in a seamless integration with your marketing automation platform. Dynamic Segmentation: While traditional segmentation methods are static, [dynamic segmentation](https://www.redpointglobal.com/resources/segmentation-and-activation/) allows you to build a segment once and use it everywhere, depending on how a customer engages throughout a customer journey. By leveraging rules-based, no-code segmentation tools, marketers can adjust targeting on the fly, responding in real time to customer behaviors and preferences. This agility ensures that your MAP campaigns remain relevant and impactful, driving higher engagement and conversions. ## **Clean Data + Marketing Automation Platform Success Stories** Clean data is a force multiplier for your marketing automation platform, enabling tangible benefits in terms of cost reduction and performance enhancement. Several organizations have already experienced the transformative power of pristine data through the Redpoint CDP. - A leading [travel and hospitality provider](https://www.redpointglobal.com/wp-content/uploads/2024/04/Xanterra-Triple-Digit-Performance-Improvements-Case-Study.pdf#new_tab) leveraged the Redpoint CDP to streamline email-based marketing campaigns, resulting in a 40 percent reduction in interaction costs and more personalized engagement. - A [prominent retailer](https://www.redpointglobal.com/wp-content/uploads/2024/03/National-Retailer-Achieves-Single-View-of-the-Customer.pdf) achieved remarkable results by deploying Redpoint as the backbone of their marketing automation strategy. With Redpoint powering omnichannel orchestration and real-time decisioning, the retailer significantly increased upsell through real-time, next-best action offerings. - One of the largest health systems in the US, with 1 billion+ page visits​, used the Redpoint CDP for breast cancer and other disease awareness to deliver targeted personalized messages for real time customer engagement. As a result, thousands of visitors signed up for their breast cancer email program. If you’re not sure you’re getting the most value out of your marketing automation platform and you’re looking to optimize your campaigns and unlock new levels of efficiency and effectiveness, the Redpoint CDP can help take you there. With advanced capabilities in data quality, identity resolution and segmentation, Redpoint supercharges your MarTech capabilities to deliver personalized, impactful customer experiences while driving cost savings. To learn more about how you can elevate your marketing efforts and ensure success in today’s dynamic landscape with Redpoint as your strategic partner, click [here](https://www.redpointglobal.com/request-demo/). **Blog categories:** Data Quality **Blog tags:** CDP, Data quality --- ### [Ensconced in Elegance: Step into a Luxury Hotel Stay with a Personalized Guest Experience](https://www.redpointglobal.com/blog/ensconced-in-elegance-step-into-a-luxury-hotel-stay-with-a-personalized-guest-experience/) **Published:** August 21, 2024 **Author:** Mike Ferguson **Content:** When you step into a luxury hotel, perhaps for a destination vacation or long-awaited getaway, you arrive with an expectation for a personalized, memorable, even life-changing experience. Whether your stay is at a wellness retreat, a beach resort, a golf destination, a safari lodge or an exotic location anywhere around the world, you know that hotel staff will treat you like royalty and cater to your every need – making your satisfaction their top priority. A luxury experience, from pre-stay through post-stay, provides a true “home away from home” atmosphere where a guest’s needs are anticipated before they’re expressed. Luxury hotel brands compete on providing this type of exemplary experience. Done well, guests are often awestruck by how well everyone on staff meets their needs with a pitch-perfect experience. An exclusive private tour offering, a dinner recommendation, a secluded cabana that has been pre-reserved. Every experience is timed perfectly, strikes the right chord and enhances the guest experience without a hint of being overbearing. ## **Luxury Starts with Data** What separates even an excellent experience from one that is truly exemplary is data. That is, to ensconce each guest in the lap of luxury, brands need to know everything there is to know about a guest, and that knowledge comes from data. To anticipate a guest’s needs, a hotel must know the guest’s preferences and behaviors in addition to a complete transaction and booking history. Collecting, aggregating and augmenting first-party data into a single view of the guest across all touchpoints, interactions and channels is foundational for providing a guest with a consistent, relevant experience that is independent of which channel the guest engages in (online or offline) or even which property the guest visits in a luxury brand’s portfolio. With an updated single view of the guest that includes all behaviors, preferences, transactions and both an identity graph and a contact graph, a luxury hotel brand is primed to leverage this knowledge to provide a consistent, superior experience. ## **Unlock a Superior Guest Experience with a Golden Record** When a single view, also known as a [Golden Record](https://www.redpointglobal.com/blog/use-a-golden-record-to-enhance-customer-experience-cx/), is accessible across an organization, everyone who interacts with a guest is working with the identical profile – whether online or offline. A concierge and a reservation agent, a restaurant manager and a website personalization marketer, an events manager and a chatbot – all have the same view of how a customer engages with the brand, in real time. This means that when a guest arrives at a property – whether it’s the guest’s first or 100th stay – the guest not only receives an experience that is perfectly tailored to their individual tastes, but one that is consistent across all interactions. A concierge proactively caters to the guest’s every whim. Housekeeping knows to set the room temperature at 72 degrees. The luxury automobile that will chauffer the guest to her private museum tour is her preferred make and model. ## **An Impeccable Golden Record and Data Quality** Many luxury hotel brands have trouble exceeding the expectations of their guests because of the limitations of their customer engagement technology. Specifically, they rely on engagement platforms or solutions that often fail to give data quality its due. Some do not store customer data. Others do not build a Golden Record, instead sitting on top of another vendor’s unified profile and performing some basic ETL for downstream activation. Others perhaps perform deterministic exact matching of disparate records or basic de-duplication and call it a day. Along with having data that is not made ready for business use, the siloing of relevant data continues to be a key problem. Performing data quality processes the moment data is ingested from any source and type of data – first-party, second-party, third-party, batch, streaming, structured, unstructured, semi-structured – is essential to build an accurate, trustworthy Golden Record. Data that is cleansed, enriched and fit for business use the moment it enters the system is needed to ensure that the Golden Record accurately reflects a guest’s journey with the brand in real time. The use of [persistent keys](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) in building a Golden Record is another important element, providing brands with a consistent view of a guest over time despite changing circumstances such as a new device, a new physical or email address, etc. Furthermore, persistent keys are an important component for how an enterprise-grade customer data platform (CDP) approaches householding within the identity resolution process. ## **Householding is Important for Understanding Contextual Relationships** Householding is the concept of understanding a guest or a customer’s contextual relationships. For a luxury hotel, it could simply mean being able to determine with a high degree of accuracy that when a device is used to book a room, whether the actual guest booked the room or whether someone booked the room on the guest’s behalf. Perhaps the guest’s husband used his wife’s personal computer. Or maybe it was a business associate, a personal assistant or an agent. A Golden Record that is compiled using householding as part of the identity resolution process will include a range of signals that link the device usage to one unified profile over another. A device may be linked to different profiles, and householding is the process that provides that contextual understanding. For a practical example, imagine a famous footballer arrives for a reservation at an exclusive seaside resort on the Amalfi Coast. Because the Golden Record includes preferences and behaviors, all staff – reception, concierge, maître ď, wait staff, etc. – know the player values discretion and wants to be addressed as “Sir” (or perhaps by an alias) rather than by name, lest it be overheard. Moreover, because householding has been used in resolving a guest’s identity in the creation of the Golden Record, the staff knows that the famous footballer is the arriving guest even though the athlete’s business manager is the person who booked the stay. ## **Meet Guest Expectations with a Customer Data Platform** The Golden Record is foundational for treating every guest interaction, every moment, as an opportunity for a luxury hotel to further enhance the guest experience. Every luxury hotel aspires to provide an exalted guest experience, and traditionally a top-flight experience is showcased through a highly trained, professional staff as well as unparalleled amenities. An enterprise-grade CDP that provides a deep, contextual understanding of each guest helps luxury hotel brands take personalized experiences to the next level, providing guests with the royal treatment that they expect every time they interact with the brand. To see how Redpoint can help travel and hospitality companies connect customer data and deliver personalized experiences before, during and post trip with [the Redpoint CDP](https://www.redpointglobal.com/cdp/), click [here](https://www.redpointglobal.com/travel-hospitality/). **Blog categories:** Travel & Hospitality **Blog tags:** Golden Record --- ### [The AI Advantage: What Health Plan Leaders Should Know](https://www.redpointglobal.com/blog/the-ai-advantage-what-health-plan-leaders-should-know/) **Published:** October 9, 2024 **Author:** Steve Zisk **Content:** The evolving landscape of healthcare has placed great importance on data-driven strategies that prioritize and automate member-centric approaches. The use of high-quality data is crucial for enabling health plans to use artificial intelligence (AI) to improve member experience and to optimize operations. When exploring how to use AI to optimize processes and outcomes, health plans need to consider that its reach expands beyond the widely known generative models, encompassing classical numerical AI models that anticipate member behavior and optimize interactions. Furthermore, the adoption of AI requires careful consideration of data privacy and the production of synthetic data, especially in such highly regulated environment. ## **AI Models and Use Case Considerations** The use of AI is not a new phenomenon in healthcare. While generative AI (GenAI) and large language models (LLMs) garner the lion’s share of attention, classical numerical AI models have been around for some time, and they remain pivotal in health plan use cases for improving the member experience. Regression and clustering techniques, for example, are foundational for tasks such as predicting member retention, correlating outcomes and optimizing interactions to improve [CAHPS scores](https://www.redpointglobal.com/blog/improve-cahps-scores-with-a-customer-data-platform-cdp-and-a-single-member-view/). Straddling the line between analytics and AI, numerical AI models have presaged a broader use of AI among health plans for the purpose of driving better health outcomes through data. In using AI to improve health outcomes, healthcare organizations typically break down potential use cases according to risk levels, with factors that include how well PHI is protected and whether AI decisioning will have a direct impact on patient care. For payers, the adoption of AI could start with low-risk applications like automating appointment reminders or enhancing patient registration processes. As confidence in data accuracy and AI grows, its use can expand to more critical areas like risk assessments and personalized care recommendations, which have a direct impact on patient outcomes and costs. AI enablement in healthcare is multifaceted, impacting various stakeholders from patients and providers to marketers and back-office operations. Identifying key use cases, understanding how AI will be applied and establishing key performance metrics upfront is important for successful implementation – particularly when AI will have a direct impact on member satisfaction and retention. ## **The Role of Data Quality on AI Performance** When expanding the use of AI to include GenAI capabilities – such as conversational AI using LLMs – it is vital for health plans to consider the health of data being used to power AI applications. [High-quality data](https://www.redpointglobal.com/blog/data-or-die-how-high-quality-data-fuels-agile-marketing/) is the backbone of any successful AI implementation. Health plans often have data siloed across various departments and disjointed systems. On top of this, structured data, unstructured data, and member communications are typically managed separately. While AI can help bring these data sets together for deeper insights, flawed, incorrectly matched, or incomplete data can hinder the accuracy of AI models. Profiling data to eliminate inaccuracies such as incorrect phone numbers and maintaining data integrity across use cases are crucial steps. For AI to be effective in tasks like patient engagement or risk assessment, the underlying data must be up-to-date, accurate and trustworthy. Trust in data is particularly important in how healthcare organizations approach the use of AI given stringent data privacy regulations such as HIPAA. Protecting PHI is paramount, and any use of AI must abide by existing safeguards. One solution is the production of synthetic data – artificially generated data that mirrors real data without compromising individual privacy. For AI training, as an example, it’s possible to alter existing data to create new, artificial data that is similar to the real data, but different in a key respect in that it does not represent an individual member – and is thus not subject to HIPAA requirements. This model of “synthetic” members can then be used to drive training and ask questions to improve member satisfaction and the member experience. ## **AI and Coordinated Care: An Ongoing Story** Leveraging AI and other tech innovations gives health payers a unique opportunity to gain an edge by focusing on building truly personal 1:1 member relationships. And what’s more personal than providing better guidance throughout each member’s care journey? AI is increasingly being used to improve risk assessment based on sound patient data, enabling timely interventions that reduce complications and costs, for patients and health payers alike. Health plans have a vested interest in ensuring members receive timely care, as delays can lead to increased complications and higher costs. AI can assist in identifying at-risk populations and guiding them to appropriate care providers, aligning with CMS guidelines of closing care gaps and maximizing incentives for screenings and preventive care. This proactive approach not only improves health outcomes but also supports financial sustainability. A historic opportunity for technological innovation is unfolding for [health payers](https://www.redpointglobal.com/healthcare-payers/), one that can radically revolutionize how individual member relationships are managed. Whether through anticipating member needs, optimizing interactions or promoting healthier behavior, AI has the power to transform healthcare by creating a more personalized, efficient and member-centric system. As trust in AI grows, its applications will become even more integral in shaping the future of healthcare. However, failing to create a strategic vision and a strong data foundation now could leave health payers struggling to keep pace with the industry in the near future. **Blog categories:** Healthcare **Blog tags:** GenAI --- ### [A Composable CDP is Not Just “Data-in-Place"](https://www.redpointglobal.com/blog/a-composable-cdp-is-not-just-data-in-place/) **Published:** November 1, 2024 **Author:** Renee Graff **Content:** In the ever-evolving landscape of customer data platforms (CDPs), terms like “data-in-place CDP” and “composable CDP” are increasingly being used, often interchangeably. While these concepts overlap, they are not synonymous. A data-in-place CDP, which is often used to describe a CDP that runs on a data cloud warehouse (data cloud), is only one aspect of what a composable CDP can be. Sometimes referred to as a “zero-copy CDP” or even a “data cloud CDP,” a data-in-place CDP is one in which all customer data remains in a centralized data warehouse or data lake, like Snowflake, Google BigQuery, or Amazon Redshift. The data does not leave this central repository; instead, tools and applications access and process the data where it resides. This approach is becoming more and more popular as companies seek to leverage existing data storage investments and avoid duplicating or moving data. ## **The Concept of a Composable CDP** The idea of a composable CDP revolves around modularity, flexibility, and agility. It allows businesses to build a CDP by selecting and integrating best-of-breed components that suit their specific needs. These components might include data ingestion tools, identity resolution, segmentation and activation systems, real-time interaction modules, and more. The key is that pieces can be added, swapped out, or customized as requirements and business strategies evolve. While a data-in-place CDP can be a composable CDP, a composable CDP doesn’t have to be data-in-place. A composable CDP can be deployed multiple ways. True, one way is data-in-place. Another is on-premises (“on prem”) or within a “private cloud.” This on prem option gives organizations complete control over their data, often a requirement for industries with strict data governance or compliance requirements. A composable CDP can also run as a comprehensive, full-featured SaaS solution that includes composable models. Here, the software vendor controls the built-in database, management, security and operational components, but gives the user the ability to select only the functionality that is needed to meet their composable workflow. This setup eliminates the need for a separate sign-on for each process. ## **Why Composable is Often Interpreted as Data-in-Place** One reason for the misconception that a composable CDP must run in a data cloud is because software vendor Hightouch evangelizes the notion that a composable CDP must centralize all customer data within Snowflake, Google BigQuery, Amazon Redshift or a comparable data cloud. Hightouch makes this claim because of its strength in reverse ETL. They excel at syncing customer data from a data cloud and activating it across various marketing and operational tools. It stands to reason that if your entire business model is built on offering reverse ETL to activate data in a data cloud that you would consistently claim that a composable CDP must run on a data cloud. But a composable CDP encompasses much more than that. You can have your composable CDP without being data-in place. As an example, the Redpoint CDP can run in any data cloud environment – and in fact is the only CDP that natively operates on the Snowflake Marketing Data Cloud using Snowflake as its primary customer database (without any data replication). But the Redpoint CDP can also be deployed on-premises, in a private cloud, or in a SaaS environment depending on the customer’s needs. Those differing customer needs also require Redpoint to offer flexibility in component functionality and the ability to operate with other tech solutions. Remember – composability is broader than where the data resides. True composability is about control, flexibility, and agility. Control your data, your MarTech stack, and your component functionality. Customize your foundational setup and your workflow according to your data storage needs and have the flexibility to connect your preferred, “best of breed” point solutions. Move with agility and respond to new opportunities or changing strategies with a setup that’s not just customized to your needs but allows different components to work well together. ## **Data Cloud or No, Don’t Forget About Data Quality** Data cloud or no data cloud, make sure your customer data is fit for purpose – for any intended use case. Wherever the data resides, and however you define composability, every CDP should take meticulous care that data is cleansed, accurately matched, dynamically segmented and ready for use. Whether you’re running on Snowflake or a legacy data warehouse, there are various tools and data pipelines that build out a Customer 360 and tools that activate it for your downstream marketing use cases. The Redpoint composable CDP goes beyond data activation to include robust data ingestion, identity resolution, data hygiene, and segmentation capabilities. Starting with clean data yields the most accurate segmentation and analysis, and ultimately the best results. Redpoint’s expertise in data quality as a core component of perfecting a company’s first-party data predates the concept of composability. Since well before the rise of the data cloud, Redpoint has prioritized choice and agility without creating an artificial limitation on the meaning of composability. Your data can reside anywhere. Whatever you choose, the composable Redpoint CDP provides you with the flexibility to select the functionality you need and get the most value out of your customer data. **Blog categories:** Composability **Blog tags:** Composable --- ### [The Power of the Golden Record for Data-Driven Retailers](https://www.redpointglobal.com/blog/the-power-of-the-golden-record-for-data-driven-retailers/) **Published:** December 16, 2024 **Author:** John Nash **Content:** Retail consumers do not put too much thought into the inner workings of “click and collect,” also known as “buy online, pick-up in-store (BOPIS).” It’s just another retail experience that the typical customer expects to go seamlessly. Of course the product you want is available in your chosen color, size and style, and of course it’s going to be ready when you want. Either an associate will run it out to your car, or it will be packaged up and waiting in a dedicated spot when you enter the store. For a service that for all intents and purposes didn’t exist five years ago, BOPIS quickly became a retail staple. The key to making the experience seamless for the customer is a Golden Record, a real time, unified profile of a customer (or household) that lets a brand know everything there is to know about a customer. It is the difference between being able to meet or exceed consumer expectations for a seamless, end-to-end experience and falling short. The [building of a Golden Record](https://www.redpointglobal.com/blog/retailers-generate-revenue-through-a-golden-record-and-a-personalized-cx/) is a key retail use case, a foundational requirement for maximizing cross-sell and upsell opportunities, reducing churn and building loyalty and customer lifetime value (CLV). Building a Golden Record is a core capability of a customer data platform (CDP). In a [CDP Institute survey](https://cdp.com/basics/cdp-industry-statistics/), 58 percent of CDP users said that their CDP delivered “significant value” and 88 percent of respondents claimed that a unified customer view (aka Golden Record) was its top benefit. Digging a little deeper into BOPIS, we will explore how a Golden Record can make the difference between a routine, run of the mill experience and one that stands out. ## **What is a Golden Record?** A Golden Record (aka Customer 360) combines data from any source (website, mobile app, eCommerce platform, POS, social media, CRM, etc.) to form a unified record of a customer and the customer’s engagement with a brand across every touchpoint. It includes behavioral, transactional, demographic and preference data. With full contact and identity graphs, all attributes and all data aggregations, a Golden Record captures every possible customer signal. By using [persistent database key management](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) and applying data quality processes as soon as data enters the CDP – including [advanced identity resolution](https://www.redpointglobal.com/identity-resolution/) and tunable matching and merging – a Golden Record provides a contextual understanding of a customer as the customer journey unfolds. When used as the basis for orchestrating [real-time decisions](https://www.redpointglobal.com/real-time-interactions/) at the moment of interaction, a Golden Record is a key component that allows a brand to deliver next best actions in the cadence of a customer journey. ## **Golden Record + BOPIS = Next Level Personalization** All else being equal, using a Golden Record to enhance a typical BOPIS experience will profitably differentiate one customer from another. In a standard BOPIS experience, a brand at a minimum has to integrate a POS system with inventory. And while it’s technically *possible* to execute a run of the mill BOPIS experience without introducing friction into the customer experience by making sure the right product is indeed reserved and ready when the customer arrives at the store, a Golden Record brings the experience to the next level. A Golden Record takes it to the next level because, knowing everything there is to know about the customer, a brand provides a hyper-relevant experience that aligns with a customer’s expectations. The brand sends customized SMS notifications and reminders because that is the customer’s preferred method of communication. The brand recommends the best time for a pick-up based on local traffic patterns. Or, because the brand knows it is a high-value customer with a high average monthly spend, it offers same-day delivery. Knowing the customer is picking up bathroom tiles – and knowing the customer just last week bought a drop-in sink – the brand includes with the order an educational brochure on do-it-yourself projects and offers a discount on vanities. At every turn, the brand provides a welcomed next best action that enhances the experience by being relevant and timely. From the customer’s standpoint, they’re receiving unexpected value. They see that the brand values them as a customer beyond the transaction. And that is why retailers invest in a CDP. In a [2023 Medallia market research study](https://www.medallia.com/blog/personalized-customer-experiences-drive-business-growth/), 82 percent of consumers say that personalized experiences influence the brand they end up purchasing from in at least half of all shopping situations. Moreover, CX leaders are 26X more likely than laggards to report YoY revenue growth of 20 percent or more. [One Redpoint CDP customer](chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https:/www.redpointglobal.com/wp-content/uploads/2024/04/International-Home-Improvement-Company-Case-Study.pdf) focused on building out a differentiated BOPIS experience using the Golden Record to great success. The multi-brand retail group, an international home improvement company, uses the Redpoint CDP to validate, transform and match consumer and trade professional data (from 38 sources) for roughly 18 million consumer and business identities. Creating a Golden Record through associated profiling, loyalty, consent, transaction, web behavior and contact history data, the retailer executes upsell and cross-sell campaigns with offers that are in the cadence of an individual customer journey as part of the BOPIS experience. For more on how Redpoint helps retailers ignite their customer data through the building of an industry-leading Golden Record click [here](https://www.redpointglobal.com/retail/). **Blog categories:** Retail, Single Customer View --- ### [Avis Budget Group Drives Enhanced Personalization with The Redpoint Customer Data Platform](https://www.redpointglobal.com/blog/avis-budget-group-drives-enhanced-personalization-with-the-redpoint-cdp/) **Published:** August 30, 2023 **Author:** Redpoint Global **Content:** In the most recent [PwC Customer Loyalty survey](https://www.pwc.com/us/en/services/consulting/business-transformation/library/customer-loyalty-survey.html), more than half (55 percent) of respondents said they would stop purchasing from a company they liked after just a few bad experiences, with 8 percent saying they would do so after just one bad experience. Most of us who have rented a car have probably experienced a negative experience at one point – a long line, a confusing contract, or having to complete an excessive amount of paperwork before you’re finally behind the wheel of a midsize sedan. Eliminating these familiar consumer frustrations and pain points was a key initiative for Avis Budget Group when it set out to modernize customer engagement by creating a seamless, omnichannel experience throughout an individual customer journey – pre-trip to post-trip. Spurred in part by the huge impact Covid-19 had on customer desires for contactless engagement and digital-first encounters, Avis Budget sought to overhaul the car rental process with a focus on making everything easier for the customer, from searching for and booking a vehicle, to pick-up and return and communicating with the company. “There was a remarkable shift in customer behaviors where customers were looking for more digital experiences with less human contact,” said Prabhakar Manuel, a Senior Solution Architect with Avis Budget Group. ## **Creating a Seamless, Omnichannel CX** The surge in popularity of curbside pick-up in retail, Manuel said, inspired the company to mirror that experience across the entire car rental process. To build an end-to-end digitized self-service experience, the company knew it needed two things: a single customer view and the ability to deliver an omnichannel customer experience. Avis Budget Group selected the Redpoint CDP for its industry-leading Golden Record and journey orchestration capabilities. “The Redpoint customer data platform is the heart of everything we do. It’s the foundation for how we engage with our customers and for creating omnichannel experiences,” Manuel said. The Redpoint Golden Record is a single customer view that is updated in real time with data from all customer sources. Using persistent keys, it provides a longitudinal view of a customer over time. Because it contains all customer behaviors, transactions, preferences, personal and device ID’s in a unified profile, Avis Budget Group provides digital-first experiences tuned to individual customer journeys. Redpoint’s omnichannel orchestration capabilities leverage the Golden Record to deliver next-best actions in the context and cadence of each customer journey, at scale, independent of channel. ## **A Frictionless Car Rental Process** With the single view of the customer provided by the Golden Record and the ability to intelligently orchestrate an omnichannel customer journey, Avis Budget Group meets customer expectations for a seamless, consistent experience across the pre-trip to post-trip journey whether the customer engages digitally or in-person. > “The Redpoint customer data platform is the heart of everything we do. It’s the foundation for how we engage with our customers and for creating omnichannel experiences.” > > – Prabhakar Manuel, Avis Budget Group For example, through the Golden Record, call center associates have immediate access to a customer’s online browsing session and know how to best serve the customer, delivering a next-best action that is hyper-relevant for the customer in the moment of interaction. Manuel said that every channel – call center, online booking, drop-off, counter agent, interactive voice recognition (IVR), mobile app – are all working with the same updated customer profile, ensuring a frictionless experience as the rental process unfolds. “We needed to have that connected, omnichannel framework vs. a multi-channel situation where one channel is not aware of (what’s happening on) another channel,” Manuel said. “And it’s not just that marketers and other business users are aware of everything that a customer is doing, it’s that based on the information available they can activate a personalized experience optimized for each customer.” As an example, Manuel said that a customer’s flight number will be a data point in the Golden Record. When a flight lands, the customer receives a welcome message (on their preferred channel) with instructions specific to their location for how to pick up their rental car. If the customer has completed a digital check-in with a biometric verification (which includes a fraud check), the customer can bypass the counter. Customer who have completed a digital check-in without that verification save an average of three minutes at the pick-up counter. Redpoint also supports triggered actions as part of the omnichannel journey orchestration. For example, when a customer who returns a vehicle breaks a geofence, fuel and mileage data are collected and an automated rental closure includes a receipt being sent via email or SMS. ## **A Closed-Loop Feedback Cycle of Enhanced CX** Manuel said that Avis Budget Group also analyzes customer Golden Records to learn how customers respond to various campaigns; results are then fed back into the CDP to enrich customer profiles, increasing the effectiveness of future campaigns which are now designed and executed in days vs. weeks and months. All because the company has better insight into its customers and uses data to build hyper-relevant experiences for granular segments – corporate or leisure customers, high-value customers, customers with high NPS scores, etc. Some initial benefits Avis Budget Group has achieved through Redpoint include: - Increased SMS click-through by 6+ percent for Quickpass campaigns promoting digital check-in - A 19 percent increase in customer identifications through IVR’s API integration with the Redpoint CDP’s advanced identity resolution capabilities in building a customer Golden Record - More relevant smart offers, i.e. real time offers in the context of where a customer is in the car rental process (booking, pick-up, drop-off, etc.) - Opt-in services tuned to customer preferences (SMS weather alerts, SMS car selection, location change alerts, etc.) - Increased capture of email and opt-in status across multiple customer touchpoints and channels - Self-service center for customers to access and/or alter their marketing preferences In a recent [McKinsey report](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/enhancing-customer-experience-in-the-digital-age) on enhancing customer experience in the digital age, 71 percent of consumers surveyed said they expect companies to deliver personalized experiences – and 76 percent said they will switch companies if they don’t like their experience. Avis Budget Group listened to its customers’ requests for digital-first, personalized experiences that demonstrate an understanding of them as the same customer from one channel to another. With Redpoint, the company is on the road to better experiences, more satisfied customers and the means to profitably differentiate one customer from another. To watch a Gartner Summit keynote presentation from Prabhakar Manuel and Redpoint Global Chief Marketing and Strategy Officer John Nash, click [here](https://event.on24.com/wcc/r/4282984/4CE09394E096A9CF6C6EFBCA92B517FE?partnerref=rpgblog). **Blog categories:** Customer Data Platform, Identity Resolution, Real-Time Personalization, Travel & Hospitality --- ### [3 Ways Retailers Can Reduce Friction and Improve Their Shoppers’ Brand Experience](https://www.redpointglobal.com/blog/3-ways-retailers-can-reduce-friction-and-improve-their-shoppers-brand-experience/) **Published:** May 17, 2023 **Author:** Thomas Kaczmarek **Content:** In a previous article in this space, we listed a few of the features in a robust, enterprise-grade customer data platform (CDP) that help retailers achieve the gold standard of a one-to-one personalized customer experience (CX), such as identity resolution and the creation of a [real-time Golden Record](https://www.redpointglobal.com/blog/5-cdp-pitfalls-retailers-need-to-avoid/). But like the dog that catches the car, even retailers far along on the path to differentiate on CX are unsure of what to do when they finally nail down a customer-centric approach. Does a personalized, omnichannel experience *really* reduce customer journey friction? Rest assured, there is indeed a wide gap between traditional retail marketing techniques and a data-driven, customer-centric approach that retail customers have come to expect – and that retail brands need to deliver. In the [2023 CX and Communications survey](https://www.retailcustomerexperience.com/news/more-consumers-want-better-customer-experience/) from Broadridge Financial Solutions, more than half of consumers (54 percent) have stopped doing business with a company due to a ***poor job personalizing the experience***. (This jumps to 63 percent for the roughly 86 million people who belong to Gen Z, outnumbering every other age demographic). Here, then, are the top three ways retailers can deliver a personalized – and frictionless – CX through the use of first-party data with a [Golden Record](http://www.redpointglobal.com/cdp/). ## *Hyper-Relevant Offers* We’re all familiar with run-of-the-mill blast offers that go out to everyone. Is this brand just trying to shed overstock? But this is what product-centric retailers do, calculating that any increase in sales will outweigh the annoyance factor for customers who receive an irrelevant offer. With a persistently updated, real-time Golden Record every offer can be tailored to an individual customer. Many brands create and market to segments as if their similarities override their differences. But not every married, college-educated woman in the 45-54 age group with a household income of $250,000+ (as one example) has the same interests, preferences or shopping behaviors. Yet by combining demographic and psychographic data (interests, hobbies, travel, pets, etc.) with data aggregates (last website visit, items viewed) and an updated identity graph (all devices, ID’s, email, etc.), brands instead can home in on what is meaningful to the customer ***at the moment of interaction***. A shopping cart reclamation email looks like it comes from a personal concierge, listing not just the abandoned items but complementary products that the customer has expressed an interest in. An email with offers “just for you” shows a hero image of the customer’s favorite hobby, with thumbnail images and links to hyper-relevant products. In a recent [Dynata survey](https://www.redpointglobal.com/press-releases/80-of-consumers-more-likely-to-shop-with-brands-that-show-they-understand-them/), 80 percent of consumers said they are more likely to purchase from a brand that sends relevant, personalized offers. - One Redpoint retail customer use the Redpoint CDP to integrate 20 data sources (comprising 2,400 data elements) into a single customer view. With a Golden Record for every customer that includes an additional 300 attributes, the company uses [omnichannel orchestration](http://www.redpointglobal.com/orchestration/) capabilities to power a real-time recommendation engine, achieving a 79 percent increase in conversions through customized product recommendations across multiple channels. ## *Path-to-Purchase Optimization* Path-to-purchase optimization gets at the heart of an omnichannel personalized CX. It’s partly hyper-relevant offers, but in a super-charged environment that encompasses a next-best action (offers included) across a customer journey. Optimizing a path to purchase through a real-time decisioning engine factors in a customer’s propensity score, loyalty status, lifetime value, average purchase size, order frequency and recency, preferred channel, contact preference and active social media footprint, among other data points that collectively provide a retailer with an in-depth view of a customer’s unique traits. An analysis of how these data points relate to one another will then provide insight into the customer journey; where will the customer engage next, what is the customer’s intent, and what action can I take to help guide the customer toward the desired result, be it a purchase, a subscription, a download, an opt-in, etc.? One customer might receive a loyalty offer through the website, while another a sees a Facebook ad while a third customer receives an app notification about a flash sale. Inbound interactions are similarly tailored to a customer’s individual journey. Each is a next-best action optimized for channel, timeliness and business objective. A frictionless CX depends on knowing everything there is to know about a customer, courtesy of the Golden Record and the ability to drive real-time engagement with cross-channel awareness. - One Redpoint customer, an apparel company, used the Golden Record to create personalized customer experiences across all channels and interactions for more than 10 million customers. ## *Enhance an In-Person Experience* Operational retail marketers are not the only ones who benefit from a Golden Record. In-store associates, call center agents and returns departments are among the customer-facing employees who, presented access to a real-time, unified customer profile during an in-person or on the phone interaction are able to enhance the customer experience as it unfolds. With a surge in buy online, pick-up in-store and curbside pickup transactions, associates using a Golden Record can present cross-sell and upsell offers when the customer reaches the store. Similarly, if a customer logs a compliant while shopping (they can’t find an item, their size isn’t available, etc.) an associate is empowered to remediate the issue through a hyper-relevant retention offer that factors in every interaction up to the moment the customer walked into the store. For any in-person encounter, a Golden Record used in a last mile engagement with the customer provides associates with a real-time, contextual understanding of an individual customer journey at the point of contact. - One Redpoint customer, a DIY retailer, hyper-personalized next-best actions for online loyalty members, resulting in a 20 percent lift in market basket size from loyalty members making an additional in-store purchase when buying online and picking up the order in-store. *For more on how the Redpoint CDP helps retailers meet every customer with highly personalized experiences in every interaction and in every channel, [click here](https://www.redpointglobal.com/retail/)* **Blog categories:** Omnichannel Marketing, Real-Time Personalization, Retail, Segmentation & Activation --- ### [Retail CX Trends and Predictions for 2025: Personalization Takes Center Stage](https://www.redpointglobal.com/blog/retail-cx-trends-and-predictions-for-2025-personalization-takes-center-stage/) **Published:** December 6, 2024 **Author:** John Nash **Content:** Retailers are facing many headwinds as we approach year end. While a potentially damaging dockworker strike was [temporarily averted](https://apnews.com/article/longshoremen-strike-ports-dockworkers-agreement-ila-f136bdcd52738e94f2938aaa79a1fa2a), barring a permanent resolution it will resume on Jan. 15. And while[ third-quarter retail sales](https://www.cnbc.com/2024/10/17/retail-sales-rose-0point4percent-in-september-better-than-expected-jobless-claims-dip.html) beat out Wall Street forecasts and holiday sales look promising, there is still looming uncertainty. Will falling interest rates continue to spur spending beyond anticipated levels? Will supply chains hold up? Moreover, the threat of tariffs looms large, potentially increasing costs for retailers and consumers. To navigate these challenges and stand out in 2025, brands recognize that they must provide personalized customer experiences through a deep understanding of their customers. This, in turn, requires brands to listen to customer signals; are there changed behaviors, new patterns of engagement, unusual activity? Responding to these signals with a personalized experience demonstrates to the customer that the brand isn’t simply paying lip service to CX, but actually prioritizes one-to-one marketing. As we head into 2025, we expect retail marketers to focus on delivering a personalized customer experience (CX), yet with a lot of variability in *how* to best accomplish their CX goals. Here, are three predictions for what we see in store for retail marketers in 2025: ## *1. Retailers close in on 1:1 personalization* 1:1 personalization is critical for success in 2025. Personalization at scale enables companies to thrive, driving higher satisfaction rates, boosting conversion rates, and reducing sales and marketing costs. Many retailers today are at the outset of their personalization journeys. They have consolidated their customer data and built a single customer view. This enables them to deliver personalized product recommendations and drive higher loyalty program engagement through personalized rewards and incentives. Or to suggest complementary products to customers based on a customer’s purchases or interests. Many have also started to optimize ad targeting with audience suppressions. Yet basic personalization, while effective, has some limitations that become exposed when up against economic headwinds. An extended dockworker’s strike that wreaks havoc on supply chains, for instance, would make it more complicated to offer real-time product recommendations – such as in parallel with an abandoned shopping cart. The retailer would need real-time insight into data from multiple systems. And if consumer behaviors suddenly change due to an economic slowdown, a hyper-personalized experience must reflect any real-time change. Perhaps the customer will have a lower average monthly spend, which should then influence how a brand decides which products to recommend. Successful retailers understand that the better they are set up to drive personalization – e.g., fewer data silos, dynamic segmentation, real-time omnichannel engagement – the better they will meet the challenges of an unsettled economy. The best are closing in on 1:1 marketing through omnichannel personalization in which the retailer seamlessly integrates personalized experiences across every touchpoint, including website, mobile app, social media and in-store experiences. [Real-time personalization](https://www.redpointglobal.com/real-time-interactions/) is also becoming a reality, where retailers personalize content or recommendations in real time based on user interactions or external factors such as location and time of day. The most advanced retailers are also harnessing the [power of AI](https://www.redpointglobal.com/ai/) to provide personalized experiences in the context of individual customer journeys. For instance, they’re using sophisticated models that continuously learn and adapt to deliver more personalized targeting and more accurate customer insights and next best actions. The longer a retailer retains a customer, the more sophisticated personalization becomes based on a deepening understanding of the customer. Retailers that embrace 1:1 personalization will be best prepared to cope with any headwinds, whether related to economic uncertainty or unanticipated changes in customer behaviors. ## *2. Data readiness will become a top priority* Personalization is not possible without a deep understanding of the customer based on high quality data. Retailers know that inferior data produces inferior results – and introduces friction into a customer journey. No one knows which new retail trend will spread like wildfire like the curbside pickup phenomenon, but every retailer wants to be ready when it hits. Meeting customer expectations for a superior customer experience requires a complete and accurate view of the customer. To get that view, more and more retailers will prioritize [automated data quality](https://www.redpointglobal.com/data-quality-and-data-ingestion/) and [identity resolution](https://www.redpointglobal.com/identity-resolution/). We will see less tolerance for having to build and execute marketing campaigns based on old or inaccurate data. Meeting a customer with a compelling marketing moment in the context of the customer journey – up to and including real time – requires a unified customer profile that accurately represents a customer or household. Armed with a comprehensive and current understanding of their customers, retailers can confidently and creatively craft 1:1 marketing that is timely and relevant, delivered through the customer’s preferred channel. Retailers will feel the data quality imperative even more when it comes to AI applications, like predictive modeling or AI-enabled chatbots. Nothing reflects the consequences of bad data more than AI. There are many recent example of chatbots misfiring, because they were trained on incomplete or outdated data – or worse, given vague directives like “make the customer happy.” And while these rogue decisions might amuse social media, they cost companies money, trust, and goodwill. Let’s be clear: AI isn’t magic. It’s only as good as the data feeding it. Smart brands will start to think about AI trust and understand that AI-enriched information depends greatly on the underlying data. ## *3. Retailers will double down on customer retention* Retailers that accomplish #1, #2 will be better prepared to [increase customer retention](https://www.redpointglobal.com/resources/the-evolution-of-customer-retention-how-a-personalized-cx-drives-loyalty-and-lifetime-value/), which was a key priority in 2024 and which will remain so in the new year, especially with so much market uncertainty. In the [Deloitte 2024 Retail Industry Outlook](https://www2.deloitte.com/us/en/pages/consumer-business/articles/retail-distribution-industry-outlook.html), retail executives identified the strengthening of loyalty programs as the top growth opportunity, knowing that trusted companies outperform their peers up to 4X, and that customers who trust a retail brand are 88 percent more likely to make repeat purchases. Everyone is familiar with the well-worn statistic that it is 5X more costly to acquire a new customer than it is to retain an existing customer. Retention becomes even more vital in an era of economic uncertainty, where customers will move to a competitor to either chase a cost savings or in response to a poor experience. If customers tighten their purse strings, acquisition becomes more than just a cost concern, with retailers then having to compete for a slice of a smaller pie. In practice, personalized CX drives retention. For example, the Deloitte study shows that retailers largely fail to meet customer expectations for a modern, personalized CX. Why? Because they lack a single customer view, and because they have siloed data. Curbside pickup was cited as a prime example of a modern CX that most retailers fail to execute without introducing friction into the customer journey. A successful curbside pickup process increases retention because it demonstrates that the brand knows everything about the customer and uses that knowledge to deliver a hyper-personalized CX. Perhaps the retailer, knowing the customer is in route to pick up a product, sends an SMS to offer a discount on a complementary item that it knows the customer may need and – importantly – has not already purchased. Or consider how a retailer might respond to a supply chain interruption. Perhaps the brand knows that a product that a customer buys regularly – e.g., a monthly dog food subscription – is going to be off the shelves and unavailable online. With an updated, trustworthy single customer view, the brand can take any number of steps to placate the customer – sending an email in advance that alerts the customer of a pending shortfall, offering a discount on a similar item, etc. To guard against churn, successful retailers will take their retention strategy beyond the basics and execute personalization better than their competitors. That means taking steps to continue to refine personalization capabilities, which comes down to having the most complete and accurate single customer view. Retailers may face some unexpected adversity in the coming year but putting personalization center stage means they can be ready for whatever change is on the horizon. **Blog categories:** 1:1 Personalization, Retail --- ### [Mastering Risk and Reward: Why Financial Services Need a Golden Record](https://www.redpointglobal.com/blog/mastering-risk-and-reward-why-financial-services-need-a-golden-record/) **Published:** December 10, 2024 **Author:** Mike Ferguson **Content:** In the realm of financial services, risk mitigation is not just a priority—it’s a necessity. The staggering costs associated with data breaches, including hefty penalties, fines, and irreparable damage to reputation, can be crippling. This is why banks, lenders, insurance companies, and other financial institutions take extraordinary measures to safeguard their technology infrastructure, often insisting that customer data be securely held [behind their own firewalls](https://www.redpointglobal.com/blog/redpoint-and-snowflake-no-data-replication-and-complete-cdp-functionality/). Equally crucial to risk management is business innovation driven by understanding the customer. The goal is the creation of a comprehensive, up-to-date profile of each customer, household or organization. This detailed profile enables financial institutions to accurately identify and differentiate individual customers across multiple accounts and contexts. A contextual understanding means having a thorough, real-time breakdown of an individual’s entire portfolio, including main and subaccounts, authorized users, secondary account holders, and whether the individual is acting as the owner or an authorized user of a personal or business account. ## **A Contextual Customer Understanding** With an accurate, real-time, contextual understanding of a customer – with “customer” referring to an individual, household, or business – financial services organizations are able to offer a next best action in real time, a critical capability for guiding a customer to a desired and advantageous outcome. For instance, consider a customer who starts an online engagement with an insurance company. A company that can instantly assess risk because it has an accurate, real-time understanding of the customer’s identity is able to produce a real-time quote. The same holds true for an online customer engaging with a mortgage calculator, and the ability of the lender to produce a quote that minimizes risk while being attractive to the customer. Now consider what a financial services institution needs to have to make this happen. It’s not enough, for example, that a company uses a basic, deterministic match to link a device ID with an individual. Will a device ID alone be enough to determine if it is a husband or a wife who is looking for a real time quote? Of, even if the company is fairly certain the device is being used by the head of household, does it know that the individual is recently divorced – which might be the reason for engaging with the mortgage calculator? There are many life events, big and small, that have a material impact on the optimal interaction a financial brand creates with a customer. The company with the deepest, most up-to-date, and accurate understanding of a customer will be in the best position to minimize risk, grow the customer base, reduce churn and drive higher revenue. ## **The Power of a Golden Record** This is the power of the [Golden Record](https://www.redpointglobal.com/blog/banking-on-change-why-a-golden-record-satisfies-customer-expectations-for-a-holistic-experience/), a consistently updated unified customer profile that is the basis for being able to profitably differentiate one customer from another. Creating a Golden Record is a key function of a [customer data platform (CDP)](https://www.redpointglobal.com/cdp/). Containing a full identity graph as well as a full contact graph, a Golden Record provides financial institutions with a contextual understanding of an individual customer that deepens over time. By using [persistent key management](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) and updating the unified profile in real time, a Golden Record accounts for any and all life changes that impact an understanding of a customer. Changes to an email or physical address, a new job, a new dependent, a marriage, a child off to university – a Golden Record contains an updated record that includes everything that is knowable about a customer and the dynamics of a customer in the context of a household, a business, and even within the parameters of various accounts. When [data quality steps](https://www.redpointglobal.com/blog/for-financial-services-a-superior-customer-experience-begins-with-data-unification-data-quality/) – normalization, standardization, data enrichment, advanced identity resolution – are complete at data ingestion, users of the Golden Record (call center, UX designers, loan officers, chatbot, bank teller, etc.) can trust that the unified profile is a precise representation of a customer at the moment of interaction – inbound or outbound. ## **Prioritize Data Quality** Financial services companies need to understand that not all CDPs approach the creation of a unified profile the same way – or with the same urgency. This is an important factor to consider, especially when there are real risks involved in not being able to understand the intricately detailed nuances of a customer in real time. There are, for example, many CDPs that do not consider [data quality to be a core competency](https://www.redpointglobal.com/data-quality-and-data-ingestion/), certainly not something that the CDP performs continuously, in real time, at the moment data is ingested from every conceivable source. Others limit [identity resolution](https://www.redpointglobal.com/identity-resolution/) to a basic, deterministic match, only linking various records if there is an exact match. The problem here, of course, is that customers are complex. They have multiple identities (nicknames, identifiers, addresses, etc.), multiple account and multiple, complex relationships. Deterministic matching alone is woefully inadequate for unraveling those complexities in real time at the moment of engagement. On top of that, there are other CDPs that operate strictly in a SaaS environment and require companies to hand over their customer data. For these reasons, when it is important to safeguard your customer data *and* develop a detailed understanding of your customer so that you can deliver next best actions that introduce the least amount of risk, we welcome you to take a look at the [Redpoint difference](https://www.redpointglobal.com/financial-services/). Redpoint is the only CDP for banks and other financial services that thoroughly addresses data security, accuracy, and accessibility challenges. With Redpoint, leading organizations gain a competitive advantage by treating customers individually and taking customer experience to the next level. **Blog categories:** Financial Services **Blog tags:** Golden Record --- ### [The Telltale Signs Your Customer Data Platform is Over-Composed](https://www.redpointglobal.com/blog/the-telltale-signs-your-customer-data-platform-is-over-composed/) **Published:** December 20, 2024 **Author:** Renee Graff **Content:** If you have or are considering a [composable customer data platform (CDP)](https://www.redpointglobal.com/blog/composable-cdp/), you’re likely familiar with all the reasons why it’s quickly becoming the preferred solution for powering a personalized customer experience (CX). You decide which components to use to build a better customer understanding. You control how your APIs are set up. You maintain flexibility, easily changing how your CDP operates to meet new trends or use cases. And, if you run your composable CDP on a modern data cloud or on-premises, you have a “data-in-place” environment where your data remains in your control, within your own security perimeter. If this is your experience – congratulations! You’ve hit a composable CDP home run. But if you’re instead finding that it now takes you more steps to accomplish the same amount of work, or that you don’t have quite an accurate view of your customer as you expected, then perhaps you’re over-composed. ## **Over-Composed? Spot the Red Flags** A CDP that is over-composed is composed at too low a level. What this means is that there are so many different components that the essence of a CDP gets lost. You find it harder – not easier – to power a personalized CX based on deep customer insight because it seems you’re just moving data from one place to another. How can you tell if you’re over-composed? Technical terms aside, one giveaway is if it becomes harder to accomplish a business function such as [identity resolution](https://www.redpointglobal.com/identity-resolution/), building an identity graph or a golden record, data transformations, data formatting, etc. Here are some other telltale signs your CDP is over-composed: - You have to navigate between multiple programs to accomplish routine tasks. - You are forced to learn SQL, or need to rely on IT to get what you need. - You are given data that is not ready for prime time (it’s messy, there is no single source of truth for the customer). - You have to make assumptions about the[ quality and the trustworthiness of your customer data](https://www.redpointglobal.com/data-quality-and-data-ingestion/) (Is it accurate, deduplicated and complete? What about householding?) - You have multiple tools with overlapping functionality - The components you’ve tasked with completing a business function (e.g., identity resolution) have various standards for the quality of data, and – for some – their standards do not meet your requirements. In short, the purpose of composability is to bring various components that you need together in a sensible way to make it easy to complete a business process. Anything that detracts from that goal is a sign that you’re over-composed. ## **Composability Done Right** What then, is the right way to compose your CDP to avoid the problems of over-composability? To understand the answer to that question, it helps to understand why over-composing is a problem. The reason is that some CDPs that tout “composability” limit the definition to having a system that sits on a modern data cloud that essentially moves data from one place to another. It might do one function well (e.g., identity resolution, reverse ETL) but does not even pretend to have complete CDP functionality. There are two points to make here. First is that while most composable CDPs do in fact sit on top of a data cloud, a [CDP can still maintain composability in a private cloud, on-premise or even in a SaaS environment](https://www.redpointglobal.com/blog/a-composable-cdp-is-not-just-data-in-place/). Second is that a CDP does have certain requirements to fulfill to be called a “CDP.” These include the ability to ingest data from every source, capture details of the ingested data, [create and maintain persistent keys](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) and create unified profiles among them. A CDP that outsources all but one core function forfeits the right to call itself a CDP. In other words, the hard things such as data quality, identity resolution and creating unified customer profiles that persist over time don’t disappear just because your system sits on a data cloud. On their own, various components may not only fail to meet your requirements, but an over-composed system becomes an operational nightmare. You have one interface to pull in your data, another for identity resolution, a third to create a segment — likely using SQL — a fourth for your reverse ETL and to [activate your segment](https://www.redpointglobal.com/segmentation-activation/), etc. Not a single application encompasses the whole business function of bringing in your customer data, creating a trusted unified profile and activating it to your end channels in a controlled way. If that describes the day-to-day responsibilities of an operational marketer working with your composable CDP, then your “composition” is really just a collection of individual applications that a marketer has to learn how to use and coordinate in order to complete a business function. That sounds like a process made more complicated and less efficient, not more streamlined and effective. The purpose of a CDP is to complete all those tasks in one place; log in, pull your data in, check to see if your data is ready, build a segment, push the segment out, look at your results, run an experiment. One interface. One source of truth for your customer data. One CDP that maintains an impeccable standard for data quality across the complete business function. To discover how the complete, composable Redpoint CDP helps companies get their data right, make it easy to use and evolve with your use cases and technology stack, click [here](https://www.redpointglobal.com/cdp/). **Blog categories:** Composability **Blog tags:** Composable --- ### [Yes, Real Time is Compatible with a Composable CDP](https://www.redpointglobal.com/blog/yes-real-time-is-compatible-with-a-composable-cdp/) **Published:** November 22, 2024 **Author:** Renee Graff **Content:** One unfortunate, and somewhat common, misconception about [composable CDPs](https://www.redpointglobal.com/blog/composable-cdp/) is that they cannot support [real-time interactions](https://www.redpointglobal.com/real-time-interactions/). Be assured that this is not the case. While SOME composable CDPs can’t support real-time interactions, *a good composable CDP CAN.* But where did the notion that real-time and composability can’t coexist come from? One observation is that some CDP vendors – coincidentally those that do not support real-time interactions – have claimed that real time is categorically outside the scope of what it means to be “composable.” The truth is that while real-time interactions might be out of scope for some CDP vendors, that’s not the case for *every* composable CDP. ## **Composability Means Choice, Not Limitations** The fact that composability means choice holds true for real time, just as it does for other components that together assemble a CDP that is purpose-built to accomplish your unique business and customer experience (CX) use cases. Whatever your requirements are for a composable CDP – real-time data ingestion, advanced identity resolution, data quality, dynamic segmentation and real-time interactions among them – true composability allows you to choose the capabilities and features you need to power personalized customer experiences. Some vendors narrowly frame a composable CDP as software that connects to a data cloud that allows you to build segments and activate data to your end channels. Boiling that down, what they’re saying is that a composable CDP requires a [reverse ETL tool](https://www.redpointglobal.com/blog/composability-is-not-just-reverse-etl/) or APIs that sit directly on the data cloud to provide needed access to individual customer records. Full stop. If that’s your definition, then by default you would view anything outside that framework – such as the ability to support real-time interaction – as outside the scope of a composable CDP. From their point of view, real time is only possible when a digital experience platform (DXP) or similar system grabs information about a customer in real time to decide on a next-best action. The underlying – and incorrect – assumption is that a composable CDP cannot make this happen because it does not support a real time cache. ## **Their Limits Don’t Have to be Yours** A complete, composable CDP *can* support real-time updates to a unified customer profile as new data comes in, without requiring integration with external systems. This is real time at every layer, from data through activation. It is different than the more limited approach of making a “real time decision” on data that is updated in a non-real-time cadence. Redpoint’s composable CDP supports real time at every layer, because for many businesses, those moments matter. A CDP can be composable and support real time updates whether the database is maintained by the CDP or a data cloud. A composable CDP can exist on-premises, in a private cloud, a data cloud or even installed as SaaS and maintain composability regardless of where the data resides or of who controls the database. Artificial constraints aside, composability simply means a CDP customer has the choice of which best-of-breed systems the CDP can connect to for the purposes of powering a personalized customer experience – an experience that may include real time, depending on your needs and goals. When you’re defining and building the framework of a composable CDP, it’s imperative to make sure you’re putting something together that accomplishes the key tasks and goals you want to achieve. There’s a little bit of a “Goldilocks” approach here – once you outline what you need the system to do, you’ll start to identify the components that can do those tasks – but you’ll need to balance being “under-composed” and not accomplishing those use cases with making sure you don’t end up “over-composed,” with a tangled web of disparate pieces that end up being too complicated, and too costly, for your organization. ## **Strike the Right Balance of Composability** A complete, composable CDP should have everything a customer needs to strike the right balance – real-time data ingestion, data quality, advanced identity resolution, dynamic segmentation, activation and, yes, real-time decisioning. It should also be flexible enough to run on-premises (in a private cloud), in your cloud data warehouse, or in the CDP’s cloud data warehouse – whichever makes the most sense for your business. After all, composability means you have the ability to customize your CDP setup to meet your needs. If you’re interested in a composable CDP, be wary of the narrative that there are, or should be, artificial constraints about what a composable CDP entails. Many vendors specialize in only one or two areas of CDP functionality (e.g., reverse ETL, identity resolution, etc.) and if it is a capability they don’t support (e.g., real time) then according to them it’s not part of a composable CDP. **Blog categories:** Composability, Real-Time Personalization **Blog tags:** Composable --- ### [Take a Personalized CX to the Next Level with Advanced Identity Resolution](https://www.redpointglobal.com/blog/take-a-personalized-cx-to-the-next-level-with-advanced-identity-resolution/) **Published:** December 22, 2020 **Author:** Steve Zisk **Content:** In a recent Dynata survey commissioned by Redpoint about the 2020 holiday shopping season, [70 percent of consumers](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/) said that they planned to shop exclusively with brands that personally understand them. A personal understanding does not necessarily mean the brand knows the consumer by name, but that it recognizes a consumer’s preferences and behaviors across every channel, and across an unknown to known customer journey. Consumers are tired of being bombarded with irrelevant messages and offers; a brand that possesses a personal understanding is able to cut through the noise, providing the right offer at just the right time and helping guide the customer journey to its logical conclusion. The difference between a brand coming across as an unwanted nuisance versus a trusted concierge is the function of [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/), a key feature of a customer data platform (CDP) for any intended use case where it is important to meet a customer with relevant information. For a retailer, this could mean presenting a hyper-relevant offer or action at the optimal moment and channel, based on the entirety of a customer’s interactions and factoring in the customer’s opt-in preferences and GDPR/CCPA or other regulatory requirements. For a healthcare organization, the relevant information may be subject to HIPPA compliance disclosure, such as a medication/dosage notification where a proper identity match is vital. As a recognition process, advanced identity resolution operates robustly in the context of whatever a business’s use cases and constraints may be, which may be very different for different organizations’ customers, for example in healthcare care management versus retail digital personalization. But not all CDPs treat identity resolution with the focus that it deserves, for various reasons. With an understanding of advanced identity resolution components, we will see why the feature is such a valuable part of a CDP. ## **Identity Graph** An identity graph is a collection of “signals” – attributes representing behavior, transactions, or information linked together with identifiers – that provides marketers with a real-time view into everything there is to know about a customer. For a marketer to trust that a piece of information on Jane Doe fits with the rest of the identity graph for *this* Jane Doe, however, a host of things must happen. Getting these various pieces of identity right is what sets advanced identity resolution apart from a run-of-the-mill match. It is essential, for instance, to accurately match the various elements that make up an identity graph. Customers engage with a brand across multiple devices/channels/platforms. Jane Doe’s interactions likely cover multiple browsers, emails, devices. She may have more than one loyalty account, phone number, address, etc. For every interaction, transaction or behavior, information is fed back to help piece together/update the identity graph. What device was used? Which browser? The identity graph ties together those fragments with the activity and/or action that was taken: an email sent, a social post, a website visit, an in-store purchase with a credit card. ## **Deterministic + Probabilistic Matching** To know which attributes are important for the purposes of identifying Jane Doe, the system must have both deterministic and probabilistic rules in place to handle all kinds of matching challenges such as nicknames, typing errors and identity relationships. Deterministic rules are generally straightforward – trusting the consistency of a machine ID, for example, to recognize that all browser activity in a single web session is from the same device. Without probabilistic matching, however, brands are still flying blind. If John Doe uses his wife Jane’s laptop to browse fishing gear, a brand that relies solely on a deterministic match may presume that Jane is in the market for a tackle box. Probabilistic rules can account for nicknames, partial addresses, other non-unique identifiers and human error, such as incorrect data entry (a misspelled name, the wrong address entered on a form, etc.). Or, as the fishing example indicates, householding. Probabilistic matching is required to sort out relationships, which a device – and usually a person – are not explicitly exposing. If a brand is going to infer behavior – “Jane likes to fish!” – it runs the risk of introducing friction into Jane’s customer journey if she’s suddenly bombarded with irrelevant offers. ## **Identity in the Proper Context** There are other ways the system should understand relationships in context beyond householding. One example is a B2B setting, where it may be necessary to know that Jane Doe works for Acme Concrete if the underlying use case is to send her information relevant to her role as CEO. IoT devices and sensors provide another example. A smart car, like a device, may be associated with a primary identity. A contextual understanding of relationships means that an accurate, up-to-date identity graph will likely be in a constant state of flux; Jane may go work for Acme’s competitor, she may buy a new car, she and John may divorce, etc. Advanced identity resolution not only makes calculations about what is (or is not) a match, it also should be robust enough to handle fragments, or pieces, that arrive in real-time and determine an appropriate action, such as breaking up a prior match, discarding data that you can’t keep or don’t want (to satisfy a regulatory requirement, for instance) or performing an aggregation. For example, if you’re tracking details of a web browsing session for the purposes of making calculations about affinity, intent, total viewing time, etc., perhaps the calculation itself – essentially an aggregation of the visit – is what you keep versus the detailed log of the visit. An understanding, then, of how you’re going to use fragments that make up pieces of an identity is another function of advanced identity resolution that, like changing life events, is also fluid in nature. ## **Basic Identity Resolution? The Customer Notices the Difference** Calling it “advanced identity resolution” – which is what is offered in the[ Redpoint CDP](https://www.redpointglobal.com/) – implies of course that there is a more “basic” identity resolution. While true, one can argue that merely matching known identifiers without probabilistic matching, householding, or other capabilities discussed here (accounting for human error, multiple devices, accounts, etc.) makes a “basic” edition hardly worth the name. The reality is, today’s always-on, connected consumer has little patience for a brand that cannot follow and recognize them across channels. A consumer expects a brand will know they have multiple devices, ID’s, or accounts, or that they’ve changed jobs, have moved or just had a baby. To the consumer, it’s incumbent on the brand to figure it all out. That’s advanced identity resolution. Basic just doesn’t cut it, not when the goal is to really understand a person in order to deliver accurate, relevant and timely actions, offers or information that respect the customer’s preferences, opt-ins and membership in a certain group (a business, a household, a loyalty club, etc.). Advanced identity resolution puts the “customer” in your Customer Data Platform. **Blog categories:** Data Management, Identity Resolution, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [What is Data Lineage and Why is it Important?](https://www.redpointglobal.com/blog/what-is-data-lineage-and-why-is-it-important/) **Published:** June 25, 2019 **Author:** Steve Zisk **Content:** ## **What is The Data Lineage Definition?** *Data lineage is defined as a data lifecycle that includes the data’s origins and where it moves over time. The ability to track, manage, and view data lineage helps simplify tracking errors back to the data source and it helps debugging the data flow process.* One common denominator for all successful data-driven marketing organizations is a recognition of the importance of data curation, or data lineage, to ensure that data is being used as the basis for innovative customer engagement or other purposes is the *right* data. Regardless of business goals, industry regulations, or the level of sophistication an organization wishes to attain with data analysis, data lineage is a vital capability of any enterprise-grade customer data platform (CDP). There are three key use cases an organization should keep in mind when determining what they’re trying to accomplish with data lineage. ## **Data Lineage Means to Ensure Compliance** [Privacy compliance](https://www.redpointglobal.com/blog/what-you-need-to-know-about-consumer-data-privacy-compliance/) is broad in scope, encompassing GDPR, CCPA, and other regulations that protect the use of consumer data, including the consumer’s right to be forgotten and governance around permissions. Different industries face different compliance rules. Banks, for instance, must comply with strict loan documentation and anti-money laundering rules. In general, all industries must document and be prepared to answer questions about how they acquire customer data, the permissions surrounding the data, how the data has been shared, and the future use of the data. These questions can be asked by regulators, boards, customers, or even lines of business, each with a different reason for wanting the information. Data lineage ensures that marketers and data managers will have the right answers. ## **Know Your Data** Another reason to care about data lineage is that it encompasses data quality metrics. Measuring how data is cleansed, merged, matched, and split produces is a roadmap, if you will, that details the history of the data that is being ingested and produces visibility into the lineage. This data roadmap also allows for tracking and measuring data movement; its route from ingestion to fueling either systems of engagement or for a specific engagement. Measurement allows marketing to understand, detect, and minimize raw data issues, common problems, and anything else that might adversely affect the quality, quantity, accuracy, and veracity of data used to create [high-quality customer records](https://www.redpointglobal.com/blog/why-data-veracity-is-the-foundation-for-a-personalized-customer-experience/) that in turn form the basis of personalized customer experiences that consumers expect. ## **Add Value to Your Data** Finally, data lineage gives greater contextual value to data. Data aggregations provide relational information that, over time, yield greater contextual awareness of the data. How has a record, for instance, changed over time? How has the real person underlying the data changed over time? How have associations changed over time, such as a customer lifetime value (CLV), or the propensity to buy or churn? Lineage that measures dynamic customer information provides marketers with keen insight into how populations change over time. This visibility answers questions about how a campaign itself or external factors have affected customers, for instance, in the context of all the data being collected. Recognizing the customer in the data sheds light on what’s needed to create an even broader, bigger view of the customer. Whereas measuring data quality, quantity, accuracy, and veracity measures an intrinsic quality of the data source, data lineage in the context of the customer measures an intrinsic quality of the actual, physical world behind the customer, the product, or the engagement. ## **Trust Your Data** The three main functional areas of the data lineage definition outlined above can also be significantly affected by non-functional areas, such as availability, performance, agility, and real time. From an operational standpoint, it might not be immediately clear why this matters, but if the marketers’ goal is a [single point of control](https://www.redpointglobal.com/blog/data-and-the-empowered-customer/) over data, decisions, and interactions, then both functional and non-functional aspects of the data solution will have an impact on the effectiveness of engagement. Data lineage, in other words, must not hold up marketers who are intent on moving in the same real-time cadence as a customer. Robust data lineage is a core functionality of an [enterprise-grade CDP](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/). Depending on the objective, having data lineage capabilities is one more reason why data-driven marketers are choosing a CDP over a DMP. Putting data through its paces and attaching metrics to various measurements is important for compliance, and for creating innovative, personalized customer experiences that make a difference. **Blog categories:** Anonymous to Known, Customer Data Platform, Data Management, Data Quality, Master Data Management --- ### [Do You (Really) Know Who I Am? Why Advanced Identity Resolution is Vital for Omnichannel Customer Experiences](https://www.redpointglobal.com/blog/do-you-really-know-who-i-am-why-advanced-identity-resolution-is-vital-for-omnichannel-customer-experiences/) **Published:** December 10, 2021 **Author:** Mike Ferguson **Content:** According to[ research from McKinsey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying), fast-growing companies drive 40 percent more of their revenue from personalization than their slower-growing counterparts, evidence that the value of getting personalization right – or wrong – is multiplying. Deeming it a business necessity, the study reveals that roughly 70 percent of consumers consider personalization a basic expectation – understood as a business recognizing them as an individual and knowing their interests. The link between personalization and revenue helps explain the deepening interest many organisations have with securing a [customer data platform (CDP)](https://www.redpointglobal.com/blog/can-your-cdp-deliver-perfect-data-how-a-need-for-perfection-is-shaping-the-market/), ostensibly for gathering all sources of customer data to better understand their customers. Yet in speaking with many companies about their underlying business challenges, I find that few have a solid grasp of the true problem they’re trying to solve. Many think of a CDP as little more than a synonym for a marketing automation platform or data centralization tool, and they harbor a misguided notion there is very little in the way of differentiation; any deployment will magically deliver a personalized customer experience, and the company will be off and running toward newfound riches. ## **A Data Quality Issue** In probing potential business use cases that these organizations have for a CDP, what is often the case is that while personalization may indeed be the desired outcome, there is little recognition that what is often holding them back is really a [data quality](https://www.redpointglobal.com/customer-data-management/data-quality/) issue. At its core, it’s not about data aggregation or automation per se, but about identity resolution. Without a good idea of who your customers are, you’re not only missing out on revenue opportunities, you’re probably also introducing loss. How many unnecessary emails are going out because of duplicate records, or because household dynamics are not accounted for? Is a retention strategy off kilter because thousands of customers who make frequent purchases have multiple customer profiles, so they’re perhaps offered promotions they don’t need, or they’re not treated as loyal customers? Marketing spend, marketing strategy and reporting all suffer adverse effects when data quality issues impede identity resolution. If you have 20 million customers, what will a 5 percent inaccuracy rate cost the business with 1 million customers potentially receiving an irrelevant offer, email or other content, or being marketed to in juxtaposition with a customer journey? Average customer spend will offer a guide, as will churn rates and acquisition costs. ## **Perfected Data & Identity Resolution** Unfortunately, many companies accept such inaccuracies as a cost of doing business. Introducing friction into the customer journey of one of every 10, 50 or 100 customers with an irrelevant message, content or offer is understood to be the price to pay for offering a personalized customer experience for the majority. The reality is, it doesn’t have to be this way. Perfect data is possible, and [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) underpinned by probabilistic matching techniques is what separates customer experience platforms from those that claim the CDP mantle but more often than not fail to do more than simply ‘unify’ data. Probabilistic matching techniques account for the fact that identities are not static, making continual updates as more customer data becomes available to build on and improve a customer record. For example, there are roughly 250,000 marriages in the UK every year, which of course influences householding. Probabilistic matching approaches ensure the creation of a layered, accurate profile that accounts for and recognizes the customer’s relationship with the brand over time, and how that relationship evolves across the complete customer lifecycle. CDP’s that purely ‘unify’ data and have no advanced resolution capabilities may render a ‘profile’ but fail to create true identities that provide the foundation and customer understanding required for personalization and effective customer engagement. ## **Contextual Awareness** From a marketing standpoint, a lack of context limits opportunities to create deeply relevant, hyper-personalized experiences that are not only in synch with an existing customer journey, but that also demonstrate a recognition of an ongoing relationship with a customer. For a simple example, consider the dynamics of a household. A simple match of a device to an IP address may tell a brand that John Smith is browsing the website. But perhaps Mr. Smith is browsing products for his wife or child. Or maybe Mrs. Smith has borrowed the device. For a financial institution, having an up-to-date, accurate customer record that includes household dynamics is vital for ensuring Mr. Smith is presented with the right product and messaging. Does he have children about to go to university? Is he newly separated? Downsizing his home? Or are he and his wife expecting a child and interested in building an extension? Another possibility is that Mr. Smith is browsing for products or engaging with the financial institution on behalf of his small business. A fleet management or car rental business provides another example of the importance of having an accurate, persistently updated customer profile. If the company knows, for instance, if a customer is renting a vehicle for business vs. pleasure, the home page may render an image of a sleek Mercedes pulling up to an office car park. Conversely, it might display a happy family piling into an MPV. Similar to the McKinsey research, a survey [Harris Poll conducted with Redpoint](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) shed light on how important it is for a brand to possess – and act on – a detailed, contextual understanding of a customer. In the survey, 69 percent of consumers said that the pandemic has made it even more important for a brand to know their individual needs and preferences, and 65 percent said they now consider personalization a standard expectation – within 5 points of the McKinsey survey. ## **Dynamic Flexibility** The persistent matching of keys at data ingestion vs. customer data that is already keyed is the difference between a brand making decisions with the dynamic flexibility that aligns with today’s dynamic customer journeys that consist of multiple physical and digital channels. [Data matching](https://www.redpointglobal.com/customer-data-management/data-matching/) at the pace of the customer adds vital context to interactions that is a cornerstone for providing an omnichannel CX, irrespective of either the volume or variety of engagement touchpoints. Done well, advanced identity resolution reconciles records across all types of data – structured, unstructured, semi-structured, batch, streaming, etc. – and all data sources. A comprehensive customer understanding is necessary to deliver an omnichannel CX in line with customer expectations. Taking shortcuts at data ingestion by using data that is already keyed will, in the end, short-change the customer. With a personalized customer experience shown to drive revenue, the era of having to tolerate inferior data is over. Customers expect and deserve nothing less. **Blog categories:** Anonymous to Known, Data Quality, Identity Resolution --- ### [What is Augmented Data Quality, and Why Does it Matter?](https://www.redpointglobal.com/blog/what-is-augmented-data-quality-and-why-does-it-matter/) **Published:** July 13, 2022 **Author:** Steve Zisk **Content:** What is augmented data quality? Augmented data quality, at a basic level, simply means augmenting human and/or automated processes with AI/ML-driven models and rules to obtain insights and capabilities that are otherwise beyond reach. The core of augmentation, in the context of data quality, is to take a known and understood process and discern where inserting some intelligence will enrich the quality of one’s data. This is true for customer data, product data, IoT data, or any entity that would benefit from a more detailed, thorough understanding. There are many reasons to augment data. In the customer experience (CX) realm, having a better understanding of a customer across multiple dimensions and contexts enables a brand to provide a more personalized experience, driving a more loyal customer base, increased revenue, and ultimately, higher lifetime value for customers. In a [2021 Harris Poll](https://www.redpointglobal.com/resources/harris-poll/) commissioned by Redpoint, for instance, 82 percent of consumers surveyed agree that they are more loyal to brands that demonstrate a thorough understanding of them as a unique customer, and 39 percent said they will no longer do business with a company that fails to offer a personalized CX. ## **Augmented Data Quality & Improved Business Outcomes** The result of augmenting data quality to improve customer experience or for any other reason should be improved business outcomes. With CX, for example, lifetime value and loyalty can be measured and tied to improvements in CX. The same is true for augmenting data quality for a deeper understanding of IoT devices and their interactions with people. Will inserting intelligence into the process improve the device, how an individual uses the device, improve efficiency, reduce costs, etc.? Augmenting processes solely to introduce intelligence can be inefficient or counterproductive. If a business does not understand the rationale driving augmentation, it may be augmenting the wrong things, “improving” processes that have little bearing on the business outcomes it is trying to achieve. Because augmenting data quality should be directly tied to business outcomes, it is worthwhile to explore how certain aspects of data quality would benefit from augmentation. A business that knows what it wants to accomplish with enhanced data quality can then drill down into the different parts of the data quality process to find targets for improvement. ## **Augmentation and Arriving at the Right Data** Turning again to CX and the customer realm, with an objective to build a golden record that tells a company everything there is to know about a customer, augmenting data quality can accomplish several things. First is to assess the scope and quality of available data across sources, driving a better understanding of what’s needed to transform isolated customer “signals” into a coherent picture of the customer. What data might allow us to understand more about customers, not limited to basic information (name, address, email, etc.) but instead based on a complete contextual understanding: demographics, behaviors (online and offline), interactions, and preferences. Handling all these signals requires mapping between devices, addresses, emails and the customer, as well as relationships like household, family, and company. Behavioral information includes applications used, websites visited, interactions, purchase history, purchase propensities – what did they buy, and what are they likely to buy, etc. Augmenting data quality in terms of a customer understanding should recognize that it’s not just information about the customer being pulled in, it’s information about everything else that is happening in the context of a customer journey, information that helps develop an understanding of how and where a customer “fits” into an overarching, continually unfolding story. How data about a customer becomes part of a golden record represents a set of steps in the data quality process that could benefit from augmentation. ## **Augmentation and Normalization of Data** Another data quality area that stands to benefit from augmentation is the cleaning, parsing, and normalization of data. A common example is to discern whether a name or address is in a usable format. More detailed normalization tasks that might be relegated to an intelligent system might be analyzing a patient record to find and extract a primary diagnosis, as one example. Perhaps a diagnosis is hidden inside a note within the patient record, and an intelligent system could create a new field, mapping data to a new target. The same exercise could apply to natural language processing, or entity and grammar parsing. One example is sentiment detection of a customer review, analyzing not just how a customer felt about a product but discerning whether the customer will recommend it, return it, or buy it again. If the first goal of augmenting data quality is assisting in discovering the data – the individual signals – that will populate a golden record, the second is to ensure that the information itself is providing cleansed, accurate signals, thus ensuring accurate mapping to end targets, be they customers, products, IoT devices or another entity. ## **Augmentation and Identity Resolution** Once augmented data quality provides a sharper understanding of the signals and mapping to various targets, a third goal of data quality augmentation is related to identity resolution itself. Here, it must be recognized that identity resolution entails far more than just matching various entities or customers using raw data, which underscores the importance of tying augmentation to business outcomes. Identity resolution in the production of a golden record matches all the elements together that are part of a singular identity, but identity resolution also identifies the relationship between those elements that form an identity. Householding is a prime example. Is a customer married, divorced, going through a divorce, engaged? Does the customer have children at home – perhaps one of whom uses the same name? Augmentation can help identity the important relationships and the entities that a company needs to understand, and only then should augmented intelligence be used to perform an accurate match of the data to produce a complete golden record. Interestingly, many vendors that claim they perform identity resolution refer only to a match of raw data. But augmenting data quality only at the point of match – leaving out how the data comes in and what it maps to – is like trying to complete a puzzle with missing pieces. Augmentation might complete a match faster, or even make better predictions for what’s a match than a human, but it will still form an incomplete picture of a customer, a household, or another entity. The augmentation of data quality processes must begin upstream, or the result will be signals with missing, dirty, or ambiguous data from which a golden record will still have to be derived once matching is complete. In the case of trying to deliver a personalized customer experience, if a company uses raw data without cleansing, normalizing, and building relationships, it introduces friction into a customer journey because it lacks a complete understanding. By not identifying a business goal for augmentation, the company produces a deleterious outcome that augmentation might otherwise have not only avoided, but produced the complete opposite – namely, a highly relevant, personalized experience that recognizes the customer in the construct of an individual customer journey. *Editor’s Note: A follow-up blog on augmented data quality will explore augmenting data itself, and the importance of closing the augmentation loop by testing and measuring augmentation results.* **Blog categories:** Data Quality, Identity Resolution --- ### [Get Real-Time with Real Time AI Marketing](https://www.redpointglobal.com/blog/get-real-time-with-real-time-ai-marketing/) **Published:** December 18, 2020 **Author:** Redpoint Global **Content:** Consumers move fast and act fast. If your marketing can’t keep up, your business performance will suffer. Today, more than ever before, the pace of marketers needs to match the cadence of the customer. Marketers no longer have time to launch performance-oriented campaigns and wait weeks to see results and then optimize. They need to take action in real time to target the right customers, personalize the customer experience and optimize on the fly. AI marketing supports the real-time analysis, decisioning, communications, and actions required for this. Right now, artificial intelligence (AI) already makes your life better as a consumer by improving convenience and personalization, reducing friction and predicting your needs. Think about your favorite app and online experiences. AI underlies everything from personalized recommendations to autofill as you type. These simplified, seamless experiences are increasingly becoming table stakes. More and more, consumers expect them. Similarly, AI can improve your marketing by helping you to make better decisions faster and to personalize the customer experience at scale. And as more and more companies adopt AI marketing, it, too, will become table stakes. Marketers who lack the “conveniences” that AI brings to their marketing strategies and tactics will be at a significant disadvantage. Plus, consumers today prefer the type of personalization that real time AI marketing can help deliver. In fact, a Redpoint Global survey of 3,000 U.S., UK, and Canadian consumers conducted by [The Harris Poll](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/) found that nearly two thirds (63 percent) expect it as a standard element of service and believe that brands have recognized them as an individual when they receive a special offer. Not surprisingly, 91 percent of consumers are more likely to shop with brands that remember and recognize them and that provide relevant offers and recommendations, according to Accenture Interactive’s “[Making It Personal](https://www.accenture.com/_acnmedia/PDF-77/Accenture-Pulse-Survey.pdf)” report. ## **Defining AI Marketing** Most marketers have been talking about and many have been using AI for years, but there’s still a lack of clarity about what AI marketing is today and how brands can benefit from adopting it. Simply put, AI marketing uses artificial intelligence technologies to automate marketing decisions and actions based on data collection and analysis. This includes predicting customer action or inaction, segmenting and targeting audiences, personalizing content selection and delivery, and optimizing channel selection. AI marketing allows marketers to enhance their analysis of, and automate decisions based on, data they’ve collected. As a result, marketers can improve the customer experience, marketing performance and business outcomes. AI marketing enables marketers to convert vast amounts of data from disparate sources into insight—in real time. This not only increases marketers’ efficiency and enables them to spend more time on strategy, but it also increases such important outcomes as time to ROI. Marketers typically use real time AI marketing where speed is essential; for example, to personalize communications and offers in real time. AI marketing enables marketers to get more from their data, faster. Aspects of AI such as machine learning and natural language processing allow marketers to predict their customer’s next preference and then take the next-best action, simplifying the customer journey. When considering how they’ll use AI marketing, marketers should keep in mind the nuances between AI and its subsets—especially machine learning, where most AI marketing happens. As Redpoint Global’s senior engineering analyst Bill Porto explains, AI replaces or augments “intelligent” human tasks with a machine, whereas machine learning uses historical data to create descriptive, predictive, or prescriptive models that users can run against current data. Think of AI as self-driving car software, which requires hardware for sense-and-control, software to recognize details of a situation (pedestrian, light, car movements, route changes, etc.), and the “executive function” that acts on the input and drives the car. In that example, machine learning is in the “recognize” and “decide” components. Machine learning’s ability to predict future outcomes based on historical data is where AI [marketing](https://www.redpointglobal.com/blog/what-is-behavioral-marketing/) can shine. Marketers can use customers’ previous behaviors to predict what they’re most likely to do, or not do. The most valuable aspect of machine learning for marketers is its ability to support real-time decisions and actions. As machine learning tools ingest more and newer data, it can make better and faster predictions in response. Marketers who spend hours each day trying to predict what customers will do based on their actions, communications, and offers, as well as what channel they use and when, can speed up and improve those decisions using machine learning. ## **Visualizing the Benefits** Using AI marketing can transform a marketing organization. It can help marketers achieve and show real impact on profitability and sales. Best of all, it can help a company to improve the customer experience and deliver greater value to customers and prospects alike. Here are some key areas where AI marketing delivers outsized results: 1. **Enhance accuracy:** Adding AI to propensity modeling significantly improves your ability to predict a customer’s likelihood to buy or churn, as well as to predict their wallet share. And adding AI to segmentation modeling will improve your ability to build customer groups based on behavior- and product-based attributes. 2. **Personalize in real time:** Give customers the personalized experiences, content, and offers they expect using AI marketing. Use real-time segmentation and personalization to engage customers when it’s most valuable to them—simplifying their customer journey and perhaps even reengaging at-risk customers when they would otherwise have churned. 3. **Increase campaign ROI:** Using AI marketing to extract insights and optimize campaigns in real time also helps marketers to get better results faster. Additionally, using AI marketing enables marketers to better allocate their budget across channels, selected the optimal messaging, and quickly analyze the effectiveness of a campaign and optimize it on the fly. As a result, they’ll get more value from each campaign. 4. **Improve reporting:** AI marketing dashboards are designed to make it simple for marketers to visualize and report on campaign outcomes. Using the insights from prior and in-progress campaigns, marketers can adjust and optimize upcoming and current campaigns on the fly. 5. **Ensure agility:** Marketers have a need for speed, and AI marketing delivers. While AI rapidly conducts data analyses on previous marketing tactics, machine learning quickly predicts next-best actions. All this heavy lifting gives marketers more time to strategize. And, as important, AI marketing enables real-time action and decisioning. 6. **Eliminate speculation:** Marketers spend their days making educated guesses on what aspects of the customer journey to improve, what product or services to promote, which messaging to use, how often to post content, what keywords will work best, and so much more. AI marketing not only eliminates the guesswork, but it also speeds the decision-making process to the point where marketers can react in real time. 7. **Dial-up digital:** With more and more emphasis on digital marketing, and more and more competition in those channels, marketers need any advantage they can get. AI marketing can help improve everything from bidding and ad placement to ad layout optimization and segmentation and targeting. ## **Gearing Up for Real Time AI Marketing** As a marketer, you need the speed that AI marketing can give you. But you also need to ramp up the right way. There are several areas to consider as you implement AI marketing, including training time, data quality and privacy. AI tools may seem smart, but they’re only as smart as you make them. They need training on information such as customers’ behaviors and needs, marketing and business goals, and campaign rules and outcomes. This takes time. It also requires high-quality data. The adage “garbage in, garbage out” is especially true with AI marketing. Data quality is of the utmost importance. The last thing you want is bad decisions faster. What you need is smarter insight in real time. You’ll only get that with accurate, timely and representative data. This means you also need technology that will bridge data silos and provide you with a holistic real-time view of your customer data. That platform should be able to ingest and then display data from across first-, second-, and third-party data in real time from all internal and external customer data sources. This includes everything from CRM and call centers to POS and email systems to preference center and website behavior data—and even unstructured data sources such as call center notes and social media posts. With access to all that data being central to your AI marketing, data privacy becomes an equally important consideration. As you launch or update your AI [marketing](https://www.redpointglobal.com/blog/what-is-experiential-marketing/) initiatives, it’s essential to consider privacy laws and regulations and how you’ll ensure compliance with them—including programming specific guidelines into your AI marketing tools. Ultimately, this will help you to ensure that your marketing is personal and personalized in a way that customers find relevant and engaging. At a time when speed and personalization matter more than ever before, AI marketing is the high-octane fuel that marketers need to rev up their customer experiences and communications in real time. The resulting customer engagement and activity will lead to super-charged marketing outcomes, as well. **Blog categories:** Real-Time Personalization --- ### [Show Me: The Importance of Proving Out a CDP Use Case](https://www.redpointglobal.com/blog/show-me-the-importance-of-proving-out-a-cdp-use-case/) **Published:** January 7, 2020 **Author:** John Nash **Content:** The need to separate hype from reality was highlighted by Missouri U.S. Congressman Willard Vandiver in 1899 when he said “…Frothy eloquence neither convinces nor satisfies me. I am from Missouri. You have got to show me.” This is still true today, particularly for enterprises actively exploring customer data platforms (CDPs) to center their martech stack and deliver a differentiated customer experience. According to the latest [semi-annual report](https://www.prnewswire.com/news-releases/customer-data-platform-industry-grew-71-in-one-year-will-reach-1-billion-revenue-in-2019-300898166.html) from the Customer Data Platform Institute, enterprises have a wide variety of options to consider when they’re ready to begin a rigorous selection process. Through the first half of 2019, the industry added 19 new vendors and was on pace to eclipse $1 billion in revenue through the calendar year. A range of choices in the market is complicated by findings that fragmentation is rising due in large part to next-generation CDPs being increasingly specialized by industry, region, technology, or client size. **What Will a CDP Do for You?** What this means for the active [CDP](https://www.redpointglobal.com/blog/whats-needed-to-be-a-realcdp/) buyer is a need to identify business value before selecting a solution that will best drive that value. Buyers can rank CDPs based on their ability to drive key use cases and corresponding metrics such as revenue growth, cost reduction, or customer acquisition, as well as functionality pertaining to analytics, engagement, offline capabilities, and other capabilities. Gartner broadly defines a CDP as a marketing system “that unifies a company’s customer data from marketing and other channels to enable customer modeling and optimize the timing and targeting of messages and offers.” The CDP Institute bestows a “RealCDP” distinction on 44 CDP vendors, Redpoint included, that meet five qualifications: - Ingests data from all sources - Retains full detail of all ingested data - Stores the ingested data as long as the user wants - Converts the data into unified customer profiles - Makes the profiles available to all external systems For marketers who want to create differentiated customer experiences and innovative journeys, the most important criteria are whether the selected CDP will support their particular use cases. If all an organization wants to do is a traditional list-based, outbound campaign using basic transactional data, then real-time functionality may not be a priority. Conversely, a requirement for a millisecond response time across all channels that is based on a real-time, underlying customer record will weed out a host of CDP vendors that do not check the “real-time interaction” box. Fortunately, the CDP Institute publishes a CDP vendor comparison for easy reference. The comparison guide breaks down CDP features and functionality for 33 vendors, analyzing 27 distinct capabilities across 10 categories. (Redpoint is the only one to receive a checkmark for each of the 27 capabilities.) **Show Me: The CDP Use Case** When you attempt to match the various customer experiences that marketers want to create within the right CDP use case scenario, the sheer number of variables and decision points can be overwhelming. When it comes to buying a CDP, that is why an increasing number of buyers insist on proof of concepts (POC), demos, and other proof points as key parts of the evaluation phase. Results of the [TrustRadius 2020 B2B Buying Disconnect Report](https://www.trustradius.com/vendor-blog/buying-b2b-software-2020) indicate that an increasing number of buyers prioritize proof over sources such as analyst rankings or vendor claims in the selection process. While the report relates to all tech buying, its findings are still illustrative for the narrower CDP market. In the fourth annual report, buyers rate product demos as “highly influential” in the selection process and rank them first overall as far as effectiveness toward moving a buyer toward a final decision. On a trustworthiness scale from 1-4, a buyer’s own experience with a product ranked first (3.88), with a free trial second (3.68), followed by referral (3.62), and demos (3.34). Further down on the list, analysts ranked sixth with 3.24. For a similar scale that rated influence, a buyer’s own experience was still first (3.83), trial second (3.60), demos fourth (3.34) and analysts falling to seventh (3.03). Asked what was important to the buyer in a demo, No. 1 was that it “answered our questions” (68 percent). Also rating high was that the demo showed the product “based on our own use case/requirements” (49 percent) that it let the vendor “drive” the demo (46 percent) and that it used real data (37 percent). Findings also show that the trend toward more of a “hands-on” experience with a product is likely to solidify. With three of five tech buyers falling into the millennial generation, the report estimates their influence and decision-making power will grow, with the demographic also more likely to rely on trials, demos and reviews than they are analysts or vendor reps. **“Proof” is in the Pudding** One final note of interest about the TrustRadius report is that buyers said that comparing products is the most difficult part of the tech-buying journey. This is certainly true of the decision process when evaluating CDPs, especially because the stakes are so high. Today’s always-on, connected consumer demands a hyper-personalized customer experience across all channels, and expects a brand to know them as the same person across all devices, both online and offline. With 37 percent of consumers claiming that they will stop doing business with a brand that fails to offer personalization, according to the Harris Poll sponsored by Redpoint, it is vital that buyers put a CDP through its paces before purchase to make sure that a solution delivers as promised in terms of being able to provide a differentiated customer experience that drives revenue. If the CDP comparison guide put Redpoint on your radar as the only vendor that satisfies all 27 capabilities, we welcome you to extend the evaluation with a [demo](https://www.redpointglobal.com/request-demo/) where you can use your own data, drive the experience, and see how the Redpoint Customer Data Platform delivers a personalized customer experience based on your organization’s own use cases. We will also gladly help set up a proof-of-concept project as another strong way to test out specific functionality according to specific business needs. **Blog categories:** Customer Data Platform, Data Management, Single Customer View --- ### [Unleashing the Power of AI: A New Era for Health Plans](https://www.redpointglobal.com/blog/unleashing-the-power-of-ai-a-new-era-for-health-plans/) **Published:** October 29, 2024 **Author:** Steve Zisk **Content:** Artificial Intelligence (AI) is transforming industries at an unprecedented pace, and healthcare is no exception. Health plans, however, have been slower to adopt these revolutionary tools compared to other healthcare sectors such as pharmaceuticals and medical devices. As the industry faces disruption, the time has come for health plans to leverage AI to reimagine their relationships with members, enhance operational efficiency and secure a sustainable competitive advantage. This blog will outline an approach to AI that health plans could consider in order to accomplish those goals. ## **Seize Control of Your Data Quality** Clean, consolidated member data is the foundation for successful AI initiatives. AI models must be built on accurate, unified data sets to drive reliable insights and predictions. Health plans interested in proving out AI use cases are first getting their data in order, using a customer data platform (CDP) to build unified member profiles that are then used to create tailored segments and power personalized member experiences. Because AI is used across any number of customer engagement touchpoints and for a multitude of purposes – analysis, training, predictions, recommendations, content generation, etc. – it is important that a CDP prioritize data quality at the point of ingestion vs. either performing data quality processes downstream or relying on a third-party platform. Performing all data hygiene and data transformation tasks at the point of entry greatly reduces process inefficiencies and eliminates the snowball effect of poor-quality data that leads to poor member experiences. Consider, for example, using generative AI (GenAI) and a large language model (LLM) to power a member portal chatbot. Effective conversational AI will depend on access to a member record that is accurate and updated in real time to include every member transaction, plan details, behavior, billing history, medical history, prescriptions, etc. > I*n a Dynata survey commissioned by Redpoint Global, nearly half (48) percent of respondents said they would interact with AI more frequently if it would make their experiences with a brand more seamless, consistent and convenient. On the other hand, 76 percent said they are less likely to trust a brand if they sense disjointed communication with AI across channels. In addition, 58 percent said they want companies to be clear about when AI is being used*. Along the same lines, AI models must train on accurate representations of members. In some use cases, this can be done by feeding models with member data that has been stripped of PHI, but for AI to power a personalized member experience it must train on data that reflects actual member experiences. ## **Test Before Scaling Using a POC Approach** With data quality taken care of, health plans need to determine a proof of concept (POC) that will prove out the value of an AI initiative. Often, initial AI implementations involve low risk projects such as helping members understand plan options or available benefits, chatbots for member support, anticipating potential detractors, fraud detection and improving services. For example, in the healthcare ecosystem a significant portion of member interactions occur in a call center where an employee is interacting with a member, helping them with an issue or challenge that often pertains to benefits. A vast majority of those interactions are repeatable, and therefore well suited to be taken over by AI. These and similar types of engagements illustrate the need for AI to train on actual member data, but health plans must also recognize that AI – as yet – is not as adept as humans in showing empathy. There is a balance between what AI and humans can each accomplish in helping members solve an issue. What health plans can do is make sure that any AI implementation aligns with ethical standards and member-centric goals, in part by ensuring that AI-driven decisions are transparent, fair and beneficial for members. Proving out the value of AI with a successful pilot implementation will help improve operational efficiency while also setting the stage for long-term competitive advantage. With the launch of a successful AI initiative based on a solid foundation of high-quality data, heath plans can begin to scale AI use cases, from automated member interactions to enhanced predictive models. Successfully incorporating AI-driven decisioning across a member’s complete interaction with a health plan significantly enhances the overall member experience, versus using AI to improve the experience on a specific channel. Health payers at the forefront of implementing AI to improve the member experience have the inside track in developing and promoting meaningful member relationships as a competitive strategy. ## **Start Your AI Journey Before You Have To** AI is revolutionizing the healthcare industry, offering payer organizations unprecedented opportunities to enhance member engagement, streamline operations and improve overall outcomes. By automating routine interactions and utilizing predictive analytics, AI can significantly reduce costs and increase efficiency. And while successful implementation of AI requires careful consideration of data organization, ethical implications and strategic deployment, health payers should not get bogged down in the planning stages. As AI technology continues to evolve, payer organizations that proactively adopt and integrate these tools will gain a competitive edge, improving member satisfaction and driving better health outcomes in an increasingly complex and demanding healthcare environment. Redpoint Global has partnered with [Engagys](https://www.engagys.com/), a healthcare consumer engagement consulting and advisory services firm, to assist health plans with maximizing the value of their member data, including how to successfully identify and implement AI use cases. For more on how Redpoint and Engagys can help you maximize member engagement through the effective use of AI, click [here](https://www.redpointglobal.com/landing_pages/healthcare-payer-poc/). **Blog categories:** Healthcare --- ### [Redpoint In Situ, Perfect Data, in Real Time, in Your Own Security Perimeter](https://www.redpointglobal.com/blog/redpoint-in-situ-perfect-data-in-real-time-in-your-own-security-perimeter/) **Published:** July 20, 2021 **Author:** Redpoint Global **Content:** Poor data quality is a universal problem that impacts nearly every company today. Whether the aim is to deliver a consistently relevant customer experience for millions of customers, or to execute internal processes without error, clean data is essential for business operations. But achieving exceptional data quality across systems, sources, departments, stakeholders and geographies is complicated. Data siloed by channel, process or department cause bottlenecks that are difficult to overcome; multiple formats, different entry systems, a lack of context, various rules for use, sharing and retention and data never being available within the cadence and consistency to meet business needs are all familiar issues to business users. This inevitably leads to negative financial and operational impacts, including higher costs of interaction, lower customer satisfaction and lost opportunities. The typical approaches to data quality and identity resolution today both come with high risk and cost. One approach is to ship data across the internet to managed services or SaaS-based customer data platforms (CDPs), which exposes data beyond an enterprise’s own security perimeter. Alternatively, an IT-centered data quality approach, which may keep data in-house, suffers from bottlenecks caused by IT and data engineer resource shortages, complex time-consuming coding and configuration, and conflicting project priorities. Presenting harmonized, perfect data to use throughout the enterprise in a way that is simple, straightforward, privacy-focused and in place within an enterprise’s own database is the premise behind [Redpoint In Situ](https://www.redpointglobal.com/in-situ/)™, the first cloud-native, data quality-as-a-service (DQaaS) that delivers perfected data and resolved identities in real time, using exclusively first-party data, where the data resides. With today’s announcement, In Situ is the first viable solution to put business decision makers fully in control of their customer data without compromise – operating seamlessly within an organization’s existing cloud subscription. With data quality, identity resolution and governance contained within an enterprise’s existing Virtual Private Cloud (VPC), In Situ yields a comprehensive identity graph and a full transactional and behavior tail as a real-time, holistic golden record for each customer with zero data exposure. ## **Quality, Reliability and Trust** Named for the Latin phrase of the same name, In Situ provides unified customer data in place, anywhere the database may exist, at unprecedented ease, speed and scale without the need to transfer data across the internet. With a simple, powerful and privacy-focused approach, In Situ provides radical transparency into the quality, reliability and trust of all customer data – empowering businesses to confidently actualize their data across all edge points of the enterprise. The newfound trust in enterprise data will be reflected in a proprietary Redpoint Trust Index™, providing the enterprise with unprecedented confidence in data that will empower positive change and revenue impact. In Situ makes it easy for business users to visualize data sources, attributes, quality dimensions and status without the need to interact directly with the underling tools or user interface. Self-service capabilities include the creation of transformations and addition of data sources – fully automated capabilities that will not require human intervention on the operations side. Because self-serve capabilities are targeted to business users rather than engineers and IT, it is visually oriented, intuitive and with graphical and visual communications of database health, quality and status – allowing business users to monitor and communicate data status across teams. ## **Break Free of the IT, Organizational Bottlenecks** In Situ removes the time, cost and complexity from data quality and integration processes – providing organizations with perfect data in real time. It continuously produces accurate, complete and enriched data from a spectrum of enterprise and external sources, using cloud resources that provide data integration, quality and governance as if it were embedded in an organization’s own application. The ensuring golden record includes a complete identity graph coupled with a descriptive tail where all the behavioral, demographics, transformations, preferences, models scores, etc. all reside. In Situ ensures that changes to a data record are made at the same cadence as technology and process changes. As a results-oriented solution built on a fully customizable data model tuned to business level metrics, the inconsistencies of misunderstood data, or inefficiencies of relying on disconnected teams to “self-service” the data all disappear. ## **Leverage Redpoint’s Expertise** In Situ was born with the vision and recognition that data quality and identity resolution are not proprietary to a customer data platform use case. Rather, sub-optimal data is an issue that affects the entire enterprise. Marketing and customer experience are key use cases, but just as a customer views a relationship with a company as one holistic experience that spans all channels and departments, a company must adopt the same mindset toward having impeccable first-party data. Leveraging Redpoint’s core data management technology, In Situ empowers business users to have complete confidence that data is rock solid, and consistent across the enterprise, with scalability and performance that deliver new data points in real time. With perfect data, organizations drive better customer experiences. Marketers can deliver real-time, dynamic, data-driven interactions across any channel, while analysts and data scientists can focus on models, visualizations and questions instead of data wrangling. With a golden record for each customer that persists over time, aggregated to various levels (e.g., household) and accessible in real time; and with processes that are automated, high performance, scalable and well-governed (quality, compliance, security, and privacy), business users no longer have to worry about the repercussions of bad data, or trade-off data quality for speed and scalability. With In Situ from Redpoint, business users can focus instead on delivering value for the enterprise, confident they’re working with perfect data as their organization defines it. **Blog categories:** Data Quality, Identity Resolution, Master Data Management --- ### [Drive Transformative Change with IoT](https://www.redpointglobal.com/blog/drive-transformative-change-with-iot/) **Published:** June 10, 2022 **Author:** John Nash **Content:** Even without a clear understanding of exactly how Internet of Things (IoT) works, savvy retail consumers have a cursory understanding that the use of sensors and RFID technology are on the rise. For instance, consumers have likely noticed how easy it is to track product delivery. They may not know that a behind-the-scenes combination of IoT and GPS is tracking their package and sending real-time notifications on the mobile app, they’re just pleased that it improves their overall customer experience (CX). According to a recent McKinsey study, [“The Internet of Things: Catching up to an accelerating opportunity”](https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/iot-value-set-to-accelerate-through-2030-where-and-how-to-capture-it) IoT will produce between $5.5 and $12.6 trillion in economic value globally by 2030, including $710 billion in the retail sector. How much of a slice of the nearly three-quarters of $1 trillion in value a brand is allocated may depend in large part on a brand’s acceptance of IoT as a transformative agent of change vs. a novelty. The dividing line will be in how well IoT helps to form a deep understanding of a customer, rather than present standalone personalization. For example, a common retail use case today is to use a mobile app notification to provide an in-store customer with an offer on a product that is in the same vicinity as the customer’s present location, e.g. this customer is near the T-shirt section, here’s an offer for a T-shirt. But is that offer based on moving excess product, or is it based instead on having a real-time, in-depth understanding of a customer journey, in which IoT just happens to be the last mile to the consumer? ## **IoT and a Golden Record** To help understand how to maximize IoT value, the McKinsey study details key opportunities and challenges, one of the latter being a recognition that capturing value at scale from IoT requires “vigorous performance management” as well as collaboration to “change people’s behavior, systems, and processes.” Another hurdle, according to the study, is that many companies are still conditioned to treat IoT as more of a technology project than a driver of change. To overcome these barriers – changing behaviors, systems, and processes as well as applying performance management – requires that brands treat IoT and RFID data as essential components of a golden record. Yes, IoT data may play an important role in manufacturing, supply chain, sales, and condition-based management, but giving it equal weight as far as what it says about a customer’s preferences, behaviors, and intent is vital for using IoT to enhance CX. Take the use case of using a mobile app to make an offer for a T-shirt when the in-store customer is in the T-shirt aisle. A brand could make an identical offer to every customer in the T-shirt aisle who happens to be on the mobile app. Yes, it checks off the IoT box, but it doesn’t offer a differentiated, personalized experience in the context of a unique customer journey. A customer who just purchased a T-shirt on a different channel might not appreciate the offer. Or perhaps there’s another customer on the app who isn’t interested in T-shirts at all, but is just making a detour to the sweater aisle or as a bridge to another department outside of apparel. And just that morning the customer browsed the brand’s website for sweaters. ## **IoT and a Closed Loop of Data, Insight, Action** In recognizing IoT as a component of a golden record, a customer’s journey through a store is analyzed in parallel with other signals and behaviors from every other channel, and a next-best action delivered only if it has the highest probability to produce the desired outcome. A mobile app notification during an in-store visit may not necessarily be the next-best action, in other words. But learning how a customer navigates a store – where a customer lingers, the route she takes, what she tries on – is indispensable for predicting with a higher degree of certainty how the customer journey will progress across a totality of channels. Analyzing in-store behavior in conjunction with online browsing history, for example, may inform how a brand responds to an abandoned shopping cart. Eliminating data siloes to incorporate IoT as a real-time component of a golden record is vital. It is what the McKinsey study means by changing behaviors, systems, and processes and to move away from implementing IoT for IoT’s sake. In this way, an IoT use case can be measured for how it helps propel a customer along a journey. Ultimately, IoT then becomes part of the closed loop cycle of data, insight, and action that continuously enhances CX. With IoT helping to formulate a better understanding of a customer in the context of an individual journey, a brand has a higher level of trust in the veracity of a golden record. Better data yields better segmentation, campaign design, and execution, which in turn generate better responses and more intended outcomes (higher average spend, reduced churn, CLV, etc.). Segmentation improves again and the cycle continues. *Note: In a follow-up blog we will explore other IoT use cases, and how brands are using it to differentiate on customer experience.* **Blog categories:** Data Quality, Real-Time Personalization --- ### [Keep Younger Generations in Play with Personalization](https://www.redpointglobal.com/blog/keep-younger-generations-in-play-with-personalization/) **Published:** March 11, 2022 **Author:** John Nash **Content:** For those of us still enduring cold and snow, the start of baseball’s spring training is supposed to signal that we’ve turned the corner and are rapidly approaching warmer days. The lockout delayed things a few weeks, but owners and players finally settled their differences, and the fields will soon be humming with preparations for a new season. Yet one issue that wasn’t addressed in the contract dispute was the game’s struggle with drawing younger generations of fans, who are more turned off than their elders by an increasingly slow pace of play and longer games. Appealing to the Gen Y and Z demographic is a similar challenge for retailers and other industries with a consumer-brand dynamic. Like baseball, brands must adapt to expectations from younger consumers or risk losing them to irrelevance. In both cases, an abundance of options and shorter attention spans drive a need for change. ## **Younger Generations of Consumers Expect Control, Personalization** A recent [Dynata survey](https://www.redpointglobal.com/press-releases/74-percent-of-consumers-believe-brand-loyalty-is-about-feeling-understood-and-valued-not-discounts-and-loyalty-perks/) commissioned by Redpoint explored the dynamics of customer loyalty. We broke down the results in a [blog](https://www.redpointglobal.com/blog/avoid-a-customer-break-up-this-valentines-day/) that examined how brands should think about developing long-lasting relationships with consumers. There were also key generational divides that clearly showed younger consumers have far higher expectations for the brands they love to evolve with their changing behaviors, and to know who they are on a personal level. Consider that for consumers 24-34, asked to cite an action that makes them feel understood by a brand, 39 percent said that it is when a brand understands that their preferences change, and they evolve as the customer does. This is a pronounced 2.5x gap between consumers 65-and-over who said the same (15 percent), and nearly a 2x gap for the 55-64 demographic (22 percent). We saw similar dynamics for all age bands grouped into Gen Z and Y (Millennials) generations vs. the older generations. Exploring more what consumers mean by evolving preferences, the survey asked what drove consumer loyalty to a brand. For the 25-34 crowd, 41 percent said that they are more loyal to brands that offer multiple ways to interact, such as an option to buy online and pick-up in-store (BOPIS). Again, this is a 2x gap for those 65-and-over (16 percent) and an 11-point differential from those 55-64. Offering new and different ways to interact, then, drives loyalty from younger consumers. The through line is that younger consumers expect brands to understand and cater to what’s important to them – adapting to changes for how and where they shop. One survey result that drives home the increasing need for a personal understanding is a 3x gap between the younger demographic (24-34) and the older demographic (65+) who claimed that a key aspect of loyalty is when a brand effectively customizes its messages “for me” – to include targeted promotions or communications tied specifically to the individual consumer (milestones, achievements, etc.). For younger consumers, this was the second most important driver of loyalty (19 percent), vs. tied for second-to-last for the older consumers (6 percent). The top driver of loyalty for those 25-34 was when a brand demonstrates that it connects its values to the consumer’s values (36 percent), a 2X gap between older consumers who say the same (18 percent). Finally, as it pertains to a personalized experience, consumers 18-24 are twice as likely to recommend a company that does personalization well as those 65+ (58 percent to 27 percent). ## **Data Privacy Value Exchange** Shifting gears to consumers’ perceptions about data quality, the survey indicates that online privacy is important across all age demographics. Younger (25-34) and older (65+) consumers were roughly equal (48 percent vs. 49 percent, respectively) in claiming they feel disrespected when a brand collects its personal data without asking and is not clear about preference management (opt-in, opt-out options). Likewise, 50 percent of those 35-44 and 43 percent of those 55-and-over said they feel disrespected when a brand is not transparent about how it uses their personal data. Yet there were interesting demographic differences in terms of consumers understanding that their data holds value to a brand. While nearly half (48 percent) of the 65-and-over crowd said they flat out do not want brands to have access to personal data, just 26 percent of those 35-44 held that view. Drilling deeper, 37 percent of the younger consumers said they’re fine with brands having access to their personal data as long as brands are transparent about its use and that value is provided in exchange for the data. Slightly fewer of those 65-and-over said the same (31 percent). ## **Strive for the Fences** To sum up the findings, it’s clear that brands do not have the luxury of standing pat and doing nothing on the false assumption that what’s worked in the past will continue to work in the future. Or that what has traditionally mattered to the consumer base – price, product – will remain a constant. Like baseball, brands need to take an honest accounting of whether they’re being too inflexible, relying on product rather than evolving to compete in an experience economy. When young consumers unequivocally demand a personalized, relevant experience, it’s clear that brands are running out of time to get it right. In closing, two findings from the survey stand out as far as making this point. Almost half (45 percent) of consumers 35-44 said they would rather purchase from a brand that “knows me” – and will spend more money to do so, a 3x gap over those 65+ (15 percent) and more than 2X among those 55-64 (21 percent). Furthermore, younger consumers are significantly less forgiving of a poor experience. While just 6 percent of older consumers (65+) said they have stopped shopping from a brand after just *one* bad experience that they or a peer had, 34 percent of those 25-34 said the same. A relevant, personalized experience does more than put fans in the seats, so to speak. It drives revenue. To remain a player in the experience economy, brands need to “play ball.” --- ### [A Personalized Customer Experience (CX) Delivers Loyalty](https://www.redpointglobal.com/blog/a-personalized-customer-experience-cx-delivers-loyalty/) **Published:** August 5, 2019 **Author:** John Nash **Content:** According to a recent Monetate survey, [93 percent of companies](https://www.businesswire.com/news/home/20190313005156/en) with an advanced personalization strategy experienced revenue growth in 2018, and 77 percent of businesses that exceeded their revenue goals had a documented personalization strategy in place. This is solid attestation that customers reward personalization with wallet share; in a Segment report, [44 percent of consumers](https://grow.segment.com/Segment-2017-Personalization-Report.pdf) said that they are likely to become repeat buyers after a [personalized experience](https://www.redpointglobal.com/blog/clear-the-hurdles-for-a-personalized-customer-experience/) with a company. This improved customer experience ultimately translates directly into an increase in revenue for companies. ## A Personalized CX Drives Customer Loyalty Personalization is now table stakes for marketers because it drives [customer loyalty](https://www.redpointglobal.com/blog/customer-loyalty-and-an-emotional-connection-for-the-new-reality/), which is why according to research from a Harris Poll survey sponsored by Redpoint Global, [37 percent of consumers](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) will stop doing business with a brand that fails to offer a [personalized experience](https://www.redpointglobal.com/blog/augment-customer-segmentation-with-a-personalized-cx/). Retaining and growing a brand’s customer base requires that marketers prioritize personalization investments across the board, for loyalty program members and non-members alike. While loyalty programs serve a core constituency, delivering personalization to non-members also enhances CLV by fostering an emotional bond that strengthens brand loyalty apart from loyalty rewards. ## Loyalty Members Crave a Hyper-Personalized CX Successful loyalty programs produce many benefits, which illustrates the importance of the customer experience. According to an Accenture Interactive study, loyalty program members generate up to [18 percent more revenue](https://newsroom.accenture.com/news/members-of-customer-loyalty-programs-generate-significantly-more-revenue-for-retailers-than-do-non-members-accenture-research-finds.htm) for retailers than non-members. By incentivizing repeat customers, loyalty programs also increase retention, reducing acquisition costs. A recent Brand Keys study indicates that brands spend [11 times more](https://brandkeys.com/portfolio/customer-loyalty-engagement-index/) on recruiting new customers than retaining existing ones, yet a loyalty increase of 7 percent can boost lifetime profits per customer up to 85 percent and a loyalty increase of 3 percent can correlate to a 10 percent cost reduction. ## A Superior CX that Drives Loyalty Means All Rewards Are Not the Same To produce these gains, loyalty programs can no longer rely on the traditional buy-and-get model that fails to impress today’s connected consumer. A Bond Brand study reports that just [25 percent of loyalty program members](https://info.bondbrandloyalty.com/2017-loyalty-report) are satisfied with the level of personalization in their programs. A loyalty program member is not incentivized to remain loyal to a brand if they receive the same benefits as everyone else. According to a Forrester report, [59 percent of US online adults](https://www.oracle.com/us/solutions/consumers-loyalty-programs-3738548.pdf) who belong to a loyalty program say that receiving special offers or treatment that are unavailable to other customers is important to them. Of regular loyalty program participants, 69 percent say that special treatment is “important”. ## To Provide a Differentiated Customer Experience, Truly Know Your Customers To provide special treatment, a brand must know and understand a customer throughout the entire anonymous and known customer journey. Offers, discounts, rewards, and other benefits of loyalty program membership must be relevant for a customer at a precise moment of interaction, and customized to align with the complete customer lifecycle. Far more than transactional, a modern loyalty program treats a customer according to the customer’s behaviors, purchases, needs, and wants in totality across the entire [omnichannel journey](https://www.redpointglobal.com/omnichannel-personalization/). This is where brands are failing customers, according to the Harris Poll survey. An analysis of the customer experience gap revealed that consumers rated brands [15 points lower](https://www2.redpointglobal.com/webinar-harris-poll-cx-2019-ondemand?_ga=2.134931698.208126881.1563800978-1570104466.1540307570) than marketers rated themselves across every dimension of customer experience – privacy, personalization, customer understanding, and consistency across channels. Based on this data, marketers are clearly underestimating the importance of customer experience. ## Close the CX Gap for Members and Non-Members A customer experience gap is also detrimental to loyalty outside the confines of a loyalty program. Just as loyalty members demand personalization across an omnichannel journey, non-loyalty program members are still capable of developing strong brand affinity when a brand provides a superior customer experience. An Acquia study reveals that [75 percent of consumers](https://www.apnews.com/f7bec4d577d04f98a00b857a2263846a) say that they are more likely to be loyal to a brand when a brand understands them at a personal level. The survey of 5,000 consumers was not restricted to loyalty program members. In addition to valuing personalization, customers develop emotional bonds with a brand when a brand shares certain values or connections, such as environmental, social, or even political issues. The Havas Meaningful Brands 2019 report reveals that brands seen as “meaningful” and viewed as making the world a better place have seen wallet share [increase nine-fold](https://www.marketingdive.com/news/consumers-see-77-of-brands-as-not-meaningful-report-says/548956/), with a 24 point greater purchase intent. While understanding a customer’s values may be getting ahead of the game for most brands, the thrust of the point is that driving increased relevance for each and every consumer is the true objective of personalization, and an imperative for increasing loyalty and customer lifetime value. At the core of this heighted relevance is the capability to know everything there is to know about a customer. ## Loyalty, CX and the Customer Data Platform To know everything there is to know, a brand must unify all sources of [customer data](https://www.redpointglobal.com/blog/evolution-of-the-customer-experience-and-the-role-of-data/) (social, transactional, marketing responses, website visits and behavior, call center interactions, demographic information, preferences, etc.). With a single view of the customer across the enterprise, also known as a golden record, marketers can deliver the most relevant offer, message, or action through whatever channel the customer shows up in next. A golden record, which contains data from first-party, second-party, and third-party data, as well as structured, semi-structured, and unstructured data, provides insights needed to treat each customer as an individual. Armed with a golden record, a brand goes far beyond understanding transactions to also understanding the customer’s needs, attitudes, preferences, and intents. It is a critical asset for driving loyalty through customer understanding, which unlocks the omnichannel consistency that customers say is an imperative if they are to become repeat, loyal customers. A customer data platform (CDP) provides marketers with a persistently updated golden record and an operational environment that ingests any volume, veracity, or velocity of data from any touchpoint. A CDP is the foundation for a single point over data, including ingestion, advanced identity resolution and curation while making this data accessible across the enterprise. By applying in-line analytics and intelligent orchestration through a customer engagement hub (CEH), marketers also have a single point of control over decisions and interactions across customer journey stages and touchpoints. With a single point of control, marketers are continuously attuned to a next-best action for loyalty program members and non-members alike, greatly increasing the propensity for repeat, loyal customers. Research from Forbes Insight indicates that [44 percent of organizations](https://www.forbes.com/sites/insights-treasuredata/2018/06/20/the-rise-of-the-customer-data-platform-and-what-it-means-to-businesses/#d7ff63a53a15) surveyed report that a CDP helps drive customer loyalty and ROI in their organization. ## A Differentiated CX Places Personalization over Product In the not-too-distant past, brands were confident that producing the best product at a fair price was enough to drive customer loyalty. But an explosion of interaction touchpoints and buying options has put the customer in charge of the omnichannel buying journey, and this trend has largely commoditized price and product in lieu of customer experience. Personalization is now what differentiates the [customer experience](https://www.redpointglobal.com/blog/a-superior-customer-experience-cx-transcends-marketing/), and customers have shown that they will reward a brand with loyalty if the brand recognizes them as an individual across channels, and across the entire customer lifecycle. Customer loyalty, in other words, is a two-way relationship based on a value exchange. With the right investments in personalization, brands can ensure that they will always deliver on their end of the bargain. **Blog categories:** 1:1 Personalization, Customer Data Platform, Real-Time Personalization, Single Customer View --- ### [3 Things You Need to Get the Most Out of a Data Lake](https://www.redpointglobal.com/blog/3-things-you-need-to-get-the-most-out-of-a-data-lake/) **Published:** July 11, 2018 **Author:** Redpoint Global **Content:** Most organizations understand that in order to compete in business, you need to understand your customer and provide the personalized experience that they have come to expect from brands. Brands are no longer simply competing based on products or services, they are directly competing on customer experience. In order to deliver a personalized experience, many of these organizations choose to utilize data lakes for their broad variety and large volumes of raw customer data. ### **What Is a Data Lake?** Data lakes are repositories that house a broad variety of data – structured or unstructured. While it is a wonderfully easy solution for housing a variety of customer data, there are several things you must have in place to reap the benefits from your data lake. One of the greatest issues in adopting data lakes is that brands fail to ensure the quality of data that is amassed within these lakes. While the ability for organizations to store high volumes of structured and unstructured data has become more accessible, there are few brands that have been able to avoid these data lakes from turning into data swamps. That is, the quality and mass of the data becomes too large and are ultimately inaccessible to the end users, making it nearly impossible for organizations to fully realize the value of their data lakes. For brands to fully capitalize on the advantages of their data lakes, they must implement automated processes to manage the data to produce accurate customer profiles for analytics and engagement. Here are the three things you need to have in place to get the most out of a data lake: 1. **Get a Tool That Can Natively Handle all the Data Source Formats –** In order to put the data to use, it needs to be sorted and easily accessible across the organization. This requires a tool that can natively handle all these different data source formats so that they can become easily accessible for the business user. This includes multiple NoSQL and document formats such as MongoDB, Avro, and Parquet. Siloed customer data is often a major barrier to creating consistent customer experiences because the data is stored separately. Data lakes solve this problem by ingesting data from all the customer engagement points into a single location. The end result is a unified customer profile, or “golden record,” that collects all that is knowable about each individual customer 2. **Ensure Data Quality –** The quality of the customer experience is entirely dependent on the quality of the data. Without correct or timely data, brands often run into the issue of irrelevant or repetitive offers. [Customer data platforms](https://www.redpointglobal.com/#cdp) (CDPs) are one of the best solutions for ensuring the quality of the data. A true CDP should be able to handle cross-source data extraction, name and address normalization, tuned deterministic and probabilistic matching, along with workflows for resolution auditing and compliance. Only a customer data platform can provide the insight into consumer preferences and analytics functionality that forward-thinking brands need to drive average revenue per user higher and succeed in the long term. 3. **Have Your Data Updated in Real Time –** In order for brands to provide a personalized experience for each customer, they need to ensure that the data is timely. Research from Dun & Bradstreet found that organizations who actively maintain their data have a 66 percent higher conversion rate than their peers. If the customer has moved homes, changed jobs, or already purchased a product, it will ultimately affect the customer journey. In the age of the smartphone, the entire path to purchase has been significantly shortened, and the ability to engage with the customer in a timely manner has become critical to attracting and retaining customers. This requires model and performance testing as well as product capability that minimizes latency. With the right tools, a company can handle the variety, velocity, and volume of data lake information to effectively utilize their data lake to meet their customer experience goals. Effective utilization of data lakes can enable companies to succeed in today’s fast-paced marketplace, but it is even more important to implement the right tool for your data lake to ensure the ability to deliver a seamless and personalized customer experience throughout the customer journey on a richer and more personalized basis. **Blog categories:** Customer Data Platform, Data Management, Data Quality --- ### [Using Predictive Marketing to Demystify AI and Machine Learning](https://www.redpointglobal.com/blog/using-predictive-marketing-to-demystify-ai-and-machine-learning/) **Published:** October 2, 2020 **Author:** Steve Zisk **Content:** Artificial Intelligence (AI) and machine learning (ML) have been shiny objects for marketers for the past several years. Chief marketers, or their CEOs, have demanded to have it—without fully grasping what “it” is. Over that same time frame, marketers have been deluged with ever-expanding sources of customer data, leading data-driven marketing to shift from being humdrum to being as sexy as branding. If data-driven marketers want to be true A-listers, though, they need to optimize the way they use AI and ML in their marketing. Many marketers grasp AI and ML at a high level. What they really need is a deeper understanding of what AI and ML can do to support and improve their marketing and how to get the most from them. So, what is AI for marketing? And how does it differ from machine learning? Redpoint Global’s senior engineering analyst Bill Porto explains it this way: AI is the replacement or augmentation of “intelligent” human tasks by a machine, whereas ML is the use of historical data to create a descriptive, predictive, or prescriptive model to be run against current data. AI might be self-driving car software, which requires hardware for sense-and-control, software to recognize details of a situation (pedestrian, light, car movements, route changes), and the “executive function” that acts on the input and drives the car. ML might be in the “recognize” and “decide” components. AI technologies enable marketers to enhance analyses of and automate decisions based on data they’ve collected. For this reason, marketers often use AI where speed is essential; for example, to personalize communications and offers in real time. Elements of AI such as machine learning and natural language processing help marketers [predict the future actions](https://www.redpointglobal.com/blog/capturing-intent-in-an-omnichannel-customer-journey-is-next-level-marketing/) of their various audiences. AI is not a replacement for marketing expertise; it augments marketing teams, allowing them to focus on activities such as strategic planning while the AI tools handle tasks such as serving contextually relevant messages at key customer interaction points—at scale. [Machine learning](https://www.redpointglobal.com/resources/redpoint-automated-machine-learning/) is the aspect of AI that most marketers are familiar with (though many refer to it as AI). Marketers primarily use machine learning to improve personalization at scale, predict churn propensity, and identify customer segments. Using algorithms, modeling, and statistical methods such as regression analysis, ML programs learn and improve over time as they process more and more data. Consider this example: Marketers might use chatbots to respond to customers on a website, and those chatbots might use text analysis, sentiment analysis, customer lifetime value calculation, persona, product rankings or recommendations, next-best-actions, customer journey analysis, etc. to respond to the customer. ML is in many of those details, but the chatbot itself might be considered AI. ML delivers startling results—when marketers feed it the high-quality, robust data it needs to analyze, learn and perform. This is especially important for marketers using ML for activities such as real-time personalization. Achieving and optimizing real-time personalization requires collecting data and updating each customer’s Golden Record in real time and then analyzing the new data in the context of any previous data in real time, which will enable marketers to then execute contextually relevant campaigns in real time. The beauty of ML is how extensively it can help marketers increase marketing effectiveness and customer lifetime value, streamline processes such as personalization and targeting, and even reduce costs and boost revenue by improving lead scoring and marketing performance and reducing “waste.” Marketers can also see benefits with activities such as ad targeting, content optimization, recommendations, and sentiment analysis. ML can help marketers meet or exceed goals in areas such as customer acquisition and retention and cross- and upselling. One simple example of the impact machine learning can have on a common marketing tactic is optimizing email send frequency. Without machine learning, marketers generally need to run frequency optimization tests to their full lists and follow each test with an ROI analysis. Automating frequency optimization through machine learning not only speeds and simplifies the testing process, but it also allows marketers to personalize send frequency to specific recipients. Let’s take a closer look at some use cases and benefits. ## **See the future:** Marketers can automate regression modeling to track and analyze data such as changes in customer preferences, responses to campaigns, and buying habits to predict customers’ responses to future actions or communications—allowing marketers to automate those actions and communications so they can happen in real time, at the cadence of the customer. ## **Get personal:** One way that marketers can improve their look-alike marketing, segmentation, and preference analysis is by automating clustering to happen in real time. These models are designed to discern optimal groupings and then assign customers to them. For example, marketers can use browsing data to segment customers in real time. ## **Optimize customer journeys:** By automating predictive analytics, marketers can more precisely guide customers through their buyer’s journey, not only improving their experience, but also increasing conversions and purchases. ## **Scale up:** Marketers can deliver personalized actions, communications, and offers at scale by automating processes such as segmentation and targeting. More relevance reduces customer fatigue and increases lifetime value and revenue. ## **Keep it fresh:** Static models can go “stale” quickly in today’s dynamic marketplace. [Automated machine learning](https://www.redpointglobal.com/blog/what-is-automated-machine-learning/) models stay current, which ensures that marketers will get the optimal predictive value from them. ## **Measure and monitor: The highest-quality customer data is fresh, accurate, robust, and detailed. So, marketers should collect all available data to create a real-time holistic view of each customer, often called a Golden Record, that feeds their machine learning activities. That single customer view should be a blend of first-, second–, and third-party data (batched or streaming) from internal and external systems. These include everything from CRM and call center data to POS and email systems to preference center and website behavior data. With the right data feeding AI and ML systems, marketers can confidently automate key marketing tactics while leaving themselves time and energy for more strategic endeavors. And that’s where the benefits of AI and machine learning are clear. **Blog categories:** Data Management, Data Quality, Omnichannel Marketing, Real-Time Personalization --- ### [Secure Centralized Marketing Benefits Even with a Decentralized Marketing Org](https://www.redpointglobal.com/blog/secure-centralized-marketing-benefits-even-with-a-decentralized-marketing-org/) **Published:** June 8, 2021 **Author:** John Nash **Content:** Deciding whether a centralized or decentralized marketing structure is the right approach for a business often comes down to which of the respective benefits are deemed more important. A centralized structure, usually defined as one with a single marketing team, is often more efficient and cost-effective. Conversely, a decentralized structure with different teams reporting into different business units or channels is often credited with being more agile, with each team highly in tune with the dynamics of its particular part of the customer journey. Because each enterprise has its own objectives, there really isn’t a right or wrong approach. But in any healthy debate about what’s right for the enterprise, the overarching consideration should be the one person who – all things considered – will notice it the most: the customer. As long as the customer receives a consistent, seamless and personalized experience at any moment of interaction across all channels, the marketing structure is of little concern. In a [Harris Poll survey commissioned by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/), consistent relevance across channels was a top priority for consumers surveyed. Asked to define a personalized experience, 43 percent said it was when a brand recognized they were the same customer across all touchpoints (e.g., in-store, email, call center, mobile, social media, etc.). Furthermore, 63 percent said they expect a personalized experience as a standard service – with 37 percent going so far as to say they will stop doing business with any brand that fails to provide it. ## **Centralized Marketing and a Seamless Experience** As it so happens, a [customer-centric](https://www.redpointglobal.com/orchestration/customer-centricity/) approach is often touted as a benefit of a centralized marketing structure. It makes sense; when there’s one marketing team devoted to the needs of the customer, the team can far more easily focus on customer-driven metrics rather than channel metrics such as opens, clicks or revenue per email. A singular focus on customer-driven metrics lends itself to a more seamless experience for the customer versus various marketing teams each being responsible for delivering an experience on a different channel. The latter approach is a problem for the customer because, as the research shows, that’s not how the customer views their relationship with a brand; customers do not consider a call center interaction, say, as a standalone interaction distinct from how they navigate a brand’s website. Rather, the entirety of a [customer journey](https://www.redpointglobal.com/orchestration/customer-journey/) is viewed holistically. ## **By Approach or Structure, Centralized Marketing Eliminates Disconnects** None of this is to say that a decentralized marketing organization must immediately reorganize. By adopting a customer-centric mindset, and with the right technology, it is possible to reap the benefits of a centralized marketing structure – namely, the delivery of a seamless experience across all channels – while retaining a decentralized structure. Marketing teams responsible for – and reporting into – different business units can adopt a customer-centric mindset as long as the teams are all working with the same customer data. It may sound like a simple concept, but siloed data is often a byproduct of siloed channels, especially when marketers are incented by channel-driven metrics. And siloed data is antithetical to a seamless, personalized experience that customers expect across both physical and [digital channels](https://www.redpointglobal.com/digital-advertising/). Consider a customer who visits a retail outlet and purchases an outfit for an outdoor summer wedding. In the meantime, an email marketing team happens to have queued up an email offer for a discount on semiformal summer attire. A customer who opens the email after making the in-store purchase may experience a disconnect – why wasn’t the email sent earlier? Can I still redeem the offer? Conversely, if the email marketing team is working in concert with the same real-time customer data as the physical location, it can perhaps use open time email personalization to change the email content to be more relevant to the customer journey stage. Maybe the offer is for a discount on matching accessories, for instance. ## **A Centralized Marketing Mindset and a Single Customer View** A centralized marketing mindset begins with a consistent single customer view that is accessible by every function or marketing department across the enterprise. A single view of the customer that integrates data from every conceivable source and is updated in real time provides a single source of truth that enables a decentralized marketing organization to act like a centralized organization in terms of how it delivers a consistent experience. Eliminating data siloes to enable various marketing teams to act as one on behalf of the customer is more than just aggregating customer data from every source. A true single customer view also requires that data quality issues are resolved in-line (i.e., at the point of ingesting or earlier). Matching, merging and cleansing functions, including [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) with both probabilistic and deterministic matching, are all essential. One could argue that data quality is especially important for a decentralized marketing organization. Because there are different teams, robust data quality measures ensure the teams can trust that they’re working with the same data as another team – and that the data are consistent across the enterprise. Certain that customer data is accurate and updated in real time, each team can be supremely confident that when a customer appears in the channel they’re responsible for, they will be poised to deliver a next-best action for the customer that is optimized for the precise moment of interaction. There is no worry of an overlap, a disconnect or otherwise inconsistent messages or offers that often plague decentralized teams when they’re each working with their own sets of customer data. ## **Centralized Marketing Puts the Customer First** Centralized marketing – whether an actual organizational structure or as an approach enabled through a single customer view – is vital to engaging customers with a consistent, hyper-relevant experience across all channels. The Redpoint CDP empowers decentralized marketing organizations to adopt a customer-centric approach and act as a centralized marketing team while also protecting their existing operational investments. Customers view their relationship with a brand not as a series of disjointed interactions, but as a holistic experience that happens to span multiple digital and physical touchpoints. An organization may choose to have multiple marketing teams, but to meet customer expectations for a [seamless, personalized experience](https://www.redpointglobal.com/omnichannel-personalization/) centralized marketing is essential, if not as an operational structure than as an approach derived from a single view of the customer. **Blog categories:** Data Quality, Identity Resolution, Real-Time Personalization --- ### [Algorithmic Optimization and the Magic of AML](https://www.redpointglobal.com/blog/algorithmic-optimization-and-the-magic-of-aml/) **Published:** July 15, 2020 **Author:** Redpoint Global **Content:** *Editor’s Note: This is the second in a two-part blog series on automated machine learning (AML), broadly defined as the automation of the process of applying machine learning to real-world problems. [The first blog in this series](https://www.redpointglobal.com/blog/what-is-automated-machine-learning/) explored the current state of the market for developing predictive assets to improve business performance.* In continuing our examination of the Redpoint approach to automated machine learning, it may be useful to first break down the full breadth of machine learning capabilities within the rg1 platform. An understanding of the types of machine learning models will reveal how the algorithmic optimization capabilities in rg1 set Redpoint apart in accomplishing intended business objectives. The difference between regression and classification models depend on whether the model is predicting a continuous, non-categorical target. Those that do are regression models, and predict a dependent, continuous variable such as revenue. A classification or categorical model, by contrast, predicts a non-continuous target, such as which category a person or thing belongs to or a segment affinity. The Redpoint AML platform also builds powerful clustering or segmentation models. Importantly, segments are not restricted to surface level groupings such as age, gender, income, zip code, etc. Rather, hyper-dimensional model clustering finds very nuanced pairings that truly predict a behavior, interest or outcome beyond a shared characteristic. Another common application for standard, predictive modeling is product recommendation, where Redpoint shines with a next-best offer, next-best product, or more accurately a “next-best X” because the decision is rendered at the optimal moment in a customer journey where it will have the best chance of success – whatever the unique sequence of interactions dictates. **The Magic of Evolutionary Programming** Robust automation and algorithmic optimization transform the models – regression, classification, clustering, product recommendation – into living, breathing sets of models that evolve over time and can be re-trained automatically without human intervention. [Evolutionary programming](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/) is the heart of automated machine learning because it ensures that the model ties directly to the metric a marketer is trying to push during development. It is called the “fitness function” because it is the metric the model must “fit” to or optimize. It guarantees that a model will be highly relevant and effective in moving the metrics the marketer intends to move – without having to rely on error-prone human judgment and experience, and the attendant resources. It works by fusing machine learning with algorithmic optimization to enable the building of dozens or hundreds of models, with a simulation process that measures the outcome of a model against a perfect solution for your particular metric. It is not only picking winners and losers, but picking a winner optimized against the metric for that moment in time. What makes this so powerful is that algorithmic optimization is done against fleets of models simultaneously, with many different algorithms run through the variation and selection process and continually assessing if the models are reducing error against the perfect solution for a particular metric. From an operational standpoint, even if we stipulate that an individual model is not as effective as one built by hand (ignoring for a moment the tremendous cost and time involved to build such a model), if the model is 80 percent as effective but there are 100 of them, that’s an 8,000 percent improvement over the capability of the hand-built model. This is what makes the platform incredibly powerful for virtually any employee in the organization to leverage. **Algorithm vs. Algorithm** The operational marketer can target virtually any metric, using hands-free evolutionary programming to solve any problem. Regression and categorical models, for instance, are essentially trying to find the best line through a set of data points – represented as dots. By extending the line, you’re predicting the pattern. Different algorithms that include linear least squares, partial least squares, neural networks, decision trees, random forests and multinomial logistic regression are all competing to determine the optimal line extension. This is where the simulator decides the winner based on outcomes measured against the metric the operational marketer has chosen. Redpoint’s clustering techniques recognize that the world is far more dynamic and complicated than traditional two-dimensional graphs. While it’s impossible for a human to make sense of a graph with 20 or more dimensions, algorithms built for a hyper-dimensional space allow for nuanced, complex groupings. These include K-Means, K-Modes, hierarchical, self-organizing feature maps, and constrained clustering – again, all competing to determine a winner. Product recommendation models include a matching function to determine the “next-best X” by listing products and weightings on one side, such as profitability, availability, type of product, etc., and people on the other side who of course have their own weightings represented by the data (preferences, finances, past behaviors, etc.) This is a commonly used feature for many Redpoint clients, with significant revenue results. One CPG company, for example, saw a 79 percent increase in conversions from web-based, real time product recommendations. **Ambitious Marketers, an Ambitious Solution** AML with Redpoint addresses the processes needed to build predictive models in a way that also reduces or even eliminates the challenges that marketers – and data scientists – typically encounter when trying to scale predictive models across the enterprise. As such, Redpoint clients commonly build fleets of models that operate in production simultaneously, and drive significant value simply by eliminating the downtime from having to code, re-build and re-train models as metrics and/or customer journeys change. By putting machine learning into the hands of the Citizen Marketer, the Redpoint AML solution gives the common, everyday marketer – and everyone in the organization – the power to create and scale a personalized customer experience that drives new revenue. With customer journeys becoming more dynamic by the day, a reliance on old school methods or manual predictive modeling will fall far short in keeping pace with today’s always-on, continuously connected consumer. **Blog categories:** Data Management, Journey Orchestration --- ### [Wave a Magic Wand: What Can You Accomplish with Perfect Data?](https://www.redpointglobal.com/blog/wave-a-magic-wand-what-can-you-accomplish-with-perfect-data/) **Published:** September 7, 2021 **Author:** Redpoint Global **Content:** In the nascent days of mass marketing, someone somewhere thought it was a brilliant idea to address a mailer to “Jane Doe, or Current Resident.” The thought was that by hedging their bet, the communication wasn’t going to waste. I bring this up both to show how far the industry has come in terms of the level of personalization that today’s savvy consumer expects, as well as an intolerance for bad data, but also to illustrate the danger of getting it wrong. In that era, when businesses lacked advanced personalization techniques and were forced to accept constraints from having imperfect data, tacking on “current resident” and similar types of half-measures were thought to correct for those imperfections. We think Jane Doe may live here, but we’re not sure, so hey – if she moved, we’re still covered. Of course, it’s easy to see the problem with that approach. First, if you are Jane Doe, you’re slightly annoyed that the business communicating with you couldn’t be bothered to be sure. So how important can the communication be? And if anyone can open it, it certainly isn’t personalized for Jane, so she tosses it in the trash. The same holds true if, indeed, you happen to be the “current resident.” In trying to be too clever by half, the brand not only wastes money, but it also creates a bad experience for any potential recipient of the communication, losing many potential customers along the way. If a brand does not care enough about me as an individual to clean up its data and ensure that every communication with me is tailored to my individual preferences, likes and patterns, then why should I bother giving it my business? Everyone knows that personalization is possible, so if they are not doing it, they must not care. Maybe I won’t care either. ## **Trust is the Central Currency** The previous example illustrates the importance of trust as the central currency of a relationship between the brand and consumer. Consumers are more apt to do business with brands they trust, particularly as omnichannel customer journeys become more dynamic and more digital. Consumers are hyper aware that they generate a wealth of personal data, and they want the peace of mind that the businesses they interact with honor their privacy and security, as well as their preferences. Likewise, to engender consumer trust a brand must have complete trust in its data. As we see with the “current resident” failure, incomplete or inaccurate data that brands try to cover up with half measures or back-handed “personalization” attempts are woefully inadequate to meet the exalted expectations of today’s savvy consumers, with far more dire consequences than a discarded mailer. A recent Edelman report, [Trust Barometer: Brand Trust in 2020](https://www.edelman.com/research/brand-trust-2020) drives the point home. From 2019 to this year, there was a double-digit increase (from 34 to 46 percent) in the percentage of consumers who say they trust most of the brands they buy or use from. That means that roughly half of the entire United States consumer brand is up for grabs for the brands that create that trust, which Edelman research indicates is a barometer for loyalty; 75 percent of consumers with high brand trust say they will buy the brand’s products even if it isn’t the cheapest – and it is the only brand of the product they’ll buy. ## **Break Free from Traditional Constraints** Yet too often, we see today’s digital equivalent of the “current resident” mishap – irrelevant or redundant emails, late messages, an overabundance of messages, an impersonal product recommendation, even the wrong image or text on a landing page. It’s a breakage in communications that solicits that very simple reaction from people; why am I bothering to interact with your brand if you aren’t investing the time and attention to get it right? It’s just not worth it. The problem for many brands, though, is that they’re accustomed to bouncing up against what they see as institutional constraints in the market, chiefly due to poor data quality. Thus, the extent of their vision for a truly personalized customer experience, made possible by perfected data, is limited. Brands must break through this barrier, because the omnichannel customer experience and vision – not just in marketing, but across the entire enterprise for the entirety of the relationship – is the reality that current market leaders are working on today, and it is the reality of what customers expect today. With trust as the currency for how brands extract value from their brand relationships every detail – however small – must be accounted for. Marketers or anyone in the enterprise who interacts with customers must not allow themselves to be constrained by the immaturity of technology. Rather, they have to find the tools that enable the vision for what can be accomplished with perfected data. ## **Wave a Magic Wand – Perfect Data is a Reality** For anyone who works with data, or interacts with customers, the Redpoint CDP truly allows them to realize the vision for engaging with customers with perfected data, without constraint. It supports and inspires bold action and data-driven decisions through the confidence that comes with knowing that data is rock solid. The Redpoint CDP fulfills those aspirations for what can be done with perfected data by putting data into a structure that supports decision-making and analytics, without requiring the traditional skills and technical expertise to do so with a high level of skill and accuracy. That, in a nutshell, is really the biggest obstacle to working with data – people avoid having to dive into the weeds or having to risk exposing their data by shipping it across the internet. Businesspeople just need it to work; they need their data to support a wide breadth of business use cases and decisions, and they want to do it without compromise. In Situ enables the trust to capture customers who will only buy goods and services from brands they trust by performing comprehensive data quality and accurate identity resolution at the moment of data ingestion. Within milliseconds, it creates an unassailable unified record of a customer, or of an entity of value to an organization, whether it’s manufacturing parts, financial entities, households or individual customers. The complexities that are now viewed as constraints fall by the wayside when identities are resolved in real time, when data is harmonized and perfected in an organization’s own security perimeter, and accurate data linkage is completed on a company’s own first-party business and customer data within the enterprise database, wherever it exists. A vision of what can be accomplished with perfect data no longer has to be a fantasy, or some intangible wish list with little reality of coming true. In Situ fulfills any aspiration for unlocking the value of customer and business data, without constraint. **Blog categories:** Data Quality, Identity Resolution, Master Data Management --- ### [Untapped Potential: In Every Industry, IoT is a Natural Fit](https://www.redpointglobal.com/blog/untapped-potential-in-every-industry-iot-is-a-natural-fit/) **Published:** August 10, 2022 **Author:** John Nash **Content:** As a bridge between the physical and digital worlds, it is no surprise that the Internet of Things (IoT) is gaining traction as a foundational technology, the martech space included. In a previous Redpoint [blog post](https://www.redpointglobal.com/blog/drive-transformative-change-with-iot/) on the merits of including IoT in a Golden Record, one key reason for doing so was the economic value that IoT is expected to produce, which McKinsey recently estimated at up to [$12.6 trillion](https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/iot-value-set-to-accelerate-through-2030-where-and-how-to-capture-it) globally by 2030 – including $710 billion in the retail sector alone. In addition: - Grand View Research estimates the IoT market in healthcare will reach [$534.3 billion](https://www.reportlinker.com/p05763769/Internet-of-Things-in-Healthcare-Market-Size-Share-Trends-Analysis-Report-By-Component-By-Connectivity-Technology-By-End-Use-By-Application-And-Segment-Forecasts.html?) by 2025 - IoT Business News predicts there will be [27 billion connected IoT devices](https://iotbusinessnews.com/2022/05/19/70343-state-of-iot-2022-number-of-connected-iot-devices-growing-18-to-14-4-billion-globally/) by 2025 With these statistics anticipating massive growth potential, we thought it worthwhile to revisit the topic to look at potential and existing use cases for incorporating IoT across a few different verticals, particularly with an eye toward leveraging the technology to deliver a personalized customer experience. ## **A Retail Game-Changer** In retail, there are countless applications for personalizing the customer experience using IoT. Much like an online browsing session of a retail website, an in-store visit provides a wealth of data about how a customer navigates through a store, such as traffic patterns and dwell times. IoT-enabled sensors can provide data about an individual customer’s patterns and behaviors, which as part of a single customer view can then be used to generate personalized offers or messages – either during the visit through a mobile app, or with a next-best action on a different channel. Amazon Go, for example, uses sensors and cameras to power its “just walk-out” technology at checkout-less stores. Customers on the mobile app scan a code to enter the store, using a credit card linked to an Amazon account. They can then pick up – and put down – any items they want and simply walk out of the store. Just as Amazon has a wealth of first-party data from online and Prime customers, cashier-less stores provide an opportunity to learn behaviors and preferences of in-store customers, which the company uses to personalize future interactions across channels. The same technology can be used to enhance the overall customer experience for every customer. While not personalization at the individual level, retailers can use IoT from ‘smart’ shopping carts or shelf units to optimize aisle layouts, reduce checkout lines, improve space allotment or otherwise creating a more seamless customer experience. Other personalization use cases in retail include IoT devices such as Bluetooth beacons sending alerts in real-time to a customer’s device based on real-time location data. A customer near a store might be prompted to visit to take advantage of a personalized offer, as an example. IoT devices in the home – refrigerators, washer/dryer, TVs, thermostats, doorbells – also provide largely untapped opportunities for retailers to partner with manufacturers to deliver a more personalized customer experience. Analyzing product usage and recommending or automatically fulfilling a product are familiar examples for bridging the physical and digital through a connected device. A smart beverage refrigerator connected to a weather app might learn over time what a household consumes when the outdoor temperate hits 80°, and automatically order a refill for that product based on both weather and consumption patterns. In this and similar examples, a customer or household will have agreed to share data in exchange for a more relevant experience. ## **IoT & Travel: At Your Service** In Travel & Hospitality, many IoT use cases approximate the smart home conveniences. In many hotels, for example, smart thermostats, lights and blackout curtains regulate room temperature and lighting to a customer’s preferences – and remember a customer’s preferences on return visits. Customers can use a smartphone app to gain room entry, order room service and use as a remote control. Disney has been a leader in using IoT to improve and personalize the guest experience. The theme park’s “Magic Bands,” which debuted in 2013, enable guests to access rooms, pay for meals and souvenirs through a credit card linked to the device, and enjoy a personalized experience across the entire park. A vast network of sensors and interconnected devices enables personalized touches such as automatically uploading pictures of customers just off a roller-coaster to their mobile apps. In detailing all the IoT-enabled use cases, an [article in Forbes](https://www.forbes.com/sites/csylt/2020/06/06/inside-disneys-internet-of-things/?sh=e5ab9bd75459) writes that “the Magic Bands and the sensors turn each park into a giant data processor or, as it is known in the industry, an Internet of Things.” Says Kelly White, Disney VP of Digital Strategy, “There is a holistic strategy for how we think about the connectivity, not only from digital experiences but also even some of those moments like getting a food delivery or accessing an attraction. … It is partially about making it a fantastic experience for our guests but also about how we can use technology in service of the story.” Other travel & hospitality IoT use cases for Disney and other tourist attractions are to use sensors to alert customers with tips for optimizing a visit, such as leveraging real-time data to reduce wait times, capitalize on flash sales and upgrades or to send updates on nearby public transportation schedules or optional services. Many, if not most, IoT use cases in the travel industry approximate a concierge service, providing customers with a personalized, frictionless experience that gives them more control and greater convenience over the complete customer journey. ## **IoT & Healthcare: Smart Devices, Healthy Patients** Opportunities for IoT in healthcare are familiar to many consumers from established technology such as Fitbits and other wearables that provide users – and connected providers – with a plethora of real-time health data that can be used to tailor exercise programs, monitor sleep patterns, create a nutrition plan, medication adherence, etc. Smart medication dispensers, glucose monitors, inhalers and thermometers are among home healthcare devices in use today to connect healthcare consumers with providers outside of a clinical setting in the rapidly growing [Remote Patient Monitoring](https://www.prevounce.com/a-comprehensive-guide-to-remote-patient-monitoring) (RPM) field that relies on digital technologies and IoT devices. In a clinical setting, there are [countless examples](https://www.hcinnovationgroup.com/analytics-ai/article/21132663/how-ai-and-iot-are-changing-daily-operations-in-hospitals) of hospitals using IoT to improve the patient experience, such as reducing wait times, helping with triage and improving diagnostic accuracy. Hospitals today are using smart hospital beds to reduce falls, mitigate bed sores and to automate the process for finding an available bed. As in retail and travel, many IoT healthcare-related use cases give the healthcare consumer more control over their journey. By bridging the gap between the physical and digital worlds, IoT technology appears here to stay in a connected economy where the line between physical and digital channels and touchpoints is becoming increasingly blurred. Consumers expect convenience, control and frictionless, personalized omnichannel experiences. IoT delivers on these expectations, which is why the medium is expected to produce trillions in economic value by the end of this decade across all industries. --- ### [A Composable CDP is NOT Just Reverse ETL](https://www.redpointglobal.com/blog/composability-is-not-just-reverse-etl/) **Published:** September 24, 2024 **Author:** Renee Graff **Content:** It seems like every time you turn around, there’s a new definition of a customer data platform (CDP). One common misconception is that a [CRM and a CDP are the same thing](https://docs.redpointglobal.com/bpd/why-a-cdp-is-not-a-crm-how-these-systems-can-work-). Another thing people get hung up on is thinking that reverse ETL is a CDP. When reverse ETL provider Hightouch came out with their since-retired “Friends don’t let friends buy a CDP” slogan, it really muddled the waters, since they also represent themselves as a “composable CDP”. In an attempt to help you make informed decisions about your customer data strategy, let’s break down the differences between reverse ETL and a composable CDP. ## **What is Reverse ETL?** At its core, reverse ETL is the process of extracting data from a data warehouse or data lake and syncing it back into operational tools, such as CRMs, marketing platforms or customer support systems. It was created to solve a problem that only existed because it wasn’t being solved upstream. Otherwise, it would be more of a “Reverse EL” than a “Reverse ETL”. The “T” (Transformation) is required when the data that resides in a data cloud is not fit for purpose, hasn’t been modeled correctly, or hasn’t got all the aggregates and insights packaged in, so they need to be created on the fly. Reverse ETL is about activating the data you’ve already collected and making it actionable for various business functions. In the CDP world, the rise in popularity of a cloud data warehouse (data cloud) as a single source of truth for customer data is a major reason why reverse ETL has taken off as a valuable tool for enabling teams to leverage the full power of their data, but it is still just one piece of a much larger puzzle. ## **Why Reverse ETL Alone Doesn’t Equal a Composable CDP** A composable CDP, on the other hand, is a more comprehensive solution that goes beyond just moving data from point A to point B. It’s about building a flexible, modular architecture that can be tailored to your specific needs, integrating best-of-breed technologies across the data lifecycle. While reverse ETL may be a component within a composable architecture, a composable CDP needs to incorporate data ingestion, cleansing, enrichment, identity resolution, and real-time data processing capabilities. > Reverse ETL is a function, not a platform. It facilitates data activation, but it does not provide the full suite of tools needed to create a unified, actionable customer profile. A true composable CDP offers unparalleled agility. It allows you to connect with and leverage existing tools and technologies, choosing the best solutions for each component of your data stack. It adapts to your infrastructure and scales with your needs, whether your data sits in a data cloud, a private cloud or even on-premise. This flexibility is not just about where your data lives; it’s about how you can use that data to drive business outcomes. ## **Real-Time Capabilities: A Key Differentiator** Another critical aspect of a composable CDP is its ability to support real-time use cases. Unlike a system limited to batch processing or static data, a composable CDP should have the ability to provide up-to-the-moment insights, enabling you to respond to customer behavior as it happens. This is achieved through API integrations, event processing systems, and real-time data streams that ensure your customer profiles are always current and actionable. ## **The Importance of Data Quality in a Composable CDP** While reverse ETL helps you move data around, a composable CDP emphasizes the importance of data quality at every step. It’s not just about pushing data downstream; it’s about ensuring that data is clean, enriched, and ready for immediate business use. This involves advanced identity resolution, data hygiene, and the creation of unified customer profiles directly within your data environment. The bottom line is that reverse ETL solves a problem that you would not have if you addressed data quality and data modeling upstream – where it belongs. It’s one thing to add some data ingestion and basic identity resolution just to tick a box, but there’s a difference between applying this to a Customer 360 that you’re required to provide vs. having data quality, data hygiene, advanced identity resolution and other features as critical parts of the CDP as it builds a Customer 360. In other words, reverse ETL solves the easy part – not the hard part. Relying on reverse ETL as your composable CDP leaves a big hole in your CDP strategy … clean data. Most companies have multiple data sources, even if they all get stored in the same data cloud. That means you could have multiple records for individual customers, and each record could have name, address, email and other variations. You are then syncing data without the rigorous cleansing and enrichment processes that a true composable CDP provides. From a marketing standpoint, skimping on data quality introduces friction into the customer journey. Duplicate or irrelevant emails or offers. Lost cross-sell or upsell opportunities. An inconsistent website experience. ## **Choosing the Right Solution for Your Needs** When evaluating your customer data strategy, it’s important to understand the roles of both reverse ETL and a composable CDP. While reverse ETL is a valuable tool for activating data, it doesn’t offer the full range of capabilities needed to create a holistic, customer-centric data ecosystem. A composable CDP, with its modular architecture and focus on data quality, provides a more robust and flexible solution that can grow with your business. Reverse ETL and composable CDPs both play roles in the customer data landscape, albeit by serving different purposes. By understanding these differences, you can better evaluate your needs and choose a solution that truly supports your data-driven goals. **Blog categories:** Composability --- ### [Segment of One: Fact vs. Fiction](https://www.redpointglobal.com/blog/segment-of-one-fact-vs-fiction/) **Published:** May 31, 2022 **Author:** Steve Zisk **Content:** A wide gap between strategy and execution as they pertain to the concept of “segment of one” marketing allowed a fair amount of misunderstanding to materialize around the approach. As a strategy, segment of one loosely translates to the delivery of a personalized customer experience (CX). On the execution side, however, operational marketers hear “segment of one” and think they will never see their families again, tasked with having to create a different offer or different content for every customer. Segment of one took off as a buzzword because the phrase succinctly captures the transition from mass marketing with very limited, manual segments to the objective of providing customers with a hyper-personalized CX. The issue, though, is the impracticality for an enterprise with millions – or hundreds of millions – of customers to segment to that level. Perhaps it just boils down to semantics, but what the segment of one concept really means is having a deep, personal understanding of a customer’s behaviors, preferences, and intent within a customer journey. At the operational level, hyper-personalization at scale is the job of machine learning, not the day-to-day marketer. That’s really what an understanding of “segment of one” comes down to; a customer may be grouped in a segment with hundreds of other customers, but with a [golden record](https://www.redpointglobal.com/single-customer-view/) of each customer, a brand will still be able to offer a personalized experience within a segment through machine learning. ## **Behind the Segment** As an example, perhaps a brand creates a segment of married women in the Pacific Northwest ages 25 to 34 who work full time, have two or more children, attend jazz festivals, grind their own coffee, wear floppy hats and are interested in taking out a mortgage to buy lakefront property. Granted, that’s a peculiar segment, but certainly well within the capabilities of technology. Jane Doe and Sue Smith may both be in the segment, but perhaps Jane is more actively researching mortgages than Sue. Within the segment, a real-time understanding of how one journey differs from another through a persistently updated golden record might then activate a rule; if Jane or Sue visit this landing page, this is the next-best action. A rules-based decision point triggers a hyper-personalized CX in the context of the customer journey. The content may be identical, but perhaps it is delivered on a different channel or at a different time. Depending on the sequence with which Jane or Sue interacts with the brand, maybe a specific piece of content is not delivered at all. A more accurate way to think about “segment of one” than just creating more granular segments is to think about it more in terms of the purpose of a segment – why it was created to begin with. This is where the construct of a [rules-based marketing platform](https://www.redpointglobal.com/blog/top-10-benefits-of-rules-based-orchestration/) vs. a list-based system comes into play. ## **Rules vs. Lists** In a list-based system – think the mass marketing approach – a list of customers is created and extracted, and messages, offers, and content is generated based on the list. Jane Doe and Sue Smith are in the same segment, and thus once the list is extracted from the database, they will receive the same communication regardless of how their customer journeys differ from that point forward. A list-based, outbound list simply does not allow for any new condition that is not defined prior to extraction. In a rules-based platform, by contrast, a rule is applied to a campaign in place of a list, and this set of logic that defines an audience is evaluated at each point in a campaign where a list would normally be used. This accounts for changing circumstances regardless of which channel that they occur, so whereas a list-based system might generate an email for both Jane and Sue, a rules-based platform re-evaluates at each decision point – up to the moment the email is sent, or even changing the content of an email until it is opened. ## **The Power of Machine Learning** Embedded [advanced analytics](https://www.redpointglobal.com/machine-learning) in Redpoint is where the rules-based system comes to life. In-line, self-training machine learning models built on a golden record can recommend an algorithm-produced next-best action for an individual customer based on a specific journey – unbound by channels, and not locked into a list-based constraint. Here is where differentiating between one customer journey and another comes into play, the rendering of “segment of one” without having to take models offline to rebuild them any time a business objective or circumstances change. With the [fitness function](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/) in Redpoint, code-free, self-training models run 24/7 continuously chasing whatever business metric an organization is trying to push. Primed to intelligently orchestrate a next-best action within the construct of an individual golden record, the resulting personalized experience is always in the cadence of a unique customer journey. Instead of “segment of one,” try “an always relevant, hyper-personalized experience in the cadence of a customer journey” on for size. --- ### [Hit a Personalization Home Run with a Five-Tool Solution](https://www.redpointglobal.com/blog/hit-a-personalization-home-run-with-a-five-tool-solution/) **Published:** April 13, 2021 **Author:** Steve Zisk **Content:** With baseball season finally here after a long winter, our attention at long last returns to the field and the exciting exploits of the “five-tool” players. The description applies to the complete ballplayer, one who excels at baseball’s five primary, distinct skills (hit, hit for power, run, throw, field). Surprisingly, there are not as many five-tool guys as one might think. Baseball is filled with guys who may hit .300 but play only average defense. Or those who can knock the cover off the ball, but are slow as molasses. Those players fill a need, and they excite at times, but there’s a reason why teams look for the rare five-tool player to build a team around. When speaking with Redpoint prospects, I’m occasionally reminded of the five-tool comparison when these businesses detail some of the issues they’re facing trying to integrate various point solutions for website personalization. They may have half a dozen or more applications, each one filling a niche specialty that together approximate a personalized experience. The problem, however, is that like an underperforming baseball team it’s often the case that a collection of parts fails to gel into a cohesive unit. Some of it can be attributed to functionality overlap; a new tool may do one thing a little bit better than a previous tool, and over time it creates unbalanced excess. A bigger problem, however, is a lack of integration. Time and resources spent trying to get the tools on the same page, if you will, is time away from creating a holistic customer experience. A patchwork of data and process siloes means that one small change in one application may affect every other tool, creating a management headache. ## **Personalization that Moves the Needle** There is a clear distinction between the personalization that a collection of niche tools delivers, and personalization derived from a single point of operational control. In the case of the former, even with tight coordination between the various tools, their purview is limited to website personalization without consideration to the broader customer journey. A personalized banner ad or a product recommendation engine, for example, may be updated based on a customer’s behavior on a particular channel, but have no insight into how the customer moves through every other channel. A single point of operational control through a platform such as the [Redpoint CDP](https://www.redpointglobal.com/#cdp), by contrast, is the five-tool player of a personalized customer experience that consistently delivers the level of personalization that drives new revenue. Personalization across channels devoid of friction aligns with the expectations of today’s always-on, connected customers. In a [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/), not only did 63 percent of customers say that personalization is part of the standard service they expect, but 43 percent defined that standard service as a brand knowing they were the same customer across all touchpoints (in-store, email, mobile, social media, call center, etc.). ## **Siloed Data Bars Real-Time Capabilities** As a work-around the data and process siloes, some personalization tools may tout APIs that send information to a call center or ESP, but these integrations lack the control needed to provide a differentiated customer experience. A key element that’s missing is real time orchestration, one of the main reasons for having a single point of operational control. Without it, a brand is essentially making – at best – an educated guess about the best content, message or offer to present to a customer at the moment of interaction. A customer may, for example, post negative product feedback on social media; real-time orchestration ensures that the product recommendation engine will not include that product. Several Redpoint customers require millisecond SLAs, guaranteeing the delivery of a next-best action for a customer that is always in the context of the individual customer journey, regardless of channel. Real-time orchestration differs greatly from what many customer experience platforms deliver when they tout real-time capabilities, but in truth what’s often the case is they may render a real-time decision, but the data the decision is based off is minutes, hours or days old. ## **Move at the Speed of the Connected Customer** A lack of a true real-time capability is a byproduct of organizations updating their personalization tools and technology on a channel basis, or by operational teams. Siloes, unfortunately, create more siloes and an endless amount of workarounds to integrate the tools will never create data that is fit for purpose to deliver the level of personalization that customers now expect. The key benefit of having a single point of operational control with rg1 is not only a seamless integration of every type and source of customer data – known and unknown – but also the fact that the data is persistently updated. A real-time decisioning engine, therefore, not only bases its decision off every conceivable piece of customer data – but the data itself is in real time. This is why Redpoint customers trust that an optimal decision at the moment of interaction – even in milliseconds – is based on an entirety of a customer journey quite literally up until that moment. With a hyper-personalized customer experience likely the difference between winning and losing in the competition for loyal customers in the near future, a customer experience platform represents an investment in long-term, transformational change. Niche solutions – a CRM, an ESP, etc. – play important roles and they’re not going away, but they’re not meant to deliver a consistent customer experience across every touchpoint. A “five-tool” customer experience platform that integrates with various point solutions, on the other hand, is a formula for a winning team over the long haul. **Blog categories:** Real-Time Personalization --- ### [Why Data Veracity is the Foundation for a Personalized Customer Experience](https://www.redpointglobal.com/blog/why-data-veracity-is-the-foundation-for-a-personalized-customer-experience/) **Published:** May 7, 2019 **Author:** Steve Zisk **Content:** At first glance, there is a seeming disconnect between the rising volume of consumer data and the ability of marketers to use data to create a personalized customer experience. With the many signals about buying patterns, preferences, and transactions that consumers provide at every omnichannel touchpoint, it appears the consumer is handing marketers everything they need to create personalized engagements. Yet the ever-increasing volume, variety, and velocity of data from the continuously connected consumer also has the potential to swamp customer engagement for the marketing organization unaware of the steps needed to effectively manage the data. Data veracity, which reflects the accuracy and diversity of an organization’s consumer data, is the chart that ensures customer engagement isn’t dashed on the rocks of a poor customer experience with untrusted, unvetted data that is not representative of either the population you’re trying to serve or the questions you’re trying to answer. Veracity requires data quality – the traditional “garbage in, garbage out” problem still exists – as well as machine learning models that ensure data precision and accuracy. Data is certain and reliable only with a diverse data set that accurately reflects the audience at large. **Representative Data and Data Diversity** Achieving data veracity can be a challenge. Marketing organizations are so focused on stemming the tide of the consumer data wave that tasks such as data aggregation, cleansing, validation, matching, linking, and even analysis become overwhelming. According to research from ClickZ and Fospha, 37 percent of marketers say their company [analyzes less than 20 percent](https://www.clickz.com/resources/the-state-of-marketing-measurement-attribution-data-management/) of the consumer data that is available to them. Ensuring data veracity can help increase the percentage, by giving marketers confidence – and trust – in the analysis. It starts with creating machine learning models that are free from bias and assumptions that undermine results. A recent [facial recognition engine failure](https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html) demonstrates the challenges marketers must overcome. An algorithm based on a data set that included a high percentage of white men was understandably far more likely to misidentify the gender of black women than it was white men. One widely used facial-recognition data set was estimated to be more than 75 percent male and 80 percent white. The facial recognition example highlights the need for data sets to be representative. Without representation, personalized customer engagement becomes far more difficult because there is less context in the data set – information that a marketer may know about a wider, representative audience that helps bring relevance to a segment-of-one. Data diversity is equally important. Just as poor representation skews results and affects downstream personalization efforts, so too does implicit bias if machine learning models are fed information that presupposes an assumption. Suppose for instance you’re training an algorithm to discern the difference between images of cars and trucks. If every image of a truck is taken at night with the vehicle’s headlights and running lights on, the algorithm will “learn” that any vehicle with lights must be a truck. Implicit bias carries a risk for an untold number of marketing engagements. A credit scoring algorithm that lacks diversity may make inaccurate assumptions about a zip code, for example. Bias is pernicious largely because we’re mostly unaware of how it can affect efforts to personalize the customer experience. The risk in not addressing bias is that marketers may believe the stories their biased data tells them, making the common mistake of thinking that data acquisition combined with machine learning is half the battle to becoming a successful data-driven marketing organization. In reality, organizations must fully close the loop by addressing issues such as data completeness, relevance, and cadence. Ensuring data veracity and data quality is an important start, but steps still must be taken to measure results that will ultimately provide confidence in a single view of the customer from which personalized engagements are generated. **Measure, Measure, Measure** Data profiling is one of the most important things a marketing organization can do to ensure that personalization ultimately stems from accurate, trustworthy customer data free from bias. If data veracity is the chart that prevents markets from being overwhelmed by consumer data, data profiling is the compass that allows marketers to navigate results. Data profiling explores measures based on audiences and goals, using visualization and analysis as aids in understanding something about the data itself rather than just something about the consumer. For example, a profiling solution could discover through analysis a high percentage of blank fields from a specific data source, or a large percentage of errors in a data set. Profiling results in quick wins for marketers, keeping marketers on the right path to personalized engagement by helping to ensure data quality and veracity baseline. Measurement is another important step to strip bias from data sets. Measurement entails, first and foremost, the instrumentation of interactions – the collection of every available piece of information pertaining to a specific interaction; the unique customer, the offer, the promotion, the response – and measuring all of these data points in relation to one another to understand what is happening at each unique touchpoint. Results themselves must also be measured – how did a customer respond to a program, an offer, or a next-best recommendation? Was the response positive or negative? Through A/B testing, marketers can measure campaign effectiveness against a base level to determine lift, which will shed light on whether a campaign’s rule sets are working and determine whether data veracity is at a satisfactory level. **Close the Data Loop with Customer Feedback** Marketers must also keep in mind of course that there is an actual customer at the end of every personalized engagement. In the context of the customer, measurement refers to understanding how a model works in the context of how it expects a customer to behave versus the customer’s actual behavior. Measurement requires gaining certain permissions from a customer and respecting the customer’s choices and expectations across touchpoints. Transparency from marketers about how they’re using customer data fuels customer trust, which in turn leads to more data being shared and even greater data accuracy. A recent [Harris Poll survey](https://www.redpointglobal.com/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) commissioned by Redpoint Global that explored this value exchange revealed that 54 percent of consumers are willing to share personal data with companies to achieve a more personalized customer experience, but 41 percent are still not satisfied with brands’ ability to tell the consumer how their information is being used. Measuring the effectiveness of customer engagement rests on trust in data. Ensuring data quality, accurate representation, and diversity give marketers the confidence to measure the right things and answer the right business questions posed by consumer data. Are they using the right approach for a particular class of customers, for example? Trust in data ensures trust in the accuracy of any category of measurement, closing the data loop for marketers and yielding confidence that personalized engagements are continually optimized. Putting consumer data through its paces is essential for marketers to harness the insights in the data to deliver personalized engagements that will ultimately win, serve, and retain customers. **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Management, Data Quality, Identity Resolution, Master Data Management, Single Customer View --- ### [Combat Message Fatigue with Personalization](https://www.redpointglobal.com/blog/combat-message-fatigue-with-personalization/) **Published:** August 29, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/08/shutterstock_376302562-e1567025101960.jpg)This year marks a watershed moment in the history of digital advertising. According to eMarketer, digital ad spend is estimated to surpass traditional ad spend for the first time (a record [$130 billion digital spend](https://www.digiday.com/uk/economist-plans-scale-time-based-ad-sales/) representing 52 percent of the total ad buy). It’s easy to understand why since Americans spend more than [six hours a day consuming digital media,](https://www.emarketer.com/content/us-time-spent-with-media-in-2019-has-plateaued-with-digital-making-up-losses-by-old-media?ecid=NL1001) more than any other form of media (TV, radio, newspapers, magazines) combined. A transition from traditional marketing spend to digital, though, is not as cut and dried as it might seem. Advertisers are wary of “ad tech taxes”, which the Association of National Advertisers (ANA) recently estimated at roughly [42 cents of every media dollar](https://www.mediaaudit.com/media-watchdog/can-the-ad-industry-come-together-to-fix-programmatic/). There are plenty of attribution and measurement concerns as well, just one of which is the traditional cost-per-click (CPC). This model is misaligned for marketers whose KPIs correlate clicks with engagement over the course of a buying journey. What can stop marketers in their tracks, however, is the fear of frustrating customers with messaging that is irrelevant to a customer or prospect’s cadence in the buying journey. Showing a customer an ad for a product that they’ve already purchased introduces friction into the customer journey, creates a negative brand impression, and leaves the customer with the feeling that the brand does not really value their individual interests or preferences. According to a Nanigans report, [77 percent of consumers](https://www.redpointglobal.com/blog/evolutionary-programming-the-survival-of-the-fittest-data-models/) say they see too many retargeting ads from the same retailers. Further, 88 percent said that they’ve seen a retargeted ad for a product they’ve already purchased. **The In-House Movement for Programmatic Advertising** Personalized digital advertising is an effective way to combat message fatigue. Personalization can be accomplished with more granular segmentation – targeting an ad audience based on demographics, interests, and behaviors. According to Adlucent, [71 percent of consumers](https://www.adlucent.com/blog/2016/71-of-consumers-prefer-personalized-ads) say they prefer ads tailored to their interests and shopping habits, and they were more than twice as likely to click through an ad if it was tailored to their preferences. More and more advertisers are bringing their programmatic advertising in-house. They want to avoid ad tech fees and enjoy having more control over targeting and measurement. By reducing the fees associated with advertising and improving its effectiveness, brands see a higher return on advertising spend (ROAS) when conducting media buying in-house. When in-house media is combined with an enterprise-grade customer data platform (CDP) advertising can be more tailored, personalized, and relevant. Pairing your CDP with an anonymous data environment paves the way for CRM KPI’s to apply in the anonymous space. **Audience Suppression: Low Hanging Fruit for Ad Personalization** Leading advertisers rely on audience suppression for multiple reasons. Audience suppression is when the advertiser onboards and activates an existing customer file in order to keep the audience from seeing particular ads. For example, a marketer might suppress existing loyalty program members from ad campaigns designed to drive new enrollments in the loyalty program. Not only does this improve results of the campaigns by messaging to people who can join the loyalty program, it also means the media spend works harder against actual prospects instead of wasted against current members and it avoids harming the brand. A brand needs to treat loyal customers well; nothing says “we don’t know you or value your business” than re-inviting them to join the program. Another suppression use case is for lapsed customers. A brand might want to place higher-value offers in front of customers whose spend has declined rather than with customers who are likely to purchase without discounts. Suppressing customers from search/keyword buys is also effective. There is no need to bid for people who already know and love you. It is better to put the search budget to work against prospects who are actively in market for your products or services. **Personalized Website Content for a First-Time Visitor** With consumers demanding personalization across the entire customer journey, brands do not have the luxury of serving up generic content, even to unknown site visitors. Delivering content that is not in the context and cadence of an individualized customer journey fails to convert customers, driving down effectiveness and brand affinity while simultaneously increasing message fatigue. The Redpoint Digital Acquisition Platform powered by LiveRamp simplifies onboarding and activation across the digital ecosystem. Within a single UI, marketers can select audiences from known CDP as well as anonymous databases by giving marketers dynamic control over audience creation. The platform simplifies onboarding, activation, acquisition, and measurement in the digital ecosystem. It combats message fatigue by giving marketers the ability to personalize a website experience for unknown, first-time visitors. In the past, if a first-time visitor to a brand’s website arrives without identifiers such as cookies, the site wouldn’t have insights to guide content, products, services, or offers. Today, thanks to a simple redirect that takes only milliseconds, the site is able to request an Identity Link from LiveRamp and use that identifier to personalize content and offers for the visitor – making it easier than ever to deliver the level of personalization consumers expect, from their first visit to your website. Personalization strategies can take many forms, and organizations rightly devote a lot of attention to tailoring digital advertising to a customer or prospect’s preferences and behaviors. Another angle to personalization, as audience suppression tactics show, is minimizing message fatigue by making sure an audience doesn’t see ads or content that is not relevant to their journey. No message, in other words, is better than the wrong message. There are many ways to mitigate message fatigue. All play an important role in ensuring a consistent personalized customer experience across an entire omnichannel journey. **Blog categories:** 1:1 Personalization, AI & Machine Learning, Customer Data Platform, Data Management, Identity Resolution, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization --- ### [Data Quality: The Missing Ingredient in Most Composable CDPs](https://www.redpointglobal.com/blog/data-quality-the-missing-ingredient-in-most-composable-cdps/) **Published:** October 3, 2024 **Author:** Renee Graff **Content:** The primary job of a CDP is to make data ready for business use through unifying customer data and creating an accurate, up-to-date unified profile. High quality data is essential for being able to trust the profile, also known as a Golden Record, for being able to trust the accuracy and validity of audience segments and, ultimately, to trust that the brand is powering a personalized customer experience (CX) across all touchpoints in the cadence of the customer – up to and including real time. You need to be confident that your CDP powers the best possible CX. This is true for *any* CDP – composable or not, and this is a key point that some self-proclaimed composable CDP vendors tend to gloss over when they tout composability. In their telling, it is enough to call yourself a [composable CDP](https://www.redpointglobal.com/blog/composable-cdp/) if all you’re doing is assembling CDP components on top of data that exists in a data cloud warehouse. Bring the application to the data, and voila – you’re a composable CDP. They make an assumption that your customer records have already been created, without [addressing the shape of that data](https://www.redpointglobal.com/data-quality-and-data-ingestion/). ## **Prioritize Data Quality** If you take a closer look at what a composable CDP is meant to achieve – less complexity, greater agility, greater ROI at a low cost – much of that is lost when you entrust what should be core functions of the CDP out to a patchwork of multiple vendors. If your view of a composable CDP is that it extracts data from a data warehouse or data lake and syncs it back into operational tools (CRMs, marketing platforms, etc.), where are data quality processes being performed? You’re either assuming data that enters the data warehouse is already 100% ready for business use – cleansed, enriched, de-duplicated – or close enough that you think a basic deterministic match somewhere downstream will suffice for your CX use cases. But how many organizations can say that their CRMs or other databases are in excellent shape and don’t have any outdated information? These vendors are playing a data quality shell game where there is no central ownership for ensuring not only that data is ready for business use, but that every activation touchpoint is using the same consistent, unified view of the customer. Without that ownership there are always going to be shifting priorities and different interpretations for what’s considered “good enough” in terms of data preparedness. For a basic, real-world example consider a marketer executing an email campaign. A composable CDP seamlessly integrates with an ESP, and in moving customer records for a John Smith from the data cloud it returns two different emails. One is John.Smith@CDP.com and the other is JohnSmith@CDP.com. They’ve both been linked to the same record. But which one is the marketer supposed to use? Some CDP vendors that claim that composability means that all you have to do is hand over your data (likely in the data cloud or data lake) and they will stitch it together. The email example is just one of many reasons why that concept isn’t ideal. A basic stitch is a far cry from an accurate Golden Record that is continuously updated in real time as data enters the system. ## **Composability + Data Quality = A Better Workflow** It goes back to trust, and providing that trust is the difference between a complete, composable CDP and one that is a loose connection of components that may be a superstar at one specific function, but in the big picture really just creates more work for both marketing and IT. Any composable CDP worth its salt should make it easier – not harder – to optimize CX by [providing core CDP services](https://www.redpointglobal.com/cdp/), starting with the prioritization of data quality from the moment data enters the system. That includes data cleansing, enrichment, advanced identity resolution using deterministic and probabilistic matching, and real-time data processing capabilities for use cases that require real-time decisioning. The right composable CDP will also make marketers’ lives easier by offering these services in a no-code environment – which applies to pulling data in, cleaning it and activating it to your end channels. When assembling various “best-of-breed” components for your composable CDP, ask if any will require your marketers to learn SQL. Getting your data right is foundational for an exemplary CX, and that should be the overarching question when considering which composable CDP is right for your business. Will the composable environment help you take the data you have – in a data cloud, a private cloud or on-premise – and make it better? If the composable CDP you choose can dramatically improve your CX, you’ve made the right decision. *Editor’s Note*: A previous blog entry on the composable CDP environment focused on the [reverse ETL function](https://www.redpointglobal.com/blog/composability-is-not-just-reverse-etl/). **Blog categories:** Composability **Blog tags:** Composable --- ### [Streamline Your Marketing Stack with a Modern IT-Marketing Partnership](https://www.redpointglobal.com/blog/streamline-your-marketing-stack-with-a-modern-it-marketing-partnership/) **Published:** February 10, 2021 **Author:** Vin DelGuercio **Content:** The partnership between IT and marketing is continually evolving, making it difficult to pinpoint well-defined roles for the two organizations. Is marketing meant to execute on IT strategy with IT taking the reins over building the martech stack? Or is IT more an enabler of a vision and strategy set by marketing? Figuring out the interplay becomes important with the stakes becoming higher for marketing to deliver a personalized customer experience (CX) that will ultimately drive revenue and profits. Consider, for example, a Redpoint survey conducted with [Dynata](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/), where 70 percent of consumers said that they will only shop with brands that personally understand them. The imperative to compete on CX helps explain why spending on marketing as a share of companies’ overall budgets rose to 12.6 percent in May, 2020 according to the [CMO Survey](https://www.wsj.com/articles/marketing-is-keeping-its-share-of-company-spending-cmo-survey-suggests-11592304300) from Duke University’s Fuqua School of Business. That number, and the 11.4 percent spend on marketing as a percentage of company revenue, are all-time highs since the CMO Survey started tracking the percentages in 2008. ## **Do More with Less: IT as an Enabler** With marketing budgets on the rise to try to meet or exceed customer expectations for a personalized CX, it is more important than ever for marketing and IT to work hand in glove to deliver optimal results. Whichever department is ultimately credited with success matters less than ensuring both are on the same page. Each makes the other look successful when there is mutual understanding of goals, and a firm grasp of the role each plays in setting and accelerating a strategy to accomplish those goals. Because marketing is now one of the biggest consumers of IT, it goes without saying that marketing should not view technology as a hindrance or a roadblock for reaching profit goals. However, many marketing organizations have traditionally proceeded on a path that took them from consuming IT services, to jumping into the SaaS world, to now being off on an island apart from the rest of the company, relatively unsupported. For many, that path has led to a patchwork of overlapping point solutions that may each solve for a specific use case, but for the most part create unnecessary complexity. It is not uncommon, for example, for an enterprise to stitch together several solutions to drive web personalization, which end up creating a worse experience for consumers than if there was no tool at all. To escape this loop of adding more complexity, marketing must rebuild a new partnership with IT – looking toward IT to help understand what each tool is capable of, and using that knowledge to streamline execution of an overall strategy. Marketers need to recognize that the organization will drive better efficiency with fewer tools that accomplish more through integration. They may not necessarily have to understand the full depth of the technology in play, but they do need a base understanding of what they want to achieve with their tools before going to IT for help executing on strategy and use cases. An examination of what is necessary for achieving a desired business use case versus what is just getting in the way will also include a look at developers, suppliers and whatever manual tools and processes are employed to achieve a specific use case. In short, understanding what may be conspiring to impede a strategy is the first step toward removing those roadblocks. An operational review will expose inefficiencies, redundancies and gaps in an infrastructure. With a better understanding of a martech stack’s configuration, marketing can then task IT with enabling the overarching strategy. As a partner with a modern marketing organization, IT can help streamline execution of the strategy by bringing all the pieces together instead of going point-by-point. Bringing in IT as a true partner and enabler of marketing strategy achieves additional benefits, including a more secure infrastructure by ceding more control. With IT handling security, infrastructure and functionality, marketing is stripped of many day-to-day operational concerns and is instead free to focus on strategy – focusing more on how to use a more functional martech stack to deliver on customers’ CX expectations. This more modern type of partnership allows IT to be successful on behalf of the marketing team, enhancing its value in the eyes of business stakeholders while also making life easier for marketers who now have the right access to the right data and are plugged into the right channels for driving a personalized customer experience. ## **A More Useful Technical Footprint** A more streamlined, more efficient footprint does not mean that IT in this newfound partnership with marketing is going to come in and rip everything up, essentially starting from scratch. There will always be multiple channels to deliver through, multiple data sources to integrate with, legacy systems, etc. To borrow a football analogy, drawing up even a simple play will still have multiple X’s and O’s. But a true understanding of the martech stack entails an understanding of each solution’s purpose – and knowing that the various tools and applications are not operating in isolation. According to the [CMO Council](https://www.cmocouncil.org/about/media-center/press-releases/gaps-and-fragmentation-plague-marketers-looking-to-deliver-seamless-engagements-to-connected-customers), however, 44 percent of marketing teams have spent more than 25 percent of their budget to “rip and replace” engagement systems. This speaks to the importance of an open garden approach for customer engagement technology when IT and marketing form a new partnership to execute on a shared strategy. A customer data platform with an open garden approach, such as [the Redpoint CDP](https://www.redpointglobal.com/cdp/), consists of tightly integrated core technology architected specifically for flexibility, integration and interoperability – as opposed to a walled garden defined by a collection of distinct technologies. An open garden architecture supports the evolving nature of customer engagement and the imperative to compete on customer experience by connecting with an organization’s existing infrastructure, significantly reducing time to value. By connecting only to what’s needed, companies leave behind superfluous point solutions and achieve a single point of data control by integrating all data into a single view. An open garden approach mitigates or eliminates many of the common problems associated with a patchwork martech stack, such as an environment where you’re performing a/b testing in three different tools, for example. Or multiple team members each building a rules-based decisioning model in a different tool and then struggling with a prioritization rule. In the end, those problems subvert whatever incremental benefit marketing may have realized from having multiple, overlapping tools cobbled together to solve for one use case. A thorough housecleaning, though, does not start with technology. Rather, it begins with marketing re-examining its relationship with IT and, if needed, forming a new partnership where each is invested in the other’s success. With brands now competing more on customer experience than price or product, marketing’s top priority is a delivering personalized experiences that drives revenue. The ultimate prize is worth a new approach to IT-marketing teamwork. **Blog categories:** Data Quality --- ### [AI and Machine Learning – Analogous, Not Equal](https://www.redpointglobal.com/blog/ai-and-machine-learning-analogous-not-equal/) **Published:** December 20, 2017 **Author:** Redpoint Global **Content:** The abundance of industry insiders using the terms *AI* and *machine learning* interchangeably is a chief cause of confusion about these two complex, interrelated topics. Generally, AI is a superset of algorithms, and machine learning is a subset of that. Simply put, AI encompasses any system that can take an action based on rules, as well as any system that can learn, adapt, and take action based on that learning. One simplistic example in marketing is “if a site visitor downloads this whitepaper, then trigger an email inviting them to register for the upcoming webinar on the topic.” AI has long meant rules-based systems. The original users and developers basically hard-coded this rules construct. But rules are fixed; there’s no learning. These systems are “intelligent” to a point, but rules-based systems are not adaptive. Humans have to adjust them. Machine learning is an adaptation mechanism. Marketers look at data or patterns and try to learn from it. One of the main goals of machine learning for marketing is prediction – to try to figure out what customers and prospects are going to do based on what they’ve done before and the information you have about them. More specifically, it’s about trying to recognize patterns of what customers have done and are doing to predict what they will be doing. ## What’s the Value of Machine Learning? When enterprises use machine learning to predict something, they can focus on the most important variables, and cut out the “noise” of the information that’s not relevant. Remember, modeling is all about learning the underlying processes that generate the data and behaviors. Marketers can use that relevant insight to personalize campaign elements such as offers and pricing, or encourage current and prospective customers to take an action. They can even use the information to optimize inventory. Another aspect of machine learning is optimization. There are myriad ways to do something and there may be multiple “best ways,” each with different positives and negatives. So, marketers can use machine learning to optimize on top of that insight. For example, machine learning techniques can optimize the channel mix based on what marketers are learning from their KPIs. Beware: Anyone who always insists on only using one specific learning algorithm, or machine learning technique for optimization can negatively impact results – look elsewhere for your machine learning support. Marketers need to find what techniques work best for their data today, and know that their data can and will change over time. The best practice is to let the data speak for itself, and select the best model(s) for that data regardless of type. Linear regression, for example, may be easy to use and understand, but it is not necessarily the best approach to use all the time. In the real world, most of the underlying processes are non-linear, so applying linear techniques can lead to the right solution to the wrong problem. Marketers need to try multiple techniques to find the one that works best for the data at hand. This doesn’t have to be a manual, time-consuming task. It can be accomplished in an automated fashion, using optimization techniques to find the best algorithm, tune the knobs, and adjust the relevant parameters. As with AI, however, too many marketers may believe that machine learning can recognize and predict things perfectly; that it’s a do-all tool for everything. Marketers need to manage expectations of what they’ll get out of machine learning. The available data may not be sufficient to create a model that can predict perfectly. It might only be possible to get 80 percent of the predictions correct, and that’s great if that’s all the information available within the data. Machine learning isn’t going to solve every problem. It’s just like predicting the weather. ## What Are the Most Common Uses for Machine Learning for Marketers? Marketers can use machine learning techniques to learn what’s inside the data, how items such as behaviors and channels affect each other, and recognize customer behavioral patterns. Consequently, one common use of machine learning is [segmentation](https://www.redpointglobal.com/segmentation-activation/) – not just to learn who’s in what segment, but also why they’re different, and what the boundary areas are that put people in the different segments. Then marketers can use the resulting models to help figure out which actions, offers, or content are likely to move customers into one segment or another. With that kind of information, it’s possible to gain tremendous market share by improving the entire business construct in terms of retaining existing customers you have and obtaining new ones. Remember, though, that what worked today may not work exactly the same tomorrow. Because people are adaptive learners too, there’s a constant dynamic shift. Fashions change, trends come and go – the world is a dynamic place. People move from one segment to another or may move from the center of one segment to a boundary. This dynamic is why [real-time decisioning](https://www.redpointglobal.com/real-time-interactions/) is so important. Creating predictive models can be time consuming and the data may be outdated by the time marketers use them. Ideally, marketers could use machine learning to automate the process. It’s like having an easy button. A marketer could say, “Hey, my data changed or my approach changed; I want to do this again,” then press that button and immediately, automatically, here’s a new set of predictions and a new set of models. Even better yet, encapsulate the learning process so that it automatically re-learns on a schedule or through some trigger event. That automation allows operations to run faster with fewer humans involved, so marketers can do their jobs more efficiently. ## What Now? Machine learning is only as good as the strategy and data behind it. So, marketers need to decide what they want to achieve by using it; what they want to automate or improve. It’s best to start with a small project that will deliver a manageable response and trackable results. Also, it’s important to minimize the data thrashing (i.e., moving data between systems). Anytime marketers have to go through multiple steps to get and pre-process (e.g., clean) the necessary data, they’re losing time, which can negatively impact getting a solution to market in a timely manner. They’ll also lose out with poor data quality. Marketers must ensure that their data is high quality, deduped, and meaningful enough to provide the insight they’re looking for. Once marketers have that strategy in place and the data to support it, they’ll get the most from machine learning by using automation to personalize marketing campaigns and content. And, by making it easy for users to use it; for example, by providing scripts or macros. Remember, managing expectations is huge. Don’t expect absolute perfection – the only perfect models with perfect predictions are created in statistics classes using perfect data. And, don’t expect machine learning to take over marketers’ jobs. Instead, it’s going to make their jobs easier and make analysts, data scientists, and marketers more efficient and effective—and more likely to positively impact the business. **Blog categories:** Customer Data Platform, Real-Time Personalization --- ### [3 Reasons Brands Need a Customer Data Platform Right Now ](https://www.redpointglobal.com/blog/3-reasons-brands-need-a-customer-data-platform-right-now/) **Published:** November 30, 2017 **Author:** Steve Zisk **Content:** [Customer data platforms (CDPs)](https://www.redpointglobal.com/cdp/) are designed for one purpose: to unify customer data across functional and channel-specific data silos into an always-on, always processing customer profile available throughout the enterprise. Many IT departments have had this capability for decades, but CDPs add the twist of making the unified data easily available to business users. This accessibility is a massive departure from traditional data management tools. With a CDP, business users such as marketing teams can access customer data in real time without putting in a request to the IT department. This ability to access data at the moment of need is transformative. With a CDP, marketing can more easily target consumers because they don’t need to wait for IT to pull the necessary data. More than that, business users in service and sales can easily see relevant information that could help them close a sale or respond to a service issue. Data accessibility is just one of the reasons brands need a CDP. Three other reasons are that CDPs: 1. **Break down** data silos. CDPs unify data from multiple point solutions into a central portal. This eliminates functional and channel-specific data silos that have developed organically over time as new point solutions were added to the [cdp marketing](https://www.redpointglobal.com/blog/what-marketing-clouds-dont-get-about-native-cdp-functionality/) technology stack. Because CDPs break down data silos, they enable access to information across the organization and streamline the day-to-day work of customer engagement professionals. 2. **Provide visibility into non-linear customer journeys.** The modern customer journey is dynamic, flowing from channel to channel and back again in a dizzying array of steps that may not end in a purchase for months – if ever. Because the journey is dynamic, interacting with the customer through multiple disconnected point solutions won’t provide a full picture of the purchase journey. The ability of CDPs to provide a complete, coherent view gives the marketer a complete picture of all customer behaviors, preferences, and interactions with the brand. 3. **Enable contextually relevant interactions.** Customers expect a one-to-one engagement experience with their favorite brands, and don’t care about the complexity involved in delivering this experience. A customer data platform provides the single point of data control that’s required to personalize interactions across channels, providing the right customer information at the right time to drive personalized customer engagement based on data-driven insights. The modern, connected customer has upended the traditional dynamic between brands and consumers. Brands now find themselves seeking ways to meet customers through their preferred channels with relevant messaging. Customer data platforms are foundational to achieving this goal. The insight into consumer behavior across engagement touchpoints of a CDP empowers marketing with the intelligence they need to personalize interactions and provide relevant messaging in a crowded field. **Blog categories:** Customer Data Platform, Real-Time Personalization --- ### [Are You Solving the Right Big Data Problem?](https://www.redpointglobal.com/blog/are-you-solving-the-right-big-data-problem/) **Published:** October 25, 2017 **Author:** Redpoint Global **Content:** Marketers and chief data officers must contend with ever-growing volumes of data in their day-to-day lives. This truth has resulted in “big data” morphing from a buzzword into an undisputed fact of modern corporate life. Between consumer-generated data and business-generated data, the sheer amount of data generated and collected worldwide has exploded. [Recent research from Northeastern University](https://www.northeastern.edu/levelblog/2016/05/13/how-much-data-produced-every-day/) found that the amount of data worldwide will grow to 44 zettabytes by 2020, with 2.5 exabytes of new data created every single day. This equates to 2.5 *quintillion* bytes of new data from social media, web traffic, and other sources created every hour of every day. The explosion of data translates into a massive expansion in the amount of data that companies collect, store, and manage, which results in a greater interest in big data technologies. And this is a good thing. Big data technologies can make a powerful impact on your business, especially from the perspective of managing increasing volumes of data and creating business value. But as with any technical solution, brands need to proceed with caution. If you’re not careful, you could end up with a solution that costs a lot of money and doesn’t do what your business really needs. ## **Technology Isn’t Always the Solution** Modern big data technologies are built to solve specific problems. These specialized solutions are extremely powerful and can streamline operations dramatically where the very large datasets commonly defined as “big data” are in evidence. That said, big data technologies are nevertheless software built specifically to manage and derive value from datasets that are too large or too complex for traditional data processing applications. These are technical solutions to a very technical set of problems. One thing that big data solutions cannot do is contend with [organizational issues](https://www.customerthink.com/a-customer-data-platform-or-any-technology-cant-solve-your-organizational-problems/). These include challenges like siloed departments, “turf wars” over who “owns” specific datasets, and so on. Seek out vendors providing big data technologies and you will find a host of companies willing to accept your business. But beware – if you begin the purchase process for a big data solution without knowing what you want to achieve, you risk losing both time and money and getting stuck with an expensive piece of software that doesn’t provide business value. ## **Goals First, Technology Second** It may be an old piece of advice, but if you start by defining the problem you want to solve, you’ll end up with a better result. A clearly defined business problem focuses any technology search on solutions that will solve your specific use-case first and foremost. You’ll likely gain other efficiencies along the way, but the core of your search should ideally focus on a small number of specific objectives. If you have a lot of unstructured data, for example, you might seek out a data lake. For massive volumes of structured data, perhaps a purpose-built data warehouse can help. If you need to blend high volumes of structured and unstructured data that travels at batch and streaming cadences, might I suggest a data hub instead? These are just a few examples of technologies that align to clearly defined use-cases businesses need to solve for. Starting with a business problem also includes enlisting the aid of any other potential stakeholders in the organization. If your company is like most, then your team isn’t the only one struggling with data volumes. If you work in marketing, consider asking sales and service about their data problems. Engaging the IT team is imperative, if for no other reason than they can tell you whether that flashy solution will play nice with the rest of your systems. If you can gain alignment with the various stakeholders who stand to benefit from a big data solution, then you also build a stronger case to seek out a new technology. Multiple departments willing to share a joint solution tends to be an easier sell than marketing or sales asking for a new department-specific system. By defining your business requirements upfront – i.e., what your goal is – you may also find that the issue is less one of technology and more one of process. In that case, you could find efficiencies that otherwise would have gone unrealized because of a too-heavy focus on deploying a new and shiny technology. If it is a problem in need of a technology solution, then you also can more clearly define its true drop-dead requirements. The idea that deploying a new technology will solve all your big data problems is a dangerous one. This kind of thinking results in companies spending thousands of dollars on unnecessary systems that provide no business value. Better to start with a clearly defined problem and only then seeking out technology solutions – if that is even what is needed. What this all boils down to is the simple reality that you need to solve the right problems, and be certain that you are doing so. **Blog categories:** Data Management, Data Quality --- ### [Are You Focused on Identity Resolution? You Should Be.](https://www.redpointglobal.com/blog/are-you-focused-on-identity-resolution-you-should-be/) **Published:** June 12, 2017 **Author:** Steve Zisk **Content:** The modern consumer is more connected and has more information at their fingertips than at any other point in history. Consumers can now interact with brands through dozens of potential channels, including online sources like social media, banner ads, and the company website, as well as offline sources such as billboards, the call center, and brick-and-mortar retail stores. The sheer number of possible touchpoints can confound even the savviest marketer, but is especially problematic in the face of the fragmented marketing technology stack and siloed data that many brands currently use. For brands to remain competitive in the age of the empowered, connected consumer, they must be able to connect customer data across channels into a coherent whole. This reality in modern marketing puts new emphasis on identity resolution capabilities, which can help marketers and other stakeholders unify data across silos into a single composite view of the customer that can then be leveraged in omnichannel customer engagement efforts. ### **Identity Resolution and Contextual Relevance** To understand the importance of [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/), it helps to first understand what it is. Identity resolution in the simplest sense is an operational process to identify an entity – for example, a person, household, or device – through an automated process that may use a combination of deterministic and probabilistic matching. It is the cornerstone process in data quality initiatives, progressive profiling, and the creation of a unified customer profile or “golden record.” Identity resolution may, in practice, include standardizing, normalizing, validating, and enhancing data as part of an automated process. Knowing that the customer who just put an item in their online shopping cart is the same person who interacted via a web form can allow you to provide a different interaction than if those two pieces of information were disconnected. In this way, identity resolution informs more contextually relevant interactions because you’ve gained the ability to leverage unified customer data in a way that was not previously possible. ### **Identity Resolution as the Key to Omnichannel Success** The ability to recognize customers across channels has taken on new importance in the modern age. The multi-channel, multi-device world that consumers operate in is only going to increase in complexity. Consider that [Winterberry Group](https://www.winterberrygroup.com/our-insights/state-consumer-data-onboarding-identity-resolution-omnichannel-environment) recently found the average consumer uses seven connected devices, up from three in 2014, and recent [Accenture research](https://www.accenture.com/us-en/insight-digital-video-connected-consumer) found that 87 percent of consumers use a second screen device while watching television. With Gartner predicting that the Internet of Things (IoT) will be 20.4 billion devices strong by 2020, it’s incumbent on brands to provide a consistent experience regardless of channel. Part of providing that consistent experience is the ability to connect data across channel-specific and functional silos, while also reconciling the different sources of customer data into a unified profile. Identity resolution capabilities play a key role in this environment ­– you need to know who your customers are, and meet them where they are, to close the gap between customer experience and expectation. This becomes even more vital in a world where customers interact through multiple channels in the course of the modern customer journey. It’s for this reason that you need to ensure your marketing technology stack has strong identity resolution functionality. If you’re able to reliably recognize customers across channels, and understand their past history with your brand, then you can provide the kind of contextually relevant interactions that improve loyalty and ensure long-term success. Without the ability to recognize customers across channels, you risk sending the wrong message to the wrong customer. The ability to quickly and efficiently recognize your customers regardless of channel can make a substantial difference in meeting customer expectations. If you’re able to accomplish this quickly and efficiently, then you can provide the contextually relevant interactions which will increase engagement and allow you to succeed in the world of the omnichannel customer. **Blog categories:** Identity Resolution, Omnichannel Marketing --- ### [What Is a Customer Data Platform? And Why Do You Need One?](https://www.redpointglobal.com/blog/what-is-a-customer-data-platform-and-why-do-you-need-one/) **Published:** May 23, 2017 **Author:** John Nash **Content:** The modern customer experience is fragmented despite our lofty goals of providing a seamless experience, complicating the ability of brands to effectively meet customer expectations regardless of channel or device. This fragmentation has become more apparent as customers are connected to more information than ever before, enabling them to decide when and where to interact with brands – and on what terms. This rise of the connected consumer has sparked a need for brands to better understand their customers’ behaviors and past histories. Without this understanding, brands fall short of providing highly relevant, personalized, and contextually aware offers and messages. A new class of solution, called a [**customer data platform (CDP)** ](https://www.redpointglobal.com/cdp/)meets this need, enabling an always-on, always-processing golden record that facilitates a unified and complete view of the customer. The ability of a customer data platform to build this golden record is vital in a world of fragmented customer experiences and disconnected marketing technologies. Without it, brands cannot hope to understand their customers’ full array of needs, wants, behaviors, preferences, and intents – or how enterprises can remove friction from their customers’ lives and create higher value for them. In an age of digital transformation, it is this deep understanding of customers that will separate the leaders from the laggards. ### **The Customer Engagement Problem** Digital transformation is forcing brands to remake their operating models at lightning speed or fall behind. Legacy brands have struggled to adapt: [McKinsey recently predicted](https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/the-strategy-and-corporate-finance-blog/think-digital-is-a-big-deal-you-aint-seen-nothing-yet) that current levels of digital disruption will shave 45 percent off incumbents’ revenue and 35 percent off their pre-tax earnings in the years ahead. More companies will soon feel the impact of digital disruption as well; in that same research McKinsey found that digitization has, as of early 2017, only penetrated 37 percent of all companies. The current crop of empowered customers, who have access to more information than any previous generation, have compounded the marketplace turmoil. There are [207.1 million smart phone users in the United States](https://www.statista.com/statistics/201182/forecast-of-smartphone-users-in-the-us/), and recent research found that [61 percent of U.S. consumers](https://www.statista.com/statistics/367740/number-connected-devices-per-person-us/) used three or more devices on a regular basis. But if each consumer has three devices, the number of *touchpoints* is much higher, and includes the company website, banner ads, social networks like Facebook and WhatsApp, email, direct mail flyers, billboard advertising, call centers, television commercials, IoT devices, and many other possibilities. Empowered customers expect a consistent brand experience across all of these touchpoints, regardless of any underlying complexities that enterprises need to resolve. The perception is that if newer internet-based organizations can do it, then any other company can as well. The combination of touchpoint proliferation and fragmentation has led to a gap between customer experience and expectations, with 86 percent of customers saying they would pay more for a better customer experience, but only one percent of consumers saying that brands have consistently met their expectations. ### **The Evolution of a Customer Data Platform** Customer data platforms are a new class of solution that enables brands to adapt to changing customer attitudes and behaviors. CDPs effectively resolve the fragmentation that resulted from the organic way customer engagement technology evolved, with multiple point solutions deployed piecemeal as each new engagement channel gained prominence. When email became popular, brands implemented email marketing solutions; when social media proliferated, brands added social media management tools to their technology portfolio. The problem is none of these walled-garden engagement systems are designed to share data. Different point solutions use unique customer identifiers, ingest data at different paces, and store data in distinct formats. Some solutions might only collect anonymous batch data, others might have phone numbers or addresses, still others may only use a social media account name. Because each system uses a different identifier, data structure, or format, brands have been unable to connect any of their customer data into the unified customer profile needed to meet customers where they are. Customer data platforms conquer this data-unification barrier. A CDP can ingest data that moves at any velocity, regardless of structure or volume, and uses deterministic and probabilistic matching algorithms to resolve customer identities across touchpoints. More than that, the CDP makes that data available across the enterprise and at the speed of the customer. This enables brands to proactively engage at the right moments, meeting customer needs with contextually relevant interactions. CDPs are best deployed as a foundational capability, with an open-garden approach to drive the customer engagement stack. This includes ingesting data, integrating, matching and mastering the data, and making the data accessible to engagement systems without replacing the existing point solutions. This implementation approach allows brands to maximize their technology investment, without the expense of ripping and replacing any current infrastructure. ### **Always On, Always Updating Customer Data** Think of a customer data platform as an always-on, always-updating golden record that is made continually available at low latency to all touchpoints and users across the enterprise. CDPs connect data throughout the technology stack, acting as a central point of data control that provides insights about customer behavior and past history with the brand at the moment of greatest need. Customers have already moved into the digital world, and are more likely to stop doing business with a brand who fails to provide a consistent and valuable experience across touchpoints. CDPs empower brands with the robust single customer view necessary to meet consumers where they are and provide contextually relevant interactions to close the gap between customer experience and expectation. **Blog categories:** Customer Data Platform --- ### [Customer Data Platforms and the New Omnichannel Journey](https://www.redpointglobal.com/blog/customer-data-platforms-and-the-new-omnichannel-journey/) **Published:** May 16, 2017 **Author:** Steve Zisk **Content:** The relationship between brands and consumers has shifted. Where once brands could define the message and pre-determine the pathway of the customer journey, the consumers’ embrace of multiple devices as interaction points has eliminated the simple journey model. The journey is still a real requirement to build and understand, but now it is a multi-dimensional, omnichannel customer journey. This new omnichannel journey means customers can appear at any time and in any channel – the path to conversion is owned by them. It always was, really, but technology just limited the possible touchpoints. Customers expect their experience with your brand to be the same, or at least similar, regardless of channel. The fragmented technology stack most marketers contend with makes that all but impossible because you can’t react at the speed of the customer across all channels. A fragmented technology ecosystem also leads to fragmented customer data, further complicating the goal of providing a consistent brand experience to the omnichannel customer. There’s never been a greater need to connect data from all touchpoints to a single platform, where each customer’s personal journey can be accommodated, typically in real time and with in-line analytics. In the age of the always-on consumer, the traditional linear customer journey, where marketers could direct the pathway from awareness to sale, is largely dead. A [customer data platform (CDP)](https://www.redpointglobal.com/cdp/) counteracts the fragmented marketing stack and the resulting channel-specific data silos. CDPs integrate data across functional and channel-specific silos into a central location, allowing you a single point of data control and visibility to make better decisions about providing contextually relevant interactions to customers at the point of engagement. The data integration capabilities of a CDP will be even more vital in the coming years as more channels arise and the customer journey becomes even more complicated. How CDPs help you engage with the always-on, omnichannel customer was the topic of a recent webinar I participated on with Brandon Purcell, senior customer insights analyst at Forrester, called “Orchestrating Optimal Interactions: Implementing a CDP to Get Moving Forward,” hosted by the CMO Council. A CDP is purpose-built to accept data inputs from a variety of online and offline engagement systems, such as Facebook, CRM, email, ecommerce, data management platforms (DMPs), call center, and in-store POS – among other sources of customer data. What the CDP does is enable you to blend all this data into a unified customer profile, also called a “golden record,” and use that knowledge to provide more relevant marketing messages to customers at the moment of engagement. Because of how a [customer data platform](https://www.redpointglobal.com/customer-data-platform) integrates data into a central location, they are often conflated with enterprise data warehouses or more generic data lakes, but the reality is that CDPs differ in that they are customer-centric. One of the hallmarks of a CDP is that it makes customer-centric data accessible to business users from across the enterprise. Because it is designed this way, it allows greater freedom to create data models than possible with a centrally managed data warehouse or data lake. Think of a CDP like the “brain” of your customer data ecosystem. Solutions that interact with customers, like a DMP, CRM, or social media tool, collect separate yet equally valuable data about customers within their defined sphere. The problem is that each of those point solutions locks the customer data it collects within its defined scope and doesn’t “talk well” with the other customer engagement solutions. A CDP sits at the back-end, ingesting data from all those point solutions, combining it, allowing you to view all the data that each solution collects about your customers – kind of like how your brain collects data from your eyes, ears, nose, tongue, and fingers to tell you about your environment. When the CDP ingests all this data, you become able to leverage customer data the same way your brain accepts dozens of inputs about the environment and determines the best course of action. CDPs are relatively new, but they are poised for success because of their core promise. In fact, Purcell said that he expects CDPs will really hit their stride within one to three years as more companies begin to understand that CDPs can reduce friction in their customer interactions, increase revenue, and enable more efficient marketing. The future of marketing lies in being able to understand your customers wherever and whenever they appear. Customer data platforms enable that capability, resulting in better customer interactions that increase loyalty and allow your marketing to be more efficient. Understanding this simple fact can make the difference between companies that succeed with the new omnichannel customer journey and those that struggle. **Blog categories:** Customer Data Platform --- ### [4 Critical Tips for Data Quality Excellence](https://www.redpointglobal.com/blog/4-critical-tips-for-data-quality-excellence/) **Published:** April 18, 2017 **Author:** Redpoint Global **Content:** The average enterprise no longer lacks for data. According to [Northeastern University](https://www.northeastern.edu/levelblog/2016/05/13/how-much-data-produced-every-day/), around 2.5 exabytes of data is generated per day. If anything, this means that companies have the opposite problem – they now face a deluge of data points every minute of every day. As a result, the debate has shifted to whether the *right data* is collected and whether the data that is collected is high quality or not. [Data quality](https://www.redpointglobal.com/data-quality-and-data-ingestion/) can make a substantial difference in the success of your marketing programs. If you have high-quality data, you can provide contextually relevant messages to your customers easily and efficiently – knowing that your targeted message will reach the right person. Without that assurance, you risk sending an offer that is irrelevant at best and tone-deaf at worst. So how can you ensure that you have high-quality data? Here are four critical tips that can make the difference: - **Invest in the right technologies.** Managing data quality effectively is often a difficult proposition because many companies use spreadsheets and other manual tools. If you invest in the right solutions that can automate some or all of your data quality processes, such as a [customer data platform (CDP)](https://www.redpointglobal.com/cdp/) that unifies data across functional and channel-specific silos, you can ensure that customer data is accessible to any stakeholder or solution that needs access. - **Link disparate data sources.** Connecting data across functional and channel-specific silos helps you create a unified customer profile, or “golden record,” which 90 percent of CMOs acknowledge they don’t currently have. The unified customer profile can enable better personalization and more effective cross-channel marketing. More than that, you can be assured that you’re sending the true next-best offer to your customers because you understand them more deeply. - **Spend the time to maintain your data.** Although data quality tools can substantially automate many of your processes, that does not mean you can set up a solution once and expect to always have high-quality data. You need to invest the necessary time, resources, and tools to augment and scrub customer records, including de-duplication and using third-party sources to both enrich profiles and fill in gaps. Only by putting in the time can you be assured of always having quality data on hand. - **Combine online and offline data sources.** Customers don’t often interact with brands solely through digital channels. There are also frequent offline interactions, such as in a retail location or via a customer service call center. Because customer interactions with your company can span the online and offline worlds, you need to combine both data flows into a single centralized portal to ensure that you have the most complete picture possible of your customer’s needs, wants, and desires. Collecting enough data to inform your customer engagement efforts is no longer an issue for the modern enterprise. Rather, the problem now is ensuring that all the data you’ve collected is of sufficiently high quality to inform the kind of contextually relevant interactions that the modern customer has come to expect. **Blog categories:** Data Management, Data Quality --- ### [What Does It Take to Build a “Customer-Obsessed” Business?](https://www.redpointglobal.com/blog/what-does-it-take-to-build-a-customer-obsessed-business/) **Published:** January 5, 2017 **Author:** John Nash **Content:** The modern customer is always-on and always addressable, which makes them empowered in ways previous generations of consumers never were. This largely stems from the substantial number of devices the average consumer uses—3.64 per digitally connected consumer, according to Global Web Index—which means that it’s likely your customer is switching between multiple devices throughout the day, and sometimes even using more than one at the same time. (Google recently found that 21% of mobile customers use their phone at the same time as their desktop computer.) This multichannel, multi-touchpoint world means traditional product-based messaging doesn’t have the same impact it once did. Now, brands must emphasize how the product solves particular customer problems and focus on cross-channel customer engagement. This has resulted in marketing strategies moving away from “pushing” the message out to consumers and instead developing ways to “pull” prospects into interactions. It’s vitally important, for long-term organizational success, that you start thinking about products from the perspective of which customer problem they solve. Forrester Research calls this a “customer-obsessed” business model, where the brand creates experiences that make customers’ lives easier or helps them accomplish specific tasks. This has a revenue impact as well, because engaged customers spend more money overall, which makes a customer-focused marketing strategy a powerful tool in our multichannel world. ### **The Components of a Customer-Obsessed Business Model** There are two specific characteristics that, according to Forrester, describe a customer-obsessed business model: - Data and insights - Digitally enhanced products and services These two components interact through the customer lifecycle to create an experience that successfully attracts and engages customers over the long term. Data and insights refers to the expansive amount of data that brands collect, through known and unknown interactions, which are then stitched together into a customer record. This data is then analyzed to derive insights into customer behavior and motivations, which leads to making better decisions about which offers to provide. Successful brands can leverage the insight derived from this data to create new products that provide additional data back for greater customer insight. This is where the Internet of Things comes into play; the smart refrigerator that knows when you’re out of milk and offers on-demand ordering of a new gallon provides feedback, which can drive future offers. This creates a virtuous cycle where brands can use data-driven insights to optimize customer experiences that create more data and provide even deeper insights into the customer’s needs, wants, and desires. ### **The Role of the Customer Data Platform** [Customer data platforms](https://www.redpointglobal.com/cdp/) (CDPs) play a key role in building a customer-obsessed business model. Unlike traditional enterprise data management tools, a CDP is optimized to collect and stitch together customer information from multiple anonymous and known customer interactions. What this allows marketers to do is create a unified customer profile, tracking the collection of data from anonymous to known, and make decisions for the most effective action with the best insights possible. CDPs thus play a vital role in a customer-obsessed business, largely because they are tailor-made to manage customer data in such a way that marketing can derive relevant insights quickly and efficiently. ### **Building a Customer-Obsessed Model** Building a customer-obsessed business model is not an easy task, but it’s certainly worthwhile. The most substantive facet of getting to this goal is gaining alignment from key stakeholders. Collecting customer data, and deriving insights from it, is key—but without aligning data collection with business goals, there is little possibility your efforts will succeed over the long term. IT is a critical partner in garnering this alignment, because they’ll need to support the adoption of the customer analytics capabilities that are required to make customer obsession feasible as a business strategy. IT also understands big data, and can help clarify technical requirements as well as advocate for new solutions if necessary. Marketers also need to embrace their inner data scientist and understand that using customer data to drive decision-making is a must in the current marketplace. The fact of the matter is that there’s a new marketing reality, and understanding this is key to eventually including customer data in your decision making. In the long run, corporate alignment and an understanding of the new marketing reality will help companies become more customer obsessed and drive better results. This will bring about better marketing decisions and more successes in the long term, as well as higher customer retention and a better brand perception among the customer base. **Blog categories:** Data Management, Omnichannel Marketing --- ### [Why Data Quality is the Key to Business Analytics Success](https://www.redpointglobal.com/blog/why-data-quality-is-the-key-to-business-analytics-success/) **Published:** November 2, 2016 **Author:** Redpoint Global **Content:** Business analysts are increasingly critical in the modern data-driven economy. They determine market trends, analyze performance data, and even present insights to executives that will help direct the future of the company. And as the world becomes even *more* data-driven, it becomes vitally important for business and data analysts to have the right data, in the right form, at the right time so they can turn it into insight. However, chances are good that your business analysts actually spend the bulk of their time focused on data quality. This is a problem because data preparation and management isn’t, and shouldn’t be, your business analysts’ primary responsibility. But, they also don’t need to depend on IT to do it for them either … ideally they should be able to do it themselves without a substantial time commitment. That way they can spend the majority of their time on algorithms and manipulating models to divine insights from clean data. IT isn’t often much help because they have dozens of other tasks on their list, including additional asks for more granular data. The flood of big data, which so far many companies have managed manually, has thus resulted in a situation where IT exports raw information for business analysts and doesn’t always have the time to ensure the data is cleansed properly. This isn’t anyone’s fault really; manual data preparation can consist of mind-numbing tedium that dominates time IT and business analysts could be using for other work. That’s why [Redpoint’s recent infographic on data quality](https://www.redpointglobal.com/wp-content/uploads/2016/10/Redpoint-Infographic_Final-DraftUpdated_20161021.jpg) offers five strategies to help business analysts and IT win at big data analytics, which includes: - **Focusing on data blending –** Data is spread across multiple locations in multiple formats, with 92% of organizations noting they have 16 to 20 different data sources. This will only worsen over time, as 95% of companies expect the number of data sources to balloon in the coming year. - **Automating data cleansing –** Poor data quality costs organizations between 10% and 20% of their revenue, but more than half of businesses spend more time cleaning data than using it. This presents a problem and is why we recommend automating data cleansing procedures so you can spend more time using your data and driving insight. - **Ensuring high data quality –** Data quality must be at a high level to execute on your overall goals; 52% of data scientists said that data quality was their biggest obstacle in 2015 and 87% of data management professionals admitted to putting bad data in their data stores. I encourage you to download (and share!) our [new infographic on data quality](https://www.redpointglobal.com/wp-content/uploads/2016/10/Redpoint-Infographic_Final-DraftUpdated_20161021.jpg) to get more tips and improve your data management to succeed in our new data-driven marketplace. **Blog categories:** Data Management, Data Quality, Identity Resolution --- ### [The Consumerization Of Healthcare: Embracing A Retail Mindset](https://www.redpointglobal.com/blog/the-consumerization-of-healthcare-embracing-a-retail-mindset/) **Published:** October 25, 2016 **Author:** John Nash **Content:** In an era where customers are always-on and always addressable, it’s time to drive customer engagement in fundamentally new ways. If customers don’t see value and contextually relevant communications from your brand, they will become disengaged—never purchasing, limiting usage, or leaving sooner. Retail marketers understand this, and have largely focused on engaging through the channels when and where their customers want to. The trend has even started to extend to healthcare, where payers and providers have begun to adopt a more retail-like mindset when it comes to customer engagement. To illustrate this consumerization of healthcare, Redpoint recently put together [this infographic](https://www.redpointglobal.com/wp-content/uploads/2017/10/RedPoint-Healthcare-Infographic-Consumer-Rebranded.pdf) to show how customer attitudes and channel preferences frequently play a key role in health plans’ member engagement efforts. This engagement has taken on new importance in recent years because of the changing market environment that increases consumer choice among health plans. In fact, 58% of health plan members would consider switching their provider if they had a poor customer service experience. There are also gaps between consumer expectations and experience, as illustrated by 88% of consumers using at least one online channel while 46% of health plans still relay on traditional mail. To succeed in attracting and retaining consumers, health plans need to change their focus to become more like retailers—engaging their plan members in the channels they prefer and creating an enduring value exchange. This includes touchpoints such as proactive reminders about their current health plan or relevant offers like discounts on gym memberships and [health](https://www.redpointglobal.com/blog/redpoint-global-and-lucerna-health-join-forces-to-advance-consumerism-in-healthcare-for-payers-and-providers/) clinics, all with the goal to deliver improved outcomes at a lower cost. This type of engagement will become more real-time and interactive, with 32% of consumers now using a medical, health, or fitness mobile app. Enhancing member engagement through tactics like data-driven personalization has the potential for a tremendous positive impact, as marketers that leverage member data with high precision enjoy a 6-times greater increase in profits over plans that don’t include a data-driven component. This demonstrates there is real value in focusing on member engagement. Healthcare payers have a timely opportunity to hyper-personalize messaging and intelligently orchestrate interactions across all communication channels. Delivering their brand promise in this way will deliver better outcomes across the board—for consumers, payers and providers. I encourage you to [download our infographic](https://www.redpointglobal.com/knowledge-center/healthcare-payers-focused-webinar/) and share its powerful insights with your colleagues. Customers will only become more empowered over time, and health plans must make changes if they’re to succeed in the long run. **Blog categories:** Data Quality, Healthcare --- ### [6 Ways to Up Your Cred as a Data-Driven Marketer](https://www.redpointglobal.com/blog/6-ways-to-up-your-cred-as-a-data-driven-marketer/) **Published:** October 3, 2016 **Author:** John Nash **Content:** Nearly two thirds of CMOs admit that “they have a long way to go in using big data properly,” according to The CMO Council. Data will continue to play a pivotal role in delivering contextually relevant experiences to customers who are always on and readily addressable. But there remain significant opportunities to improve data-driven marketing to ultimately engage with customers in ways that meet their rising expectations. Most companies are either overdelivering or underdelivering against customer expectations today. Overdelivering might mean using data in ways the customer doesn’t value or communicating too frequently. Underdelivering might mean not using data to provide relevant personalization or, worse, not using data at all. Success is about striking a balance between what customers need and what the business hopes to achieve. Customer engagement optimization is the most efficient way to achieve that balance, and in doing so, cultivate new customers and retain existing customers. But engagement optimization requires a robust focus on data and on customer-centric, data-driven interactions and decisioning. There are six ways marketers can improve their data-driven marketing acumen that will support their customer engagement optimization efforts—and the technologies that can help. #### **Improve data quality** Poor data quality leads to analytics and insights that don’t accurately reflect customers, as well as to misaligned moments of engagement and negative brand experiences. Marketers can fix data-quality issues by exposing their root cause. One of the biggest challenges is correctly identifying customers that may have interacted across several channels and subsystems. There are other common culprits including data entry errors, discrepancies in similar data provided by different systems during data integration, erroneous data matches resulting in lost data, and data structure limitations and inconsistencies. Remedying the data-quality issue starts with precisely resolving identities across customers, households, and devices along with all of their variations. These efforts are enhanced by fixing the data in the source system, as well as fixing the system itself. The newest generation of marketing technology offers a data layer to aggregate, augment, scrub, and transform customer data. It’s the fastest and easiest way to solve for data quality issues. #### **Tap into an open ecosystem** The ability to ingest data and connect with customers across an array of systems is a basic requirement for marketers today. Doing so is difficult to accomplish with disparate tools. There’s demonstrable value in open and connected marketing technology. The ability to leverage the best tools for a brand— from the newest channels to new innovations in AdTech and MarTech—in ways that also align the data from these tools and facilitate communications across all of these systems is critical to customer engagement optimization. The fact is, not all companies are ready to rip out legacy technologies that still have strategic value. Additionally, there are best-of-breed tools and enabling technologies that marketers can implement not only to supplement and enhance their existing marketing stack, but also to differentiate the customer experience. These tools can provide the flexiblity marketers need to adapt quickly to customers’ ever-changing expectations and the agility they need to optimize the customer experience. #### **Spin customer records into gold** The concept of a “golden customer record” refers to a single source of truth for any and all customer interactions. Insights into the frequency of engagement, device use, household, purchase cycles, media mix, and conversion activity can inform all facets of customer engagement optimization. Marketers can translate these data points into listening activities, new segments, trigger-based campaigns, and paid media investments to micro-target customers with relevant messages. Most brands attempt to solve the golden customer record challenge by creating a customer data warehouse or federated database to consolidate customer interactions from source systems. But connecting disparate data to a unique customer record (a CID, email, or fuzzy logic) only solves for half the equation; it’s also essential to use this data to improve the customer experience. Legacy tools and lack of integration force marketers to personalize messaging using only basic customer profile data. The next generation of customer engagement platforms can ingest data from any source system and then put it into action via automated triggers, business logic, advanced modeling, and real-time personalization. Marketers can learn a great deal about individual customers during anonymous interactions if they have mechanisms to connect the activity to a known customer later, building a progressive profile as the customer engages over time. #### **Implement real-time personalization** Most personalization efforts involve manual intervention from marketers: contact lists have to be downloaded and uploaded from multiple systems; paid media campaigns have to be configured for display, search, and social; and channel-specific copy and creative must be created from scratch. This is less than ideal for customer engagement optimization. The data-driven personalization needed for engagement optimization is real-time, contextual, and relevant. That means marketing technology must be capable of using first-, second-, and third-party data to inform the right messaging at the right time in the right channels. The new generation of engagement platforms use advanced computing, business intelligence, and marketing-friendly user interfaces to make data-driven personalization easy for marketers to manage and implement. These next-gen customer engagement tools offer assets such as three to 10 times faster processing than traditional campaign tools; triggered and transactional messages; and dynamic segmentation and suppression rules. #### **Provide role-based insights** Marketing execution involves a complex tapestry of roles and responsibilities. Rarely are the reporting capabilities of most marketing technologies equally diverse. While there’s certainly no shortage of charts and reports from today’s marketing tools, many of them go unused by marketers. Dashboards and reporting are only valuable if they provide context that can inform a decision or action. Different marketing stakeholders have very different requirements with respect to reporting. The CMO needs holistic visibility across performance, spend allocation, alignment with marketing targets, and back-office operational execution. A campaign manager needs detailed insights about the performance of in-flight campaigns and real-time insights about improving performance. The next generation of customer engagement platforms has invested heavily in intelligent reporting so marketers can easily translate relevant, role-based insights into actions that benefit customer engagement optimization. #### **Adopt advanced analytics and machine learning** Marketers are responsible for managing exponentially growing volumes of data that they’ve acquired via channels such as web analytics, social media monitoring, email, mobile, display, search, and third parties. The focus on extracting value from these large and ever-changing volumes of customer data has led to new roles and responsibilities such as data scientists, machine learning, and advanced predictive analytics. But extracting the insights needed to deliver real-time personalization at scale also requires analytics technologies that enable marketers to foster 1:1 relationships. Today, advances in cloud computing, data access, and analytic models provide the power marketers need to hyper-personalize interactions. The next generation of customer engagement platforms allow marketers to drag-and-drop a library of analytic models on top of massive volumes of customer data. Modeling and operational execution happen from a single platform where marketers can apply models and real-time, trigger-based optimization to optimize offers, targeting, media mix investments, and messaging at the point of execution. #### **Driven by data** Customers’ appetite for relevant customer experiences has no bounds. Marketers must invest in customer engagement optimization—delivering contextually relevant experiences that are informed by data. But being “data-driven” is about more than relying on data to inform customer strategy or operational execution. The future is about real-time marketing, personalization, and continuous optimization—and data is the means to this end. What’s exciting for marketers is that the data challenges they have all faced for decades are being addressed in next generation engagement platforms. These systems allow marketers to connect and interact with existing marketing tools, build a single view of the customer; deliver real-time personalization; personalize dashboards by role; *and* unlock the potential of big data and analytics for operational execution. It’s up to you to determine how quickly you’ll set off on the journey to hone your data-driven marketing acumen and succeed at customer engagement optimization. These six steps will certainly set you on the right path. **Blog categories:** Anonymous to Known, Data Management, Data Quality, Real-Time Personalization --- ### [Understanding the Differences Among First-, Second-, and Third-Party Data](https://www.redpointglobal.com/blog/understanding-the-differences-among-first-second-and-third-party-data/) **Published:** April 27, 2016 **Author:** Redpoint Global **Content:** In many ways, the collection, management, and use of data is a central activity in the modern world. Certainly, it is the foundation of today’s marketing, and the partnership between marketers and database experts grows increasingly closer year after year. That’s why it’s extremely important for marketers — and their audiences – to understand the different kinds of data that can be collected and the rules regarding its use. Generally speaking, data is divided into three categories based on how it was acquired: first-party data, second-party data, and third-party data. In any marketing situation it’s obviously critical to have as much information about a customer or prospect as legally possible. In the customer acquisition scenario this is just as important, but of course it can be more difficult. Find them; get them to react to call to action; drive them first to one channel, then another until they buy or don’t buy. This is the domain where alternative data sources become extremely important to the marketing team and just as importantly, we need to understand their relationships and treatment. First-party data is information a company or organization acquires directly from a person or device. They buy something from you, or regularly interact with you in some way such that you begin to collect plenty of information about their buying behavior and their device usage when they visit you in person or digitally. For marketers, that information is voluntarily provided during a transaction, and can include the person’s name, address, color and size preferences; or any information about products shopped or purchased. First-party data can also include information submitted when a person fills out a web form, even though they maybe haven’t purchased anything at all. First-party data is generally thought of as the most reliable and valuable to marketers since the person is known to the marketer and is first-hand information. Also, unlike other kinds of data, first-party data comes into the company’s possession free of charge. Companies own their first-party data and usually store it in their CRM, marketing and/or loyalty-program systems to help them get and retain customers by making offers that meet their preferences or simply to maintain their customers’ awareness. Second-party data is data that is acquired as a result of a relationship, typically a partner company, or a co-op partner. Second-party data offers the promise of combining the unique, highly-personalized information first-party data provides with someone else’s first-party data. Many companies share first-party data with a second party and when you receive someone else’s first-party data, it is “second-party” data to you. Usually a company that has a similar or linked business with yours is that kind of partner. A simple example or mutually beneficial second-party relationships would be ski resort partnering with an outdoor winter clothing retailer. The resort could provide its first-party data of its guests to the retailer (the resort is a second party to the retailer), which could then make direct offers advertising skis, helmets, boots, and even ski jackets if it carried apparel. The arrangement is typically reciprocal, with the retailer providing its customer list for the resort’s marketing (which is second-party data to the resort). In large companies, a consumer may be opted-in by their agreement to make their information “available to our partners,” which many times means other business units of the corporation, for example. Let’s jump to third-party data now. Third-party data is…everything else. This includes data that you buy. Some examples are data from list brokerages, acquisition through digital means via online digital data providers like reverse phone append, email append, and so on. Third-party data is used to help identify potential customers for generally anything that you’re selling in a “look-alike” way – by looking at your good customers and trying to buy access to people or device access (display ads, search words) that appear and behave like those good customers. A prime source of third-party data for many years has been data aggregators, companies that specialize in identifying people’s attributes via hundreds or thousands of sources, and aggregating that information in a proprietary way for sale. With digital extensions now, these “on-boarders” can take your first- or second-party data digitally, and match them to the aggregated data set and also provide details about whether you can perhaps reach them on a social platform or generally on web properties around the internet. The key to the marketing usefulness of third-party data is in the volume of data and how precise the segmentation of that data is. If a seller has a large database of people who have shown an interest in cooking, for example, that data could be sold to kitchen remodelers, food merchants, cookbook sellers, major appliance retailers, pot and pan outlets, and cutlery houses, among others. The aggregating product would in effect sell the same database to an array of clients in different ways. Because third-party sellers are in the data business, they charge for their information. Even though first-party data is regarded as the most valuable, a Forrester Consulting survey confirms that marketers must rely on all three types of data to [build their campaigns](https://www.emarketer.com/Article.aspx?R=1012439&ecid=MX1086). The same survey also reported that [privacy concerns and security controls](https://www.emarketer.com/Article.aspx?R=1012439&ecid=MX1086) were very much on the mind of digital marketers which makes sense because there are so many considerations — from maintaining trust in customer relationships to making sure data use in in compliance with applicable regulations. One of their most significant considerations is Personally Identifiable Information (PII). In its 2010 Guide to Protecting the Confidentiality of Personally Identifiable Information, the National Institute of Standards and Technology (quoting earlier Office of Management and Budget memorandums on the subject) defines PII as “―any information about an individual maintained by an agency, including (1) any information that can be used to distinguish or trace an individual‘s identity, such as name, social security number, date and place of birth, mother‘s maiden name, or biometric records; and (2) any other information that is linked or linkable to an individual, such as medical, educational, financial, and employment information.” Though this definition is quite broad and only applies to federal agencies, it serves as a marker for what private data collectors can have and use. In 2012, the Federal Trade Commission released a [report](https://www.ftc.gov/sites/default/files/documents/reports/federal-trade-commission-report-protecting-consumer-privacy-era-rapid-change-recommendations/120326privacyreport.pdf) with recommendations for how businesses should implement best practices in protecting consumers’ private information. Of course, data would be pretty useless to marketers if it didn’t contain sufficient information to shape the offer and reach the customer. So all three types of data have some PII information, the difference is the amount and type of fields shared. First-party data has the most personalized and detailed PII, but some it may not be passed onto a second-party cooperative data sharing situation, leaving second-party data with a greater degree of PII than third-party, but both partners in a second-party arrangement need to be very cognizant of any restrictions as these agreements can be “[a bit of a legal minefield](https://marketingland.com/second-party-data-digital-marketers-128254),” as one observer commented. Third-party data usually is assembled for transfer to the purchaser by an automated device and it might have some PII but that is usually limited to digital life: location, position, device, emails, and phone number. This complexity is why everyone who has an investment in these three types of data can’t just dump it all into a Data Management Platform (DMP) and hope for the best. First-party data isn’t fully at home in the DMP – many pieces of PII have no business in the DMP. Besides, most DMP data is a one-way trip: It goes in but getting all the detailed data and decision history back out isn’t a pretty sight. The larger a company is in terms of transactions and the more of these three classes of data they have, the more imperative it becomes to create a data layer that specializes in keeping the keys aligned and the classes of data clear. **Blog categories:** Data Management, Identity Resolution --- ### [CDP Myths Debunked: More Than Just Another Data Platform](https://www.redpointglobal.com/blog/cdp-myths-debunked-more-than-just-another-data-platform/) **Published:** January 9, 2019 **Author:** Steve Zisk **Content:** The standard definition of a customer data platform (CDP) invites some misconceptions, primarily because the definition put forth by the [Customer Data Platform Institute](https://www.cdpinstitute.org/) in 2013 doesn’t really address business value. The Institute defines a CDP as a “packaged software solution that creates a persistent, unified customer database that is accessible to other systems.” If we infer by the definition, as many do, that a CDP is simply another martech solution owned by marketers, we fall prey to misconceptions by focusing too much on the technology and less so on the business challenges it aims to solve. The standard definition focuses its lens on the “what” and not the “why” you might need a customer data platform. A calculus that accounts for and applies business context to a CDPs purpose will dispel many of the common fallacies that a literal definition invites. **Not Your Father’s CRM System** The common misunderstanding that a CDP is “owned by marketers” and is just another data system that marketers must manage and maintain likely arose from marketers worrying that a CDP was just a newfangled CRM or DMP. It’s an understandable notion. After all, a CRM stores customer data and engages with customers, and a DMP is used to deliver seemingly personalized content, and those are both marketing-managed systems. The key difference is that a CDP connects all types and sources of customer data – batch, streaming, structured, and unstructured – from across the enterprise. By generating a single view of the customer, a CDP differentiates itself from other customer data systems. A CDP is owned by marketers in the sense that it is built with the marketing function in mind, but marketers already swimming in data will naturally resist another solution if they think it will entail having to go through IT for every change request and having to wait months for any change to be implemented, however small. Rather, if we think about the business purpose behind “packaged software”, we get at what marketers really care about: agility and the ability to keep up with the customer. As new devices crop up, new channels emerge, and as customer journeys become less easily defined, marketers must keep pace. A CDP enables that agility, which further distinguishes it CRM or DMP systems. The reality is that creating a persistent, unified customer database ensures that a CDP is never going to be wholly owned by marketers simply because IT needs to be a trusted partner to build, maintain, and customize the CDP solution according to unique business needs. Creating a single view of the customer through use of a CDP is a different process for each company and is largely dependent on what type of data and data sources make up a unified customer profile. “Packaged software” doesn’t mean you flip a switch and an out-of-the-box CDP spins up a unified customer profile unique to specific business requirements. ## **Simplicity, Debunked** Other misconceptions around CDPs relate to the notion of simplicity. While a CDP is indeed a persistent, unified customer database accessible to other systems, the literal definition reinforces a belief that possessing a common data platform will miraculously result in optimal continuous engagements and dynamic customer journeys. The reality is that setting up a single platform for a continuously updated customer data record is just the first step in a process to improve the customer experience. Identity resolution is an enormous challenge that requires linking records across an anonymous-to-known customer journey. Robust identity resolution solutions must know how to handle ambiguous or conflicting information such as name and address variants, missing fields, shared devices, and multiple devices, and need to offer both deterministic and probabilistic data matching to match the range of data consistency and quality found in CRM, POS, and other enterprise systems. Marketers should rightly be wary of a vendor that promises simplicity. All CDPs are not equivalent, either in the breadth of functionality or the depth of capabilities. A CDP is not primarily about combining lightweight data assembly, analytics, and activation. An organization that undertakes an honest assessment of CDP use cases for the business will understand that a CDP must operationalize data across the enterprise to support real time decision-making. The core task of building an accurate, timely golden record must not be overshadowed by trying to “kitchen-sink” fit the entire martech stack into the CDP. ## **All Data is Created and Valued Equally** This brings us to a similar and intertwined misconception, that a CDP can provide high value with a smattering of martech-provided data (think web visits, device IDs, email opens, etc.). The reality is that today’s empowered customer controlling his or her own customer journey, uses several online and offline touchpoints in an undefinable pattern often lacking rhyme or reason. Additionally, customers are providing many different sources of behavioral, transactional, and sociographic information at every touchpoint, both in traditional marketing “channels” and across the broader enterprise landscape. This broad set of customer touchpoints, if used effectively, can deeply enrich customer engagement and personalization. The challenge for a marketing organization is that the more information it collects – a necessity to paint a complete picture of a customer journey – the more it is challenged with identity resolution. An open garden approach provides accessibility to any type of data source which helps complete a unified customer profile and underscores the importance of a CDP as far more than a collection of martech-provided data. How that data is used to deliver real-time insight is what gives a CDP value. When organizations considering a CDP understand the complexity of building a unified customer view while still moving at the speed of the customer, the misinterpretation that a CDP delivers simplicity with a one-size-fits-all solution for all customer data problems will diminish considerably. ## **Near-Real Time is the Same as Real Time** One final mistaken belief about a CDP is that it will deliver optimal value by supplementing batch data with data that is only a few minutes old. The problem with this myth is that it undermines the entire reason for using a CDP in the first place, which is to obtain an up-to-date single view of the customer to inform marketers of a next-best action specific to an individual customer journey. Simply layering new information on batch data doesn’t enable you to create a dynamic model for delivering the next best action for the needs of the customer. This misconception ignores the value of real-time, which means responding with timely and relevant information for the customer at every interaction and channel. Website visitors, mobile app users, and in-store visitors all expect personalized and immediate engagements, which means knowing where the customer is along every step of the customer journey. If a customer uses a brand’s mobile app just before dialing the call center, an account rep that knows what the customer did on the mobile app can guide an optimal customer journey that would otherwise be a missed opportunity. Ultimately, the true purpose of a CDP is to deliver a satisfied customer. Starting with that baseline understanding and thinking about business goals before the technology will help to dispel the myths that a CDP is a panacea that will instantly solve all customer data problems or, worse, that it is just another in a long line of data systems that needlessly adds complexity to a marketer’s job. Peeling away the misconceptions uncovers a CDPs core value and the impact it can have on the bottom line. **Blog categories:** Customer Data Platform --- ### [Demise of the DMP, Long Live the CDP](https://www.redpointglobal.com/blog/demise-of-the-dmp-long-live-the-cdp/) **Published:** July 10, 2019 **Author:** Steve Zisk **Content:** The Data Management Platform (DMP) is like a once dominant golfer now retired to the Senior PGA Tour. While there is still a time and place for the elder statesmen to show off their considerable skills, it is with far less fanfare than when they competed at the highest level. The customer data platform (CDP), meanwhile, is the dominating upstart with a more complete, all-around game. While the reputation of a DMP as the true single source for customer data has been in steady decline for some time, two relatively recent trends have helped accelerate its demise. One is the increased restrictions on web cookies, led with high-profile examples of Apple and [Google](https://fooshya.com/2019/05/08/google-is-limiting-cookies-and-promising-customers-extra-privateness/). More restrictive policies hamper digital advertisers from reaching their intended target. Second, there is now an ability to link identities anonymously in a CDP. Through solutions such as the Redpoint Digital Acquisition Platform powered by LiveRamp, marketers can activate and match activity from downstream ad platforms and networks back to a CDP, giving them control over the customization of a customer journey across digital touchpoints. ## **It’s All About Personalization** Recent trends aside, the writing has been on the wall for some time that DMPs just don’t have the “game” to keep up with the power and capabilities of a CDP. Customers now demand a seamless, hyper-personalized customer experience across an omnichannel journey, and DMPs are simply not built for that purpose. According to research from [The Harris Poll](https://www2.redpointglobal.com/webinar-harris-poll-cx-2019-ondemand?_ga=2.138769333.743388908.1561985490-1570104466.1540307570) in a survey commissioned by Redpoint, 37 percent of consumers said that they will not do business with a company that fails to offer a personalized experience. Moreover, 31 percent report being “very frustrated” when a company does not recognize them as an existing customer, which would include receiving an untargeted digital ad. Such ads are, unfortunately, quite common. According to research from [Nanigans](https://www.businesswire.com/news/home/20180508005275/en/Consumers-Advertisers-Agree-Retargeting-Change-Nanigans-Research), 88 percent of consumers say they have seen retargeted ads for an item they’ve already purchased, and 77 percent say they see too many retargeted ads from the same retailers. Because each touchpoint is part of a dynamic journey, and a specific customer engagement is influenced by the totality of a customer’s actions or behaviors, personalization should not be left to whim. A frustrating experience for a customer anywhere along a customer journey, in other words, will undo even the best efforts at hyper-personalization. A relevant email offer in pitch-perfect sequence with a customer’s buying intent, for instance, will be offset if it’s followed by a digital ad that is irrelevant or, worse, in conflict with the previous offer. The ability to serve up personalization at scale in the context and cadence of an omnichannel journey, and tracking customers along the full anonymous-to-known interaction cycle has never been the purview of the DMP. It is, however, what customers have come to expect. ## **Why a DMP is Not a System of Record** DMPs, which have largely been acquired into the belly of the marketing cloud beasts, do provide capabilities for look-alike modeling, onboarding, and audience buying, which are valuable for select business use cases. A marketer might be interested in finding home loan prospects, for instance, and uses a DMP to extend reach beyond first-party data to build or buy that audience. The misconception that DMPs could serve as a customer engagement system of record took hold because a DMP can handle first, second, and third-party anonymous data – and interface with demand-side (DSP) and supply-side (SSP) platforms. There was a belief that a DMP could do anything from a data perspective. The misconception has largely been dispelled, mostly because they lack first party PII, and lack reporting and measurement capabilities that become more and more important as personalization becomes a competitive differentiator. According to [London Research](https://www.emarketer.com/content/what-concerns-ad-buyers-about-programmatic-advertising), working in concert with media agency Truth, 49 percent of media buyers surveyed were concerned with a lack of consistent measurement/metrics of programmatic ads. Another 42 percent cited a lack of agency transparency as a major concern, with a lack of visibility on third parties (39 percent) and fraud (37 percent) also rating as top concerns. Further devaluing a DMP is the fact that data ingestion, standardization, matching, and keying all infringe on a marketer’s ability to deliver a real-time personalized experience in the context and cadence of a customer journey. As the demand for personalization rises and it becomes even more clear that a DMP is out of its league in this particular arena, taking it off the playing field has the perhaps unintended result of increasing its value by restricting its focus to what it does well – a channel solution for facilitating lookalike advertising. ## **Precise Audience Targeting with a CDP** Niche DMP use cases aside, a CDP is the preferred customer engagement solution among data-driven marketers for many reasons, not the least of which is the fact that it generates a single customer view that is the backbone of hyper-personalized customer engagement. From the advertising perspective, a CDP provides better audience segmentation – an important buffer against the very real problem of ad fatigue when engaging with customers across the anonymous to known lifecycle. According to the same Nanigans study, 57 percent of consumers say that retargeted ads have no influence on their decision to make an online purchase. An enterprise-grade CDP accomplishes more granular audience segmentation than a DMP while maintaining privacy compliance by keeping third-party data adjacent to first-party data in an anonymous data repository. Advanced identity resolution that produces a full representation of an individual customer across online and offline touchpoints differentiates a CDP as a true engine for personalized engagement. A CDP in concert with the Redpoint Digital Acquisition Platform further reduces the DMPs usefulness because mapping an anonymous record to an identity graph allows a marketer to individualize ads to the micro level for more precise, robust, and finite audience targeting. Unlike most DMPs, response data can be returned back which allows for increased contextual relevancy, optimized interactions, and marketing attribution. Marketers are empowered to seamlessly manage communications across all online and offline touchpoints, giving them control over the full customer relationship and digital ecosystem. Serving up personalized customer experiences that consumers demand depends on brand marketers having this end-to-end control. ## **The DMP as a Sidecar** In an open garden environment, which does not lock marketers and business users into pre-built data models or predefined technologies, a DMP retains value when it serves as another CDP data source to contribute to the single customer view. By interfacing with a DSP to serve up a digital ad or build an audience, a DMP provides at least broad information about customers, even via metadata that may help the business understand which audiences are interacting with the brand. Marketing can combine these insights with everything it already knows about a customer to deliver an even more hyper-personalized customer journey across all online and offline touchpoints. Some Senior PGA Tour players can still hit the ball 300 yards, but they lack the complete, all-around game to compete at the highest level. Like a DMP when stacked up against a CDP, legacy status affords them an opportunity to shine – even if the game has mostly passed them by. **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Quality, Identity Resolution, Omnichannel Marketing --- ### [Top 10 Benefits of Rules-Based Orchestration](https://www.redpointglobal.com/blog/top-10-benefits-of-rules-based-orchestration/) **Published:** October 30, 2019 **Author:** Redpoint Global **Content:** There is not a single multinational company thriving today that would deploy an integrated supply chain system that failed to account for volatility, disruption, and consumer demands for immediacy in the delivery of customized products and services. Putting the right product at the right time with the right price in front of a customer is simply too important to rely on static, list-based decisions and systems that fail to account for the dynamics of a global economy. The same is true for keeping pace with always-on, connected consumers as they move through an omnichannel customer journey. Consumers demand a personalized customer experience, and evidence shows that delivering such an experience provides a direct line to revenue. Yet, unlike the universal acceptance of a dynamic and highly functional supply chain, many organizations are slow to embrace empowering marketers with the same power and flexibility under the spirit of “it’s just marketing” or “marketers just don’t think that way”. The hard truth, though, is that if “marketers don’t think that way”, they won’t be long for their jobs as marketing is the direct line between data and revenue and expectations have risen both inside and outside the organizations for marketing to be the tip of the spear for the enterprise as a revenue-generating engine. ## **A New Rules Based Approach That Leaves Audience Lists Behind** Marketing, perhaps more than any other line of business, has complexities that require dynamic systems of engagement. An influx of communications channels. Online and offline engagement touchpoints and personas. Continuous updates to preferences and permissions. The bottom line is that no line of business deals with things like data lists anymore, and none should be bound by legacy technology designed for the age of outbound, batch, or drip communications through audience lists. Everything in marketing is now dynamic and enterprises cannot afford to have toys standing in for hardcore data-driven tools needed to achieve the ultimate marketing objective: omnichannel (all outbound and inbound channels) optimized messaging. With this context in mind, here are 10 reasons why a customer engagement platform that derives audiences, channels, actions, and preferences dynamically at the time an action is taken is a requirement for keeping pace with a dynamic, omnichannel customer journey. 1. **Rules Are Dynamic:** In a rules-driven environment, marketers are not locked in by pre-defined, static lists of data, customers, or prospects that paint a picture of a moment in time at odds with the entirety of a customer journey. The dynamism and flexibility of a rules-based system underscores every reason why a rules-based approach is superior to a reliance on lists. 2. **Rules Are Data-Model Agnostic**: Being dynamic, rules can organize and re-orient themselves into whatever a changing business requirement or KPI dictates. Lists, being inflexible, are static in a table. Models based on lists will therefore become outdated the moment a business objective or KPI changes. 3. **Rules are Multi-Channel Driven**: A marketing campaign based on a static list – such as sending a credit card application to a list of prospects – is likely not just impersonal, but it runs a risk of being irrelevant if a prospect signs up for the credit card after the list is created. Because a rules-based system is dynamic, eligibility for one channel versus another channel – or multiple channels – is decided at the time the communication is initiated based on the totality of a consumer’s actions. 4. **Rules Are Journey-Based**: A list-based system is locked in time and cannot keep pace with a [customer](https://www.redpointglobal.com/blog/a-superior-customer-experience-cx-transcends-marketing/) in a non-linear, non-sequential journey. A rules-based system dictates a next-best action relevant to a customer’s journey at the precise moment and channel of engagement. It does not rely on a list that recommends an action for a customer based on an engagement that may have lost its relevance. 5. **Rules Are Re-Usable:** Lists are a one-and-done proposition. As soon as a customer takes an action – signs up for the credit card, buys the product, etc. – the list is obsolete. Rules are dynamic, which means they can be updated in real time based on a customer’s behaviors and re-used as a “living” document. A rule about what constitutes a gold customer means that the “list” of gold customers is fluid and is re-usable within an automated machine learning model. 6. **Rules Are Flexible:** Once created and activated, a list cannot adapt to an unforeseen change in conditions. When an item on a grocery list is not in stock and the shopper makes a substitution on the fly, the list is relegated to the trash bin. Likewise, a static list of customers loses relevance when any change occurs that is not directly accounted for in the list. 7. **Rules Are Not Batch-Driven:** Running batch-based business processes means that decisions are – by definition – based on lists of data. Businesses – and connected consumers – move faster, which require that decisions rest on dynamic, analytically driven machine learning models. 8. **Rules Account for Behavior Post-Campaign:** An “if this, then that” dynamic rule builds on its own success, infusing value into campaign results by moving the chains, if you will, providing marketers with an – automated – logical next step to drive desired behavior. Results from a campaign derived from a list can be matched back to the list, but a new list must be created to advance any findings. 9. **Rules Do Not Require Coding for Dynamic Personalization:** A list-based email campaign set up for personalization requires scripts, as an example. The time-consuming, resource-intensive effort devalues the benefit of personalization because by the time a customer receives a personalized email, they may be steps ahead in a customer journey. Scripts simply cannot be written to keep pace or to account for every step of a dynamic customer journey. By not requiring coding, rules are unencumbered by manual intervention for dynamic personalization. 10. **Rules Are Secure:** A previous [blog in this space](https://www.redpointglobal.com/blog/why-customer-permissions-must-be-applied-dynamically-in-the-customer-lifecycle/) focused on the importance of applying customer permissions dynamically in the customer lifecycle. The fact of the matter is that regulations frequently change; new provisions, bylaws, and legislation crop up continuously. A list of customer preferences and permissions becomes outdated with each new provision, introducing compliance risk and creating a self-defeating cycle where a new list becomes irrelevant almost the moment it’s published. Applying rules dynamically at the time of each customer interaction seamlessly integrates all segment and preference data into the process at each stage of the journey, making it dynamic and eliminating the potential of frustrating a customer by ignoring or mishandling their preferences, or by ignoring where the customer is in their journey. The[ Redpoint Customer Data Platform](https://www.redpointglobal.com/cdp/) is rules-based. It creates rules for how, where, and when customer data is ingested. It creates rules for how it is managed, and for how it provides advanced identity resolution. It creates rules for how code-free automated machine learning models recommend a next-best action that is always perfectly in the context and cadence of a customer journey. These rules are the most powerful part of the solution, providing the foundation to enable segment-of-one marketing at scale. Any system that relies on lists is simply incapable of matching this power. A rules-based platform embraces the enormous complexity of providing a personalized customer experience for every customer at every stage of a dynamic customer journey. The reward for tackling this complexity is to have a mission-critical solution that is accountable for generating revenue. **Blog categories:** Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [What is Customer Lifetime Value (CLV)?](https://www.redpointglobal.com/blog/what-is-customer-lifetime-value-clv/) **Published:** March 30, 2022 **Author:** Steve Zisk **Content:** What is customer lifetime value (CLV)? According to the [Economic Times](https://economictimes.indiatimes.com/definition/customer-lifetime-value), CLV is the “present value of the future cash flows or the value of business attributed to the customer during his or her entire relationship with a company.” In this calculation, customer lifetime value can be understood as the revenue generated by a customer, less expenses, as well as a brand’s estimation of the projected total value throughout the relationship. Customer lifetime value is often thought of in parallel with loyalty programs, where steady customers accumulate points or rewards based on expenditures. Brands assign value to a customer based on a customer’s spend or the duration of the relationship, and reflect that value through a tiered system of offers, discounts, or other rewards. Outside of loyalty programs, customer lifetime value is increasingly viewed as an important metric that can be instrumental in a brand’s transition to becoming customer-centric. According to [Forrester](https://d3w3ioujxcalzn.cloudfront.net/item_files/9a1f/attachments/779837/original/make_customer_lifetime_value_your_polaris_for_long-term_growth_brandon_purcell.pdf), CLV “plays a highly strategic role in helping firms achieve organizational alignment, make important strategic business decisions, and pivot toward becoming customer-obsessed.” Customer lifetime value aids the pivot toward customer-centricity by giving brands a different way to measure the effectiveness of a personalized customer experience (CX). Before exploring how it does this, we should specify that pursuing a personalized CX is a worthy goal as a key revenue driver. In a recent [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) commissioned by Redpoint, 39 percent of consumers said they will not do business with a brand that fails to provide a personalized CX. A recent [McKinsey article](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying#:~:text=Research%20shows%20that%20personalization%20most,intimacy%2C%20the%20greater%20the%20returns.) on the value of getting personalization right found that a personalized CX drives up to a 25 percent revenue lift depending on an organization’s ability to execute. Conversely, traditional KPIs for measuring campaign ROI such as open rates or time on page are not as effective in measuring the impact of personalization. Success in one channel – an email campaign, a display ad, direct mail, etc. – offers little to no insight into how success bleeds into a customer’s subsequent actions and behaviors throughout a customer journey. That limitation gets to the heart of why brands are turning toward customer lifetime value to gauge personalization. Because a customer’s perception of the experience a brand delivers transcends the channel of engagement, CLV looks instead at the effect of a personalized customer-centric approach throughout not just a single journey, but as the term suggests over the course of the entire relationship with a customer. ## **Relevance and CLV** In practice, what does it mean when customer lifetime value is the arbiter of success? Take email as an example. Every consumer knows first-hand that most brands aren’t shy about sending emails. Beset on all sides with emails from retailers, health practitioners, service companies, the kids’ school, etc., keeping up with the never-ending parade of communications can feel like a full-time job. What, though, does your opening – or not opening – an email say about you as a customer or a potential customer? Thinking about your own experience, are you more or less likely to open an email from a brand that sends you a daily email, or one that stands out from the crowd because it is relevant to your journey as a customer? Maybe it was related to a product you expressed interest in, or perhaps it was a perfectly timed offer for a complementary item to a recent purchase. For data-driven, customer-centric brands, that relevance is not an accident. Rather, it speaks to the strategy of focusing on actions that increase lifetime value rather than the channels themselves. Perhaps a high-value customer prefers not to receive emails at all, or has indicated via a preference center that one email a month is enough. How many brands even ask? In a true customer-centric approach, the value of an email or any other communication channel is assessed by its contribution to the overarching customer journey. Email frequency is just one example. With CLV as a metric, emails as a next-best action might not even be related to an offer at all. A brand might simply want to thank a customer for their business, or invite a customer to an exclusive closed-door event. ## **A Personal Understanding and CLV** The throughway to enhancing customer lifetime value is to ensure that every next-best action is in the context of the customer journey. From the customer’s perspective, a seamless, highly relevant omnichannel experience demonstrates that a brand makes a concerted effort to know the customer as an individual. In the Harris Poll survey, consumers ranked omnichannel consistency as the most important component of customer experience. Furthermore, 82 percent of consumers surveyed said that they are loyal to brands that demonstrate a thorough understanding of them as a unique customer. With customer lifetime value a measurement of success for executing a thorough understanding, brands must develop a [single customer view](https://www.redpointglobal.com/single-customer-view/) in order to measure the impact of a personalized CX at the individual customer level. A single customer view, or golden record, is what allows a brand to deliver a [next-best action](https://www.redpointglobal.com/next-best-action/) to a segment of one. Real-time data processing from every conceivable data source, combined with advanced identity resolution capabilities and other data quality steps within milliseconds of data being ingested, ensure that a golden record tells a brand everything there is to know about a customer at an individual (or household) level. Automated machine learning and a real-time decisioning engine are key components of an omnichannel CX platform that allow a brand to intelligently orchestrate a next-best action at scale. ## **Measure Success with CLV** Customer lifetime value is an easy concept to understand – a customer’s worth to a brand over the course of the relationship. Yet because brands and campaigns have been for the most part organized around channels, CLV has often taken a backseat to channel-centric metrics to gauge the effectiveness or ROI of personalization. With the technology available to provide a hyper-personalized CX to a segment of one, customer lifetime value instead becomes a key metric for how well a brand provides a thorough understanding of an individual customer. **Blog categories:** Data Quality, Identity Resolution --- ### [What is Customer-Centricity?](https://www.redpointglobal.com/blog/what-is-customer-centricity/) **Published:** April 29, 2022 **Author:** Steve Zisk **Content:** What is customer-centricity? According to the [Gartner marketing glossary](https://www.gartner.com/en/marketing/glossary/customer-centricity#:~:text=Customer%20centricity%20demands%20that%20the,customer%20satisfaction%2C%20loyalty%20and%20advocacy.), customer-centricity is an ability of an organization to understand customers’ situations, perceptions, and expectations. It “demands that the customer is the focal point of all decisions related to delivering products, services, and experiences to create customer satisfaction, loyalty and advocacy.” Conceptually, customer-centric is easy to comprehend. “We put the customer first” has been a tagline for as long brands have interacted with customers. But, as the Gartner account makes clear, customer-centricity is more than ascribing importance to the customer. It encapsulates an overarching enterprise mindset in which data, people, processes, and technology are all organized around the customer. This blog will examine why customer-centricity is a priority, look at some of the key challenges organizations face in transitioning to a customer-centric approach, break down steps to becoming customer-centric, and finally touch on the benefits that result from an organization having a deep, personal understanding of an individual customer and leveraging that knowledge to deliver an omnichannel customer experience, at scale. ## **Customer-Centricity Translates to Customer Loyalty** To understand why brands are pivoting to a customer-centricity approach, consider the findings from a [2021 Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) commissioned by Redpoint, where 82 percent of consumers said they are loyal to brands that demonstrate a thorough understanding of them as a unique customer. Consumers equate a thorough understanding with a brand recognizing them as the same customer across all channels, and knowing their preferences and behaviors on an individual level vs. those of another customer. Customers want to feel valued beyond a transactional basis. Value that reflects an individual understanding of a customer stems from a personalized customer experience. Irrespective of channel, every interaction must be in the cadence of a unique customer journey, relevant to the customer’s situation the moment of engagement. In the Harris Poll survey, 65 percent of consumers agree that personalization now meets the threshold of a standard service. Consequences for failing to meet consumer expectations are stark; 39 percent of consumers surveyed said they will not do business with a company that fails to provide a personalized experience. ## **Customer-Centricity Transcends Marketing** The expectation for a personalized CX is not restricted to a marketing/advertising use case. True customer-centricity that makes a customer feel valued as an individual encompasses any possible way a customer interacts with an organization, from service and sales to the call center or collections. The type of personalized CX that drives loyalty – and revenue – requires consistency across any channel or department; from the customer’s point of view, it is as if the brand is speaking to them with one voice. The challenge for many organizations in making the transition to becoming customer-centric is that operationally they are not set up to support consistent interactions with a customer across the enterprise. It’s one thing to say the customer comes first, but when people, processes and channels are organized around a product or service it’s difficult to pull it off successfully. A true customer-centric approach transcends not only product, but the context with which a customer interacts with the brand. How organizations measure the success of CX initiatives is telling for whether they’re on the path toward becoming customer-centric. Channel-specific or department-specific metrics may reveal the success of an email campaign, for example, or whether a direct mail team is producing effective content, but those successes are not tied to their influence across an entire customer journey. An email team awarded bonuses on open rates, for example, is not incented to enhance a customer journey after the email is opened. In addition, the team is likely not provided with customer data not directly related to its channel of interest, which might be helpful in deepening the customer relationship with more relevant emails. ## **Customer Centricity and Perfected Data** Inferior data quality is another barrier preventing companies from becoming customer-centric. Inaccurate and/or outdated data is perhaps the biggest impediment keeping brands from moving in the cadence of a customer journey. From an accuracy standpoint, the simple truth is that if you don’t know who your customer is – or the dynamics of a customer relationship in a household or B2B setting – consistent relevance is virtually impossible. Reconciling a customer’s identity across all devices, data sources, social, IoT, etc., gives a brand the confidence to make bold decisions on behalf of the customer, certain that a next-best action is directed to the right audience – an audience of one. Brands that struggle with customer-centricity because of subpar data quality will often hedge their bets, sending blanket offers or other content that cast a wide net. The trade-off is that communications are likely not relevant to the majority of customers on the receiving end of the content, nor in the context of an individual journey. The same holds true for inbound communications; if an inbound caller could be John A. Smith, Jonathan A. Smyth or another close variation, an agent will not be primed to deliver a next-best action at the outset of the call. Outdated data presents similar challenges to becoming customer-centric. If just prior to dialing the call center John Smith had gone onto his bank’s mobile app to fill out a mortgage loan application, that behavior is probably the reason for the call. But an agent, likely without real-time data from the mobile app, will probably have to ask probing questions to secure information the customer has already provided. That is just one example among many possibilities for introducing friction into a customer journey when there is a lack of integration between two or more channels, or a lack of real-time data updates. Just as with inaccurate data, it creates the same hesitance from business users of customer data to make bold decisions; real-time visibility into an omnichannel customer journey is a necessity to deliver a hyper-personalized CX in the cadence of an individual journey. ## **Customer Centricity and an Omnichannel CX Platform** Knowing that siloed data and channels, operational constraints, inferior data quality and outdated data are the culprits preventing customer-centricity, the solution to making the transition becomes obvious. Companies serious about customer-centricity must organize people, processes, channels, and technology around the customer. An omnichannel customer experience platform provides business users of customer data with the single point of operational control that is needed to act as one brain – one voice – on behalf of a customer. An omnichannel CX platform orients data, insight, and action around an individual customer. The customer – not a product or service – is at the core of every decision. Communication is aligned with any channel; a cross-channel awareness encompasses every stage of an entire customer journey lifecycle. Having all customer data – from every source and of every type – in one platform is a foundational requirement that allows organizations to eliminate the data siloes that prevent customer-centricity. Most customer data platforms promise that functionality, but what separates many vendors from offering what can truly be called an omnichannel customer experience platform is the ability to perfect data within milliseconds of data ingestion. Advanced identity resolution capabilities that include probabilistic and deterministic matching, performed the moment of data capture, helps ensure that business users of the data are communicating with the right person, household, or business entity. A unified customer profile, or golden record, must include real time updates and both a long-tail transactional record and a complete set of unique identifiers. A physical address, email address, name and other traditional customer associations is combined with devices, IP addresses, household information, social footprint and any other identifier to provide a complete profile that users can trust lets them know everything there is to know about a customer. Creating a golden record is a vital component of becoming a customer-centric organization. Data integration alone – simply combining data from any number of sources – is not the same as creating an unassailable, real-time customer profile that is accessible to any user who needs it at the precise moment it is needed. Immediate access to a real-time record is the key to being relevant to a customer’s unique situation as the customer moves through a customer journey. Automated machine learning (AML) and a real-time decisioning platform are key components of an omnichannel customer platform that allow marketers and business users to deliver a next-best action for a customer at scale. With AML as a component of the platform, in-line, self-training models enable users to test and run an endless number of models to allow for audience segmentation at a granular level. The models determine the optimal result based on whatever metric a business user is chasing, and a real-time decisioning engine calculates a next-best action for an individual customer the moment of interaction. ## **Rules, Not Lists, for a True Customer-Centric Approach** Another key component of a customer-centric approach is that a next-best action is not confined by channel. An ability to separate messages from channels across the enterprise, dynamically and in real time, is what allows an omnichannel CX platform to deliver an experience that transcends context. In this way, a customer interacting with a brand through, say, a returns process will have an experience consistent with the experience the customer has online or in-store. The ability to respond dynamically in the cadence of a customer journey requires that an omnichannel CX platform follow a rules-based rather than a list-based approach for generating messages, offers, and communications. In the latter, once a list is created and extracted it is separated from updates and thus becomes static, prone to decay. Any change in circumstance is not reflected in the list, meaning unless a new condition is defined prior to extract, a list will not accurately reflect the change. Customers impacted by the change will then receive a message or offer that is not up-to-date with their customer journey. A rules-based platform, by contrast, uses a set of logic that defines an audience and is evaluated at every point of a campaign where a list would normally be used. This guarantees an extract happens at the latest point possible, ensuring a much higher level of precision and relevance regardless of changing circumstances. Used in conjunction with a golden record, a rules-based approach ensures that the right audience or customer is always matched to the right communication – inbound, outbound, any channel, any time, every time. An OCX platform ensures precise engagement through every moment in time, with exact data and an exact understanding of a customer. ## **Customer-Centricity: More than a Slogan** A customer-centric approach is welcomed and rewarded by customers. In the Harris Poll cited above, consumers ranked omnichannel consistency as the most important dimension of customer experience, up from the No. 2 spot in 2019. And about one-third (31 percent) of consumers surveyed said that a brand’s failure to recognize them as the same customer across devices will make it less likely for them to do business with a brand. A recent [McKinsey study](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) on the value of getting personalization right suggests that companies that excel at personalization will generate 40 percent more revenue just from personalization efforts than their peers, and that organizations that shift into the top quartile performance in personalization would generate over $1 trillion in value across US industries. “We put the customer first” is not just a catchy, nebulous slogan that brands use to declare affinity for a customer. Rather, it is an attitude backed by processes and technology that put action behind the words. A customer-centric approach that puts the customer at the center of every interaction, in the precise cadence of an individual customer journey, manifests itself through a consistently hyper-personalized experience. Customers on the receiving end of such an experience feel valued and understood, which translates into customer loyalty. Curious about how to get started transforming into a customer-centric organization? [Click here](https://www.redpointglobal.com/request-demo/?utm_source=website&utm_medium=footer) to learn how Redpoint can help your organization put its goals within reach. **Blog categories:** Data Quality, Identity Resolution --- ### [What is Automated Machine Learning?](https://www.redpointglobal.com/blog/what-is-automated-machine-learning/) **Published:** June 26, 2020 **Author:** Redpoint Global **Content:** *Definition: Automated machine learning (AML) automates the process of applying machine learning to real-world problems. AML covers the complete pipeline from the raw dataset to the deployable machine learning model. The high degree of automation in AML allows non-experts to make use of machine learning models and techniques. (Wikipedia)* *This two-part blog series will examine the Redpoint approach to machine learning with a solution that eliminates the need for data scientists to build complex models that need to be re-built as dynamic customer journeys evolve. By stripping away complexity, the Redpoint solution puts the power of analytics into the hands of operational marketers – and thus closer to the customer.* The jury on artificial intelligence (AI) is in. Data-driven organizations using new technology to drive new revenue streams are fully on board with AI as a competitive differentiator. According to a recent [McKinsey & Co. survey](https://www.mckinsey.com/featured-insights/artificial-intelligence/global-ai-survey-ai-proves-its-worth-but-few-scale-impact), 80 percent of companies that have adopted an AI marketing and sales use case report a revenue increase – with half reporting an increase of 5 percent or more. [MIT Technology Review](https://www.technologyreview.com/2020/05/06/1001227/the-global-ai-agenda-north-america/) examined AI use cases in a “Global AI Agenda” survey of more than 1,000 AI leaders and found that personalization of products and services – arguably the heart and soul of marketing – was a leading contender. In financial services, for instance, 58 percent of respondents cited personalization as the leading AI use case. Despite AI having proven its efficacy across the board and with widespread confidence in its ability to deliver business results, organizations struggle with scaling an enterprise use case. One reason for the struggle is the general misconception that AI still must depend on data scientists to build, code and program highly complex models. Companies that espouse this route often discover a self-fulfilling prophecy; the complexity they invite at the outset becomes an insurmountable obstacle to enterprise-wide scaling. ## **Evolutionary Approach to Machine Learning** At Redpoint, we believe there is a better way. Automated machine learning (AML) in the Redpoint rg1 solution scales beyond proofs of concepts and special projects with code-free models and automated algorithmic optimization that democratizes the utilization of analytics for the everyday operational marketer. This blog series will break down the important characteristics that define automated machine learning, examine the process itself, explore the types of models favored by marketing and sales, look at the opportunities, and how algorithmic optimization allows for automated re-training without human intervention. A full understanding of how AML delivers enterprise value will allay the misconception that AI must entail a repeat cycle of manually built models that go stale over time, requiring a continual infusion of resources that deliver minimal value. ## **Three Tenets of a Machine Learning Model** There are three basic characteristics important for any machine learning model, and a closer look reveals where the problem of scale first asserts itself. The first characteristic for success is that a model be predictive. This may seem obvious, but only insofar as it’s true as the core of what analytics is all about; if you can predict something even a little bit better than random, you can better align resources to take advantage of opportunities. This is true for any line of business and any AI use case. Second is optimization and adaptation, which is where the struggle to scale an AI use case begins to emerge. Companies tend to invest heavily in the initial building of models, but because the world is dynamic they are then faced with having to repeat the process again and again as time goes on and new data brings new patterns to be detected – and they balk at throwing good money after bad. Particularly in marketing and the creation of a personalized customer experience, customer journeys are becoming more dynamic by the day. A static model constructed for today’s journey loses predictive value – goes “stale” – quickly, which will entail an expensive rebuild/retrain of the models in a few short months. Which takes us to automation as the third characteristic for a successful approach, and an imperative for delivering on optimization and adaptation. Automation scales, people do not. Later in this blog series, we will explore in more depth how Redpoint differentiates with automation, in particular automated re-training where models are tuned to perfectly optimize for a chosen metric. ## **An Automated Process, Reduced Complexity** A closer examination of the standard machine learning (predictive modeling) process itself helps explain why some companies turn to armies of data scientists, and why Redpoint eliminates this necessity by putting its AML solution in the hands of the citizen marketer. First, of course, is the acquisition and curation of data in preparation for activation. Traditionally, this is where companies lean on data scientists to correctly prepare the data. It is where the Redpoint solution sets itself apart with powerful analytics that determine what data is valuable and what isn’t, without human interaction. Next, the preparation phase – transforming, joining, filtering – similarly requires intensive resources to create the features in the data necessary for comparisons, aggregations and other metrics that need to be composed into the data set. Typically, the next step is selecting code – the modeling approach, accounting for considerations such as the quality or richness of the data, the sparsity of the data, and the type of questions you’re trying to answer. Here again, most companies rely on very skilled resources to make these judgment calls, which the Redpoint solution does as a matter of course and eliminates the mistakes made by relying on human judgment and experience. Next is configuring parameters and inputs for the models, which is another step traditionally put into the hands of experienced data scientists vs. having an algorithm that automates the process. After training and review, you select code again and repeat the final steps as necessary until you’re comfortable that the model predicts what you want it to predict. Only then do you deploy the model for activation. Again, for a code-based algorithm (Scala, Python, etc.) the entire process requires an enormous amount of resources, financial, temporal and human capital. Automating the entire process with code-free algorithms is the key to the Redpoint AML solution democratizing analytics for the everyday marketer. It leverages computing speed to analyze thousands of variations in the models and resolve the most powerful model in short order. This removes the built-in complexity of building models that has historically posed the biggest challenge for organizations to scale an enterprise AI use case. Hopefully, this short outline has set a context that reveals the current state of the market when it comes to developing predictive assets to improve business performance. In the next blog in this series, we will explore the various types of models in the Redpoint AML solution. We will also look at how the entire machine learning process is fused with an algorithmic optimization that allows for automatic re-training without human intervention and a simulation process that guarantees outcomes are measured against a perfect solution for whatever business outcome you’re trying to achieve. **Blog categories:** Data Management, Omnichannel Marketing, Real-Time Personalization --- ### [What Does Real Time Mean to You?](https://www.redpointglobal.com/blog/what-does-real-time-mean-to-you/) **Published:** June 20, 2019 **Author:** John Nash **Content:** The definition of real time in the context of marketing varies by use case. For a retailer implementing a buy online, pick up in-store (BOPIS) model, real time could mean engaging with a customer with a personalized message or offer within five minutes of the online transaction. For a web services company, real time will likely require a much faster interaction, likely within milliseconds of a customer appearing in a channel of engagement. Determining the meaning of real time depends on what a marketing organization ultimately wishes to accomplish by eliminating latency between the ingestion of data from any source, applying analytics, and taking action. Business goals must be weighed against operational and cost considerations, and measuring those considerations on a sliding scale against potential lift from the introduction of real time. Will making a process faster generate revenue that will offset the additional cost and resources? ## **Real Time and the Next-Best-Action** While real time can transform many marketing strategies, including onboarding, retention, and acquisition, it’s important to understand that real-time data is only one of three pillars that make up true, transformational real-time capabilities. Real-time access to every customer data source, combined with real-time decisioning through in-line analytical models and real-time action – the ability to enact the decision at the precise moment of interaction – together make up the core capabilities of real-time marketing. For a web services company to know everything there is to know about a customer exactly when that customer engages with a chatbot is of minimal benefit unless that recognition generates insight for a next-best action for the marketer to take during the interaction. For the BOPIS retailer that recognizes an in-store customer as the same person who just purchased a chainsaw online, that real-time action could be a discount offer for an accessory that was placed in an abandoned shopping cart. Real time in the context of generating a next-best offer does not mean that a brand has a static offer queued up ready to go regardless of where a customer next appears in their buying journey. An important distinction is that the next-best action is generated based on the specific interaction or channel. Machine learning models that drive intelligent orchestration and real-time decisioning start firing on all cylinders and make a calculus according to a precise moment of the customer journey. [In the BOPIS example](https://www.redpointglobal.com/blog/if-your-cx-strategy-doesnt-include-bopis-youre-doing-it-wrong/), a next-best action may differ based on the customer’s behavior when picking up the purchased item. A discount offer for a chainsaw cleaning kit might be proffered if the customer goes right to check-out, but if the customer opens the mobile app while lingering in the home improvement aisle, the next-best action might be a personalized direct mailing with kitchen remodel ideas. ## **Real Time Quick Wins** Factoring in revenue lift that can be generated by introducing real time into a marketing strategy can be the deciding factor on whether real time for a business means five milliseconds, five minutes, somewhere in between or longer. On-boarding is a particularly high-value journey that stands to benefit from real time. Introducing real time into a strategy for welcoming a new customer, introducing them to the brand, and encouraging the customer to follow the brand on social media can have a significant impact on customer lifetime value (CLV), in line with the old “no second chance to make a good first impression” adage. A brand that asks a customer to follow them on Facebook when the customer is already a follower will likely introduce friction into the onboarding experience. Armed with a real-time single customer view, a brand can engage with each new customer in the context and cadence of an individual customer journey, offering a personalized experience that recognizes the fact that customers onboard at different paces. Customer renewal strategy is another area where real time can deliver big wins. Marketers know that it’s less expensive to keep an existing customer than acquire a new one, and real time allows a marketer to throttle retention engagement according to a customer’s unique behaviors. A lapsing customer, for example, might be met with a more enticing retention offer during interactions than might a frequent transactor who would convert with a different message. Real-time capabilities often significantly lower the cost per acquisition/retention of a customer. Continual testing of models can help place a monetary value on real time. What percentage of customers did you retain with a same-day email offer versus a second-day email? Does conversion rate improve on your website when content is presented based on propensity scores and clickstream data? ## **Real-World Considerations** A decision of whether to implement real-time capabilities must include real-world considerations. Marketing must be wary of overpromising. A customer service chat complaint that an item wasn’t delivered in the timeframe promised might generate a next-best action of an apology with refunded shipping costs, but marshaling additional resources – re-routing trucks, white-glove service, etc. – might be out of scope. Marketers must balance a fine line between being responsive enough without adding unnecessary cost or resources. Cost considerations are not the only consideration when making the determination for how and whether to implement a real-time marketing strategy. A millisecond response time might be ideal for a web services company but introduce the dreaded [“creep factor”](https://www.redpointglobal.com/blog/how-to-personalize-retail-interactions-without-being-creepy/) for a retailer. ## **Real Time Runs on Metadata** A brand with a single customer view; a golden record for each customer, will have at their fingertips everything there is to know about a customer – behaviors, transactions, preferences. Real-time marketers score their customer file for various outcomes such as persona, segmentation, LTV score among many others. Delivering a real-time engagement in the context and cadence of a customer journey, machine learning algorithms determine next-best-action or recommended products based on metadata as well as event data. Did the customer buy or not buy an item? Did they fulfill or abandon a shopping cart? A trigger or interaction that generates a real-time response may only need yesterday’s data, for instance. Not every part of a golden customer record is going to be live, event data. When creating new models, the decision about what data is needed can be as important as deciding how fast a real-time engagement needs to be. For a real-time “before you go” message for a customer who abandons a shopping cart, will mining 10 years of transactions create a more personalized engagement than looking at just the past 30 days? If so, does that outweigh the extra millisecond, second, or minute that churning through more data will take? Real time is about collapsing the time between data, decisions, and interactions in an outcome-focused way. Measuring outcomes such as revenue lift or customer satisfaction will help marketers make important decisions from a time, cost, and resource perspective about what real time means in accordance with their business goals. **Blog categories:** AI & Machine Learning, Customer Data Platform, Data Management, Real-Time Personalization --- ### [The New-Look Customer Experience in 2022: First-Party Data & Omnichannel will Drive Innovation](https://www.redpointglobal.com/blog/the-new-look-customer-experience-in-2022-first-party-data-omnichannel-will-drive-innovation/) **Published:** November 22, 2021 **Author:** Dale Renner **Content:** At the beginning of 2021, there was a lot of speculation about what a “new normal,” if there is such a thing, would look like as the world began to emerge from the depths of the pandemic. Questions included whether digital-first customer behaviors would continue, if employees would return to an office environment, and whether contactless engagements such as telehealth would continue on an upward trajectory. As we close out the calendar year, in addition to gaining some clarification on [changing consumer behaviors](https://www.digitalcommerce360.com/2021/04/27/more-than-50-of-large-retail-chains-offer-curbside-pickup/), [office dynamics](https://www.accountingtoday.com/news/pwc-launches-full-time-remote-work-policy-for-u-s-staff-members) and [telehealth stabilization](https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality), there appears to be a common denominator to the topline changes likely to endure in these unprecedented times. Consumers are simply more empowered and increasingly aware that they are in control of the shape of their brand relationships. An expectation for a seamless experience across all channels and engagement touchpoints has hardened as foundational. The fact that brands are still striving to meet this base expectation sheds some light on what we can expect as we turn the page on a new year. To wit, here are my predictions surrounding customer experience for 2022: - **Companies will innovate with the use of first-party data to drive brand equity** – Consumers understand that first-party data is what drives the seamless, omnichannel experiences they have come to expect. In new research [Redpoint conducted with Harris Poll](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236), 66 percent of consumers surveyed said they are willing to provide brands more information about themselves if it is used to create a more valuable customer experience. Thus, with the impending shift away from third-party data, first-party data will become the standard for brands to gather insight. Businesses that succeed in a soon to be cookie-less world will be those that foster consumer trust, building brand equity through the proper use and accountability for data entrusted to them. Data protection and stewardship will be a big focus, and successful brands will set the standard by ensuring all customer data never leaves control of the organization, becoming increasingly reluctant to push their data out to managed services. - **Consumer preferences and consent will become integral to the marketing team’s approach to driving revenue** – In line with the increased focus on data stewardship, 2022 will see accountability for consumer data extend beyond a siloed IT function into marketing. Preference and consent management will become a new key function for marketing teams, as they take on additional responsibilities for how data is used to orchestrate consumer experiences. With the willing exchange of first-party data for more personalized experiences, and with an expectation that this data is used in accordance with stated preferences, we can expect to also see leading brands establish data contracts with customers. This will go beyond checking a box of adhering to a new regulation to instead focus on adding long-term value and consideration to the consumer experience. - **Only the top 15-20 percent of brands will execute on a holistic omnichannel strategy –** In yet another recent Harris Poll finding that underscores the importance of delivering a personalized experience, 82 percent of consumers (up 5 percent from a 2019 Harris Poll survey) say they are loyal to brands that demonstrate a “thorough understanding” of them as a unique customer. Consumers increasingly demand omnichannel experiences that reflect such a thorough understanding, with consistency across all touchpoints. In fact, a lack of consistency was cited by 74 percent of consumers in the Harris Poll as a major reason why brands fall short of delivering an “excellent” customer experience. What holds many brands back from meeting this expectation is that they often strategize and execute around the channel, not the customer. Putting the customer at the center from a business *and* an operational standpoint is the only way to truly achieve omnichannel customer engagement. However, this customer strategy will only be possible by the brands that embrace omnichannel capabilities for data, decisions and orchestration – enabling them to understand and act in the moment at the cadence of each customer, regardless of their channel. We anticipate that moving beyond ‘multi-channel’ or ‘cross-channel’ to omnichannel will only be achieved by the top 15-20 percent of brands that truly get creating, delivering and sustaining superior customer experiences right. - **The 90s are back, not just in TV reruns and fashion, but in marketing methodologies, too. Marketers will finally be able to market to a segment of one –** Popularized in the early 1990s, one-to-one marketing is no longer just a concept – but now finally an executable strategy. Even though marketers have become very sophisticated at selections, offers and messages to package and send out, it has always been a segment of many. There never was a true market of one….until now. We finally have a way to drive this market of one in real-time, using in-the-moment insights to engage consumers. With machine learning and AI, marketers can generate thousands or millions of models to establish a market of one, versus building a model around a segment. Technology – underpinned by trusted data – is what enables this. With this in mind, brands will finally start to leverage machine learning to gain a single point of control over their decisions, tapping into the ability to engage a true market of one and advancing customer strategies exponentially. In closing, I see 2022 as a tipping point in terms of how brands think about customer experience. By this time next year, I think we will see many more brands and organizations appreciate that first-party data is the cornerstone for providing omnichannel customer experiences that align with customer expectations. While some of the coming changes will be driven by new privacy regulations and the end of third-party cookies, compliance – however important – will finally be understood as secondary to the overarching reason brands will better protect data they’ve been entrusted. That is, they know the exchange of first-party data in return for a continually improved omnichannel customer experience forms a relationship with a customer based on trust, which becomes central to the brand promise. **Blog categories:** 1:1 Personalization, Data Quality, Identity Resolution, Segmentation & Activation --- ### [The Sky is the Limit with AI and Machine Learning: Capitalize on the Moment of Interaction](https://www.redpointglobal.com/blog/the-sky-is-the-limit-with-ai-and-machine-learning-capitalize-on-the-moment-of-interaction/) **Published:** July 22, 2019 **Author:** Redpoint Global **Content:** *This is the second blog in a two-part series that explores how to use advanced digital technologies to create revenue lift.* [*Part one*](https://www.redpointglobal.com/blog/harness-the-power-of-digital-technology-as-a-revenue-driving-engine/) *examines the transformative power of embedded AI and machine learning to engage with a customer with a personalized customer experience in the context and cadence of an omnichannel journey.* A previous blog on the topic of AI and machine learning as vital tools for producing revenue lift referenced an Aberdeen Group study that found brands that “create personalized experiences by integrating advanced digital technologies and proprietary data for customers” are seeing revenue increase by [6 percent to 10 percent](https://www.bcg.com/en-us/publications/2017/retail-marketing-sales-profiting-personalization.aspx). In some cases, that estimate undersells the revenue-generating power of advanced digital technologies. According to Gartner, the difference between up to a 10 percent lift and a 20 percent lift more in line with what some Redpoint customers achieve is the difference between being “persona-centric” and “customer-centric”. In the former, an enterprise uses transactional, preference, historical, and purchase data to form an engagement strategy. In the latter, an enterprise also looks at behavior across devices, analyzes device usage, IoT and sentiment analysis, and first-party, second-party, and third-party data across an anonymous-to-known customer record. In other words, what the [Redpoint CDP](https://www.redpointglobal.com/) delivers. One Redpoint client directly attributes our platform to a 19 percent revenue lift. Another produced a 3X ROI on the entire system in the first year. This is the reality of what embedded, in-line analytics and machine learning models produce; automated revenue-generating interactions that directly impact the bottom line. These and other Redpoint customers are executing on customer engagement strategies to drive revenue with personalization, and with machine learning as an indispensable partner to achieve the ultimate objective: capitalizing on the moment of interaction with each customer in the context and cadence of an individual customer journey. ## **From “Nice to Have” to “Must Have”** Many companies misunderstand or mislabel advanced analytics and machine learning as strictly a cost concern, which can be understandable if the goal or use case is to produce a superficial personalized experience on par with a ‘smart’ drive-thru at a fast-food eatery. A truer cost consideration considers the price of inaction, combined with the cost of existing manual engagement systems, processes, and strategies that fail to seize on the moment of interaction. Existing technology that cannot offer a consistent personalized experience across an omnichannel journey has two strikes against it. One, it’s ineffective. According to a [Harris Poll survey](https://www2.redpointglobal.com/white-paper-customer-experience-harris-poll?_ga=2.46111788.94145515.1561385605-1570104466.1540307570) commissioned by Redpoint, 37 percent of consumers said they will no longer do business with a company that fails to offer a personalized experience. Two, it is counterproductive. Failing to keep pace with the consumer in the channel of their choosing – such as an online offer for an item the consumer recently purchased in-store – introduces friction into the customer experience. In the same Harris Poll survey, 34 percent of consumers say it is “very frustrating” when a brand does just that, with 33 percent also frustrated when a brand sends offers that are not relevant. Viewed through this lens, there is a sunken opportunity cost marketers must factor when they’re unable to seize the moment of interaction. Taking a longer view, automated machine learning models that retrain themselves based on current data and are always primed with a next-best action regardless of where and when the customer appears in their buying journey are indispensable for augmenting a customer’s lifetime value (CLV). Opportunity, then, must be weighed not only against the cost of a standalone personalized customer experience but rather against the customer loyalty and lifetime value a consistent, personalized CX delivers. According to research from the [Aberdeen Group](https://lmistatic.blob.core.windows.net/document-library/boldchat/pdf/en/omni-channel-customer-care.pdf), companies with an extremely strong omnichannel customer engagement have an 83 percent customer retention rating, versus a 53 percent rating for those that do not. In a [2018 Boston Retail Partners survey](https://brpconsulting.com/download/2018-digital-commerce-survey/), more than half of consumers (51 percent) said that it is important to have a personalized experience across all digital channels within a brand. An increase in orders over a customer’s lifecycle, a greater average order value, and growing wallet share from a loyal, growing customer base are all provable ROI metrics from introducing AI and machine learning. ## **Leave No Data Behind** A casino offers a real-world example for how embedded AI and a next-best action drive revenue. One casino approached Redpoint with a problem: it was taking 36 hours or longer to ingest and aggregate customer data to create an identity proxy to be used for customer engagement. If a weekend guest earned a craps windfall on Saturday afternoon, for instance, that information wouldn’t be in an updated customer record until long after the guest had left the premises, making real-time offers to help the guest spend their winnings in the casino – a discount on a luxury fur, a room upgrade, a spa treatment – no longer relevant to the customer’s experience. The Redpoint customer engagement platform consists of three layers that enable personalization at scale, in real time, in the cadence of the customer. A next-best action spurred by machine learning begins with the golden customer record or 360-degree customer view, a persistently updated record that captures data from any source or type in real time. The instant data comes in it moves through a model and is ready to be used to form a relevant engagement with a customer in the context and cadence of where the customer is in their buying journey or path-to-purchase, independent of channel. The lack of latency between ingestion and activation of the golden record is a key functionality of the Redpoint CDP. As the above casino example illustrates, knowing everything there is to know about a customer in real time is the foundation for providing a next-best action that is hyper-relevant to the customer experience. The crucial importance of running advanced analytical models on an updated customer profile across an unknown-to-known state is a unique enabler of the Redpoint CDP to enterprise customer engagement at scale. Real-time decisioning powered by Redpoint Automated Machine Learning (AML) is the second layer of the Redpoint Customer Engagement Hub that, in concert with the CDP, breathes life into a unified customer profile by personalizing an omnichannel journey. AML runs 24/7 looking for opportunities to monetize the golden record; a simulation engine continuously monitors campaign models, and when a model predicts a better outcome than an existing model, a new model will automatically be put into production. This lights-out modeling capability can be tuned to any business goal, whether it’s acquisition, cross-sell, retention, customer value score, or another metric. Intelligent orchestration is the third and final layer, activating the real-time decision with a next-best action relevant to the customer’s real-time experience, regardless of channel. A customer’s cadence may dictate an email or a push notification, for instance, but the key point is that an action is not determined in advance. Rather, a dynamic next-best action is perfectly synchronized to that precise moment of interaction. ## **Shed Complexity with a Single Point of Control** The customer referenced earlier which attributed Redpoint software to a 19 percent revenue lift has a service level agreement (SLA) that stipulates a 50 millisecond response time to engage with a customer with a next-best action independent of channel, with the platform regularly returning a next-best action in roughly 15 milliseconds. The immense complexity of continuously updating a golden record, applying real-time decisioning, and intelligently orchestrating a next-best action happens, literally, in the blink of an eye. This is made possible because the platform offers a single point of control for all data, decisions, and interactions that is indispensable to keeping pace with an omnichannel customer journey. Because a personalized customer experience is largely overtaking price and product as a competitive differentiator, marketers can no longer rely on intuition to create engagement that moves the needle. Automated machine learning in a platform finely tuned to a customer’s needs, wants, and desires unlocks revenue opportunity for customer-centric, data-driven organizations. **Blog categories:** AI & Machine Learning --- ### [Segmentation is Evolving, But Data-Driven Insights are Here to Stay](https://www.redpointglobal.com/blog/segmentation-is-evolving-but-data-driven-insights-are-here-to-stay/) **Published:** November 16, 2022 **Author:** Steve Zisk **Content:** In the world of real-time, personalized customer experiences that drive revenue, it is fashionable to think of [segmentation](https://www.redpointglobal.com/orchestration/segmentation) as a relic of a bygone era, kicked to the dustbin of history by the concept of one-to-one marketing. While it is true that customers expect omnichannel personalization and for brands to recognize them as individuals with unique preferences and behaviors, segmentation is still a valuable tool that allows marketers to target an individual customer with a [next-best action](https://www.redpointglobal.com/next-best-action/) based on what the data says will produce the desired result as opposed to basing an action on gut instinct or a hunch. An organization might, for example, want to understand how likely a customer is to churn. A machine learning propensity model trained to find commonalities in the data will produce segments that reveal this insight. Segmentation does not preclude personalization; even customers with the same likelihood to churn may be presented with a different next-best action. ## **Adieu, “Batch and Blast”** Before expanding on the role of segmentation to derive actionable insights from customer data, first some background on why segmentation became a popular marketing tool. Historically, the adage of “the more the better” held true as far as customer data in an organization’s database. “Batch and blast” campaigns were the order of the day; for email, direct mail or other channel-based campaigns, responses were a numbers game – the more you sent, the more responses you’d get. As the volume of data and the number of channels increased, however, drawbacks emerged – beginning with cost and inefficiency. A response rate may have stayed static at, say 10 percent, but the company paid more for the same return, with more unresponsive customers. In addition, more channels and different ways for a customer to move through a customer journey meant more customers who weren’t being reached at all, further diluting the effectiveness of a channel-centric campaign. ## **Limitations of Basic Segmentation** For a simple, illustrative example, consider a neighborhood pizza parlor that sends an email to every customer promoting a free dessert for any party of three or more patrons. The email’s hero image shows a meat-lover’s pizza next to a frosty mug of beer and a bowl of ice cream. Without segmentation, the offer is immediately irrelevant to any customer who regularly dines alone or as a couple, or for those who are lactose intolerant. Maybe a party of three – a couple with their child – would love the free dessert, but the parents are non-drinkers, or they’re vegetarian and they’re put off by the hero image. The promotion loses more luster when the establishment opens another location or adds delivery and take-out options. Even basic manual segmentation will mitigate many of the drawbacks associated with a blanket offer. The free dessert promotion goes to customers who have previously purchased desserts. A different 2-for-1 large pizza offer goes to those with an average order of $40 or more. Segmenting out an audience using only transactional data, the company is more relevant and more cost-effective. Recognizing that a small pizza shop will all but certainly not utilize advanced segmentation techniques, the concept itself applies equally to enterprise businesses. Marketers experimenting with segmentation quickly realize that customer data has endless stories to tell. Working on a hunch, or gut instinct, marketing teams can come up with any number of ideas for how to manually segment an audience, and target a segment with tailored content. College students are offered a free appetizer on karaoke night. Seniors are offered an early-bird discount. The more data, the more stories – and the more difficult it becomes to decipher those stories manually. A hunch, after all, can only be based on a finite number of data points. With more data points to consider, a hunch has less chance of hitting the mark. The pizza parlor will be less confident, in other words, that a manually created segment reveals what’s most meaningful about the customer. ## **Enter Machine Learning** Trusting that an audience segment reveals what’s meaningful about a customer is where unsupervised machine learning models come into play. Unsupervised machine learning models are used to discover data correlations that reveal something interesting about an audience, and are a primary use case for audience segmentation. An unsupervised model will segment an audience based on what – according only to the data – is important, interesting or unique about a particular audience. The model exceeds the threshold of what’s realistic for marketers to divide an audience manually, finding patterns in the data that may not be apparent to the naked eye. In the pizza parlor example, machine learning models might be used to find a correlation between a customer’s physical address and the time of day the customer visits the establishment, the frequency of visits and order size, of the number of pizza toppings and how many soft drinks a customer buys. The use of machine learning to find patterns in customer data does not preclude a personalized experience, or what is often referred to as one-to-one marketing. Rather, it allows a marketer to deliver a hyper-personalized experience the marketer can trust is meaningful and relevant to the customer because the data says it’s meaningful and relevant. **Blog categories:** 1:1 Personalization, Journey Orchestration, Segmentation & Activation --- ### [The Art of Listening in Mastering Omnichannel Marketing](https://www.redpointglobal.com/blog/the-art-of-listening-in-mastering-omnichannel-marketing/) **Published:** May 9, 2019 **Author:** John Nash **Content:** The traditional customer buying journey or path-to-purchase followed a mostly linear track. A customer considered several brands, narrowed choices through an evaluation phase, and ended up buying a product in-store or online. Marketers mostly followed suit with a funnel-like engagement strategy that viewed each touchpoint or customer interaction through a transactional lens. A linear, static transaction-based approach is no longer tenable when trying to engage with the continuously connected consumer. The empowered consumer is now in charge of his or her own customized, dynamic buying journey that is nearly unrecognizable from the traditional, straight-line path. An explosion of interaction points across multiple smart devices, channels, and applications coupled with unprecedented consumer choice makes today’s omnichannel buying journey more like a Whac-A-Mole experience. A consumer can, and will, surface in no readily discernable pattern across a host of touchpoints without a defined beginning, middle, or end. Omnichannel does not necessarily mean the same thing as “multi-channel”, which refers to consumer options for how to engage with a brand. Mastering omnichannel marketing is more about the seamless orchestration of physical and digital touchpoints into a holistic experience. It’s a recognition that, for the customer, each touchpoint complements another in an instantaneous, visceral manner based on changing variables, conditions, or events. While viewing a 30-second ad, a consumer might begin an online search. Or a customer could be one of [the 45 percent of American consumers](https://www.mediapost.com/publications/article/328672/report-mobile-shopping-yes-mobile-payments-no.html) who say the smartphone is an “essential” shopping tool, checking out in-store inventory while simultaneously on their device looking for a better price. For the marketer, managing such a journey requires viewing the journey through the lens of an overall customer lifecycle in which a relational construct that is built over time through loyalty, growth, and retention supersedes any single transaction. Mastering omnichannel marketing entails always being on the lookout for customer signals and viewing each interaction not only in the context of the last interaction that preceded it but also in the context of always-changing, trigger-based variables that are unique to each customer for every moment in time. ## **An Ear to the Ground** To achieve the consistency that consumers expect throughout an omnichannel journey, marketers need to refine how they listen to consumer signals. A famous quote from educator and leadership expert Stephen Covey illustrates how marketers need to change tack. “Most people do not listen with the intent to understand, they listen with the intent to reply,” he said. This describes the key difference between transactional and relational marketing. In the former, marketers listen with the intent to reply: the customer did X, in response we will do Y. An action expected a response. In the latter, marketers listen non-stop for cues that will inform their knowledge about the customer in the context of the overarching lifecycle. Listening to understand allows marketers to see precisely what’s happening underneath the Whac-A-Mole cabinet. Instead of having to make even an educated guess about where and when the customer will resurface, finely tuned listening lets us engage with a customer in the right context and cadence every time. This inner-workings view is made possible by having a single view of the customer, a persistently updated golden record that lets us know everything there is to know about a customer. A single view of the customer strips away the guesswork by providing marketers with every piece of consumer data from first-party, second-party, and third-party sources, including structured, semi-structured, and unstructured data. Any new trigger or variable – a like on social media, a page view, a product review, a new job, a new home – instantly becomes part of the unified customer profile and builds on not just what we know about the customer, but also informs our expectation for the customer’s likely course of action. ## **New-Look Attribution** In transaction-based marketing, attribution has been a challenge for a long time. Marketers struggled to understand a 1:1 cause and effect for which action spurred a purchase. Was it the 30-second ad, the product placement, the packaging? In a relational, omnichannel approach, there is no magic bullet. Everything is intertwined, and the complexity of the dynamic customer journey forces marketers to re-think attribution. Linking a specific offer to a specific outcome fails to recognize the breadth of a true omnichannel marketing experience and discounts the power of the consumer to chart a unique path based on a narrow orchestration of events and variables. The traditional way to think about purchase intent – predicting the likelihood of purchase based on past behavior – offers an example of why it’s important for marketers to adapt. A transaction-based construct uses imperfect measures, such as forecasting buying intent based on a set interval between purchases. Making an offer based on old-school purchase intent analysis fails to account for or recognize the power of the consumer to instantly pivot based on any number of variables. Imperfect assumptions based on a straight-line, transactional customer journey run the risk of introducing friction into the customer experience by making an offer that is not relevant to a consumer at that particular moment and touchpoint, or by showing the customer that you do not recognize them as an individual across an omnichannel environment. According to Forrester, [70 percent of consumers](https://www.forrester.com/report/Bridging+The+CrossDevice+Chasm/-/E-RES120054) have a negative view of inconsistent cross-channel messaging, with a subset of that population claiming that an inconsistent experience factors in a decision to switch brands. ## **Real-Time Decisioning is the Omni-Channeling Lifeblood** Data aggregation is an important first component of listening with the intent to understand. Data activation is the second crucial component; in totality, listening is a combination of applying everything we know about a customer with a real-time decisioning engine powered by advanced analytics and machine learning. Real-time decisioning is the fuel that powers the management of an [omnichannel](https://www.redpointglobal.com/blog/capturing-intent-in-an-omnichannel-customer-journey-is-next-level-marketing/) experience. A real-time engine provides an understanding of not only how to interact with the customer, but to interact with the customer in the right context, cadence, and in the right channel at the right moment. A real-time engine breathes life into last-mile connections by ensuring you’re moving at the pace of the customer in a consistent manner across all touchpoints. Buy online, pick-up in-store (BOPIS) illustrates the importance of real-time in fueling the last-mile connection to keep pace with a consumer’s customized journey. When a customer buys a drill online and chooses to pick-up in-store, real-time decisioning that includes geo-fencing as a consumer data source will let the marketer know when the customer is near the physical location. A next-best action could be a mobile app push notification for a discount offer on an accessory such as drill bits that may have previously been a partial cart abandonment. Or maybe the customer recently purchased drill bits, and the next-best action is an offer for a woodworking class. Without a real-time engine, a brand would be unable to facilitate a seamless customer experience. ## **Staying In-Step with the Customer** Data aggregation and real-time decisioning provide marketers with a single point of control over data, decisions, and interactions that is an absolute necessity to engage with customers in an omnichannel environment. A [single point of control](https://www.redpointglobal.com/blog/data-as-a-revenue-engine/) levels the playing field for marketers by intelligently orchestrating a next-best action in the context and cadence of the customer at every interaction and on any device or channel. Whether a customer chooses to webroom, showroom, BOPIS, or even just check in on social media occasionally, a single point of control puts marketers in-step with the customer throughout every dynamic journey. A recent [Harris Poll commissioned by Redpoint Global](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) underscores the importance for marketers to manage an omnichannel experience with a data-driven approach. Consumers rated omnichannel consistency as the second most important dimension of customer experience (CX) when rating brands’ ability to meet their expectations, just behind privacy and just ahead of customer understanding (recognizing the customer as an individual). Together, the CX dimensions inform how consumers define the personalized customer experience, with many (43 percent) saying personalization means a brand knows who they are across every touchpoint, and 42 percent defining it as a brand’s recognition that the consumer is in charge of customizing and controlling how, when, where, and why the brand interacts with them. Customers are clear; they want the brands they frequent to know who they are. They want a personal relationship. While it may seem counter-intuitive for a brand, eschewing a transactional marketing approach in favor of a relational one to engage with the omnichannel customer will produce the outcome every brand wants: more transactions, and more satisfied, loyal customers. **Blog categories:** Omnichannel Marketing --- ### [Machine Learning Misconceptions, Dispelled](https://www.redpointglobal.com/blog/machine-learning-misconceptions-dispelled/) **Published:** May 11, 2021 **Author:** Redpoint Global **Content:** There are many misconceptions about machine learning and artificial intelligence (AI). One prevalent misconception is that the terms are interchangeable. We’ve dispelled this notion in a previous blog. Machine learning is a subset of AI. Self-driving cars provide a good illustration of the differences between the two. Autonomous vehicles require hardware for sense-and-control, software to recognize situational details (traffic, lights, lane changes, etc.) and an “executive function” that acts on the input and drives the car. Machine learning provides the pattern recognition component, learning and predicting what might happen next. AI replaces or augments “intelligent” human vehicle control tasks. Machine learning uses historical data to create descriptive, predictive, or prescriptive models that users can run against current data, whereas AI uses these predictions to control one or more processes. Often machine learning, AI or learning systems in general are bandied about as synonyms for “analytics.” The truth is, straight-up analytics – a graph or a plot of numbers, a calculation of sales over time, etc. – do not necessarily involve a learning system. Machine learning can produce analytics as an outcome, but not all analytics is machine learning. There are other common misconceptions about machine learning that are important to debunk, especially for marketers looking to tap into the power of machine learning to help deliver hyper-personalized customer experiences at scale. Machine learning can be more useful, and provide better insight than run-of-the-mill analytics. ## **Machine Learning, a Trusted Ally** One misconception is that, for whatever reason, machine learning systems in general cannot be trusted. This notion seems to be hardwired; unless we see it with our own two eyes, we tilt toward disbelief – it is human nature to think a humanized customer experience must, by definition, include directed human involvement. Another common mindset is that something that has worked before must therefore work again – the “we’ve always done it this way” syndrome. It can be difficult to break out of this rut; we’re conditioned to think the techniques we’ve learned must always deliver the right answers. In a highly dynamic world, however, mistaken assumptions may often lead to right answers to the wrong sets of problems. To minimize the number of assumptions, marketers must accept that the world moves faster than we can keep up doing things manually. The world is dynamic – markets, consumer behavior and interactions change all the time, and some of this is reflected in data that changes by the day, hour and second. A great model may go stale quickly. Marketers, therefore, need to be open to alternative technologies and to understand that these offer the ability to keep pace with dynamic environments. One way to alleviate the mistrust is for marketers to understand that machine learning does not need to automate everything in a system; there can be parts that are manually controlled, or that require some level of manual intervention such as approving results. A recognition that machine learning is not an all-or-nothing proposition will help debunk the similar misconception, which is that automation in general will end up replacing humans (read: marketers and data scientists.) There is a perceived job security factor in play. Or rather, job insecurity. Alleviating this involves adopting a new mindset that machine learning is, at its core, about making things more efficient – outcomes, processes and, yes, people. Testing and applying machine learning in the context of the business will always need smart people. With the understanding that machine learning is a collaborative partner rather than a system designed to eliminate marketing jobs, marketers will more fully appreciate the learning system as an enabler of more powerful human-centric experiences. ## **Real Problems, Real Solutions** Another common misconception about machine learning is that it is a magic bullet that solves all problems perfectly, just by throwing enough data at it. Some of this misconception can be attributed to the buzzword factor. Like “Big Data” of a few years ago, machine learning and AI suddenly became something every business had to have – even though few understood *why*. To dispel this notion, it’s important to understand not only what problems machine learning is good at solving, but also what areas are (currently) not a good fit. Underlying this thinking is the need for businesses to be crystal clear about a use case. Deciding in advance on a judicious application will help debunk the notion that machine learning, provided enough data, will solve any problem. Just like human-driven solutions, machine learning also must work within the constraints put on the business by regulatory agencies, data privacy laws, overarching corporate objectives and even physical constraints. The saying that “to a hammer everything looks like a nail” is analogous to how some marketers view machine learning – as a tool that solves every problem. The reality is, just as a toolbox is filled with different gadgets, the key is to use the right tool (or tools) to solve the problem at hand. ## **Freed from the Coding Burden** Another myth about applying machine learning is that it must require heavy coding by data scientists. But because the world is dynamic, with data and customer journeys changing rapidly, is precisely why machine learning needs to be an easy-to-configure, automatable process – from data inputs through to model building, deployment and optimization. Automating these processes without the burden of coding is key to keeping pace with business cadence. Marketers that harbor a belief that ML requires coding then think they will either need to hire more people, learn how to code, or send data off to a third-party vendor to build a model. It’s even worse if the third-party vendor requires a licensing agreement to use the model. Having to repeat this tedious manual process every time there’s new data is very inefficient. Automation ensures the model comes back in time to still be relevant to the problem you’re trying to solve. ## **Predictive Modeling vs. Business Optimization** Another misconception, somewhat in line with the hammer-nail analogy, is that many marketers fail to distinguish between using machine learning for predictive modeling and for optimizing a business process. Optimization is an integral part of any successful business operation, and is a key component of learning systems. Think of optimization as ‘knob-tuning’ – finding the best set of configurations, parameters, process sequences, etc. to meet one or more goals. I.e., basically a search through a set of ‘what-if’ scenarios, subject to applicable constraints (resource limitations). Obviously, this involves prediction – this is where machine learning modeling comes into play. The optimization part takes these predictions, uses them to evaluate how well things would be, then tunes the salient parameters to get the ‘best’ expected outcomes. For example, in the world of customer engagement segmentation models are often used to predict which offer or recommendation will be most relevant to a customer. Once predictions are available, they might be used to evaluate profitability. The optimization piece can use these predictions toward tuning the parameters with a goal of producing a business outcome – such as increasing customer lifetime value or reducing attrition. Airplane scheduling is another good example. If the business process is to optimize gate turnover to increase profit, machine learning will be used to model a host of processes, from predicting time spent onboarding and offloading, to cleaning, fuel consumption, routes, weather, maintenance, etc. With the ultimate goal to increase profit by having more planes takeoff from one or more locations, optimization will find the best setting(s) for each variable to produce the desired outcome. This misunderstanding, as it were, brings us full circle to the first point mentioned – the notion that machine learning is a synonym for analytics. In the airline example, analytics are the statistics – jet fuel consumption from Point A to Point B, average onboarding time for a 737, an Airbus 340, etc. Machine learning could be used to predict and generate these statistics. Artificial intelligence might be used to control the flow of people, or direct support services. Optimization would decide the ‘best’ allocation of resources, where to locate them, and what schedules to fly, all with the goal of optimizing profit. The combination of these technologies create the opportunities for business to stay ahead of the game. With a finer appreciation for what machine learning is – and what it is not – marketers will sidestep the common misconceptions, and begin to appreciate machine learning as an indispensable enablement tool for the delivery of hyper-personalized experiences at scale. **Blog categories:** AI & Machine Learning --- ### [Is Your Marketing Platform Ready for Prime Time?](https://www.redpointglobal.com/blog/is-your-marketing-platform-ready-for-prime-time/) **Published:** February 27, 2020 **Author:** Redpoint Global **Content:** With few exceptions, marketing technologies have fallen short of being enterprise-grade technology capable of driving revenue and innovative customer experiences. The rise of software-as-a-service (SaaS) applications helped contribute to this gap, creating a mindset that the enterprise could piecemeal and outsource marketing functionality to a public cloud, chasing one channel at a time. Another reason for a lack of enterprise-grade technology in the martech stack is a general failing to accept marketing as a mission-critical department with the potential to be the top revenue driver for the enterprise. A recent [post by Scott Brinker](https://www.archive360.com/a360-news-blog/more-than-sixty-percent-of-enterprise-it-executives-plan-to-retire-current-saas-applications-amid-growing-security-fears) highlights the inability of most SaaS providers to meet even minimal IT security requirements. ![](https://www.redpointglobal.com/wp-content/uploads/2020/01/Security-300x225.jpg) The result is often a fragmented stack, fragmented data, and a fragmented marketing strategy diametrically opposed to an expectation for innovative customer engagements. This fragmentation creates a series of problems for the always-on, always-addressable consumer, in that marketing fails to meet their expectations for timely, relevant, and consistent interactions. Finally, these SaaS-based marketing systems also fail to handle the complexities of other, non-marketing types of messages, which are such an important part of the overall customer experience. A growing acceptance that marketing is indeed a mission-critical, revenue generator elevates the need for scalability, speed, security, and flexibility in the martech stack. At the core of the flexibility and innovation capacity is a platform requirement that messaging be freed from any channel constraints. A previous blog in this space explored why marketing is the new standard-bearer for driving a data-driven transformation, responsible for delivering a holistic, personalized customer experience across all channels and interactions with a brand. Doing so while fulfilling all enterprise requirements with a solution that can sit in a variety of cloud configurations – including your own private cloud where you maintain control over the security perimeter – is a distinguishing feature of a modern marketing platform that creates differentiated, personalized customer experiences that drive revenue. ## **Messaging and the Overall Brand Experience** Beyond fulfilling enterprise requirements for speed, scale and security, unlocking messaging from channels is a key operational benefit of the Redpoint platform. Whereas marketing campaigns are traditionally organized on a channel basis around list-based processes, this technology is dated as it was tuned to large-scale batch sending of direct mail and email communications. Today’s marketers are best served by a rules-based system, providing for a dynamic (vs. static list-based) customer experiences that are personalized at a segment of one at scale. A [rules-based platform](https://www.redpointglobal.com/blog/for-marketing-to-drive-revenue-say-goodbye-to-lists-forever/) has the power to enable unique customer journeys with the flexibility to dynamically alter every single customer interaction based on a customer’s behavior at the precise moment of a journey. A marketer, then, is always ready with a next-best action for an individual customer at the optimal time, irrespective of a preset expectation, which is impossible to do when marketers are restricted by lists of data or customers tied to a channel. In addition, rules-based systems offer very high levels of re-use which list-based systems cannot touch. The ability to separate messages from channels across the enterprise not only means the difference between being rules-based vs. list-based, it also means that messaging does not have to be restricted to just marketing-oriented considerations. Because a customer interacts with a brand across touchpoints that extend beyond traditional marketing channels, an enterprise-grade system must remove all barriers that prevent interacting with a customer in real time. This is the future of what real time means for the enterprise. Separation of messages from channels is a prerequisite for delivering a personalized customer experience that from a customer’s perspective places the consumer in control and effectively meets their expectations. ## **Development on Five Key Dimensions** A platform that satisfies enterprise technical and security requirements while also manages rules-based messaging across the enterprise clears the final barrier for giving marketers the proverbial head of the table. There are five requirements that a platform should satisfy that distinguish an enterprise-ready marketing system from a SaaS application that is fine for a departmental or channel-specific function. Satisfying these five dimensions ensures all new customer experiences are squarely aligned with current and future requirements of the enterprise. - **Enterprise-Grade** – Enterprise technologies must support common requirements and protocols for critical enterprise applications such as security, scaleability, HA/DR, and single sign-on. The category ensures flexibility across different types of infrastructures, and operates at the cadence of the consumer. - **Cloud Exploitation** – Applications must take full advantage of cloud-based technologies such as containerization (Kubernetes, Docker Swarm) to ensure flexibility and scalability in a multi-channel environment with different types of workloads. Being cloud native is a key consideration to meet the sophisticated demands for a seamless, personalized CX across channels and audiences. - **Platform Convergence** – One single platform that spans data to analytics to orchestrated interactions, while allowing the last-mile channels to remain in place, is a foundational element of an enterprise-ready system. Convergence essentially entails breaking down the traditional application boundaries with a unified, highly integrated platform to drive consistent and hyper-personalized messages, offers, and engagement. - **Deprecation of Unnecessary Capabilities** – Eliminating (or refactoring) unused capabilities greatly simplifies the martech stack. A clean, clear platform is often the first order of business for creating a seamless, holistic experience. - **User Experience** – UI development must continue to simplify and expand the transparency and reusability of the application for all levels of users. There is high value in not just re-using a rule, but in knowing what it is you’re using – quickly and easily, to glean a commonality of understanding across all data types. In summary, flexibility and an innovation capacity defined by scalability, speed, security, and flexibility are fundamental requirements of an enterprise-grade marketing platform freed from traditional channel constraints that delivers hyper-personalized customer experiences in the cadence of a customer journey. **Blog categories:** Data Management --- ### [Identity Resolution and its Effect on CX](https://www.redpointglobal.com/blog/identity-resolution-and-its-effect-on-cx/) **Published:** December 11, 2018 **Author:** Steve Zisk **Content:** One of the definitions of the word “identity” is the distinguishing character or personality of an individual. Since the beginning of advertising, brands and marketers have tried to arrive at the core of this definition in their marketing efforts to achieve what many consider the holy grail of marketing, true one-to-one personalization. Since the onset of the digital revolution, an added challenge for brands in this effort is that consumers now engage with brands across a growing number of touchpoints. A consumer can be seen as having multiple identities, depending on how and when they choose to engage with a brand or retailer. Brands that can recognize the behavior patterns of customers across touchpoints and build accurate identities will create a basis to market to them as a segment of one, delivering content and offers in the right context and cadence to drive revenue and loyalty through an extended customer lifecycle. This is identity resolution, and it is imperative to creating a seamless, omnichannel customer experience. ## **You Don’t Know Me, But You Should!** Personalized experience is a primary brand value for connected consumers. Accenture, in its [2018 Personalization Pulse Check,](https://www.accenture.com/us-en/service-propelling-growth-through-personalization) found 91 percent of consumers are more likely to shop with brands that recognize and remember who they are and deliver relevant offers and recommendations that prove the brands really understand them at an individual level. One issue for brands in fulfilling consumer expectations for more personalized experiences is that these expectations are increasing faster than brands can handle. This has led to a growing gap between the personalization that customers want and what they are actually receiving. A 2015 Infosys report, “[Rethinking Retail](https://www.infosys.com/newsroom/press-releases/Documents/genome-research-report.pdf),” revealed 31 percent of customers surveyed want a more personalized shopping experience than they currently receive. And, 74 percent feel frustrated when they visit a website that doesn’t offer any personalized content. ## **Changing the CX Game… Forever** While brands may be trying to understand customer behavior and construct solutions for personalizing every interaction, their struggle grows with each new social or commerce app, as does the frustration among consumers who do not understand why a brand provides such disparate experiences across touchpoints. Enter [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/). Identity resolution powers every piece of marketing technology, for it allows the brand to identify customers across all touchpoints with high precision, resolving and matching information across all data sources and identity proxies to get the most accurate understanding of every customer. In laymen’s terms this means that marketers can finally deliver on a true one-to-one personalized experience using identity resolution to “power up” their existing technology investments. Put another way identity resolution provides an effective way for brands to understand their customers’ unique buying journeys along the path to purchase. Armed with this understanding and advanced analytics tools, a brand can more effectively predict, shape, and influence purchase behavior. Getting personalization right allows brands to ask for more consumer data. We know from one study, for example, that 83 percent of consumers will share their data in exchange for a personalized experience if the brand is transparent about how it uses the data. If a brand provides consumers with the personalization and experience that they expect, they will in turn reward the brand with trust and loyalty. The better a brand can incorporate timing and intent into personalization efforts, the better chance it has to deepen its relationship with a consumer across the full customer lifecycle. ## **Identity Resolution and a Unified Customer View** Data silos are a barrier to effective [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/). A lack of integration and a lack of communication between various marketing stakeholders and departments can negatively impact the customer experience as well as result in brands failing to recognize multiple interaction across channels as belonging to the same customer. As part of their identity resolution initiatives, marketers must eliminate the issue of siloed systems and data that fragment the customer experience – for instance, using a Customer Data Platform to unify, cleanse, and merge customer data from all relevant sources. Only by breaking down silos and accurately matching customer data across the enterprise can a brand provide the most optimal experience across every channel. The ultimate goal of identity resolution is to: - Gain a better understanding of customers by building an accurate and usable representation of them (anonymous or known) - Predict and shape customer behavior to improve engagement and value - Improve retention by meeting increasing customer expectations - Increase sales by providing relevant, timely offers - Reduce friction in the customer experience using accurate, complete knowledge of customer history and preferences Identity resolution is a key imperative for brands to deliver hyper-personalized interactions and experiences across all digital and traditional channels or enterprise touchpoints. **Blog categories:** Identity Resolution --- ### [Here’s Why Householding Matters for a Seamless Customer Experience (CX)](https://www.redpointglobal.com/blog/heres-why-householding-matters-for-a-seamless-customer-experience-cx/) **Published:** August 20, 2021 **Author:** Steve Zisk **Content:** Like an unexpected detour that brings a driver down unfamiliar roads, a customer journey often has stops and starts that brands may not have anticipated but still need to prepare for. The dynamics of a household present an example familiar to marketers who may suddenly find the degree of difficulty raised a notch for presenting the perfect next-best action in the context of a customer journey. Imagine a customer who visits a brand’s website and searches for video games, maybe the latest Halo or Fortnite release. The customer also visits the physical store and puts in a pre-order, engages in a gaming community forum and posts a Tik Tok video showing a mastery of an older release. Throughout this journey, the brand knows that the customer is the same individual across every channel because it has a complete identity graph, which links all the identities through which a person, a customer or an entity presents themselves to the brand. It also has a descriptive tail, with the customer’s behavioral, demographics, transformations, preferences and model scores. With this [Golden Record](https://www.redpointglobal.com/single-customer-view/), the brand is primed to present a next-best action wherever and whenever the customer appears in a channel. Maybe it’s an offer for a discount on the new release if the customer becomes a brand advocate on social media, or agrees to review the new game. Or perhaps a real-time recommendation engine presents the ideal game – not because it’s popular in the 18-to-35 demographic or based on past transactions, but because an automated machine learning model selected the recommendation based on what the Golden Record dictates. ## **Without Householding, You’re Driving Blind** So far, so good. The brand is moving in unison with the customer throughout the customer journey and delivering a seamless customer experience. But then a roadblock occurs – a laptop associated with the customer’s identity graph takes actions during a session that are uncharacteristic of the customer – unsubscribing from notifications and removing products from a saved items list. What to do? Did the customer find the product somewhere else for less money? Did the customer lose a job? Decide that gaming’s a waste of time? Or, perhaps, the IP address is associated with the customer’s physical address, and maybe someone else in the household is using the customer’s device – a mother adamant that no child of hers is going to be a brand advocate. The above scenario shows the importance of householding as part of a complete Golden Record, specifically the importance of [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) that uses a combination of probabilistic and deterministic matching to accurately determine household dynamics. A customer may present to a brand as an individual, but also within the context of the household, and it’s vital to make an accurate match to know how to keep the customer journey rolling along. A brand that knows the household dynamics may know, for instance, that the gaming customer typically uses a credit card that is tied to the same physical address, but that is also associated with two other devices used in that same household. Through probabilistic matching, an algorithm may determine with 90 percent accuracy that the customer’s mother was driving the outlier session. The brand then defuses the situation by pulling the offer to become an advocate, instead offering the mother a discount on a mesh system where she can control when each household device can access wi-fi, gaming consoles included. ## **Bank on a Perfect CX with Householding** Householding is important for more than retailers, of course. Financial institutions have a vested interest in recognizing various relationships and households, and with linking or matching multiple accounts to one household. Knowing household dynamics is of critical importance not only for marketing – to know which products or services may be of interest for a household or household member – but also for any department that engages with a customer, whether a call center, a teller, a lender, etc. In addition to the probabilistic matching difficulties like the above example, banks and financial institutions often run into deterministic matching challenges – different addresses (St. vs. Street), names (John Smith vs. John Smith Jr.) or simple human error such as an account number keyed incorrectly. Add in commercial accounts, or multiple members of a household with multiple physical addresses, and determining and serving the centers of influence becomes difficult. A seamless customer experience depends on the inclusion of householding as part of a complete, accurate and up-to-date Golden Record. Advanced identity resolution that relies on both probabilistic and deterministic matching presents a holistic, single customer view of an individual customer across the enterprise, spanning every device, channel and behavior. It also importantly lets a brand or institution know whether an individual is presenting to the brand as an individual – or as a member of the household. Depending on the answer, the difference in how to engage with that customer might be small or large, but without household information in a Golden Record, a brand will be doing little more than hazarding a guess. More than a detour, it might lead the brand to a complete dead end – a promising customer journey stopped dead in its tracks because householding was not given the attention it deserves. **Blog categories:** Identity Resolution --- ### [The 2024 Redpoint Crystal Ball: A Need to Ignite Customer Data Sparks Four Key Trends](https://www.redpointglobal.com/blog/the-2024-redpoint-crystal-ball-a-need-to-ignite-customer-data-sparks-four-key-trends/) **Published:** January 4, 2024 **Author:** John Nash **Content:** In reading the tea leaves of a Gartner survey on business strategy, it seems likely that in 2024 organizations will prioritize igniting their customer data in pursuit of revenue, but doing so cost-effectively, finding ways to innovate while maximizing their existing investments. In the [2023 Gartner CMO spend and Strategy Survey](https://www.gartner.com/en/newsroom/press-releases/2023-05-22-gartner-survey-reveals-71-percent-of-cmos-believe-they-lack-sufficient-budget-to-fully-execute-their-strategy-in-2023), 75 percent of CMOs report facing pressure to cut technology spend and having to “do more with less” as inflationary pressures and fears of a looming recession threaten growth and experimentation. Because of this pressure, a majority of marketers (86 percent) said they must make significant changes to how the marketing function works to achieve sustainable results. In effect, even with customer expectations for privacy and personalized, omnichannel experiences, becoming more pronounced, marketers must be judicious in how they use and activate customer data. This spotlight on marketing spend along with a need to ignite customer data will bring four trends to the forefront in 2024, all related to the need for marketers to do more with less. **1. Companies will continue to embrace the data cloud** In an era of constrained IT resources, data clouds simplify access to data, making it more efficient to deliver a single source of truth for customer data. Combined with customer data platform (CDP) technology, this eliminates the siloed data that traditionally prevent companies from becoming customer-centric because they lack a single view of the customer across channels. Data clouds help prioritize the efficient collection and use of first-party data, an important capability for meeting consumer expectations for a real-time, personalized customer experience. With a CDP sitting directly on top of a data cloud, it is easier and less expensive to build a unique customer profile and then to activate that profile across multiple systems. Data clouds align with the need to prioritize the efficient collection and use of first-party data, an important consideration particularly with marketing budgets strained, with the disappearance of third-party cookies and with customers only willing to share personal information if it is used to enhance CX. In a 2023 survey from [Invesp](https://www.invespcro.com/blog/data-driven-marketing/), 87% of marketers said that data is their organization’s most under-utilized asset, while 54% said that the lack of data quality and completeness is their biggest challenge to data-driven marketing. Moving to a data cloud and coupling it with a CDP’s automated data quality and identity resolution addresses these issues head-on, and that trend will continue – particularly for customer-facing brands who see a data cloud with an enterprise-grade CDP as a relatively fast, inexpensive way to monetize their customer data. **2. AI will continue to gain momentum, with companies taking a methodical approach** Generative AI is another trend that will dominate data management in the coming year. Generative AI and large language models (LLMs) took the world by storm in 2023, contributing greatly to a wider enterprise recognition that artificial intelligence (AI), in its various forms, is a vast untapped resource for driving business innovation and efficiency. ChatGPT and other natural language processing tools have made it abundantly clear that AI has enormous potential to help marketers and other business users of data do their jobs more effectively – even more than it already has. Whether through the delivery of more personalized insights and predicted actions, or through the alignment of marketing programs and customer experience (CX) – e.g. copilot driven segments, campaigns, journeys and next-best actions – there is tremendous opportunity to use AI to improve results and outcomes. To avoid being swept up in the considerable hype and jumping full bore into the first opportunity, however, enterprises should ensure that AI is used in a pragmatic way that is both profitable and can adapt to new data and technology innovations over time. By experimenting with AI use cases, organizations will discover the importance of having an accurate and up-to-date unified customer profile as the foundation for delivering a personalized CX, which ties back to the continued emergence of data clouds as a means for establishing a single source of truth for data. Organizations should also remember that customers are still somewhat leery about the widespread use of AI as it relates to creating a personalized CX. In a 2023 Dynata survey that explored customer sentiment around AI, 58 percent of respondents said that it is important that a company is transparent about when AI is being used. And while nearly half (48 percent) said they would interact with AI more frequently if it made their experience with a brand more seamless, consistent and convenient, a majority (77 percent) said that AI still needs an element of the human touch. **3. Efficient, effective utilization of the marketing tech stack will be a top priority** Continuing with the overall “more with less” theme, organizations will face pressure to optimize the marketing tech stack. Constrained marketing budgets plays a major role, forcing companies to eliminate waste and overlap. But there is also a need to protect the veracity of customer data, ensuring adherence to various rules and regulations for how customer data is collected and used on behalf of the customer. Gartner’s 2023 Marketing Technology Survey reveals that [marketing tech utilization has plummeted](https://www.gartner.com/en/marketing/topics/marketing-technology#:~:text=Gartner's%202023%20Marketing%20Technology%20Survey,maximize%20returns%20on%20existing%20investments) to 33%. One factor driving low utilization is the lack of an accurate unified customer profile that is easily accessible across the stack. In the coming year, more companies will make an honest assessment of their existing marketing tech stack to curb problems that stem from having data siloes across the enterprise. Greater recognition that a pristine, unified customer profile is a direct link to profit will generate scrutiny on whether a company’s existing marketing tech stack is set up to best achieve that pristine, unified view. Marketers will be tasked with optimizing the customer data that for the most part already exists across the enterprise, albeit siloed, but doing so cost-effectively with agility so enterprises can pivot as needed to quickly capitalize on emerging trends. **4. Companies that get their data right will solve the “Personalization Paradox”** The [personalization paradox](https://www.forbes.com/sites/forbestechcouncil/2023/08/29/can-a-superior-customer-experience-and-data-privacy-co-exist/?sh=4f764d8e28e9) refers to the seeming contradiction in consumers increasingly expecting a more personalized CX, while at the same time they are more protective of their personal data. In a Harris Poll survey, for example, [66 percent of consumers](https://www.redpointglobal.com/resources/harris-poll/) said that they are willing to provide brands more information about themselves, but only if it is used to create a more valuable customer experience. We explored the customer data value exchange in detail in [last year’s predictions](https://www.redpointglobal.com/blog/2023-will-be-a-watershed-year-for-customer-experience-cx/) from the point of view of the consumer, i.e., why consumers became so intent on protecting their data as an asset. From the perspective of the enterprise, the shift toward consumers being more protective of their data cast the spotlight on what the enterprise is doing to satisfy consumer expectations for data privacy and transparency. The adoption of preference centers and opt-ins as default data protections are among the steps companies are taking to be more transparent, but an increased focus on data privacy is also a big reason for the shift to data clouds. Yes, data clouds prioritize the efficient collection and use of first-party data, but an enterprise-grade CDP on top of a data cloud also means being able to complete all CDP functionality – automated data quality and ingestion, tunable identity resolution, transformations, the building of a unified customer profile, segmentation and activation – without any data replication and without persisting any data outside of the data cloud. With complete CDP functionality in a data cloud, organizations can execute marketing campaigns directly within the data cloud, accomplishing key objectives of delivering a personalized CX while, protecting customer data and maximizing MarTech stack efficiency. ***Editor’s Note: John Nash discusses these predictions along with other insights into the changing customer experience landscape in a webinar, that you can watch [here](https://event.on24.com/wcc/r/4460389/1473A8C8797F5B5991C0188A63BBD4B8?partnerref=rpblog).*** **Blog categories:** Customer Data Platform, Data Quality --- ### [Drive Revenue and Reduce Costs with a Customer 360](https://www.redpointglobal.com/blog/drive-revenue-and-reduce-costs-with-a-customer-360/) **Published:** February 5, 2024 **Author:** Redpoint Global **Content:** *Editor’s Note: Redpoint Global recently connected with Ed Scrivani, Chief Operating Officer at Munvo, to discuss why organizations should be interested in developing a Customer 360. Ed touched on some key considerations and challenges in building a Customer 360, the steps to take, and some tangible use cases. This is an edited version of the conversation, which you can watch* [here](https://event.on24.com/wcc/r/4486698/D9B5A00DDF2B0DF93A36128EF6B828C6?partnerref=blog)*.* **Q**: *Ed, thanks for joining us today to discuss the importance of having a Customer 360. Can you touch on the basics of what it is and what it consists of for those unfamiliar?* **A**: Absolutely, and first off, thanks for having me. The ultimate purpose of a Customer 360 is to provide [a comprehensive view of customers](https://munvo.com/navigating-the-customer-360-landscape-unveiling-insights/) by consolidating data from various sources. It is a hyper-clean set of profile records – all that is known or knowable about each customer – and this set of records is made available to business users across the enterprise to create an audience and to activate messages across all devices, channels, and touchpoints. **Q**: *What does the Customer 360 consist of?* **A**: There are essentially four key components. The first is the profile, consisting of centralized customer data – demographics, purchase information, interactions, preferences, and behavioral data. Second is the data sources, which integrate various data sources such as CRM systems, a marketing database, social media, support tickets, and an EHR in healthcare – anywhere customer data is stored. There should be an ETL process to ensure consistency and accuracy of the incoming data, which can also be in any format: batch, streaming, structured, unstructured, etc. Next, attention must be given to data quality. To ensure the accuracy and completeness of a customer profile, processes must be in place for deduplication and standardizations and for creating persistent identity keys at the individual, household, address, or entity level – whether an individual customer, a business, product, etc. With persistent keys, a profile may then be enriched by creating and attaching data aggregates related to recency, frequency, monetary value, and other signals that provide layers of context to the profile. The final component is analytics and insight – how to drive meaningful insight from the Customer 360. That could involve predictive analytics, segmentation, lifetime value analysis, sentiment analysis – anything that brings the Customer 360 to life for the intended business purpose. Most importantly, the analytics should all be automated with minimal latency from data ingestion through activation and at the enterprise scale. **Q:** *How will a Customer 360 help an organization increase revenue and reduce costs?* **A:** Right away, you’re removing duplicates within and between dozens of data siloes, which both cuts the expense of duplicate marketing efforts while also boosting responses and conversions by communicating the right message to the right person at the right time and on the right channel. And that personalized customer experience – the activation of the Customer 360 that demonstrates an understanding of the customer – leads to increased customer loyalty, higher lifetime value, increased spend, improved customer satisfaction, and higher retention. There are countless benefits from delivering tailored recommendations and offers based on past behaviors or effectively addressing a customer’s preferences. **Q:** *What are the steps for creating a Customer 360?* *A:* There are a few key steps. First is to define the objective and scope, the goal for having a Customer 360. It’s essential to know the depth of the customer understanding that is going to be required for your intended use cases. The second is to assemble the right team, consisting of IT, data specialists, marketing, sales, and maybe even customer service. There should be a shared understanding of what a successful creation and utilization of a Customer 360 will look like. In that same vein, having an executive sponsor is vital to keep everyone on the same page. When you’re ready to create the Customer 360, the following steps are to identify the data sources to be integrated, what analytics will be in play, what testing processes will consist of, and what mechanisms will be in place to ensure continual maintenance and improvement. The common thread throughout the creation of the Customer 360 is data quality. It’s important to ensure data quality is in place at every step. Global address standardization, email normalization, phone verification, parsing, and other routine data quality steps will ensure the trustworthiness of the customer profile. It is also important that these fundamental data hygiene routines are run lights-out 24/7. Similarly, there must be security, compliance and governance protocols in place throughout the creation of the Customer 360. Lastly, things to watch out for include an awareness to bake in some time for user adoption and training. These are new tools and processes; users should know how to extract meaningful insights. A Customer 360 becomes far more valuable when users trust it will solve real-world problems. **Q**: *To that point, what do you see as some of the more profitable use cases?* That depends on what industry you’re in. I’ll touch on a few. In telecommunications, a Customer 360 offers tremendous potential for driving profitability through improved services and an enhanced CX. By utilizing insights from Customer 360, telcos can tailor personalized offers and plans based on usage patterns, preferences and other customer behaviors. Using Customer 360 to create more granular segments, they can execute more personalized marketing campaigns to capitalize on cross-sell and upsell opportunities. By equipping support teams with Customer 360 profiles, telcos can improve issue resolution, leading to higher customer satisfaction and lower support costs. In healthcare, an actionable Customer 360 can be used to enhance patient care, improve operational efficiencies and drive better health outcomes. By centralizing and analyzing data from CRM systems, EHRs, labs and other sources, healthcare organizations can communicate with a patient with a single, consistent voice across all interactions and channels. That might entail personalized treatment plans, reducing care gaps, improving response rates, and helping patients make and keep more appointments. Ed Scrivani is the Chief Operating Officer at Munvo, responsible for professional services, sales and software products. A marketing software system integrator, [Munvo](https://munvo.com/) provides expert consulting services and software solutions, delivering Implementation, Marketing Transformation, Data & Analytics, and Marketing Operations services in North America and across the globe to enable organizations to make data-driven decisions, enhance customer experiences and maximize MarTech investments. ## **Related Redpoint Blogs** **Blog categories:** Single Customer View --- ### [Let's Get Back to CDP Basics](https://www.redpointglobal.com/blog/back-to-cdp-basics/) **Published:** March 22, 2024 **Author:** John Nash **Content:** In a little more than a decade since the [customer data platform (CDP)](https://www.redpointglobal.com/cdp/) was coined to describe a standalone application that creates a unified view of the customer and makes it accessible to other systems, there has been so much jostling about who is or is not a CDP and what’s required to be a [RealCDP](https://www.cdpinstitute.org/vendors/) that the term has almost lost all meaning. Everyone’s talking about composability, reverse ETL, marketing cloud, smart hub – no wonder there is so much confusion. It reminds me a bit about the “sabermetrics” craze in baseball, where arcane stats that no one understands – WAR, wRC+ and wOBA to name a few – replace the easily understood batting average, RBIs and runs as tools to measure player value. Baseball purists complain, rightfully so, that there is so much attention paid to the complex stats that no one seems to care anymore about the ultimate objective – winning ballgames. Likewise, lost in the shuffle amid all the back and forth about a CDPs definition is its ultimate purpose. What are we trying to accomplish? Is the goal to have the best widget such as reverse ETL, or is to have a better understanding of your customers and use that understanding to achieve better outcomes through more personalized experiences? ## **Ignite Your Customer Data** Along the same line, because a CDP for all intents and purposes is a marketing tool, it seems that everyone has also lost sight of what marketers want. Everyone is talking about empowering the marketer, as if marketers are losing sleep thinking about how composability will help them bring data together more efficiently. Is that really what marketers care about, or do they want the best data to create the best audiences? To some extent, a focus on integration, on infrastructure or on tools that promise to bring data together better, faster, easier – is understandable, even encouraging. It shows that there is now a consensus on how important it is to ignite customer data. With everyone in agreement that pristine data is required to create real-time personalized experiences, the battle lines naturally shift to who can produce the biggest spark. The bluster, though, doesn’t take the marketer into account, whose primary concern is that the CDP provide them with a deep understanding of the customer that adapts over time. ## **Core CDP Functionality** In that light, it’s time we return to the basics and remember that providing that deep customer understanding is the CDP’s main responsibility. To that end, a CDP should have a few core capabilities, starting with [ingesting and fixing messy customer data](https://www.redpointglobal.com/data-quality-and-data-ingestion/), and [resolving identities at individual and relationship levels](https://www.redpointglobal.com/identity-resolution/) to create an accurate unified customer profile. A CDP should also make it easy to [build reusable segments without code](https://www.redpointglobal.com/segmentation-activation/), and then activate those segments against any marketing or CX use case. Marketers want to maximize the value of customer data quickly, but in a pragmatic way. They don’t want to have to add staff or rely on data scientists. They just want to be able to trust the data so that, in turn, they can better trust their campaigns, programs and results for whatever their use case might be that rests on having a better understanding of the customer. ## **Maximize Value of Your Customer Data** It’s time to cut through the smoke and mirrors and think back to the original concept of a CPD that starts with creating a unified view of the customer. Redpoint’s mission from the start (and we were included in the [2013 list of original CDPs](https://customerexperiencematrix.blogspot.com/2013/04/ive-discovered-new-class-of-system.html)) has been to power superior customer experiences based on a foundation of cleansed, pristine and unified customer data. For more than a decade, we have stayed true to the vision that a CDP’s ultimate purpose is to extract value from customer data. That is what marketers want. And it’s what Redpoint delivers. We welcome you to join our CDP Back to Basics series, a five-part webinar presented by Beth Scagnoli, Redpoint Global Vice-President of Product Management, and Kris Tomes, Redpoint Global Vice-President of Engineering. You can watch the first episode of the webinar series, “Understanding the Core: Elements of the CDP” [here.](https://event.on24.com/wcc/r/4544523/CE314B45C2ECDA81C6085670897CECBD?partnerref=blog) **Blog categories:** Customer Data Platform --- ### [Superior Data Quality and the Role of a Customer Data Platform](https://www.redpointglobal.com/blog/data-quality-and-role-of-cdp/) **Published:** April 2, 2024 **Author:** Steve Zisk **Content:** According to research from [McKinsey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying#:~:text=Research%20shows%20that%20personalization%20most,intimacy%2C%20the%20greater%20the%20returns.), personalization drives up to a 25 percent revenue lift depending on an organization’s ability to execute. The more skillful a company becomes in applying data to grow customer knowledge and intimacy, the greater the returns. In every industry with a customer-brand dynamic, companies are competing with one another on the experience they deliver to customers, patients, members, donors – anyone who interacts with the brand. As the McKinsey article notes, the competition is fueled by data – the line between winning and losing comes down to the skillful application of data to develop a deep understanding of the customer and orchestrating the perfect experience based on those insights, anywhere, anytime. The challenge for organizations is that the actualization of benefits largely comes down to successfully harnessing people, processes and company culture to truly become data driven. When an organization’s goal is to make data-driven decisions, data quality becomes paramount. Companies must trust that their customer data is accurate, updated and ready for business use. This is where a customer data platform (CDP) comes into play in helping organizations deliver an omnichannel customer experience. There is a common misconception among companies that a CDP is a magic bullet that will miraculously bring about a hyper-personalized customer experience. The problem with this line of thinking, however, is that data quality is not given its due as a key element of the equation. If we think about a CDP’s purpose to enable marketers to access the data they need to create segmentation models and ultimately to create campaigns based on insights, what’s missing from the majority of CDPs in the market is the correct approach to data quality. ## **Just Say No to Reference Files** Asked how they handle data quality, many CDP vendors will talk about identity resolution, touting basic matching techniques that anyone can do. But really what they mean is that they’re relying on someone else’s data quality; they’re outsourcing data quality by bouncing customer data against a reference file – gathered by a third-party organization and enhanced in some way. The problem? Any organization intent on delivering a hyper-relevant omnichannel customer experience does not have the luxury to wait for a quarterly update of a reference file where keys change with every iteration. We know customers expect brands to precisely match the cadence of their unique customer journey. In a [Harris Poll sponsored by Redpoint](https://www.redpointglobal.com/resources/harris-poll/), 82 percent of customers surveyed said that loyalty to a brand is dependent on the brand’s ability to demonstrate a *thorough understanding* of them as a unique customer. A thorough understanding, from the customer’s perspective, means the brand knows them as the same customer across every channel and offers consistent relevance because the brand knows their individual behaviors and preferences. Adopting this mindset as an approach to data quality is what differentiates the [Redpoint CDP](https://www.redpointglobal.com/cdp/) from also-rans who rely almost exclusively on reference files. What makes Redpoint stand out from the crowd is that our platform *is* the technology with which to build reference files. Redpoint’s advanced identity resolution capabilities do not rely on a third-party organization’s customer data, but rather use probabilistic and deterministic matching to enhance a company’s own first-party data. ## **Democratization of Data** Redpoint solves other common problems as well, namely the data discovery challenge. For an understanding of the problem, consider the [Alation State of Data Culture Report](https://go.alation.com/hubfs/Resources_Assets/Alation-State-of-Data-Culture-Report-Q2-2021.pdf) where 34 percent of data leaders listed data discovery (do not know what data exists or who has what data) as a top challenge for using data to drive business value. Furthermore, 36 percent said data democratization was a top challenge, where not everyone can access data on their own, with 35 percent citing organizational siloes as a barrier for using data to drive business value. Redpoint solves these problems by managing data governance at the point of data use (which the report said is indicative of top-tier, data-driven companies). This task used to be a central IT function, but is more often now moving to the point of use where data is applied at the business level. Pushing governance, curation, and the perfection of data out to the business unit breaks down the siloes that too often lead to inefficiencies in driving business value from data. Taking care of all data hygiene and data transformation tasks at ingestion solves for these inefficiencies. *All* data harmonization means that even if two strands of data are identically labeled, the platform will make sure they mean the same thing. From ingesting to creating an industry best Golden Record, Redpoint covers the entire spectrum – from the edge to the business unit, or even in central IT if that is what a client prefers. Importantly, all standardization and data management tasks are completed in the same application, greatly reducing process inefficiencies born of having a multitude of solutions in differentiated states. ## **Foster a New Data-Driven Culture** Lastly, let’s talk a little bit about data culture. It’s important to remember that there is a difference between having quality data and *knowing* that you have quality data. That, in a nutshell, describes our approach toward changing the deep-seated mindset that it’s too hard to adopt a data-driven strategy. With Redpoint, business users don’t just have democratized access to data, they see it and have the tools to answer any question they might have about it. With a [data observability](https://www.redpointglobal.com/data-observability/) dashboard that reveals all dimensions and metrics behind data quality, Redpoint makes it possible to correlate good data with good business outcomes. The business users who interact with the data drive the improvement of data quality, becoming part of the curation process at the business unit level. This involvement establishes a line of sight between the use of data and outcomes, a key factor in developing and fine-tuning a data-driven culture. With consistent visibility of data quality throughout the platform, marketers and business users no longer have to fly blind as they try to drive business outcomes. Importantly, as a [previous blog notes](https://www.redpointglobal.com/blog/scale-new-heights-with-a-rules-based-omnichannel-cx-platform/), Redpoint is a rules-based platform. Trust in the quality of data is fortified because marketers and business users know that, as a rules-based system, data reflects every change – however recent. This is an important concept for delivering an omnichannel customer experience. By building audiences using rules instead of lists, marketers and business users are able to package different versions into content assets, building once and using an audience or asset across every channel and in every context. Lists are static and subject to decay, rules are dynamic and re-evaluate at every inflection point, guaranteeing precision with every customer interaction. Ubiquity. Connectivity. Process efficiency. Supporting perfected data and a new data-driven culture around the curation of perfected data. This is the Redpoint difference, and it is why ambitious business leaders are turning to Redpoint to rapidly transform customer experience and drive tangible ROI. For more on how the Redpoint CDP handles data quality, click [here](https://event.on24.com/wcc/r/4544523/CE314B45C2ECDA81C6085670897CECBD?partnerref=blog) to join Redpoint VP of Product Management Beth Scagnoli and Redpoint VP of Engineering Kris Tomes in the Redpoint “CDP Back to Basics” webinar series. **Blog categories:** Customer Data Platform, Data Quality --- ### [For Marketing to Drive Revenue, Say Goodbye to Lists Forever](https://www.redpointglobal.com/blog/for-marketing-to-drive-revenue-say-goodbye-to-lists-forever/) **Published:** November 12, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/11/nash-blog-marketing-revenue-11-14-19-300x200.jpg)Closing the customer experience gap while transforming marketing into a mission-critical enterprise is a matter of both strategy and execution. Marketing must accept that organizing activities around a product line, the calendar, batch and blast processes, or any strategy not directly attuned to the customer will fail to provide a personalized customer experience that provides a direct, fast path to revenue. On the execution front, fragmented systems and siloed data that cloud a single customer view will derail even the best-laid plans for personalization. Eliminating data fragmentation requires a top-down, organizational buy-in that recognizes marketing as the tip of the spear for creating differentiated, personalized customer experiences. The customer experience gap is the divide between consumers’ growing expectation for personalization and the experience that marketers deliver. According to the [2019 Harris Poll](https://www2.redpointglobal.com/white-paper-customer-experience-harris-poll?_ga=2.11071668.1675196484.1571927192-1570104466.1540307570) commissioned by Redpoint, 48 percent of US-based marketers surveyed report an “excellent” ability to deliver an exceptional personal experience, while just 22 percent of consumers agree. That’s more than a 2x gap. In rating customer experience, consumers say that marketers fall short in every facet of customer experience – privacy, personalization, cross-channel consistency, and an understanding of the customer as a unique individual. The survey shows that consumers now consider personalization table stakes; 63 percent say it is now part of the standard service they expect, 53 percent say they expect a brand to know their buying habits and preference, and 37 percent say they will reward personalization with loyalty, making them more likely to purchase from the brand in the future. Alarmingly, 37 percent said they will stop doing business with a brand that fails to offer a personalized customer experience. **Lists: Obsolete** One of the biggest hurdles preventing marketers from closing the customer experience gap has been list-based processes, approaches, and decisions that are not designed to deliver segment-of-one personalization. Traditional push-based marketing campaigns that are organized around lists of customers and data simply cannot deliver personalization at scale; by definition, static lists lack the dynamism required to be in synch with an omnichannel customer journey. By dismissing a list-based approach in favor of rules-based decisions and processes, marketing removes the biggest obstacle that stands in the way of transforming the department into a revenue-driving engine. Lists are inadequate because customer journeys are dynamic. As the Harris Poll shows, consumers expect personalization at every touchpoint, online and offline. But it’s not enough to personalize a standalone interaction, such as showing a customer who logs onto your website that you know their preferences and purchase history. Meeting consumer expectations for personalization entails presenting the customer with a hyper-personalized, relevant experience at every touchpoint; it’s a recognition not just of what’s transpired from a transactional or even behavioral perspective, but a recognition of how a specific interaction affects the customer journey. Has the logged-in app on a mobile device just broken the geo-fence of a local store? Have the interactions with your brand across email, website, and social touchpoints been connected? Every action taken by a customer in an omnichannel journey should influence a brand’s response to optimize the journey with a relevant, hyper-personalized experience. Lists do not support a dynamic customer journey because they can never be in support of – or in deference to – the always-on, connected customer. When a marketer creates a list of customers that according to the dataset should receive a piece of direct mail, followed by an email with a discount offer in three days, it allows for no flexibility according to a customer’s unique journey. Further, by ignoring the myriad of possibilities that could potentially occur between outreaches – the customer buys a product in-store, for example – the static process has a strong likelihood of introducing friction into the customer journey by making an offer or an action that is not relevant to the journey at that moment in time. **Rules: Here and Now** Basing decisions and processes on rules that are optimized with automated machine learning models and fine-tuned to always deliver a next-best action ensure that marketing is always in the right context and cadence of a customer journey. In a rules-based campaign that begins with a direct mailing, for example, follow-up actions are dictated not just according to each customer’s response (or lack thereof), but also according to a customer’s behaviors and actions apart from the mailing. Did the customer visit the website or a store? Did she post on social media? Call the call center? The campaign, like the customer journey, is dynamic; the marketer is prepared to offer a next-best action irrespective of a preset determination or expectation of a certain behavior. Offering a customer a next-best action or recommendation – the perfect offer at the perfect time – requires that a brand has real-time access to a continuously updated golden record, a unified customer profile that includes every source and type of data. This ensures that a brand will always be in cadence with the customer at every stage of a customer journey. The golden record is a foundational requirement for giving marketers the capability to deliver a personalized, relevant next-best action. Arriving at this single customer view requires breaking down all data and operational siloes that have traditionally served product-based (and list-based) marketing efforts. Integrating the martech stack with a single platform that ingests all customer data in real time accomplishes this. Applying in-line analytics with automated machine learning and an intelligent orchestration layer that generates a next-best action gives marketers a single point of control over data, decisions, and interactions that is the key to turning data into revenue with hyper-personalized customer experiences that move the needle for a customer. Bolstering the Harris Poll survey, [78 percent of consumers](https://marketinginsidergroup.com/content-marketing/content-marketing-personalization-imperative/) in a Marketing Insider Group study said that a personally relevant customer experience ups their purchase intent. Furthermore, Boston Consulting Group estimates a potential revenue increase of up to [10 percent](https://www.bcg.com/en-us/publications/2017/retail-marketing-sales-profiting-personalization.aspx) by brands that create personalized experiences by integrating advanced digital technologies and proprietary data for their customers. Abandoning list-based decisions and processes is a critical first step for marketers to reap these potential rewards. **Blog categories:** Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Evolution of the Customer Experience and the Role of Data](https://www.redpointglobal.com/blog/evolution-of-the-customer-experience-and-the-role-of-data/) **Published:** January 2, 2019 **Author:** Dale Renner **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2018/12/Omnichannel_525896374.jpg) Customers have the power in today’s competitive marketplace. Traditional loyalty measures no longer apply; instead, consumers are loyal to the brand that provides the best customer experience. In one recent study, [80 percent of customers](https://www.slideshare.net/EpsilonMktg/the-power-of-me-the-impact-of-personalization-on-marketing-performance/1) indicated that they prefer brands that offer personalized experiences. Moreover, customers who find personalized experiences very appealing are 10 times more likely to be a brand’s most valuable customer. Many brands already understand that they must compete on customer experience to remain competitive. The question brands must answer in this rapidly changing landscape is how they can evolve their customer experience delivery. With the number of new technologies being introduced into the market and digital transformation heavily adopted across all areas of business, how can brands meet the changing nature of the customer experience and ensure that they remain ahead of the competition? According to a study by [Deloitte](https://www2.deloitte.com/insights/us/en/topics/emerging-technologies/mit-smr-deloitte-digital-transformation-strategy.html), only 44 percent of brands are prepared for digital transformation. The most essential step for a brand to meet its digital transformation goals is to create a strategy and vision for transforming the customer experience. **Foundational Change for Better Customer Experience** So how do brands develop a customer experience strategy for the always-connected, always-addressable customer whose journey involves multiple touchpoints and variable entry points into the funnel? It needs to be grounded in a deep understanding of customer needs and preferences. They need to create what is called a “golden record” to know all that is knowable about the customer. A golden record is a single 360-degree customer view that combines the freshest, most complete and accurate data about the customer from across the entire enterprise and is accessible at all points of engagement. This record includes identifying information, the touchpoints a customer uses, all prior interactions and purchases, the number of days since the last purchase, recent offers they received and how they reacted, and how the customer transacts with the brand. Because a golden record consists of data from multiple sources accessible across the business, it allows for a constantly updated, dynamic profile that reflects who the customer is, their preferences, and how the relationship has evolved. It also provides a window into that customer’s intent. Determining a customer’s intent is powerful because it shows what a customer is looking for in an exact moment and where they are looking to find it. This is where brands can provide superior experiences and separate themselves from the competition by fulfilling each customer’s individual need. **Execute on Your Customer Experience Strategy** Once a brand possesses the most accurate and timely data for each customer and can make this accessible across the organization with a single point of control over data, decisions, and interactions, the brand is able to deliver a superior customer experience. Artificial intelligence (AI) and machine learning are also increasingly necessary for a marketing organization to deliver on this promise. Powerful advanced analytics embedded in these emerging technologies can deliver personalization at scale previously not achievable to provide customized offers and messages to all customers in real-time across the enterprise. Use cases include next-best-action predictions that automatically score customers and automate segmentation to determine the optimal content type as well as the optimal timing and placement of that content. In this scenario, AI and machine learning are invaluable tools to deliver messages and offers in the cadence and the context of the customer journey, which is critical to create the personalization and relevance that resonate with customers. For the satisfied customer, this could mean placing an online order for a same-day, in-store pickup and immediately receiving an offer for a relevant accessory. Or it could mean developing an ever-deepening bond with a favorite brand because each interaction triggers a welcomed personal touch. Consumers are taking note of these types of next-level customer experience. [Research](https://lmistatic.blob.core.windows.net/document-library/boldchat/pdf/en/omni-channel-customer-care.pdf) from the Aberdeen Group found that companies with the strongest omnichannel strategies retain an average of 89 percent of customers, compared to 33 percent of companies with weak omnichannel strategies. Brands across all industries have embraced the notion of digital transformation, but most of these companies do not yet have the foundation in place to effectively execute on a strategy to deliver a superior customer experience. Once armed with a single view of the customer, brands are presented with unlimited opportunity to optimize engagements and provide customers with the experiences that they desire and deserve. **Blog categories:** Data Quality, Omnichannel Marketing --- ### [SaaS vs. PaaS in the Pursuit of Perfected Data](https://www.redpointglobal.com/blog/saas-vs-paas-in-the-pursuit-of-perfected-data/) **Published:** March 23, 2022 **Author:** Steve Zisk **Content:** For a hands-on marketer intent on delivering personalized, next-best actions at scale, the difference between software-as-a-service (SaaS) and platform-as-a-service (PaaS) may seem academic. Whichever delivers customer data to the marketer in the time and condition needed to run effective campaigns is their top concern; upstream technical details are secondary. If both SaaS and PaaS solutions may claim to deliver perfected data, however, then the next logical question for marketers is how can they be sure the solution will deliver the visibility and change control a brand needs for its customer data? The reason this question is important is because visibility and change control are important components for ensuring that perfected data remains fit for purpose. Marketers need visibility into how changes are managed such as attaching to a new data source, and visibility into enterprise data. Both ultimately impact the experience delivered to a customer. ## **PaaS vs. SaaS?** Before diving into whether a SaaS or PaaS provides better visibility, it may be worthwhile to make the distinction from a functional standpoint. Standard descriptions of SaaS vs PaaS tend to focus on delivering a finished solution (SaaS) versus providing a ready-to-use platform to configure or customize a solution (PaaS). But for the marketer shepherding high-value customer data, the more important distinction will be where that customer data is held. In a SaaS, the managed service includes a database inside the service and effectively has custody of the data, whereas in a PaaS the data storage is inside the client’s own cloud services so the client has custody. In a PaaS infrastructure, the client – the company using the solution – owns and controls the security perimeter around their data, rather than turning it over to a software service vendor to manage. In healthcare, financial services and other highly regulated industries with sensitive customer data, maintaining custody inside the company’s security perimeter is a requirement; they will simply not consider another option. Many will even demand even more stringent data controls, such as PII vaults, data clean rooms, user-based and role-based controls, etc., to meet regulatory and corporate governance standards as well as customer expectations for data safety and privacy. Conversely, for smaller organizations with limited IT resources and less stringent requirements, having the software vendor manage the data – turning over custody – may make more sense. ## **Custody vs. Control** On the visibility front, one consideration in turning over the company’s data to a vendor to manage is the level of limitations placed on the data, or more accurately limitations on the company’s choices for the control of the data. Beyond the standard SLAs for HA and DR, there will typically be guarantees that compliance needs are met, but there is a difference between satisfying generic compliance mandates by industry or geographic area and satisfying a company’s specific compliance needs to support their vision of perfect customer experience. For example, a SaaS vendor may guarantee that GDPR/CCPA needs are met. For many companies, checking off the compliance box to avoid fines might be sufficient. For others, giving up custody of the data might make it more difficult to control the experience that they’re delivering to customers with respect to preference management. A significant part of the value exchange that a brand builds with a customer – receiving first-party data in exchange for a personalized experience – entails meeting customer expectations around channel preference, frequency, product affinity, as well as personal details like clothing sizes, gift lists, and purchase preferences. This type of active engagement may be best handled with a preference center where the customer can see, select, and refine their choices for how they will interact with a company. SaaS vendors will likely offer basic features around preference center controls (for showing information to the customer as well as implementing the actual preferences in marketing, e-commerce and other interactions), and brands will need to weigh whether those are enough to meet customer expectations. ## **Single Cloud, Single Version?** Another visibility issue is the ability of a SaaS solution to deliver a consistent single view of the customer, particularly if it uses a collection of different infrastructures and clouds. One good alternative is to put all customer data records into a single database, such as you might find in a CDP, and use that database to control views, exports, and interactions, applying both user preferences compliance rules to every interaction. This can work with the database inside a brand’s own cloud subscription using a PaaS or as part of a SaaS solution, with the data held in the SaaS vendor’s database. Finally, if customer experience remains a priority, a company considering SaaS and PaaS solutions must consider how readily changes and updates to customer details can be made for each scenario. That, in fact, is one key Redpoint differentiator and a solution to the single view question. With the [Redpoint CDP](https://www.redpointglobal.com/), master data management provides the option to push preference and compliance requirements and all notifications into upstream systems. All changes are applied, and records are updated as soon as data is ingested. Managing customer experience, closing the loop on data quality with end-to-end visibility, and meeting compliance and customer privacy needs are among the factors a brand should consider when deciding between a SaaS or a PaaS solution. Both options may offer the ability for a brand to have a singular database that carries all customer software and allows them to drive the results they want. Custody of the customer data is the main distinction. If driving a personalized customer experience is a priority, before turning over precious data, brands should put vendors through their paces, knowing exactly what a vendor means – and what they can deliver – when they talk about data quality and a single source of truth for customer data. **Blog categories:** Data Quality, Identity Resolution --- ### [The Cloud Advantage: Control Your Own Security Perimeter](https://www.redpointglobal.com/blog/the-cloud-advantage-control-your-own-security-perimeter/) **Published:** December 11, 2019 **Author:** Redpoint Global **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/12/cloud-advantage-blog-300x200.jpg)December is a popular month for year-end lists and ‘best of’ compilations. One list that no company wants to appear on, though, is a year-end summation of the worst data breaches. A cloud storage company, a Fortune 500 financial services company, and Facebook are among the half dozen companies that make the [*Security Boulevard* list](https://securityboulevard.com/2019/12/biggest-2019-data-breaches-some-of-the-worst-of-the-worst/) of the worst 2019 breaches, affecting roughly 4.3 billion customer records worldwide. With the [California Consumer Protection Act (CCPA)](https://oag.ca.gov/privacy/ccpa) taking effect in January and other state data privacy rules in legislation, 2020 will be a watershed year for marketers to resolve privacy and compliance challenges in a cloud or hybrid infrastructure without risking security or sacrificing the ability to provide a personalized customer experience. There’s a lot at stake, which makes it important to understand what people mean when they talk about a cloud environment. Traditionally, a “hosted” environment meant that an organization entrusted their data to an external vendor, either in a dedicated data center (i.e., Acxiom), private cloud or public cloud, the latter of which being equivalent to a software-as-a-service (SaaS) application. Conversely, the classic definition of on-premise simply meant that data resided inside an organization’s own data center. The traditional lines between on-premise and cloud have blurred, however. Decisions about cloud or on-premise deployments are not really about who hosts or manages a company’s data. Rather, they’re about who owns and controls the security perimeter. **Why Perimeter Control Matters for Customer Engagement** Organizations entrust marketing cloud vendors with their customer data because they make an assessment that the reward – the delivery of a personalized customer experience – outweighs the risk. The calculation that there is some acceptable level of risk demonstrates the critical importance that marketing gets customer experience right. According to a much-referenced [Frost & Sullivan report](https://inform.tmforum.org/data-analytics-and-ai/2016/08/customer-experience-overtake-price-product-differentiator-2020/), 2020 will be the year that customer experience supplants price and product as the most important factor for customers when making a purchasing decision. The problem for organizations is that while potential rewards are increasing, so too is the risk – as GDPR, CCPA and other data privacy regulations make clear. Simply put, organizations are reluctant to give up control of the security perimeter, and are trying to find a way to strike an equilibrium between providing a differentiated customer experience while still safeguarding customer data in a cloud deployment. Traditionally, the SaaS value proposition was one of convenience; ceding control of the security perimeter was balanced by outsourcing some functions of an organization’s IT department to the vendor. But in addition to rising security concerns, the convenience of a public cloud is less of a selling point because SaaS applications have evolved to become functionally specialized and industry-specific, which locks companies into pre-defined functionality and prevents changes to a data model. For marketers striving for innovative customer engagement strategies, data model restrictions and unintegrated customer data in various niche SaaS applications are non-starters antithetical to the notion of personalizing the customer experience. **Control and Security – the Best of Both Worlds** The argument from marketing cloud vendors justifying ceding control of the security perimeter is, in a word, trust. They claim the highest levels of dev and cloud security, along with unparalleled SLA, RTO, and RPO levels that inspire peace of mind. There are two problems with the argument. First is the lack of an absolute guarantee; a contract might stipulate compensation for a data breach, an “act of God”, or other failure, but that’s shutting the barn door after the horse has left. Risk can be minimized but not eliminated. Second, and more importantly for the purpose of customer engagement, is the concern over a perimeter breach is a separate discussion from control over the data once it’s inside the perimeter. Putting your application inside of someone else’s security perimeter cedes a level of control over how the data will be used, where it’s used, and who has access. Further, it prevents full integration of the martech stack and enterprise functionality. For enterprise companies, owning and controlling the cloud security perimeter is quickly becoming the default option in response to tightening regulations. SME companies are at a different decision point, especially if they do not fall under CCPA (or GDPR) jurisdiction. But if enterprise-grade security without sacrificing enterprise functionality is the path you want, Redpoint eliminates the need to balance security concerns with providing a personalized customer experience with a hybrid cloud solution that sits inside of a company’s own security perimeter, akin to a private version of a SaaS deployment. Redpoint clients can manage their own security perimeter encompassing their own cloud subscription, and provide Redpoint with permission to access and update the software without having to move data outside of the perimeter. This deployment model mitigates risk and meets enterprise-grade security requirements while preserving the control and integration needed to provide a hyper-personalized customer experience that drives revenue. *Editor’s Note: A follow-up blog will explore in greater detail benefits of having a holistic, consistent security operation without the need to cross security perimeter boundaries.* **Blog categories:** Customer Data Platform, Data Management, Data Quality --- ### [Clear the Hurdles for a Personalized Customer Experience](https://www.redpointglobal.com/blog/clear-the-hurdles-for-a-personalized-customer-experience/) **Published:** January 20, 2020 **Author:** Steve Zisk **Content:** *![](https://www.redpointglobal.com/wp-content/uploads/2020/01/shutterstock_593935445-e1579193237977.jpg)* Providing a hyper-personalized experience for the always-on, continuously connected consumer across the complete customer lifecycle is the holy grail for a modern marketer. But a seemingly endless number of channels and touchpoints, multiple shared devices, and the customer expectation for immediacy present personalization challenges that are often beyond the capabilities of all but the most sophisticated marketing technology solutions. Personalization is too important to let these challenges go unchecked. Consider the [Harris Poll survey sponsored by Redpoint](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/), where 63 percent of consumers said that personalization is now a standard service they expect, and 37 percent said they will stop doing business with a company that fails to offer personalization. Further, asked to define what personalization means to them, 43 percent of consumers said that it means a brand knows they are the same customer across all touchpoints – email, in-store, mobile, social media, etc. With few exceptions, today’s “marketing clouds” (a common reference to SaaS solutions hosted by a third party for marketing purposes) are not constituted to deliver the type of personalized customer experience that is in line with consumer expectations. This blog series will explore in depth four primary areas where traditional marketing clouds fall short in providing a data-driven personalized experience in the context and cadence of a unique customer journey. **Siloed Data and Personalization, Oil and Vinegar** For the most part, modern marketers are on board with recognizing the importance of integrated customer data for a personalized customer experience. But while most customer engagement technology will offer the promise of a single customer view, it’s important to understand what this promise really entails. To begin, data unification does not necessarily mean that fragmentation of the data layer is eliminated. There are, for example, many well-known marketing clouds with multiple cloud technologies that lay claim to providing a unified data view, but a closer look reveals that this is accomplished not by having data in a single solution, but by moving data to various infrastructures. Having separate e-commerce, sales and service, and marketing clouds, as well as other potential siloes in web apps, mobile apps, IoT data, and other information cannot truly be called data unification just because they fall under the same cloud umbrella. Providing a personalized experience in real time across an omnichannel customer journey requires a unified data layer marked by low latency, precision depth, and real-time access to updated core customer data as well as operational data such as transactional and behavioral information, metadata, and campaign information. **Keep Up with the Cadence of the Customer** Infusing real-time capabilities throughout the data layer is a key function for preserving a cadence that is critical for delivering a personalized experience. The sticking point for many marketing clouds is that individual data sets from separate cloud technologies will have individual sets of cadences and views; moving data across those views serves as a roadblock to a real-time cadence typical of a customer journey. Real-time data processing is a key capability of an enterprise-grade customer data platform (CDP). Moving at the speed of the customer requires real-time capabilities throughout the data lifecycle, which requires real time at the data layer, at the analytics layer, and at the orchestration layer. Combining events-based data that is updated in real time with slower moving data without losing accuracy or cadence is a key function for moving at the speed of the customer. **Resolve Customer Identity** Advanced identity resolution is another common failing of a traditional marketing cloud. Some marketing clouds may claim to offer identity resolution, but matching customer data records for known customers across multiple cloud infrastructures is vastly different – and inferior – to deciphering often imperfect identities encompassing bits of online and offline, or known and unknown identifiers. A true single customer view, in other words, entails far more than unifying customer records across multiple cloud infrastructures. Advanced identity resolution that is required to deliver personalized interactions across all touchpoints of an unknown-to-known customer journey also means deciphering customers across shared devices, shared IP addresses, or multiple devices that may represent the same person. **Control of the Security perimeter** Last but not least among the limitations of traditional marketing clouds for personalization of the customer experience is the fact that most ask the enterprise to cede control of the [security perimeter](https://www.redpointglobal.com/blog/the-cloud-advantage-control-your-own-security-perimeter/) in exchange for putting customer data in their cloud environment. While a personalized customer experience is important, so too is safeguarding customer data – as a host of data privacy regulations make clear. In addition, the convenience of a public cloud is becoming less of a selling point for the enterprise because it locks companies into pre-defined functionality and prevents changes to a data model, which can be roadblocks to creating innovative customer engagement strategies. Marketing cloud vendors claim that high levels of trust mitigate concerns over yielding control of the security perimeter. Even if true – and of course there is no absolute guarantee – the problem as it pertains to customer engagement is that ceding control of the perimeter also gives up some control over how the data is used, where it is used, and levels of access, which are all important for delivering a personalized experience that customers crave. **The Multiple Layers of “Single Customer View”** In summary, an enterprise-grade CDP does far more than provide a single view of the customer. In the coming weeks, we will use this space to explore in greater detail how the Redpoint platform unifies data to create a complete golden record, with real-time cadence, built-in tools for advanced identity resolution, and operation inside of an enterprise cloud security perimeter. It is encouraging that more and more marketing clouds recognize the importance of a single customer view to ultimately deliver personalization, but there is a lot more than meets the eye for delivering a real-time personalized customer experience that drives revenue. **Blog categories:** Customer Data Platform, Data Governance & Security, Data Management, Journey Orchestration, Real-Time Personalization --- ### [Cadence of the Customer and the Corner Store Experience](https://www.redpointglobal.com/blog/cadence-of-the-customer-and-the-corner-store-experience/) **Published:** March 13, 2020 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/03/3-13-blog-cadence-TJ-300x180.jpg) Henry Ford’s famous quote about customers being allowed to have a car painted any color they want as long as it’s black describes some marketers’ approach toward personalization. For this type of marketer, every customer can have a personalized customer experience – as long as it’s an ad on Facebook, a direct mail piece sent three days after an email, or a discount offer on an item viewed on the website. A one-size-fits-all campaign with a personalized touch, however, does not meet the modern standard for a revenue-driving, personalized customer journey. The reason adding a personalized touch based on a list of customers falls short of the standard is that it does not account for the unique cadence of a customer journey. Recognizing the cadence of the customer is the key factor in delivering the level of personalization customers expect. An analogy can be made to a proprietor of a mom-and-pop corner store greeting every customer by name. Yes, it’s personalizing the customer experience. Introducing cadence, though, is when the astute business owner personalizes the journey in accordance with each customer’s unique interaction preferences, beyond how often they frequent the store and their regular purchases. One customer may ask questions about every product she pulls from the shelves. One may prefer to dart in and out without making small talk. Another likes to call ahead and asks that an item be set aside. Cadence is more than the pace at which a customer moves from discovery to purchase, or channel preference, it’s a deep understanding of *how* a customer engages with a brand. Data-driven marketers with a golden record – a single customer view that tells them everything there is to know about a customer – will know how an individual customer responds to personalization. They will know the optimal engagement frequency. They will know that providing relevance to a customer’s experience means different levels and types of personalization. Delivering a personalized customer experience in the cadence of the customer is the secret to driving revenue. Consider the [2019 Harris Poll](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) commissioned by Redpoint, where 63 percent of customers said that personalization is now a standard expectation. Asked to define what personalization means to them, 43 percent said it was a brand recognizing them as the same customer across all touchpoints – with an equal number saying that it means they, the customer, control *how*, *when*, *where*, and *why* the brand interacts with them. That, in a nutshell, is cadence. **Moving Toward Customer-Centricity** Like the famous quote from author Stephen Covey, “most people do not listen with the intent to understand; they listen with the intent to reply,” delivering the corner store experience is about empathy. It’s about paying close attention to what every interaction with a customer says about their preferences – and shaping a proactive response that tells the customer that you know who they are. List-based marketing campaigns are wholly inadequate in meeting this expectation because they are tied to an action irrespective of a non-linear journey. Lists are incapable of recognizing cadence. In contrast, a [rules-based campaign](https://www.redpointglobal.com/blog/for-marketing-to-drive-revenue-say-goodbye-to-lists-forever/) where the rules are orchestrated in accordance with a unique journey is akin to a corner store proprietor knowing precisely how to interact with each customer, however the customer chooses to guide the journey. A rules-based campaign draws its power from a persistently updated golden record that ingests customer data of every type, and from every source. Because rules are updated in accordance with a golden record, they will always reflect the optimal response (or lack of a response) to a customer’s absolute latest preference, behavior, or action – ensuring that every inbound or outbound interaction is exactly in step with a customer journey, irrespective of channel. Providing an exceptional customer experience at scale requires automated machine learning with self-training models that are based on current data. In-line analytics – without channel constraints – generate a next-best action specific to a unique customer journey. Embedded intelligence produces relevant engagements at any stage of a journey, and is the key to producing [significant revenue lift](https://www.redpointglobal.com/blog/the-sky-is-the-limit-with-ai-and-machine-learning-capitalize-on-the-moment-of-interaction/). Incorporating the cadence of the customer into marketing strategy aligns with what Gartner calls a “customer-centric” approach, versus a “persona-centric” approach. The difference is that customer-centricity recognizes an entirety of behaviors and actions across an entire anonymous-to-known journey, rather than basing a strategy on historical or transactional data or aligning it on a channel basis. According to Gartner, a customer-centric approach produces a revenue lift of up to 20 percent, roughly double that of a persona-centric approach. **Do You Really Know Your Customer?** There is a general appreciation that personalization is an effective marketing strategy for engaging with a customer. But personalization for personalization’s sake is not the same as delivering a truly relevant customer experience where a brand shows that it recognizes a customer as an individual. A recent Domino’s Pizza ad is an example of how well-placed intentions can fall short. The brand uses the [Norm Peterson character from “Cheers”](https://www.youtube.com/watch?v=Ve7GLRr_PWY) (the iconic “Norm!” greeting) to promote its name recognition pick-up service. Customers who order online are asked to check-in on the mobile app when they approach, and staff members are alerted to prepare the order for hand-off – and greet the customer by name. Because a customer opts-in, there is no risk of introducing friction into the experience by welcoming a customer who would rather remain anonymous. But what about a customer who checks in and for whatever reason doesn’t immediately enter the store. Will the person be greeted by the wrong name – perhaps mistakenly addressed as the next person who checked-in using the app? What if the customer uses the app to check-in, then sends her teenage son to run in to pick up the order? Or there’s an order mix-up with customers who share the first name? The example illustrates a persona-centric approach rather than the [differentiated customer-centric approach](https://www.redpointglobal.com/blog/harness-the-power-of-digital-technology-as-a-revenue-driving-engine/) that truly drives revenue. Delivering a hyper-personalized customer experience in perfect synch with the cadence of the customer tips the scale to the latter. **Blog categories:** AI & Machine Learning, Customer Data Platform, Journey Orchestration, Omnichannel Marketing, Single Customer View --- ### [Avoid a Customer “Break Up” this Valentine’s Day](https://www.redpointglobal.com/blog/avoid-a-customer-break-up-this-valentines-day/) **Published:** February 9, 2022 **Author:** John Nash **Content:** In grade school these days, Valentine’s Day card distribution is an egalitarian exercise. Children who wish to hand them out are usually asked to give one to every classmate. To spare hurt feelings, no one is left out. A one-size-fits-all approach, while sound policy for the classroom, is not conducive for brands trying to establish a long-lasting relationship with a customer. New research from Dynata on customer loyalty, in a [survey](https://www.redpointglobal.com/press-releases/74-percent-of-consumers-believe-brand-loyalty-is-about-feeling-understood-and-valued-not-discounts-and-loyalty-perks/) commissioned by Redpoint, reveals that across-the-board discounts are far less important to consumers than a brand that demonstrates a personal understanding. A discount available to everyone, much like an interchangeable Valentine’s card, does not make the heart aflutter. In the survey, 74 percent of consumers said that feeling understood and truly valued is more important than discounts and loyalty points. Being “understood” by a brand was defined as the brand recognizing a customer as unique, knowing preferences and behaviors on an individual level vs. just another customer. Being “valued” was defined as a customer knowing that their worth to a brand is more than transactional. ## **Customer Experience X’s + O’s** Consumers were also clear that they are willing to reward brands that make an effort to develop a personal understanding. In the survey, 64 percent of respondents said that they would rather purchase a product from a brand that knows them. Conversely, 39 percent of consumers claimed that they have stopped shopping with a brand after just one bad personal experience. The bar for excellence, it appears, is high. Always-on consumers are now accustomed to omnichannel customer journeys consisting of a combination of digital and offline channels, and they clearly will not tolerate an inconsistent brand experiences. Diving deeper on what it means to be understood, more than half of consumers surveyed (52 percent) said that it is when a brand provides relevant product and/or service recommendations, with 44 percent claiming that it means a seamless navigation of in-store and online channels. Consumers increasingly demand that brands deliver a real-time [omnichannel customer experience](https://www.redpointglobal.com/omnichannel-personalization/) that matches the cadence of how they engage. One expectation, for example, is for a call center agent to possess an in-depth understanding of the customer so that issues are resolved to a customer’s satisfaction without burdening the customer with endless questions or explanations. In addition to up-to-date transactions, a call center agent might possess the customer’s search history, social footprint, household status and other identity graph components. A key distinction between a multichannel experience and an omnichannel experience that drives revenue growth is that the latter entails an unbroken, consistent understanding at every touchpoint – no exceptions. An understanding derived from linking two or three channels will have gaps akin to blind spots in how a customer navigates a journey. Those gaps translate to a brand unable to provide a customer with a hyper-relevant experience at every touchpoint. At best, a brand compensates by delivering a static message or universal discount or offer, which as the survey makes clear is met with ambivalence. Worse, though, is a brand makes a false assumption that is based on an incomplete, outdated view of the customer, thus introducing friction into the customer journey. The traditional example is when a brand makes an offer for a recently purchased product, but common CX failures also include missed upsell or cross-sell opportunities. Think about a customer who buys a product online and picks it up in-store. An omnichannel customer experience may consist of a brand offering a discount on the perfect complementary item as the customer arrives to pick up the item, using SMS as the delivery mechanism because that is how the customer asked to receive notifications. According to the survey, 34% of consumers said they are more loyal to brands that offer multiple ways to interact with them and shop – with buy online, pick-up in-store (BOPIS) listed as one of the favored options. Brands that organize campaigns around a channel, or do not link every channel, cannot provide this type of experience that demonstrates the personal understanding customers covet. ## **A Hallmark of a Good Relationship: Transparency** The survey also explores consumer sentiment around data privacy. Nearly half (47%) of consumers said that they feel disrespected when brands collect their personal data without asking, or fail to provide them with an opportunity to easily opt-in or opt-out. And 42 percent feel disrespected when brands are not transparent about how they will use their personal data. All consumers are asking for is something in return. We’ve written at length about the [consumer data value exchange](https://www.redpointglobal.com/blog/build-a-comprehensive-customer-understanding-through-perfect-data/), where consumers make clear that they are willing to provide personal data – as long as brands use it to create more personalized experiences. In the [2021 Harris Poll](https://www.redpointglobal.com/resources/harris-poll/?utm_source=website&utm_medium=homepage&utm_campaign=HarrisPoll&_ga=2.119651976.1059602439.1632149195-1096058990.1552515236) commissioned by Redpoint, two-thirds (66 percent) of consumers surveyed said that they will give trusted brands more information about themselves if it is used to create a more valuable customer experience. The key to building customer loyalty is to honor these preferences, establishing an unshakeable trust that you, the brand, always have the customer’s back. Brands demonstrate the value and personal understanding with every personalized experience, which in turn results in deeper loyalties and more shared data, which leads to even more personalized experience in the cadence of an omnichannel customer journey. The cycle continues, with the end result a long-lasting, trusting relationship that outshines all competing brands with their uninspired, rudimentary messages, content and offers. **Blog categories:** Data Quality, Identity Resolution, Real-Time Personalization --- ### [A Superior Customer Experience (CX) Transcends Marketing](https://www.redpointglobal.com/blog/a-superior-customer-experience-cx-transcends-marketing/) **Published:** December 10, 2020 **Author:** John Nash **Content:** For years, a prevailing thought was that a superior customer experience began at the store. It’s where a brand’s identity took shape, formed by knowledgeable, friendly and experienced associates trained to put the customer first. As commerce moved online, the website was viewed – at least initially – as a complementary experience, its unmatched convenience secondary to its primary role as reinforcing the image honed in-store. Even though the trend has been gravitating toward an online-first mentality for some time, brands could usually depend on the in-store experience as a fallback, counting on the in-store advantage to potentially make up for a subpar or even run-of-the-mill experience online, over the phone or in other channels. Amazon and other digital native companies helped turn this notion around by making convenience and exemplary service central to the online experience, but 2020 has completely upended the remaining “store-first” dynamic. More than simply accelerate the online trend, the coronavirus pandemic has for all intents and purposes eliminated the fallback option. With customers expressing a clear preference for [contactless experiences](https://www.mytotalretail.com/article/shifts-in-shopping-how-businesses-are-prioritizing-the-contactless-customer-experience/) and other new methods of engagement, brands recognized that the in-store experience would no longer be the primary touchstone for building trust and loyalty. ## **An Integrated Business, an Integrated Superior Customer Experience** The modern customer experience calls for more than merely transitioning resources to an online-first approach. Rather, it demands that companies completely re-think how they communicate with their customers. Whereas in the past, the in-store experience was the last mile of a largely linear customer journey, today’s dynamic multi-channel journeys require the entire business to coordinate to deliver an integrated experience. Gone are the days, then, of clearly delineated roles for marketing, sales, service and store operations; revenue growth now depends on enterprise collaboration to deliver a holistic, [personalized omnichannel experience](https://www.redpointglobal.com/omnichannel-personalization/). An ad-hoc approach divided by channel or function simply will not work because it does not reflect how customers view their relationship with a brand; anything that separates pre- or post-event engagement from the actual transaction event will be seen as uneven by a customer. These fragmented siloes are precisely what the Redpoint CDP was engineered to overcome. ## **CX Outside of a Marketing Realm** Redpoint is currently working with several clients on delivering such holistic experiences that fall outside the boundaries of traditional marketing. A national retail chain is intent on optimizing operational messaging – notifications, pick-up logistics, etc. which involve precise coordination between third parties and ‘right time’ messaging, but have little or nothing to do with acquisition or upsell or moving product. Sending a notification that an order is ready for pick-up, delivered on the customer’s preferred channel, with pertinent information about payment, returns or customer service creates a seamless, [positive experience](https://www.redpointglobal.com/blog/goals-breadth-and-depth-three-dimensions-of-cx-strategy/) for the customer. Knowing where and when to upsell or market to a customer and when not to is critical for building a relationship with each customer. A financial services institution similarly wants to manage all aspects of a customer relationship through a single platform; acquisition, marketing, customer service, collections, etc. A key starting point for the program was identifying customers likely to churn, an effort often driven more by customer service than marketing. The initiative entailed tapping into any possible interaction a customer has with the institution (call center interactions, emails, social, transactions, etc.) and mining the totality of engagements for any negative sentiments or steps in a customer’s journey for signs of churn. ## **Eliminate Daylight Between Channels or Departments** Where traditional customer journey analytics is considered a marketing exercise – analyzing journey steps for the purposes of optimizing future engagements and with an intention of driving sales – this client sought to instead complete the exercise to reduce churn, [using the platform to render a next-best action](https://www.redpointglobal.com/next-best-action/) for a customer likeliest to accomplish the objective. Importantly, while in a financial institution marketing is usually considered the acquisition engine, a next-best action such as an outbound call, a survey or a special offer can be initiated and executed from any part of the organization. The approach considers a seamless customer experience as the overarching objective, which by necessity precludes any daylight between channels or departments. If churn is a KPI, marketing outreach for a new product – to use one example – must coordinate with every part of the organization that touches a customer. A call center interaction illustrates the need to have [a single view of the customer](https://www.redpointglobal.com/single-customer-view/) and an integrated experience that transcends the marketing purview. When an inbound call takes place, for a next-best action to be at the fingertips of an agent, the agent must know – in real time – everything there is to know about a customer. The purpose and timing of the call, as part of the customer journey, also influence the calculation for optimizing the customer experience at that moment. The [real-time decisioning engine](https://www.redpointglobal.com/real-time-interactions/) in the Redpoint CDP will render a next-best action at the moment of engagement laser-focused on delivering a superior customer experience above all, one that is hyper-personalized and relevant to the customer’s journey in the moment of time. A next-best action for one customer may intend to reduce churn, while a next-best action for another may be a special offer meant to increase customer lifetime value. ## **Innovative Uses Cases Beyond Marketing** Geo-fencing use cases offer another window into how companies are competing on customer experience, often in ways that stand apart from traditional marketing outreach. To be sure, geo-fencing has tremendous marketing potential. In retail, for instance, sending a personalized offer to a customer who breaks a geo-fence is a common use case with an opportunity for significant ROI. For non-traditional use cases, healthcare offers a blank canvas. A pharmacy with a geo-fencing app could easily alert customers with relevant and timely information about a prescription pick-up, for instance. Healthcare organizations could likewise help patients manage chronic conditions, pushing helpful information to help with disease management. One study from the [University of California, San Francisco](https://www.ucsf.edu/news/2017/03/406101/geofencing-shows-promise-tracking-chronic-care) provided participants with a geofencing app that monitored the frequency and duration of participants’ hospital visits, which triggered a survey for any visit exceeding four hours. The intent of the study was to identify the use of a smartphone and geofencing as a resource for tracking visits and reducing the error of respective reporting, vital for assessing quality of care metrics. Just like a call center agent delivering a next-best action for a customer, using geofencing outside the bounds of traditional marketing depends on a provider knowing everything there is to know about a patient. Here, the tracking of hospital visits becomes one more important piece of information that combine to form a single view of the healthcare consumer. How a single view is used is less important than what it does, which is empower an organization to deliver a holistic customer experience that transcends channels, processes and departments. ## **A Single Platform; a Single View** One of the lessons learned of 2020 is that customers crave new experiences that often combine physical and digital channels, curbside pick-up and grocery delivery service among them. The pandemic may have accelerated the trend toward digital-first interactions, but the writing was already on the wall that, as far as customers are concerned, however they choose to engage with a brand is all part of the same experience. Even brands that recognize the need to provide a holistic experience have struggled to break free from fragmented channels, competing department objectives or even a mindset that marketing is the final arbiter of creating and delivering innovative customer experiences. A unified experience needs to be managed by a single platform that combines a single view of the customer with a real-time decisioning engine that intelligently orchestrates a next-best action. Many customer engagement technology vendors tout an ability to bring together data, insights and personalization, but they fall short of executing on a vision for a holistic omnichannel customer experience at scale because they still think of the software primarily as marketing technology. The Redpoint CDP makes a clean break of this narrow mindset. For many Redpoint clients, the digital customer experience platform is the top revenue-producing solution in the organization. That is the power of the single customer view; it is the foundation for providing the seamless customer experience that customers have come to expect. Customers on the receiving end do not think of it as marketing. To them, it’s a relationship. By adopting a similar mindset, organizations will begin to demand a lot more of their marketing technology. **Blog categories:** Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [4 Steps to More Efficient Data Preparation](https://www.redpointglobal.com/blog/4-steps-to-more-efficient-data-preparation/) **Published:** April 3, 2018 **Author:** Redpoint Global **Content:** ![4 Steps to More Efficient Data Preparation](https://www.redpointglobal.com/wp-content/uploads/2018/04/data-preparation-steps.jpg)Data preparation has long been one of the most time-consuming tasks in data management. Today, 80 percent of data scientists [spend more time preparing data](https://tdwi.org/research/2016/07/best-practices-report-improving-data-preparation-for-business-analytics.aspx) than analyzing it. But you can make data preparation far simpler and less painful than it has traditionally been. This post presents four well thought out streamlined data preparation steps, helping you spend more time *using* your data to engage with customers based on their true needs, behaviors, and desires. 1. **Fix the Data in Your Source System** Imagine that you capture customer and prospect data from a website where consumers voluntarily register. Or, perhaps, your call center agents capture information from inbound callers who are responding to advertising or trying to solve problems. Even assuming this information is being validated on the front end – for example, with a web form script that checks to make sure a U.S. ZIP Code consists of five or nine numbers – it’s surprisingly common for data fields to be truncated before they reach your database. This can especially happen if your data source is external, and built with code you don’t control. Obviously, truncated data fields such as last names can wreak havoc with marketing. Fortunately, advanced [customer data platform](https://www.redpointglobal.com/solutions/customer-data-platform-cdp-redpoint-global/) technologies can quickly recognize and fix most problems like these, before bad data feeds into your CRM system or enterprise data warehouse. 2. **Fix Your Source System to Correct Data Issues Going Forward** Some data problems are easier to fix than others. For example, missing ZIP Codes can usually be added through familiar address standardization tools. However, prevention is always better than cure, and even fixable data quality problems may signal deeper issues that require your attention. For example, while it’s perfectly acceptable for a data source to report middle initials for only 20 percent of its records, if it’s missing last names for more than five percent of them, that’s a problem. A record that contains gaps might have enough information to permit a mailing, but not enough for accurate identity matching – degrading your ability to avoid duplicate offers and engage with customers as individuals as they cross channels. With the right tools, you can profile any data source to quickly understand key aspects of its quality. You then have detailed information you can use to address the problems with your internal or external data provider. Profiling is, of course, equally valuable when you’re bringing a new data source onboard: for example, social media posts or other unstructured data that may have unique attributes and flaws. So, too, it can help you determine which external data you should (and shouldn’t) purchase. 3. **Apply Precision Identity/Entity Resolution** To gain an actionable 360-degree view of your customer, you must ensure that *one and only one* *record* is associated with each customer or household. Otherwise, you’ll send duplicate mailings that alienate instead of engage. Your “next best offers” won’t be “best” at all, because they don’t fully reflect your customers’ latest behaviors. Identity matching techniques such as fuzzy matching are well-known, but they still miss many duplicates. Of course, sometimes you know exactly who visited you, because they made a purchase with a credit card, authenticated themselves by logging in, or used a mobile app tied to their identity. But many customers and prospects visit you anonymously. Fortunately, you can now use a wide variety of technical attributes – including IP addresses, cookie data, and network adapter MAC addresses – to tie anonymous web visitors back to specific customer records. Handled carefully, this can significantly increase the number of accurate matches you generate. There are industry best practices for identity matching, but sometimes the rules need tweaking to reflect the characteristics of your own [data](https://www.redpointglobal.com/blog/3-elements-of-ironclad-customer-data-quality-management/) sources. An advanced system such as a customer data platform can allow you to easily adjust thresholds to maximize matches without generating false positives, so you can get the most value from your data. 4. **Automate, Automate, Automate** Today, there’s way too much data for any individual to manage on their own. But you can gain data preparation “superpowers” through automation, and it can help you focus data stewards’ limited time where it’s most valuable. Once you understand a data source’s characteristics and know how to “fix” its flaws, you can use tools like a customer data platform to run those fixes automatically on every new batch of data from that source. By automatically running new datasets through a series of validation rules, you can surface problems as soon as they emerge. For example, a call center might have consistently delivered high-quality data for years, but it has just introduced a new data entry system, and error rates are spiking upwards. Problems like this need to be fixed ASAP — and a customer data platform enables your data steward to get involved, with incontrovertible information to back them up. **Refocus on What Matters Most: Using Your Data to Engage and Delight** Data isn’t an end in itself. It’s a means to the end of delighting customers, increasing loyalty, and growing profitable sales. Use technology to help you prepare your data more efficiently and painlessly, so you can focus on the end, not the means. A customer data platform can empower you to do that by making it easier to fix data problems upfront, quickly identify their root causes, match identities more reliably, and automate the majority of your data preparation tasks. **Blog categories:** Data Management, Data Quality --- ### [3 Top Identity Resolution Use Cases for Marketers](https://www.redpointglobal.com/blog/3-top-identity-resolution-use-cases-for-marketers/) **Published:** July 3, 2018 **Author:** Steve Zisk **Content:** ![Identity Resolution Software Use Cases for Marketers ](https://www.redpointglobal.com/wp-content/uploads/2018/07/identity-resolution-marketing-use-cases-1.jpg)Identity resolution capabilities are increasingly critical in an age where consumers leverage multiple devices and multiple channels to interact with brands. Only a few years ago, [Global Web Index](https://blog.globalwebindex.com/chart-of-the-day/digital-consumers-own-3-64-connected-devices/) found that digital consumers own 3.64 connected devices each. Each of these devices represents another engagement touchpoint for the average consumer, and it’s common for brands without identity resolution capabilities to consider each device as a new customer record. This means consumers could receive the same offer via SMS text message, app notification on a tablet, or via an online ad on a laptop or desktop. The number of devices is dwarfed by the number of channels. Customers who use their smartphone and tablet can engage via social media, email, the website, and even mobile app for digital experiences; and they can walk into a brick and mortar store, see a billboard, or call into a contact center for offline experiences. It’s because of this range of options that brands need to resolve customer identities across devices and channels to ensure the offers they send are relevant. This is further complicated by the fact that most internet users [have an average of two email addresses they regularly use](https://www.radicati.com/wp/wp-content/uploads/2014/01/Email-Statistics-Report-2014-2018-Executive-Summary.pdf), which complicates determining which email to use for messaging. ## Understanding Identity Resolution Software & Use Cases Understanding that multiple contacts are from the same person can be a tremendous window into consumer behavior and communication preferences, which can result in more personalized interactions and a better ROI on marketing’s activities. Beyond that, three of the most powerful identity resolution use cases for marketers include: - **Building a multi-device customer journey** – The customer journey is increasingly a multichannel, multi-device pathway from awareness to sale with [50 percent of customer interactions happening during a multi-event journey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/from-touchpoints-to-journeys-seeing-the-world-as-customers-do). With consumers leveraging multiple devices, brands need to be able to identify current or prospective customers regardless of the device they happen to be using. Strong identity resolution capabilities allow brands to determine that multiple devices belong to the same person and then track that user across devices. By doing this, brands gain greater insight into the consumer’s communication preferences and their behavior. Even if the person remains anonymous, tying multiple devices to the same person can still be a powerful tool to personalize interactions. - **Resolving multiple users on the same device** – Although certain devices are linked to individuals, such as a smartphone, there are vastly more devices shared by an entire household. The most common examples of this are a television and a desktop computer. A teenage boy streaming his favorite television show on the family’s desktop computer should ideally see different ads than his mother who logs on later to research software for her business. Identity resolution capabilities can ensure marketers understand the difference in usage periods so they don’t deliver messaging intended for the teen to his mother and vice versa. This capability moves marketers into semi-known territory, where they may not have customer names but can still identify different preferences. - **Delivering richer interactions with known customers** – [According to Winterberry Group](https://www.winterberrygroup.com/our-insights/theory-practice-roadmap-omnichannel-activation), 40.3 percent of brands said that better identity recognition capabilities for matching consumers across channels would do the most to advance their organization’s omnichannel marketing efforts. Marketers need to be able to tell when a known customer is interacting with their brand, regardless of which device the interaction occurs in. This is especially vital because it’s impossible to predict which channel a known customer will engage through at any given time. Possessing this sort of identity resolution capability allows marketers to deliver more contextually relevant interactions, like what Starbucks does with its mobile app. Starbucks regularly provides proximity alerts to mobile app users, and broadcasts announcements of sales like its Happy Hour promotion. Marketers who recognize a known customer, as Starbucks does, can then provide a richer interaction for a better customer experience. The ability to resolve customer identities across channels ultimately leads to greater nuance in understanding customer preferences as well as the ability to deliver more personalized interactions. Customers like to be recognized, within reason, and anything that empowers brands to do that with greater regularity is a net positive. It’s especially vital in a marketplace where successful brands are the ones who more easily deliver personalized experiences, and more readily recognize consumers as the loyal customers they are. **Blog categories:** Customer Data Platform, Data Management, Identity Resolution, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Advanced Personalization: Complex Yes, Complicated No](https://www.redpointglobal.com/blog/advanced-personalization-complex-yes-complicated-no/) **Published:** November 14, 2019 **Author:** John Nash **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2019/11/ww-blog-11-14-300x200.jpg)Consumers increasingly demand personalized experiences across all channels, and will look elsewhere if a brand fails to deliver. In the recent Harris Poll survey commissioned by Redpoint, [43 percent of consumers](https://www.redpointglobal.com/blog/addressing-the-gaps-in-customer-experience-redpoint-global-harris-poll-benchmark-survey/) expect a brand to know they are the same customer across all touchpoints, and 63 percent say they consider personalization a standard service. In an Epsilon survey, [80 percent of consumers](https://us.epsilon.com/pressroom/new-epsilon-research-indicates-80-of-consumers-are-more-likely-to-make-a-purchase-when-brands-offer-personalized-experiences) say they’re more likely to choose a brand that offers a personalized experience. [Personalization](https://www.redpointglobal.com/omnichannel-personalization/) is clearly an imperative to engage with and retain customers, and ultimately to drive revenue. While there is a consensus that personalization is indeed important, there is less agreement over what form it should take. A personalized email or direct mail piece is a start, but limiting personalization to a single channel fails to satisfy the consumer expectation that a brand knows who they are across every touchpoint. Personalizing the experience when a customer or prospect makes an anonymous, first-time visit to a brand’s website – when there is no first-party cookie attached to the browser and no log-in – is an advanced form of personalization that more than satisfies consumer expectations. **Turn More Prospects into Existing Customers** Consider a camping aficionado researching tents and gear for an upcoming trip to the backcountry who makes an anonymous, first-time visit to the website of a sporting goods retailer. A landing page with an image of a lakeside campsite and discount offers on camping accessories will likely be of far greater interest to the prospective customer than a generic landing page – and help lower the estimated average bounce rate of [46 percent](https://conversionxl.com/guides/bounce-rate/benchmarks/) for an ecommerce store website. The [Redpoint Digital Acquisition Platform](https://www.redpointglobal.com/wp-content/uploads/2019/06/SB-RedPoint-Digital-Acquisition-Platform-0519.pdf), powered by LiveRamp, provides brands with the capability to meet the challenge of a personalized website experience for the first-time, anonymous visitor. To execute on website [personalization](https://www.redpointglobal.com/omnichannel-personalization/) for first-time visitors a brand starts out by onboarding a list of prospect records to LiveRamp. A prospect list can consist of known website visitors – perhaps lapsed customers, or those who’ve yet to make a purchase – as well as external lists the brand purchases from a third-party. LiveRamp strips away any personally identifiable information (PII) that the brand may have that exists in a customer data platform (CDP) – name, physical address, phone number, date of birth – and creates an IdentityLink. An IdentityLink is the result of LiveRamp’s approach to identity resolution. Minus PII, it resolves all first-party and third-party cookies that exist across the web to an identity – the IdentityLink, which becomes anonymized – known only as a series of letters and numbers, such as “XYZ123”. When a visitor makes an anonymous website visit, the brand fires a pixel off to LiveRamp – essentially asking LiveRamp if an IdentityLink exists for the browser/device. LiveRamp returns the IdentityLink in milliseconds, which the brand looks up in its anonymized database (without PII) to find an attribute to use for personalization. In this case, the prospect is a hardcore camper – and will thus see website content that is customized accordingly. The site continues showing camping equipment to campers, golfing equipment to golfers, and running gear to runners all in order to improve conversion rates and drive revenue. **Make a Difference with a Single Point of Control** Managing anonymous audiences has long been a struggle for marketers who, despite recognizing the value of personalization, often lack a single point of control over customer data. Multiple data sources and siloed legacy solutions have traditionally made it exceedingly difficult to create a unified customer profile, or golden record, which is the foundation for creating a personalized customer experience. The Redpoint Digital Acquisition Platform provides marketers with the single point of control that is needed to essentially anonymize a golden record and personalize first-time, anonymous website visits. The adage that you only have one chance to make a good first impression is just as true for the impression you provide a first-time website visitor as it is for an in-person introduction. A personalized experience helps turn first impressions into what brands really covet – conversions. *Editor’s note: This is the first part in an occasional blog series that will focus on use cases for the Redpoint Digital Acquisition Platform, powered by LiveRamp.* **Blog categories:** Customer Data Platform, Data Management, Data Quality, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Segmentation & Activation, Single Customer View --- ### [Spread Holiday Cheer with a Frictionless Omnichannel Customer Experience](https://www.redpointglobal.com/blog/spread-holiday-cheer-with-a-frictionless-omnichannel-customer-experience/) **Published:** September 30, 2022 **Author:** John Nash **Content:** Major retailers bracing for a more difficult holiday shopping season due to inflation and rising interest rates are pulling out all the stops to reach customers who need to spread out their holiday budget. Target and Walmart are among major retailers who have started or will start [holiday programs](https://www.washingtonpost.com/business/2022/09/22/target-walmart-holiday-hiring/) the first week of the month in anticipation of a “subdued” holiday season. While last year’s holiday season started earlier due to fear of supply chain disruptions (and an easing of pandemic restrictions), this year’s early start is expected because consumers are wary of rising prices. The Bureau of Labor Statistics monthly consumer price index showed an overall [8.3 percent increase](https://www.washingtonpost.com/business/2022/09/13/inflation-cpi-august-fed/) in prices in August, down from June and July but still higher than analysts expected. Target and Walmart are starting the holiday season on a small scale, with “deal days” events and curbside gift returns, respectively, but just the announcement itself is expected to have smaller retailers quickly follow suit. ## **Eliminate Customer Journey Friction** With consumers exercising care in discretionary spending over a longer holiday season, retailers are compelled to remove friction from the customer journey to capture whatever spend they can. Retailers who are already pressured to deliver a personalized, omnichannel customer experience, will now be facing additional demands for a consistent and seamless experience. Consider, first, how important a personalized experience is to the average consumer. In a recent [Dynata retail survey](https://www.redpointglobal.com/press-releases/80-of-consumers-more-likely-to-shop-with-brands-that-show-they-understand-them/), the overwhelming majority of consumers (80 percent) said they are more likely to shop with brands that show they understand their needs by sending relevant, personalized offers during the holiday season. And 78 percent of consumers said that it is frustrating when a retailer’s communications and marketing messages are inconsistent depending on the channel they visit (in-store, online, social, mobile app, call center, etc.). Friction, in other words, is borne of inconsistency and irrelevance. Take “blast emails” as one example of what, in most cases, is sent without any contextual understanding of a customer journey. Yet with more than [300 million emails](https://www.statista.com/statistics/456500/daily-number-of-e-mails-worldwide/) sent and received every day in 2020, it is clearly still a [favorite marketing technique](https://www.theatlantic.com/technology/archive/2021/08/why-stores-send-you-so-many-emails-spam/619670/) even if the vast majority of marketing emails are not in the cadence of the customer journey. While receiving an irrelevant email may not have introduced friction during earlier economic downturns, such as the great recession of ’08, the rising demand for consistency across channels and for brands to know and honor consumers’ preferences reduce its effectiveness. In today’s experience economy, consumers regularly move between digital and physical channels. Understanding a consumer’s needs translates to a personalized, omnichannel CX that is consistently in the cadence of a journey, which often means a real-time, next-best action the moment a customer appears in a certain channel. ## **A Profitable Golden Record** The ability to remain consistent and hyper-relevant for an individual customer while still being able to quickly adapt to changing consumer circumstances on a macro level is why data-driven brands prioritize identity resolution using a brand’s own first-party data as a cornerstone of a personalized customer experience. A [recent blog](https://www.redpointglobal.com/blog/a-bountiful-harvest-continual-identity-resolution-and-a-pristine-profitable-golden-record/) covered why continual identity resolution using first-party data is a key revenue driver. One example cited – using real-time open email – is certainly applicable for retailers trying to compete on experience during the holiday shopping season. If, for example, a customer buys a product at full price that later goes on sale, a real-time open email might switch up content to offer the customer a coupon equal to the price differential. Advanced identity resolution that produces an accurate, updated Golden Record allows the brand to achieve this hyper-relevant consistency even if the customer has multiple identifiers and completes a transaction using multiple channels. Instead of a static email that may introduce friction into the customer journey, identity resolution helps the brand deliver a contextually engaging, dynamic customer journey with precision throughout. This holiday season, it pays to recognize that customers worried about higher prices may be even less tolerant of friction in a customer journey than usual. For more on why leading brands trust the Redpoint CDP to leverage a single, comprehensive customer view for cross-channel engagement in real time, [click here](https://www.redpointglobal.com/customer-data-management/identity-resolution/). **Blog categories:** Identity Resolution --- ### [The Retailer Roadmap for Personalizing CX While Reducing Costs](https://www.redpointglobal.com/blog/the-retailer-roadmap-for-personalizing-cx-while-reducing-costs/) **Published:** February 16, 2023 **Author:** John Nash **Content:** An [Accenture report](https://www.accenture.com/us-en/insights/retail/store-tomorrow-future) on the state of retail highlights the gap between how consumers derive value from brands and where brands are in their ability to meet elevated consumer expectations for digital-first experiences. Drawing on previous research findings that showed a [169 percent increase](https://www.accenture.com/us-en/insights/retail/coronavirus-consumer-habits) in ecommerce purchases from new or low-frequency online shoppers since the pandemic – with nearly all of them expected to continue digital usage in a variety of channels – the report stressed why it’s important for brands to close the pronounced gap to “maintain brand relevance and cultivate stronger loyalty.” Traditional value drivers such as store proximity and competitive pricing have given way to a desire for a personal connection between a consumer and a retailer or brand. That connection is reflected by a personalized customer experience that demonstrates a deep understanding of the customer. The challenge for many traditional retailers is that digitally native brands are chasing after those same customers – and have quite the head start with providing personalized content that gives consumers an incentive to continue a relationship with a brand. To help close the gap, Accenture details a retailer maturity curve as part of a roadmap for becoming customer-centric. At the Wave 1 starting point (where it claims most retailers are today), retailers are simply trying to understand a customer. At the Wave 3 end point, retailers provide one-to-one personalization at scale, creating long-time loyalty and persistent customer value. Using the maturity curve as context, this blog will focus on the underlying purpose behind refocusing on the customer over price and product; i.e., is revenue growth, cost savings – or both – the intended value driver in delivering omnichannel personalization? We will first look at the cost side of the value drivers, and detail how Redpoint helps break down some of the common barriers standing in the way of achieving an organization’s customer-centric objectives. ## **The Retail Maturity Curve** Before examining cost-cutting measures, some additional detail on the maturity curve stages. Most retailers will likely recognize Wave 1, identified as a recognition that a personalized CX must start with understanding the customer. As a workaround for not having a personal understanding, many Wave 1 retailers utilize basic segmentation with minimal customization of content. In the meantime, progress is marked by identifying the current gaps, setting a customer strategy and at least starting to apply analytics to customer data. In short, these retailers are getting their house in order. Not yet customer-centric, they’re still assessing the people, process or technology limitations to omnichannel personalization at scale. Wave 2 retailers are getting their data in order, summarizing insights and key themes, and are generally at a point where they’re using multidimensional segmentation. Their limits are mostly operational; even with structured data, clear governance and ownership across the business, they have yet to break down people, process or data siloes necessary to evolve from channel-centric to customer-centric. A progression to Wave 3 and one-to-one personalization starts to take shape when retailers, by pairing analytics and augmented third-party data with advanced technologies, begin to orchestrate customer journeys for individual customers. Having made significant investments in their business model, technology and workforce, these retailers are aligned around an enterprise approach in which customer strategy has become the core priority for the business. An individual understanding is more than segmenting an audience by demographic and behavioral characteristics, it’s taking predictive action at the individual customer level, at exactly the right moment during a digital engagement. Cutting edge personalization consisting of unique content, messages and treatment is a core part of a customer engagement strategy. ## **Retail Cost Savings** At each stage of the retail maturity curve, digitally mature retailers can achieve cost savings, either through cost-cutting or cost avoidance, using [the Redpoint CDP](https://www.redpointglobal.com/cdp/). In a time of economic uncertainty marked by rising interest rates, inflation and fears of a recession, cost-cutting often takes priority over revenue growth, which we will address in a follow-up blog. To be sure, the two are not mutually exclusive, with a lot of potential for synergies. Retail cost levers impacted by customer data and personalization are primarily in three categories: data operations, technology operations and marketing operations: ## *Data Operations* Precise customer data is a key element that allows digitally mature retailers to achieve “cutting edge” one-to-one personalization at scale. To deliver a next-best action for an individual customer in the context and cadence of a unique customer journey, retailers must trust that a single view of the customer is a comprehensive, accurate and real-time representation of a customer (or household). Data ingestion, data quality and identity resolution are the core operational components for creating a Golden Record, a unified and persistently updated single customer view. The Redpoint Golden Record helps reduce cost, first and foremost, by improving match rates and eliminating wasteful spending. Hyper-relevant campaigns deliver next-best actions to the intended customer or household on the optimal channel (inbound or outbound) and, importantly, optimally aligned with the cadence of the customer. Blast email, batch uploads, mis-targeted promotions and surface-level segmentation become a thing of the past. ## *Technology Operations* Fixed infrastructure is a big sunken cost for retailers who often must pay for processing power they’re not using, such as preparing for daily, monthly or seasonal variations. There is another cost for software components of a MarTech stack that are either redundant or ineffective, particularly when functionality is replicated across channels without planning for an omnichannel experience. Elastic scaling and an open garden infrastructure in the Redpoint CDP solve for both of these issues, the former by automatically scaling technology and adjusting real-time controls to account for workload variations, and the latter by allowing retailers to drive digital transformation by leveraging all of their existing stack without an expensive rip-and-replace. ## *Marketing Operations* Finally, digitally mature retailers achieve cost reduction/avoidance by not only having a single customer view, but by being able to leverage the Golden Record to orchestrate omnichannel customer journeys and execute real-time interactions. A single point of operational control with Redpoint’s Journey Orchestration and Real-Time Interactions empower marketers and business users to design and deliver simple or complex multitouch campaigns, executing true omnichannel customer experiences across all touchpoints. Hyper-relevant offers also reduce the number of interactions required to achieve marketing objectives. In essence, marketing is able to drive personalization at a much more granular level without the need to add any staff. Our next blog in this series will explore how the Redpoint CDP helps retailers on the farther end of the maturity curve increase revenue. **Blog categories:** Data Quality, Identity Resolution, Retail --- ### [Synthetic Data in Marketing, Acceleration of a Personalized Customer Experience (CX)](https://www.redpointglobal.com/blog/synthetic-data-in-marketing-acceleration-of-a-personalized-customer-experience-cx/) **Published:** March 24, 2023 **Author:** Ian Clayton **Content:** Let’s say you had some time on your hands and for some reason decided to create a machine learning algorithm that could count the oranges on an orange tree. Far-fetched? Not according to data scientists who published “[Fruit Counting Based on Deep Simulated Learning](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5426829/).” Welcome to the world of synthetic data, which is now in the news because of how it relates to AI models, which are also trending thanks in part to ChatGPT and other generative artificial intelligence (AI) models. In the orange tree example, synthetic or simulated data are the thousands of computer-generated images of orange trees fed into the machine learning algorithm to train the model. One alternative – feeding the model thousands of images of actual orange trees – would be far more costly and time-consuming, requiring a lot of legwork and the permission from the grove owners. Another alternative would be even less appealing; manually having to count the number of oranges on each tree. ChatGPT is somewhat similar in that it produces content that could be classified as synthetic; the text that’s created is generated using existing text scraped off the internet according to the programmed inputs. The output is synthetic in that it has been artificially generated, and the text it mines is also synthetic – it is trained not to copy text, but to collect bits and pieces mimicking style and tone. Like the made-up orange tree images, the text is a distorted amalgamation. ## **Synthetic Data in Marketing** So while synthetic data is familiarizing a wider audience of laypersons to machine learning, marketers have long been aware of its more practical use cases in creating datasets for training or testing purposes. In an era of heightened data privacy regulations, synthetic data is also gaining traction as a privacy-compliant way to train a machine learning model without exposing any personally identifiable information (PII) or otherwise run afoul of data security regulations. A main reason for using synthetic data to train machine learning models is that, like the orange tree example, it saves time and resources. For a midsize company – really any company without the data of an Amazon, Google, Walmart, etc. – that wants to learn more about its customers or determine which prospects to go after, it is faster and more inexpensive to train a model with synthetic data rather than wait until you have enough customers with which to train a model. The other option of course is to forget about the modeling, and make marketing decisions based on intuition, or to test a campaign on a small percentage of actual customers and hope it accurately represents the target audience. But because customers now expect [hyper-personalized, omnichannel experiences](https://www.redpointglobal.com/blog/a-bountiful-harvest-continual-identity-resolution-and-a-pristine-profitable-golden-record/), more and more brands are data-driven and base decisions only on the most accurate, up-to-date information about an actual customer. Enter synthetic data. ## **Accelerate Training of Machine Learning Models** Using simulated data to discern something about an actual customer might seem counter-intuitive, but an example might make it more clear. First, it’s important to know that machine learning models are only as good as the data they’re trained on. If the goal of a marketer is to use the best predictive model, it would be highly counter-productive to feed a model with biased data, i.e., the self-prophecy example of creating a model that will predict pet owners and feeding it only with pet owners, or creating a model that assumes everyone in the world is already a pet owner. Likewise, for a fraud detection model to be effective it is necessary to feed it with thousands of examples of (synthetic) fraud, lest the model see only a few cases of legitimate fraud and thus be biased against detecting fraud. With that in mind, consider a fictitious travel company that wants to market a certain vacation package – say a budget weekend getaway to Atlantic City. To market to the right audience, it needs to know which customers would be most likely to purchase the package. If it’s a newly created offer, the company can’t go to the well with existing customers. Again, it might test the offer on a small percentage of existing customers – taking the chance of creating friction for those customers who find the offer irrelevant. Instead, it purchases an anonymized dataset that includes thousands of customers who have visited the same or a similar destination – a dataset of synthetic customers created from real customers (anonymized), or algorithms that can generate variants of real customers. It trains its model on this anonymized dataset to determine which customers will buy the vacation package. Providing real-life synthetic data, particularly in industries with a need for security and privacy, effectively accelerates the training of models to apply them more quickly to real-life scenarios. The caveat for the travel company is that it must obviously trust that the anonymized data is representative of the audience it is trying to influence. If your campaign is for the budget weekend in Atlantic City, you wouldn’t feed the model with anonymized data of people who summer on a yacht in the Mediterranean. There is a ChatGPT liar’s paradox [making the rounds](https://twitter.com/DrEliDavid/status/1617762423972429824?lang=en) that shows the potential distortion when a model has been improperly trained. Trained that “my wife is always right,” the model eventually agrees that “2+5=8” because someone’s wife says it is, even though it also has been trained that “2+5=7.” For the travel company, using a large enough dataset mitigates such a possibility because the data are founded in reality, used to replace or mirror the type of behavior you’re trying to predict. With enough of a dataset that approximates the type of audience it wants to reach, the travel company can then train its own models on that database, apply it to its own customers and enhance the model over time. ## **“Synthetic” as a Synonym for Metadata** Another way marketers tend to think of synthetic data is first-party data that has been augmented in some way using machine learning, such as by creating a model score. A propensity model score, a clustering index – it is a framework that has been generated by something else to append to the customer record. In this type of instance, the use of the word “synthetic” is more analogous to metadata than it is to simulated data, which is the marketing use cases we’ve explored above. The similarity is that in both the creation of a model score and training a model with an anonymized dataset, the result is data that have been generated by a machine. One key difference is that in something like a model score, the synthetic data is telling you something about your own customers. If you ask a model to classify a group of customers into five buckets according to the completeness of a unified profile, for example, that 1-5 grouping is synthetic and whichever group a customer belongs to might determine how they’re marketed to. But it’s synthetic only in that it didn’t exist prior to you creating it; unlike synthetic data that trains a model, it does not simulate actual customers – it is data about actual customers. **Blog categories:** Segmentation & Activation --- ### [How Does Your CDP Handle Data Transformations?](https://www.redpointglobal.com/blog/how-does-your-cdp-handle-data-transformations/) **Published:** April 6, 2023 **Author:** Steve Zisk **Content:** Everyone agrees that the secret to great customer experience is great customer data. There is less agreement, however, on the precise steps that need to be taken to ensure customer data is fit-for-purpose. Poll a few multichannel marketing hub (MMH) or customer data platform (CDP) vendors about how they handle data transformations, and you will likely have a few different responses. This article will explore how the various approaches to data transformations ultimately impact customer experience. ## **Control, Visibility and In-System Data Transformations** The first and most obvious form of customer data that every CDP handles is Martech touchpoint data, i.e. data that flows between the CDP and the various channels. In the case of a MMH, this may also include the orchestration and pushing of offers to the channel. One thing to be aware of with Martech data is how quickly or frequently a data source returning data to the CDP is updated. In this pre-transformation phase, there is the possibility of delay before data becomes usable, with background activities that must be performed such as redoing data aggregates or recalculating segments and propagating the changes throughout the system. You don’t want to perform data transformations on data that is out of date. If control over the timing of how and when customer data is updated is important, such as preserving the option for real-time interactions, then performing data transformation online in real time will solve for the potential of an outdated source. And that’s really the crux of any discussion about data transformation; does data management belong inside the platform, or is it permissible to have it happen upstream or even downstream? This becomes clear when looking at a data API as a second source of customer data. Different vendors will have different methods for loading data, and how that data is mapped or matched to existing records. An advantage of data transformations occurring upstream is that data is then presumably ready for use from a marketing standpoint when it reaches the system, with the major caveat that again you’re ceding control – not just pertaining to the timing, but in terms of the specificity of data transformation. As it pertains to matching, for example, if data cleansing happens upstream, what happens when records for jon.smith@email.com and jonathan.smith@email.com are returned from two different sources? How will you know whether those should be matched, particularly if neither one aligns with an existing signal for a customer, such as a physical address? Unpacking one more layer of this conundrum, even if the match itself can be trusted, that is not the same as compiling an updated, accurate and precise Golden Record, which might also need to determine – in the jon.smith vs. jonathan.smith example – which email to actually use when engaging with said customer. The second advantage of performing data transformation internal to the platform, then, is that when matching new data with existing records, you have visibility and control that the data is matched and merged correctly, yielding complete confidence in the validity of a [Golden Record](https://www.redpointglobal.com/cdp/). ## **Identity Graph vs. Golden Record** If less control is a result of data transformation happening upstream when a Golden Record is created in the system, the same drawback holds true for yet another way in which data enters a CDP or MMH, via files such as an FTP or one of any number of cloud buckets. As with an API, if you’re picking up the data expecting – hoping? – that data transformations have already occurred, there is going to be some uncertainty over whether the data is fit-for-purpose. One end-around to this problem is to simply hang on to all the raw data and do some piecemeal matching to associate it with a customer. Some vendors take this approach and deem a resulting record an “identity graph,” referring to a collection of every customer signal and its attendant attributes, and then attaching all identifiers within a signal to a customer. Once again, though, that definition of an identity graph is not on equal footing with a Golden Record. Taking a signal coming in, matching it using a known identifier and attaching it to an identity graph does not represent a data transformation per se because really all you’re doing is keeping raw data and pushing the problem off to someone downstream who must then figure out what to do with it. This is a key distinction when [identity resolution](https://www.redpointglobal.com/identity-resolution/) comes up as a core capability. There are vendors who embrace a purely identity graph-based approach, claiming they will figure out which signals are attached to which real-world customer, so you can throw in as much raw data as you want and it will all be resolved. Yet the same issue crops up where identity resolution alone does not tell you how to accurately reach out to a customer according to the customer’s own preferences or per compliance rules. If you have five different phone numbers, do they all belong to the same master record? Which should you use? Keeping raw data is fine, but there is an important distinction between an identity graph and a Golden Record that cannot be overlooked. The latter may accept raw data but ensures that a corrected, cleansed and normalized version is created and persisted for later use, with identity resolution a key component – but certainly not the only component. Native connectors to various enterprise data sources in addition to Martech touchpoints represents a final common source of customer data, with an advantage being control from within the platform the cadence, the choice of touchpoint and the depth and breadth of data coming in. One consequence is a requirement to transform that native data to the format that’s needed, which is where an ETL tool comes into play. As with sourcing data from an API or a file, the ideal place for data transformations are within the customer data layer internal to the platform. Many CDP and MMH vendors slide past the problem of making data fit-for-purpose and, in doing so, also slide past the problem of ensuring that data is at the cadence that’s needed to drive the needed results. If a solution makes a disconnect between data cleansing going on upstream, versus the data cleansing and creation of the Golden Record somewhere downstream, what happens is that problems are offloaded to someone else somewhere else, and the result is data that is not fit-for-purpose which will translate into an inferior customer experience. **Blog categories:** Data Quality, Identity Resolution, Master Data Management **Blog tags:** CDP --- ### [Redpoint Secures ISO 27001 Certification](https://www.redpointglobal.com/blog/redpoint-secures-iso-27001-certification/) **Published:** April 10, 2023 **Author:** Redpoint Global **Content:** Redpoint Global recently received its ISO 27001 certification for 2023. The certification, issued by ISOQAR, demonstrates that Redpoint has implemented and maintains robust information security controls and processes to protect the confidentiality, integrity, and availability of its customers’ information assets. ISO 27001 is the most widely recognized international standard for ISMS. By holding ISO 27001 certification, Redpoint ensures that its customers and prospects maintain organization-wide protection, including against technology-based risks and other threats. Redpoint customers also benefit by: - An increased resilience to cyber-attacks - A centrally managed framework that secures all information in one place - Secured information in all forms, including cloud-based and digital data - Heightened response to evolving security threats - Protection for the integrity, confidentiality, and availability of data According to Redpoint Chief Information Security Officer Ron Sanderson, ISO 27001 demonstrates Redpoint’s commitment to information security and data protection and validates through an independent third-party auditor that Redpoint protects its clients’ most valuable resource – customer data. “To have the (ISO 27001) certification for our prospective or existing clients, gives them an independent, third-party subject matter expert seal of approval and that our ISMS policies and controls are in-place, to secure all data, under our administration, for our hosted clients,” says Sanderson. According to the ISO website that details the specific requirements for ISO 27001 certification, an organization that receives an ISO 27001 certification provides interested parties with confidence that security risks are adequately managed by virtue of an independent third-party auditor verifying that ISMS policies are “part of and integrated with the organization’s processes and overall management structure and that information security is considered in the design of processes, information systems, and controls.” More information about ISO/IEC 27001 is available [here](https://www.iso.org/standard/82875.html). --- ### [The Power of Email in Patient Engagement: 5 Key Benefits](https://www.redpointglobal.com/blog/the-power-of-email-in-patient-engagement-5-key-benefits/) **Published:** April 14, 2023 **Author:** Erik Kangas **Content:** Even with the transition from multichannel to omnichannel communication, email still remains at the heart of patient engagement. Email addresses are individual identifiers and are crucial data points in the single patient view. Let’s explore how the email channel can deliver key benefits to your patient engagement strategy. ## 1. Patients Prefer Email Communication Despite its popularity among patients, the email channel is often overlooked. However, it plays a critical role in an omnichannel patient engagement strategy. Not every patient will be on every channel, but most do have an email address. In 2022, [92% of Americans](https://www.statista.com/statistics/1332443/us-users-top-communication-methods/) reported using email, and [49% checked it every few hours](https://www.statista.com/statistics/911592/frequency-consumers-checking-work-emails-outside-work-hours/). As such, email is an appropriate communication channel for important patient communications. Not only is email popular, but patients also prefer asynchronous communication that doesn’t interrupt their lives. Patients are more likely to engage with your messaging when you are communicating according to their preferences. When patients’ preferences are met, they have a better experience and will be more likely to return for follow-up appointments and care. Best of all, email can be secured to meet HIPAA requirements and protect patient privacy, all while providing a patient-first experience. ## 2. Dynamic Personalization Today’s patients are seeking out healthcare organizations that can provide a digital experience that fits into their lives. Many people have become accustomed to the personalized digital experiences they receive from other sectors, like retail and personal care. A [Redpoint Global survey](https://www.redpointglobal.com/press-releases/81-of-consumers-say-a-good-patient-experience-is-very-important-when-interacting-with-healthcare-providers/) conducted by Dynata Research found that 45% of patients surveyed expected healthcare brands and providers to proactively contact them at the right time and in the proper context with relevant information about their healthcare journey. Healthcare providers are at an advantage regarding how much first-party data they can access. However, security and privacy are paramount when using protected health information (PHI) to personalize email content. Securing data to comply with HIPAA regulations and obtaining patient consent for marketing communications is essential to engaging patients with personalized emails. Email communications are easy to opt-in and out of- giving patients full control of how their healthcare data is used. Once secured, marketers can use PHI to create customized patient journeys that build trust with their healthcare providers. With the single patient view, creating scalable, unique messages is possible. Email is an extremely versatile channel that allows for a high level of personalization. By using dynamic content, it is possible to create messages that address social determinants of health (SDoH). For example, messages can be delivered in the recipient’s primary language, use imagery that is culturally appropriate, and be personalized further to support their preferences. ## 3. The Power of Automation For email to be an effective channel in your patient engagement efforts, it can’t rely on one person typing one message to a single patient. The power of omnichannel marketing platforms is that they enable the automatic sending of personalized messages at the right time based on information in patient records. Setting up trigger-based campaigns makes it easy to pivot based on new patient information. Once the [Golden Record](https://www.redpointglobal.com/single-customer-view/) is updated with new information about the patient, their healthcare journey can be automatically re-directed according to the new data. Of course, you may be familiar with workflows triggered by email behavior, such as when a patient opens one email and is sent a different type of email five days later. However, when using an omnichannel platform, actions performed on another channel can also trigger email messages to provide a seamless and unified patient experience across different channels. For example, when a patient reschedules an appointment in the patient portal, the email workflows automatically adjust to send the appointment reminder at the correct time with updated information. Email is a multidirectional channel. It can also be used to encourage patients to visit other channels like social media, patient portals, and websites to further their journey and nudge them into taking specific actions. ## 4. Alleviate Staffing Stress The healthcare industry is not immune to recent staffing challenges. Staffing shortages have left fewer employees available to do more tasks, including patient care. Introducing programmatic email into your patient communication strategy can help automate and streamline common workflows. By using dynamic personalization and automation, your staff can spend less time with their fingers on keyboards and more time assisting patients. Trigger-based email flows can remind patients of appointments, collect insurance information, ensure proper medication adherence, and send other relevant healthcare communications. This frees up time for staff to focus on other tasks and relieves some administrative overhead. ## 5. Real-Time Analytics and Reporting Email can also provide near-instant performance analytics, so it’s possible to tell what messages are resonating and which are not. In addition, A/B testing makes it simple to test components of your message on a small scale and then send out the winning formats. It’s easy to test email subject lines, calls to action, imagery, and other messaging without a massive investment. Because of these features, personalized email messaging can provide better conversion rates and return on investment than other digital channels. For every $1 spent on email marketing, it delivers a $36 return on investment according to the [Litmus State of Email Survey](https://www.litmus.com/blog/infographic-the-roi-of-email-marketing/). Since email addresses are tied to a single patient profile, it’s possible to determine what works at an individual level and adjust workflows to meet their preferences based on data. ## Secure and Personalized Email Improves Patient Engagement Email is essential to an omnichannel strategy because of its popularity among patients and its technological advantages. It provides a frictionless digital experience that patients want and improves IT and administrative staff efficiency. Making email a central pillar of your omnichannel patient engagement strategy improves satisfaction, leading to better engagement and retention. ## About LuxSci LuxSci is a security-focused communications company that provides secure email solutions to help healthcare organizations meet compliance requirements and protect patient data at scale. LuxSci’s SecureLine encryption technology enables patient engagement without sacrificing patient privacy. We have protected personal health data since 1999 and are HITRUST certified for HIPAA and GDPR compliance. Visit [our website](https://luxsci.com/) today to learn more. **Blog categories:** Healthcare --- ### [What is Customer Data Debt?](https://www.redpointglobal.com/blog/what-is-customer-data-debt/) **Published:** May 10, 2023 **Author:** Steve Zisk **Content:** What is customer data debt? The simple definition is the amount of money required to fix data problems. Natural follow-up questions are what data problems, and how did they happen? As the word debt implies, the problems — lack of [data quality](https://www.redpointglobal.com/data-quality-and-data-ingestion/), governance, security, repeatable processes and a lack of data standardization – can be caused by making trade-offs (“borrowing” data before you’ve “paid” for it), and these problems accrue over time. Business users “borrow” data by using data from various sources without ensuring the data meets enterprise standards for quality, consistency, availability and security. This is like technical debt, where software may be released with design flaws or performance problems that later must be dealt with. Like any debt, customer data debt is insidious in that it tends to spiral quickly, making it more and more difficult to get it under control, to identify the source and reverse the accrual. With customer data debt, that build-up also hinders the delivery of a personalized customer experience (CX). Marketers burdened with customer data debt essentially can’t trust that the customer view they’re working with is [accurate, up-to-date or a precise representation of the custome](https://www.redpointglobal.com/data-observability/)r. Like an IT technical debt in which outdated or rushed software prevents applications from reaching their potential, having to “pay off” (fix) customer data debt uses valuable resources that might otherwise be spent making optimal use of incoming first-party data, [building your own generative AI models](https://www.redpointglobal.com/machine-learning/model-builder/) or really just advancing a customer-centric strategy. ## **To Solve the Problem, Identity the Problem** To solve customer data debt, organizations must first recognize whether they indeed have it and, if so, recognize how pervasive it is. Observing and measuring the extent of the problem boils down to profiling your customer data to understand how fast it’s coming in, its cadence, and in what state; i.e., what has been done to the incoming data? Marketers need to understand where (or whether) data transformations, identity resolution, tuning, matching and the creation of a [Golden Record](https://www.redpointglobal.com/blog/use-a-golden-record-to-enhance-customer-experience-cx/) have taken place. Consider, for example, a situation where a company stores incoming customer data in a data lake. Perhaps a CDP vendor provides an assurance that incoming data has been matched. If you’re an operational marketer with multiple records for one customer and you need to know which of two different email addresses to use for a campaign, you may have to use one without knowing if it’s the right way to reach the customer. That’s customer data debt. There may be dozens of customer or transaction tables, tons of redundancy and a complete lack of consistency that prevent the delivery of a hyper-personalized CX. To remedy the problem, the marketer has to know the source of the data, its recency and the context under which it was collected. Like a polluted river mouth, it’s easy to spot the detritus but you need to go upstream to determine the origin. ## **Customer Data Debt and a Composable CDP** Customer data debt is not a new phenomenon nor a new term, but it is becoming a larger part of the discussion around a personalized CX in large part because of the composable CDP trend. Many CDP vendors that offer a [composable architecture](https://www.redpointglobal.com/blog/the-pros-and-cons-of-composable-architecture/) framework do not consider customer data debt the responsibility of the platform. That is, they will say that a modular CDP will integrate customer data from all sources and provide a single customer view, but with the assumption that the steps needed to prevent customer data debt from accumulating – cleansing, identity resolution, security, transformations, etc. – have either already been completed or will be outsourced for completion downstream. In the example of the marketer with two email addresses, that “single view” might contain two (or more) identifiers for the same customer, but as we’ve seen a simple match does not make it actionable for the marketer. Will a database administrator clean up the transaction or customer tables? A third-party data transformation tool? Will that be done in real-time, or at the very least in the time needed to keep up with the cadence of the customer? ## **Solve Customer Data** **Debt with Redpoint** The [Redpoint CDP](https://www.redpointglobal.com/cdp/) composable architecture framework is unique in that Redpoint solves for customer data debt at the source. The platform performs all data quality, data transformations, data enhancements, identity resolution and the creation of the Golden Record as data is ingested. Customers using one or all of the platform’s three main features – [CDP](https://www.redpointglobal.com/cdp/), [journey orchestration](https://www.redpointglobal.com/journey-orchestration/) and [real-time interaction](https://www.redpointglobal.com/real-time-interactions/) – can choose any of the underlying services to support business use cases from ingesting data through to the delivery of content. Whatever the business use case, marketers and business users can trust that customer data has been made fit-for-purpose because the platform handles all the data cleansing tasks as data arrives. All customer data is ready for prime-time, in other words, the moment it is accessible. Composable architecture applies object-oriented principles to software, but if assumptions are made about data, the danger is that any problems with the data will result in compounded customer data debt throughout the composable framework. The problems with data don’t even necessarily have to be problems, per se, but even different definitions. Perhaps one table uses a month/day/year field, while another uses year/month/day. Or in one source a data clerk is instructed to enter 0’s for data not captured, while in another source the clerk enters X’s. In and of itself, those issues may not seem insurmountable, but that’s what’s so insidious about debt. Its presence requires attention but the organization may not even know it’s accumulating. Speed-to-value may be one of the stated benefits of a composable CDP, but customer data debt will negate any value until the debt is paid. Left unchecked, customer data will lead to an inferior CX. Your customers expect better, and they will go elsewhere if their expectation for a personalized, omnichannel CX is not met. **Blog categories:** Customer Data Platform, Identity Resolution, Master Data Management **Blog tags:** CDP --- ### [First-Party Data: Transforming Insights into Action](https://www.redpointglobal.com/blog/first-party-data-transforming-insights-into-action/) **Published:** June 2, 2023 **Author:** Vin DelGuercio **Content:** In the IDC FutureScape [“Future of Customers and Consumers”](https://www.idc.com/getdoc.jsp?containerId=US46909620) survey, brands were asked at which part of a customer journey do they collect the most data to help shape customer experience. Not surprisingly, most data are collected *after* a brand has established a relationship with a customer (i.e., customer support, loyalty programs, renewals, etc.). Yet asked which data provides the most *value* in terms of driving loyalty, lifetime value and profit, customer support polled last. Instead, the most value was seen in collecting data before a brand has established a relationship with a customer (before/during advertising process, during sales process). The lesson learned for marketers is that to continually improve customer experience through personalization, it is important to take into account all data, even if you’re not certain of the immediate payoff. Like a dogged detective leaving no stone unturned at a crime scene, a marketer may not know what “evidence” will lead to a better CX, so the wisest course of action is to collect it all. The leave no stone unturned approach has always been prudent, but is particularly important now for two reasons. First, with the third-party cookie’s demise marketers will lose a favored, if flawed, method for attaching signals to a customer. Second, the need to gather as much first-party data as possible is heightened due to the increasing complexity of omnichannel customer journeys, and the resulting expectation from customers for brands to know them as the same customer across all channels. According to SmarterIQ, [72 percent of consumers](https://c.smarterhq.com/resources/Retail-Personalization-Playbook.pdf) will only engage with marketing that is tailored to their interests. That depends on a brand knowing everything there is to know about a customer, and demonstrating a deep understanding with consistent messaging across channels. ## **Omnichannel Orchestration** The traditional method of gathering first-party data – gather only what you need (or think you’ll need) – may have sufficed in a product-centric or channel-centric approach to marketing, but not with omnichannel. The main reason for this is because an omnichannel CX is like a mosaic that stitches together various elements into a complete picture. It may not be readily apparent to a marketer how data from one channel ties to data from another channel, which is why it is necessary to collect everything. Over time – whether it’s a second, a minute, an hour, a day, a week, a year or longer – how those various elements fit together may make themselves known, thus enriching the customer [Golden Record](https://www.redpointglobal.com/single-customer-view/) and delivering an even more personalized experience across the omnichannel journey. > As potential signals of customer intent, all customer data are essential inputs into the analytics that will ultimately power better engagement. Just like holding onto browsing session data from an anonymous device ID in the event a match is made to a known record, perhaps notes from a call center engagement will be useful for providing a more relevant, personalized experience when the same customer initiates a return. Capturing first-party data at a granular level and in a unified way reveals more than an identity, it reveals how a person engages, when the person engages, the content and/or topics they’re engaging with – when does the customer read emails, how many curbside pickups does the customer schedule vs. an in-store visit? As potential signals of customer intent, all customer data are essential inputs into the analytics that will ultimately power better engagement. By capturing everything, you’re drawing insights which will support better, more targeted and more productive engagement over time, allowing you to capture more information in a closed loop cycle of data, insight and action. ## **Get on Board the First-Party Data Train** The good news for marketers and other business users of customer data is that as it’s becoming more important to collect first-party data, it’s also becoming easier. The mass adoption of data clouds in a composable cloud data architecture democratizes data engineering and creates an inexpensive way to manage and deliver data across the enterprise. This type of open data environment encourages a bring-your-own-analytics (models) approach, which supercharges the closed loop of data, insight and action to perpetually enhance CX. Without being locked into one data model (or vendor), organizations are free to cast a wider net for collecting more data, for running more or different models and to draw entirely new inferences than they might in a walled garden environment. To summarize, with technology making it easier and less expensive to collect and analyze first-party data, combined with heightened customer expectations for personalized experiences across omnichannel journeys, now is the time for organizations to finally embrace a true customer-centric strategy centered around first-party data. --- ### [Consumers Take the Pulse of Personalized Healthcare](https://www.redpointglobal.com/blog/consumers-take-the-pulse-of-personalized-healthcare/) **Published:** June 20, 2023 **Author:** John Nash **Content:** Healthcare consumers increasingly favor digital channels of communication with their providers and insurance plans, but the convenience of digital interactions has not lessened the expectation that caregivers demonstrate an understanding of them as an individual, beyond basic patient data, across a healthcare journey. An expectation for seamless, personalized experiences across digital and physical channels is a main takeaway from a new [Dynata survey](https://www.redpointglobal.com/press-releases/unlocking-personalized-care-half-of-consumers-believe-digital-tools-hold-the-key-to-better-health/), commissioned by Redpoint, that explored consumer perspectives on care access. More than half (57 percent) of respondents reported that they have used digital tools to engage with healthcare providers and insurers more than in previous years, and half (50 percent) said that they believe using digital tools (e.g., prescription reminders via app, personalized health recommendations based on medical history, etc.) can help them take better care of their health. Even with a transition to digital, 62 percent of consumers said that they expect online communications to match the in-person experiences they receive (in terms of relevance, consistency and outcomes). Bolstering the viewpoint that consistency and relevance are top of mind for consumers, survey respondents listed “complex or confusing experiences” (such as a provider’s digital tools being hard to understand or use) as the No. 1 reason they would consider changing healthcare providers. Furthermore, an inconsistent experience across channels and a lack of personalized engagements with a provider came in as the No. 2 and No. 3 reasons, respectively. Rounding out the top five were slow responses or a difficulty in engaging with a provider (No. 4) and a lack of understanding that where a patient lives or their financial situation might influence the care they need (No. 5), which demonstrates the importance of including social determinants of health in the knowledge a caregiver has about a patient. > Survey respondents listed “complex or confusing experiences” (such as a provider’s digital tools being hard to understand or use) as the No. 1 reason they would consider changing healthcare providers. Interestingly, nearly half of Gen Z consumers (45 percent) said they want non-medical information to become a part of the health care assessment and management process (social determinants of health, behavioral, economic factors, etc.) if their inclusion resulted in more personalized and comprehensive healthcare experience. This is more than double Baby Boomers (17 percent) and Gen X (21 percent), showing that having a full understanding of a patient, beyond basic health data, will matter more as the population ages. ## **Taking the Temperature on Relevant Communications** Showing an overall need for improvement in providing relevant communications, just 45 percent of respondents said that proactive communications from providers feel *completely* relevant to them. Examples of proactive communications cited included wellness tips, seasonal health recommendations and reminders for preventive screenings. Providers, however, are doing a better job than insurance plans (25 percent) pharmaceutical companies (19 percent) and retail pharmacies (28 percent) on the same metric (providing proactive communications that feel completely relevant). One reason providers rank ahead of insurers and pharmaceutical companies in providing completely relevant communications could be that healthcare consumers still value the in-person visit. Asked about methods of interaction, half of respondents (50 percent) said that they engage with their provider in person most of the time (vs. online portal, mobile app, website, phone, SMS or chatbot options). Yet 34 percent of respondents said they engage most of the time through an online portal, and 27 percent cited the mobile app as the preferred method of communication. To discover why Redpoint is the preferred choice for leading healthcare organizations, visit [Redpoint for Healthcare](https://www.linkedin.com/showcase/redpoint-for-healthcare/) on LinkedIn, or to see how The Redpoint CDP can solve your company’s unique business challenges and improve outcomes, click [here](https://www.redpointglobal.com/healthcare/). **Blog categories:** Healthcare --- ### [Take the Fast Lane to Innovation with a Fully Integrated Composable CDP](https://www.redpointglobal.com/blog/take-the-fast-lane-to-innovation-with-a-fully-integrated-composable-cdp/) **Published:** July 24, 2023 **Author:** Steve Zisk **Content:** Rising interest in a composable martech architecture is due in part to organizations demanding greater flexibility to respond to rapid market innovations, such as generative AI. When the need for continuous innovation aligns with use cases for delivering exceptional customer experience, the concept of composability might specifically refer to an integrated, composable customer data platform (CDP). We’ve covered the composable architecture concept in [previous articles](https://www.redpointglobal.com/blog/the-pros-and-cons-of-composable-architecture/) where we detail the features and functions of a tech stack architecture composed of different best-of-breed technology solutions, brought together through APIs to operate as a whole in a cloud-based open ecosystem of services and platforms. This article will address composability more from the point of view of a marketer or IT, specifically how a fully integrated composable CDP such as [Redpoint](https://www.redpointglobal.com/rg1/) enables continuous innovation. It does this, in part, because a composable platform wraps self-contained and complete business functions within the composable service. There is tremendous value for a marketer in being able to accomplish a complete business process in a small set of steps in one environment without having to duplicate their efforts elsewhere. It is similar to the services of popular travel sites – the Expedia’s and Kayak’s of the world – where a customer can do everything (explore destinations, select options, book flights, hotel and car, plan activities) all in one environment rather than have to go to different websites to compare prices and try to cobble together an itinerary. As a user of the system – in this case a marketer – it simplifies innovating in areas you care about like trying new campaigns rather than having to focus resources on having to navigate from app to app or solution to solution to build out a process. The catch is that a single environment only works if the user can trust that the available flights, rooms and travel packages are accurate and up-to-date. Accuracy is ensured through a careful and thorough vetting of the underlying data. The same holds true for building transformative CX; built-in data quality and identity resolution ensure that processes in a composable environment are built on high quality data that represent a real-time, accurate view of the customer. ## **A Composable Architecture and an Intelligent Hub** Once the data in a composable environment is cleansed and fit-for-purpose, another aspect of a composable environment in a CDP is how well the system architecture enables the orchestration of a consistent, omnichannel CX. To some extent, every martech stack is composable in that it will consist of systems from multiple vendors. The difference in a fully integrated composable CDP is that it will not only make inbound/outbound connections as automated and as capable as possible, it will manage the orchestration of experiences across every channel. In a fully integrated system, an email marketing team, a website team and a loyalty app team no longer have to operate in a siloed fashion, focused mostly on whether the data they care about is updated. Instead, a central source of truth allows each team to see and validate how one experience impacts a broader customer journey. An intelligent hub is more closely aligned with the real purpose of a CDP; it’s not just to push data out, it’s about designing a perfect CX through the coordination of experiences across channels. Next-best actions, product recommendations, loyalty recommendations, etc., are all driven by the same intelligence. A real-time decisioning engine – the heart of a digital experience platform – advances the concept of composability from simply swapping out components to an idea in which best-of-breed connectors are configured with a goal of optimizing the single point of operational control. ## **Innovation is an API Away** Optimization is, of course, a continual process, particularly with dynamic customer journeys always seeming to veer off in new directions. To take generative AI as one example, there are already dozens of use cases where the technology is being used in real-time decisioning to enhance CX, such as with personalizing the dialect or conversational tone of a chatbot. A fully integrated composable CDP will enable capturing the latest and greatest trends to make better offers, generate better content and to better analyze and evaluate customer behavior because new models can be up-and-running in days, not weeks. A fully integrated digital platform future-proofs CX. Users can bring their own models and interact with the real-time interaction engine at the API level, enabling continuous innovation to match the ever-changing consumer across a dynamic omnichannel journey. Flexible and adaptable at a microservices level, a fully integrated composable CDP meets an organization’s changing needs and requirements to drive transformative CX with a customer-centric approach. **Blog categories:** Customer Data Platform --- ### [Your Customers Are Global, is Your CDP?](https://www.redpointglobal.com/blog/your-customers-are-global-is-your-cdp/) **Published:** June 7, 2023 **Author:** Mike Ferguson **Content:** In our global economy, organisations that work directly with end customers need to know everything there is to know about a customer in order to deliver a personalised customer experience (CX) – and that knowledge cannot be limited by country borders. Many customers leave data footprints around the world. A retailer may have a customer who is partial to American fashion trends, currently lives in Beijing but signed up for a loyalty program when she happened to live in London. Does the brand first and foremost recognize a customer as the same individual across multiple regions and languages? If so, is the brand able to deliver a holistic experience across those dimensions that is in the context of a customer journey? In a Harris Poll commissioned by Redpoint, [82 percent of customers](https://www.redpointglobal.com/resources/harris-poll/) said they are loyal to brands that demonstrate a thorough understanding of them as a unique customer. Again, this expectation does not stop at the water’s edge. Consider our retail customer who lives in Beijing, who used to live in London, and who travels frequently. Which brands is she partial to when she travels abroad? Which email address does she use? How are her loyalty points calculated from country to country and brand to brand? Developing a single, global view of the customer across multiple regions is necessary to deliver a personalised experience, but the reality is that global companies struggle to manage customer data on a global basis. One reason is different countries and regions often have different regulations for how to collect, cleanse and store customer data. Different approaches to [identity resolution](https://www.redpointglobal.com/identity-resolution/), for example, may make it very difficult to tell if the Jane Smith from Berlin is the same Jane Smith who works in Los Angeles. And for a global company that allows each regional location to build and maintain its own MarTech stack, different technologies will prevent synergies, such as whether a platform loads characters in Asian, African and Middle Eastern languages. In addition, when there are different regional protocols for data standardisation, no one speaks the same data “language” if you will. If, for instance, each region takes a different approach to deduplication, it becomes impractical to compare customer counts. ## **Support a Single View with an Enterprise CDP** To deliver a personalised CX across multiple regions, languages and cultural differences it is important to understand how or whether a customer data platform (CDP) supports a global roll-out. One way to do this is for a CDP to support a different database for each region of business operations. Without sharing personally identifiable information (PII) across regions, the CDP can create an anonymized [Golden Record](https://www.redpointglobal.com/cdp/) for use in a global context, thus enabling a global brand to keep up with the customer across a multi-region journey. A brand may also elect to have a global database, but use different schemas to adhere to various global regulations such as GDPR and CCPA, among others. One thing to look out for is whether a pure-play SaaS offering will be able to support a multi-region approach. Adopting a global approach to customer experience (CX) brings benefits both in delivering personalised experiences and from an analytics perspective. Instead of focusing on individual customer differences across regions, we can analyse their similarities. This allows us to better understand and cater to customers’ culture, language, behaviours, and preferences, while also leveraging data-driven insights for improved decision-making. Consider, for example, a multi-country retailer with a curious increase in churn from its customers in North America. The retailer develops a churn propensity model and drives a campaign that reduces churn by 5 percent. If those same churn indicators arise in Europe or Asia-Pacific, the retailer tests the existing model against other datasets. There’s no guarantee that it will work as effectively as it did in another region, but the point is that you’re not starting from scratch. Because you’re working from the same set of data standards, you’re able to think globally and act locally and vice-versa. **Blog categories:** Retail --- ### [Why Trust Unlocks a Superior Customer Experience (CX) and Builds Brand Equity](https://www.redpointglobal.com/blog/why-trust-unlocks-a-superior-customer-experience-cx-and-builds-brand-equity/) **Published:** May 16, 2022 **Author:** Dale Renner **Content:** When a journalist agrees to protect the anonymity of an “off the record” source, it is a code of honor between the two parties. In providing information under the terms, a source extends a level of trust, and a reporter’s reputation rests on honoring the agreement. To a reporter, protecting the relationship is a sacred duty. Good reporters have been known to face jail time rather than betray the confidence of a source to a grand jury. The trusting relationship between a brand and its customers should be viewed from a similar lens. In the brand-customer dynamic, information is customer data and insights derived from that data. Customers expect a brand they engage with to safeguard their data and use their data only in the context of permissions granted and for intended purpose. At all times, a brand is bound by permissions and preferences granted by a customer, and a brand’s reputation hinges on honoring its agreements – written, unwritten, or otherwise. Just as journalists know they’ll never work again if they break trust with a source, brands that violate a consumer’s trust will lose the customer, likely forever. Heavy fines for violating data privacy regulations are certainly one consideration, but at its core building a sacred trust with a consumer is about building brand equity. A foundation of trust is the basis for delivering a superior omnichannel customer experience that sustains profitable revenue growth for a brand and sustained relevance for the customer. Customers provide brands with more data if they trust the brand. That is, they know their data will be used only in accordance with their wishes, and also because they know it will be used to create deeper, more meaningful, personalized and valued experiences over time. ## **Consumers Embrace the Value Exchange** Consumers embrace this value exchange. In a [Harris Polls survey commissioned by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/), 54 percent of consumers said they are willing to share more personal data with companies to achieve a more personalized experience. Super majorities of Gen Z (72 percent) and Millennial (70 percent) consumers say they are “very willing” to share their data for that purpose. As with the reporter-source dynamic, sharing data comes with the understanding that a brand will honor a customer’s wishes. In the same survey, 71 percent of consumers said that it is essential or very important that a brand allows the customer to give explicit authorization for how personal data will be used, with similar majorities saying that it is very important/absolutely essential a brand reveal how the information is being used (73 percent) and allow the consumer to set specific permissions for the data they allow the brand to collect (68 percent). Brands that fail to honor permissions or preferences will face the wrath of its customers, with 88 percent of consumers claiming they are likely to switch brands if a company sells the customer’s data to other companies for marketing/advertising purposes without authorization. ## **Brand Equity: A Personal Experience** Survey results are telling, but to bring the concept of consumer trust and brand equity to life it’s best to hear from a company that recognizes the importance of data in building and persisting that relationship. With summer around the corner, many people are making travel plans. For millions of travel enthusiasts each year, vacation plans start with Xanterra Travel Collection. In business for more than 100 years, Xanterra offers unique travel experiences on six continents. Stewards of six national parks, the company’s extensive hospitality portfolio includes cruises, international walking and biking tours, scenic rail travel, and destination vacations to several pristine locations such as the Grand Canyon, Glacier National Park, and the Tuscany coast. One reason for the company’s continued success is that its loyal customers know that their data will be used exclusively to improve their experiences. A customer who books a guided biking tour of the French countryside, for example, has no concern their data will be sold to a bike company. “We decided as a company several years ago that we are not in the business of sharing our data with third parties for profit. When we had those conversations, we developed 10 guiding principles around the collection and use of data that Xanterra and our sister companies adhere to as a best practice,” says Director of Marketing and CRM Andrew Heltzel. “Customer data is extremely important and valuable to us, we want more of it, and we want to secure and enrich the data that we have. What’s interesting for us as a travel and hospitality company is that if we can secure your trust, if you feel good that we’re managing your information responsibly and to your satisfaction, you will have that same level of care and confidence moving from one Xanterra brand to the next because we all share the same guiding principles about your data.” ## **Customer Controls and Personalization** Xanterra partners with PossibleNOW to develop and manage its preference center, and with Redpoint to use those preferences and other data points to develop a single customer view to help manage the entire customer experience. Customers control how Xanterra communicates to them by refining email preferences, easily accessible via a personalized landing page. Customers of Windstar, for example, which operates cruises, have the option to opt in or out of a monthly newsletter, exclusive offers, “close-in” sailings (next 60 days), or new voyage announcements. Customers also have the option to manage a travel interest page, selecting where and when they’d like to travel. Xanterra then uses those preferences to enhance personalization. “Customers have the flexibility to manage their communication preferences a little differently from one Xanterra brand to the next, but the underlying guiding principles never change. We still secure and treat a customer’s data like it’s gold. We never sell it – ever,” says Heltzel. ## **Build A Trust Relationship Using First-Party Data** The deepest, most valuable relationships in life are based on an in-depth understanding of the wants and needs of the other person. The more each invests in the relationship, the more mutually beneficial and sustained the relationship becomes. Each must receive value commensurate with the effort put forth. This is true of the relationship between friends and partners and true for a brand and its customers. The deep understanding of a customer is built on knowing all that is knowable about the person. And the best insights come from first-party data that already exists in any number of silos across the enterprise. The key is to bring all that data together into a golden record or single view of the person that is complete, always up-to-date, accurate and available across all customer touchpoints and all in real-time. By harnessing first-party data, companies have the fuel needed to effectively drive the customer experience engine. The better the data, the better the insights gained, and the better the outcomes will be for both parties. Being able to achieve this at the cadence of the customer is key to a brand sustaining relevance to its customer. Naturally, the more data customers share about themselves, the better this entire cycle works. And this is where it all comes back to a trust relationship. Again, in our daily lives, we share more with people we trust. We also know that what we share will be held in confidence by those we trust, or we will cease trusting them and stop sharing. Keeping the commitment to protect and use a customer’s data only in the way that has been mutually agreed is the same principle and is fundamental to having a trust relationship that builds and persists real mutually beneficial value. On the other hand, instead of building brand equity through trust relationships, a brand that breaks that trust, destroys brand equity. Winners will be the most trusted brands. Be one of those! --- ### [Here’s What You Need to Consider When Choosing a Customer Data Platform (CDP)](https://www.redpointglobal.com/blog/heres-what-you-need-to-consider-when-choosing-a-customer-data-platform-cdp/) **Published:** September 8, 2023 **Author:** Steve Zisk **Content:** According to IDC, the worldwide customer data platform (CDP) market is expected to reach [$5.7 billion by 2026](https://www.idc.com/getdoc.jsp?containerId=US49456122), up from [$2 billion in 2022](https://www.idc.com/getdoc.jsp?containerId=US49456122), with the growth rate attributed to customer experience (CX) being a top business driver for brands transforming into digital-first businesses. The reason CX is a top business driver is because personalized experiences across all channels of engagement is what consumers expect. Consider a [2023 Broadridge CX and Communications survey](https://www.retailcustomerexperience.com/news/more-consumers-want-better-customer-experience/) where more than half of respondents (54 percent) said they will stop doing business with a company that does a poor job personalizing the customer experience. Because there are many CDP use cases and equally many variations of the CDP itself, organizations considering a CDP should have a firm understanding of their business objectives – now and in the future – when evaluating which type of CDP will best suit their needs. Those objectives should align with other considerations, including the needed level of sophistication as well as the overall reach of the CDP into the enterprise, i.e. what depth and breadth of features and functions are required to meet the desired level of transformation? ## **The Reach of a CDP** On the sophistication front, one question to ask is whether the CDP is intended solely for marketing use cases, or intended more broadly for CX use cases across the organization. If the former, the breadth of the types of interactions that you need to support will be somewhat limited, and so will the data needed to handle these. For the latter scenario – supporting CX use cases across the organization – the CDP will be responsible for improving all interactions on all channels – digital, offline and in-person. A greater level of sophistication will be needed in terms of how you approach the data, as well as how to configure the CDP to handle various use cases for marketing and beyond. Going a level deeper, what we mean by the reach of the CDP into the enterprise is what is needed in terms of breadth and depth to support specific use cases. Reach can be understood in terms of three components: data handling, connectivity and enterprise reach. Data handling considerations involve what will be required of the CDP in terms of functions and features such as data cleansing, identity resolution, parsing, third-party data enrichment, etc., with the knowledge that different CDPs handle these functions differently. Other points to consider involve how much reach into the data world does the CDP need to have, i.e., whether the CDP needs to handle message queues, web logs, a loyalty app or other types of data in an active and real-time fashion, or whether it’s enough to simply feed files into the CDP or drive data through an API. Connectivity is a measure of how much reach into your Martech and channel world your CDP needs. By this, we mean where do you need orchestration to take place, and how do you expect to manage either single-channel, multiple-channel or omnichannel orchestration. In other words, if your organization is happy with existing channel-specific teams, orchestrating experiences across those teams is not a priority, and what you really need from a CDP is high quality segmentation, the scope of the CDP narrows considerably. But if you need to understand a customer’s journey and meet a customer in the journey with a consistent voice by orchestrating real-time, omnichannel experiences, then your CDP will require additional capabilities. ## **Standalone vs. Integrated CDP** When considering enterprise reach, you will need to consider other parts of your enterprise stack, determine whether the CDP will operate as a standalone technology or will need to be composable, integrated and work tightly together with other elements of your stack, such as ecommerce, digital experience or content platforms, or even product or supply chain systems. This consideration goes beyond just working in tandem with the Martech stack or different channels to whether the CDP will need to integrate with new technologies and trends such as generative AI. > If a CDP is intended to be the enterprise hub to orchestrate CX through a single point of operational control, a key consideration will be knowing how a CDP is set up to optimize services for data management, journey orchestration and real-time interactions to support business functions across the enterprise. If the primary goal of a CDP is to ingest all customer data to create a single customer view and you’re not concerned with, say, attribution, personalization or CX improvements, that narrows the scope of the CDP and there can be a much simpler set of integrations. But if the CDP is going to be additionally tasked with calculating propensities using internal or external models, content customization, next-best-action decisions, etc., then your CDP will have to either have those capabilities or be designed to integrate smoothly with solutions that handle those aspects of customer experience. Another broader question to answer in the context of a CDP’s intended use cases is where you anticipate your sources of intelligence coming from. Beyond the internal data, how will the CDP support experimentation, optimization and orchestration at the API level? This is more than simply tapping into data that already resides in a data warehouse, but instead requires bringing results back from channels in ongoing campaigns and analyzing those results for attribution, journey optimization, and additional CX prediction or measurement. It is a recognition that there may be other enterprise sources of information and other consumers of (and actors on) CX insights the CDP offers. If a CDP is intended to be the enterprise hub to orchestrate CX through a single point of operational control, a key consideration will be knowing how a CDP is set up to optimize services for data management, journey orchestration and real-time interactions to support business functions across the enterprise. A final point to make about deciding what type of CDP will best suit your business is to think about how your needs will change, and whether a CDP will be able to adapt to future change. You may have simple use cases today, but as you anticipate meeting the changing needs or demands of your customers – or business – your use cases may become more complex. Will your CDP offer an opportunity to ask and answer different questions about your customers, will it be able to automate some of the things that may have been manual in the past such as segmentation, visualization of customer differences and a calculation and understanding of aggregates around customer journeys? You will not know precisely what’s in store for your customers or your business next year and beyond, but you do know that there will, in all likelihood, be a certain level of disruption. The type of CDP you choose may very well determine what side of the disruption your company is on. **Blog categories:** Customer Data Platform --- ### [Healthcare Providers' Path to Sustainable Patient Acquisition](https://www.redpointglobal.com/blog/healthcare-providers-path-to-sustainable-patient-acquisition/) **Published:** September 22, 2023 **Author:** Steve Zisk **Content:** Patient acquisition via word of mouth or walk-ins are nearly as antiquated as the midnight house call from the neighborhood pediatrician. Personalized outreach, search engine optimization, a regular cadence of new content and a focus on a holistic patient journey from pre-care to post-care are among the modern acquisition initiatives that are taking on greater importance. In the realm of patient acquisition today, it’s imperative to recognize that every single encounter holds immense importance, especially with the rise of competition spurred by the growing healthcare consumerism trend. ## **Why Explore Patient Acquisition?** The need for healthcare marketers to consider shifting their patient acquisition strategy is evidenced by a 44% decline in the usage of provider referrals and a 70% drop in insurance referrals since 2018, according to a doctor.com survey, [“Customer Experience Trends in Healthcare.”](https://www.managedhealthcareexecutive.com/view/consumer-survey-signals-death-referral) Still, patient acquisition remains a top priority, with 90% of respondents in a [State of Healthcare Marketing](https://martech.health/articles/the-state-of-healthcare-marketing-2022) survey reporting that increasing patient volumes is a top goal. ## **Where Do We Fall Short?** Historically, providers prioritized creating loyalists over acquisition, often with the rationale that acquisition was 5X more expensive than retention, citing a well-known statistic. Cost aside, creating loyalists was also easier to accomplish for the simple fact that providers were dealing with known patients, and often in face-to-face encounters. And that gets to the heart of what makes acquisition challenging in the era of the dynamic healthcare consumer journey; how do we find and target an audience for a preventative screening, elective procedure or new movers campaign when the audience is exposed to so many more channels and options for care? Today’s continuously connected, always-on healthcare consumer has expectations for convenience and a personalized experience with every engagement, online or in-person. From the consumer’s perspective, the expectation for personalization exists even if it’s the first time they’re visiting a provider’s website. The challenge for providers is to market to an audience of known and unknown patients to include first-touch personalization, recognizing that an addressable market exists outside of existing patient data in an EHR. What does this mean? Consider what happens when an unknown patient makes a first-time visit to the website. Does this person see static content, or based on the ongoing browsing session is content dynamically updated in real-time to personalize the experience based on the website behavior? Is the device ID saved to a unique master profile, and then attached to a name on the next visit, when the person fills out a form seeking education for a chronic condition or inquiring about an elective procedure? ## **What is the Sustainable Approach to Patient Acquisition?** - **Using all available data sources is an important step for learning all there is to know about an addressable market.** When a unique patient profile is continually updated, using [persistent keys](https://www.redpointglobal.com/blog/what-is-persistent-key-management/) to construct a longitudinal view of the patient (or prospect’s) actions and behaviors over time, marketers begin to understand a patient as an individual. Marketers can then create and activate granular segments without any guesswork, patients and prospects dynamically move in and out of segments based on what the real-time profile indicates is important at the precise moment of engagement. Is the patient seeking education about a chronic condition? A consultation with a specialist? Does the patient wish to do a lab test or to understand how to improve their overall wellbeing? By knowing everything there is to know about a patient, healthcare marketers will always be ready to [deliver personalized content in the context](https://www.redpointglobal.com/blog/context-is-king-deliver-a-more-personalized-customer-experience-cx-through-a-contextual-understanding/) of the individual patient journey. - **With high-value content at the ready, another key step is to deliver that content wherever a patient might possibly engage.** The website and the provider office, yes, but engaging with a dynamic healthcare consumer requires engaging in the right channel at the right time. A mobile app, a self-service kiosk or electronic check-in, email or direct mail, pharmacy, an outpatient phone consult – all are opportunities to present a patient or prospect with a [next-best action](https://www.redpointglobal.com/blog/without-real-time-next-best-actions-may-be-second-best/) that will further acquisition objectives. - **An important distinction between delivering a multichannel vs. omnichannel experience is to recognize that the latter is what aligns with consumer expectations for a holistic healthcare experience.** It’s possible, in other words, to deliver static content simply wherever a patient or prospect appears and still check the box for delivering a multichannel experience, but that experience often fails to account for a patient’s real-time healthcare journey. If a blast email is queued up to notify a segment that they are eligible for a preventative screening, is that email removed – up to the point it’s opened – if a new patient has just scheduled an appointment using the mobile app? A hyper-personalized healthcare experience means engaging with a healthcare consumer not just in the right channel at the right time, but also engaging with a next-best action optimized for the channel of engagement, and optimized via a real-time decisioning engine for that specific moment of the healthcare journey. - **Accurate assessment of acquisition efforts enables optimization and increases value over time.** When results of an acquisition campaign are fed back into segmentation models, marketers can test and learn, create new segments or dynamically update segments in real time based on the most up-to-date data and real time unified profile. A closed loop feedback cycle ensures continual optimization of a patient healthcare journey, both for a segment of one and at scale. ## **Patient Acquisition is a Balancing Act** When marketers are continuously primed to deliver a next-best action that aims to increase and optimize patient encounters, the line between patient acquisition and retention becomes blurred. That is, the definition of a new patient vs. an existing patient isn’t determined by how long it’s been since a patient has sought care, nor does it dictate the type or cadence of content you’re delivering (i.e. every patient we haven’t seen in 12 months receives an email to schedule an appointment). Rather, an updated patient profile with a longitudinal view of a patient’s behaviors, actions and preferences makes a new vs. existing category almost superfluous. The distinction does not matter to the patient; what’s important is that the patient receives the message, content or action that is perfectly timed to where they are in the healthcare journey. ## **Patient Acquisition with the Redpoint CDP** The Redpoint CDP powers patient acquisition initiatives by helping healthcare organizations transition from a doctor or clinic-centric model to a consumer/patient-centric model. At its core, patient acquisition is about understanding an individual patient’s needs, and leveraging a deep understanding to guide a patient on the right care path. To match a new patient with the right provider, for example, it helps to know as much as you can about the patient. What language does the patient speak? Does the patient have a preferred gender for their doctor? What is the patient’s health condition? Social determinants of health? Redpoint drives a patient-centric approach by bringing all the data about the patient into the platform, including consumer data. This broadens the target pool for providers, allowing them to reach new patients while also staying relevant to the patient journey. Advanced identity resolution steps then create a real-time, unified view of the patient. By eliminating disparate siloes of patient and consumer data, providers know everything there is to know about a patient, and are empowered to guide a patient along the appropriate care path, tuned to an individual patient’s healthcare journey. With a unified patient view, providers can better predict the likelihood of care for a specific diagnostic use case, for example, and can [personalize the patient experience](https://www.redpointglobal.com/blog/personalized-patient-experience-the-new-competitive-battleground-for-healthcare/) accordingly. Redpoint helps connect the right patient with the right message at the right time – targeted, relevant, personalized and at scale, and all HIPAA compliant. For more information about how Redpoint helps drive patient acquisition, visit [Redpoint for Healthcare](https://www.linkedin.com/showcase/redpoint-for-healthcare/) on LinkedIn, or request a demo at the [Healthcare homepage](https://www.redpointglobal.com/healthcare/) on redpointglobal.com. **Blog categories:** 1:1 Personalization, Healthcare, Identity Resolution --- ### [A Technology Primer: MDM vs. CDP](https://www.redpointglobal.com/blog/a-technology-primer-mdm-vs-cdp/) **Published:** September 29, 2023 **Author:** Diptesh Singh **Content:** *Editor’s Note: This is a guest blog from Diptesh Singh, Cognizant Data Management, Governance & Privacy Practice Lead* There is a lot of confusion in the IT and MarTech space about the differences between a Master Data Management (MDM) solution and a Customer Data Platform (CDP). As a Practice Lead with Cognizant for Data Management, Governance & Privacy, I’m asked about this by clients often: which solution do we need? Do we need both? How much overlap is there? I recently had the pleasure of discussing this topic with Redpoint Global, and you can see a recording of that conversation [here](https://event.on24.com/wcc/r/4353516/DD7D2CBF0A0830982AABE38F58BD6ADA). I’ll use this space to shed more light on what people need to know when considering a data management tool. First, some quick definitions. MDM is a key pillar of Cognizant’s Data Management practice. An MDM tool concentrates on data assets that are typically fixed across the enterprise, i.e., master data. An MDM solution takes a holistic look at the functions and domains of those fixed data assets, identifies the key critical assets and makes sure those assets are governed, available and valuable for the entire enterprise. In comparison, a CDP concentrates on using consumer and customer data to learn as much as possible – customer master, transaction detail, and behavioral attributes – every interaction across every engagement touchpoint. Ingesting all of this data and creating a single view of the customer is the number one thing that a CDP is known for. Secondly, a CDP makes a single view actionable, enabling decisions to present a customer with the right content, on the right channel and at the right time. An MDM and CDP are both mission-critical for the enterprise, with the key distinction that an MDM looks at various assets throughout the enterprise – location, supplier, vendor, etc. – whereas a CDP concentrates primarily on consumers and customers. By consumers, I mean the B2C context – shoppers, goods and services. ## **For the Common Good: MDM + CDP Working Together** The biggest misconception is that an enterprise needs one or the other. In our view, MDM and CDP are more complementary technologies, with an MDM implementation feeding into an effective CDP implementation. Again, keeping in mind that a CDP is meant to enable decisions based on a single view that benefits the consumer, and that master data impacts that decision. Inventory is a good example of a typical master data asset that can easily impact a decision made on behalf of a consumer. In a revenue-based or experience-based use case – standard for most CDP implementations – all information that is relevant about or for a customer may have the potential to influence a moment in the customer journey. An MDM, therefore, feeds into the CDP ecosystem; quality master data is integral to building an updated, accurate single view of the customer which again is essential for orchestrating a next-best action. ## **Maximize Value from Your MDM and CDP Technologies** Lastly, from the perspective of driving enterprise value, a successful data strategy accomplishes three main objectives: revenue growth, cost optimization and/or operational efficiencies, and brand reputation. Through that lens, both an MDM and CDP are important because a positive customer or consumer experience is closely tied to revenue growth. Time and time again, we’ve seem that an increase in customer satisfaction and response rates drive revenue. Operational efficiencies and brand reputation are solidified because quality master data helps ensure data is used only for its intended purpose. From a CDP perspective, irrelevant offers and blast campaigns are eliminated; a next-best action optimizes the use of data in a particular moment. As for brand reputation, a master profile includes such data elements as a customer’s opt-ins and other privacy preferences. Adherence helps cement a level of trust between the brand and the consumer – of particular importance in a cookie-less world that prioritizes the respectful use of first-party data. For more on how to maximize your implementation of a CDP, you can learn more by contacting Redpoint’s Global Alliance Director John Dodd, Cognizant’s partner liaison, via [email](mailto:john.dodd@redpointglobal.com) or [LinkedIn](https://www.linkedin.com/in/john-dodd-007a4313/). **Blog categories:** Customer Data Platform, Data Management --- ### [What Can a Fully Integrated CDP Do for You? Exploring Value Creation with the Redpoint CDP](https://www.redpointglobal.com/blog/what-can-a-fully-integrated-cdp-do-for-you-exploring-value-creation-with-the-redpoint-cdp/) **Published:** October 10, 2023 **Author:** Redpoint Global **Content:** The global customer data platform (CDP) market size surpassed $4.8 billion in 2022 and is anticipated to reach [$19.7 billion by 2027](https://www.marketsandmarkets.com/Market-Reports/customer-data-platform-market-94223554.html). The accelerated growth rate may be attributed to the simple fact that an enterprise CDP delivers on the promise of a personalized customer experience (CX) that drives loyalty, lifetime value and revenue. Gartner estimates that a personalized CX drives [66 percent of customer loyalty](https://www.cmswire.com/customer-experience/how-personalized-customer-experience-delivers-brand-loyalty/), more than price and brand combined, and that 80 percent of customers prefer to buy from brands that offer a personalized experience. This article will examine some of the use cases that Redpoint clients have leveraged using the Redpoint CDP to develop a single customer view, power personalized experiences and ultimately reducing cost and driving revenue. Whether using Redpoint as a fully integrated CDP, or for its capabilities around journey orchestration and real-time interactions, customers are finding value and proving ROI through improved data quality, segmentation and/or activation. ## **Data Management for the Win** Xanterra Travel Collection’s mission is to improve the guest experience for its more than 20 million annual guests. The travel brand owns and operates 34 worldwide hotels, numerous cruise ships and golf courses, and manages eight million acres of land on which it offers hiking, walking or cycling tours through iconic destinations and national parks. A superlative guest experience for Xanterra customers includes receiving highly targeted, relevant communications precisely in cadence with an individual guest journey across one or several of Xanterra brands. Relevance stems from Xanterra using Redpoint’s data management capabilities to build a comprehensive unified customer profile for each customer, a real-time, single customer view that it uses to profitably differentiate one customer from another. With a real-time, accurate understanding of an individual customer Xanterra identifies key segments and personas to significantly reduce the number of touches, achieving triple-digit performance improvements in revenue per email. Targeted email campaigns with relevant offers and unique imagery, content and subject lines now generate about 73 cents per email vs. an average of 8 cents for previous campaigns. Xanterra also attributed Redpoint with seamlessly combining data from more than 100 sources to deliver significant savings in time and resources because the company was able to keep its existing systems intact. Reservation platforms and various data entry processes across multiple systems all stayed the same, with the Redpoint CDP completing data quality processes and identity resolution at the point of data ingestion to provide the 360-degree view of the customer. Also in the travel realm, AAA uses Redpoint to automate 90 percent of its data processes. This is a key revenue driver because 47 federated AAA clubs across the country pay for the national club’s data services. With Redpoint simplifying data alignment, the regional clubs gain actionable insights from each other’s data even with each club having unique processes and business rules. With the same number of FTE resources since before deploying Redpoint, AAA has gone from managing millions of rows of data to billions of rows, attributed to Redpoint’s data management capability, specifically the ease of managing the complexity of 2,500+ customer attributes. ## **Identity Resolution and De-Duplications** Switching to media & entertainment, the Contributor Development Partnership uses the Redpoint CDP to increase donor files and net revenue for a coalition of public TV and radio outlets, including WGBH. The CDP’s advanced identity resolution capabilities provided WGBH with a 10 percent increase in matched records, eliminating thousands of expensive duplicate accounts and contact records. Cate Twohill, the Contributor Development Partnership Director of Strategic Solutions, said that not having to manage the duplicate accounts that Redpoint identified “nearly paid for” the cost of Redpoint. Beyond the immediate cost savings, the partnership uses the Redpoint CDP for granular segmentation and omnichannel orchestration, increasing revenue through personalized B2C marketing campaigns that are optimized by channel. With Redpoint Journey Orchestration, the Contributor Development Partnership optimizes customer journeys across direct mail and digital channels and is able to measure campaign effectiveness with a single point of operational control. ## **Omnichannel Orchestration** In the healthcare space, a healthcare marketing engagement company that strives to transform healthcare through better outcomes and experiences with a consumer-first approach, achieved a 60 percent reduction in onboarding costs and time to onboard, and a 25 percent reduction in marketing operations costs with Redpoint. Healthcare insurers and providers across the country have used Redpoint to run multi-channel marketing campaigns, supporting renewal and onboarding efforts even across multiple different business models. Campaigns for a diverse partner community are managed through a single orchestration layer across the entire customer lifecycle: acquisition, onboarding, member engagement and retention/renewal. This provides payers with full visibility into the customer lifecycle of members, translating to highly coordinated and relevant omnichannel engagements, more effective onboarding and better retention. Turning to finance, a Fortune 100 global financial services firm achieved a 96 percent improvement in speed to marketing campaigns with Redpoint, as well as a reduction in customer data queries from hours to minutes for advanced analysis. Prior to implementing Redpoint, separate databases for international and specialty customers prevented the company from having a unified customer view, especially with a latency issue that made timely data extraction from a 5-terabyte database difficult. With a limited data model, the company lacked the necessary speed and efficiency to deliver new segmented campaigns, offers and multichannel customer journeys. With the Redpoint CDP, the company streamlined access to its customer data, making it possible to provide contextually relevant experiences at the pace of its customers. The company now has a single point of operational control over customer data across the enterprise, and builds personalized customer journeys at scale. For a webinar on CDP Success Stories with Redpoint, click [here](https://event.on24.com/wcc/r/4366853/7D25C798D2B4717551D36ECB6B394432/5047315?partnerref=rpgblog). **Blog categories:** Customer Data Platform, Data Management, Single Customer View --- ### [A Complete, Robust CDP Does More than Unify and Democratize Data](https://www.redpointglobal.com/blog/a-complete-robust-cdp-does-more-than-unify-and-democratize-data/) **Published:** October 17, 2023 **Author:** Steve Zisk **Content:** The [CDP Institute](https://www.cdpinstitute.org/learning-center/what-is-a-cdp/) defines a customer data platform (CDP) as “packaged software that creates a persistent, unified customer database that is accessible to other systems.” That’s a somewhat broad definition, which permits any vendor with a data management strategy to lay claim to the CDP mantle, even if they’re referring to a combination of tools and/or support a very limited range of functions. One way to separate the haves and have-nots in terms of who is or is not a CDP is to dig further into the CDP Institute definition. What, for example, is meant by “unified” in a unified customer database? How is a unified record accessible to other systems? From Day One, the CDP Institute has included Redpoint as a [RealCDP](https://www.cdpinstitute.org/resources/redpoint-global-realcdp-audit-certificate/), so the intent here is not to point out *why* the Redpoint CDP belongs in the “haves” category, but rather to point out some of what we feel are the platform’s differentiators even among other CDPs that have rightfully earned the title. ## **Intelligence in a Robust CDP** Taking the second part of the definition first (making a unified record accessible to other systems), one distinction to make about democratizing data is that a robust, enterprise-grade CDP should have as a key feature adding intelligence on the handling of data. Providing interfaces for both technical and business/marketing teams is certainly an important part of that, but a CDP’s interfaces – particularly for the business team – need to directly support the intended use cases. That support comes from an ability to visualize, measure, manage and distribute the right subset of customer data, aggregates, results and metadata. The distinction becomes apparent when use cases depend on complex, multi-channel data orchestration. Single touch, single channel use cases could arguably be done in use case specific applications like a digital experience platform (DXP) or event stream processing (ESP), but moving toward complex use cases such as managing shopping cart abandonment, for example, the intelligence functions must be centralized. This is the bread-and-butter of a robust CDP. ## **Hidden Complexities in “Co-Locating” Data** Digging further into the nuances of a CDP, there is wiggle room in what it means to “create” a persistent, unified customer database. One could theoretically argue that it simply means to co-locate customer data into a single application, but that fails to explain how the CDP will handle the many complexities in that process. In the process of “co-locating” customer data for instance, what is to be made of data integration, data federation, real-time and performance or latency considerations? Are those native to the CDP? At what point in the process of “co-locating” do those events happen? The point is, data sourcing and distribution are complex, critical responsibilities for a CDP. On the data sourcing side, data availability, timeliness and accuracy all contribute to the success or failure of CX initiatives. Bringing together “messy” data – even if you can do profile unification on it – simply pushes the problem off to someone else, and may lead to problems with consistency, velocity and relevance of the CDP data store. Similarly, if the intent is simply to integrate data into use case specific tooling to act on it, there could be problems associated with latency, inaccuracy and redundancy downstream. A complete, robust CDP will include the right mix of data tools for business and IT users to make good decisions about centralization/distribution of data management processes. ## **A Complete CDP and a Lower TCO** Left unsaid in the CDP Institute’s definition of a CDP is its purpose; i.e. that it exists and operates as part of a CX/Martech stack. While it is certainly true that the core mission of a CDP is to power that Martech stack with clean, fresh customer data, there is also a hidden mission of reducing the cost of operating that stack. Plumbing that hidden mission is another way to differentiate a complete, robust CDP from an also-ran. That is, a complete, robust CDP will – in addition to accomplishing the core mission – also simplify marketers’ tasks, reducing redundant data both inside the CDP and flowing through the system, and reducing redundancies in the stack itself. Furthermore, it will add intelligence to targeting, making CX interactions more effective and less costly. A CDP should support cost management through its entire value and data chain. This means including: - Data readiness / observability to let the marketers control timing / pace of campaigns with clean, fresh data. - High-performance integrations to reduce the storage, network, and compute burdens imposed by the CDP. - Smart ID resolution to handle multiple use cases with aggregates and controls for preferences, relationships, and affinities. - Intelligent segmentation to simplify the marketers’ job of preparing campaign data, especially for multi-touch, multi-channel campaigns. - Simple and effective visualization, experimentation, and optimization tools to help the marketer be agile in deploying campaigns and interactions. - Options for smart real-time decisions (not just the engine itself but rule/offer design, caching, and results tracking. Again, a lot of this might be overkill for single touch, single channel use cases, but a data-smart CDP in the core of a CX stack will find use cases that are out-of-the-box, i.e., awkward, expensive or impossible to manage if the burden of coordinating data flows and metadata is placed on use case specific tooling. For more on why the Redpoint CDP is the most complete, robust CDP in the market, trusted by leading brands to deliver 1:1 personalized interactions that drive higher revenue and lower interaction costs, click [here](https://www.redpointglobal.com/cdp/). **Blog categories:** Customer Data Platform, Data Quality, Identity Resolution --- ### [Why a CDP + CRM is the Right Prescription for Pharma & Life Sciences](https://www.redpointglobal.com/blog/why-a-cdp-crm-is-the-right-prescription-for-pharma-life-sciences/) **Published:** November 2, 2023 **Author:** Steve Zisk **Content:** According to an [EPG Health survey](https://www.linkedin.com/pulse/2-years-how-has-hcp-engagement-evolved-epg-health), pharma companies have major challenges when it comes to digital engagements with healthcare professionals (HCPs), a designation that includes physicians, nurses, pharmacists and other healthcare providers. A majority of pharma companies (77 percent) say they face a major difficulty providing real value, and 63 percent struggle to provide an effective customer experience (CX). Customer relationship management (CRM) software has been a vital tool for pharmaceutical and life sciences field reps for many years, but the reason many reps struggle to provide value and/or an effective customer experience has a lot to do with access to HCPs trending toward digital-first encounters across multiple channels. A CRM is an extremely valuable planning tool, indispensable for providing rich account details – a call report, product information (including indications, contraindications, dosages, side effects, and clinical trial data), sample management and tracking, sales data, prescribing patterns, adverse event reporting, scientific events and conferences, etc. – but a CRM is also limited in its ability to guide an omnichannel customer journey. A CRM may, for example, show a detailed history of an account’s calls and visits, with notes, action items, and follow-ups, but it will not incorporate an HCP’s real-time website activity, nor use that activity to guide a customer journey across all channels. This is not to fault a CRM, it’s only to point out that the solution was designed and intended for human-driven campaigns, focused almost entirely on human-driven channels for outreach. In the life sciences industry, that mostly means in-person visits, calls and email. In addition to web activity, a CRM is not intended as a destination source for a digital or mobile platform. The offshoot is an incomplete view of the HCP, which may translate into an inferior customer experience simply because a field rep lacks a full breadth of understanding. When managing a human relationship extends to managing interactions beyond a handful of channels, a CRMs limitations are laid bare: it doesn’t handle automation requirements, and without a full understanding of an HCP across the entire channel ecosystem it will be of limited value in keeping pace with a customer journey. ## **Deepen the HCP Relationship with a CDP** An increase in digital-first interactions and the pressure to provide an effective customer experience explain why life sciences companies are turning to customer data platforms (CDPs) as a complement to a CRM solution. If managing a relationship with a customer is a primary CRM use case, a CDP takes it a step further – improving the relationship by creating a unified customer profile and making it fit-for-purpose for any engagement channel – website, mobile app, email, contact center and CRM as both a source and a destination channel. With access to a unified profile that is updated in real time – without IT and without manual entry – a field rep making an office visit will have a holistic view of not only interactions on every channel, but an understanding of the interplay between those interactions and how that influences the customer journey. How soon after downloading a meta-analysis did a doctor review the product information, or use a cardiovascular disease risk calculator? How would the marketing team segment the HCPs that are more likely to prescribe to target them for a launch of new medication? With insight into behaviors across all channels, medical reps and pharma marketing professionals have a firmer grasp on customer intent and can be more proactive in how they manage the customer relationship. ## **A CDP and a Complete, Contextual Understanding** A CDP provides a more complete view of the relationship by managing the data that supports the relationship. CRM data is one source of HCP data, but a CDP builds a unified customer profile by ingesting all forms and types of data, cleansing it, normalizing it and performing identity resolution to resolve an identity at the individual or business level. In life sciences, this level of advanced identity resolution could mean having a profile of a physician, nurse, or pharmacist as an individual, and basing certain interactions with that understanding, but also knowing the makeup of the larger group or practice and marketing to an individual within that context. As a destination for the unified profile, the CRM is then working with an accurate, updated and complete record for the customer, providing the user with a much more complete contextual understanding. A complete, robust CDP will also perform data enrichment, i.e., firmographic data, surge information or any third-party information that might help add to the CRM record, such as in the case of identity resolution in a B2B context. Also included in the enrichment category – and typically beyond the scope of a CRM – are calculation modeling, i.e, interests, affinity, predictive models around viewing or prescribing habits, etc. Based on that deep customer understanding, a CDP is able to identify the next-best action and communication channel for each HCP or patient. Whether that is used to automate website personalization or to enhance one-to-one conversations, a CDP applies a holistic approach to consumer experience. A CRM is an invaluable tool for field reps in managing a relationship with an HCP. With a CDP creating a real-time, unified customer profile that includes all there is to know about an HCP, pharma reps and marketing professionals are poised to manage the relationship at an entirely different level, delivering hyper-relevant experiences across the customer journey. **Blog categories:** Customer Data Platform, Healthcare --- ### [Shine a Light on Customer Retention](https://www.redpointglobal.com/blog/shine-a-light-on-customer-retention/) **Published:** January 25, 2023 **Author:** Steve Zisk **Content:** In November I purchased some outdoor Christmas lights from a specialty online retailer you likely know. My house and yard decorations looked pretty good (humble brag), but the more than 100 emails I received from the retailer in the roughly two months between the purchase and mid-January left me questioning whether the festive scene was worth it. It wasn’t uncommon to receive multiple emails a day, which ultimately led me to unsubscribe. The experience got me thinking about the importance of retention marketing, particularly as we start a new year with rumors of an impending recession. Most marketers are likely familiar with the statistic that it is roughly five times [more expensive to acquire](https://www.huify.com/blog/acquisition-vs-retention-customer-lifetime-value) a new customer than retain an existing one, or that you’re about three times more likely to sell to an existing customer than a new customer. It took just one poor experience to personally bring those numbers to life. Because for a few weeks after the purchase, I was a satisfied customer. My house looked great. The kids were excited. I thought I detected a hint of envy from the next-door neighbor. But now it’s an open question of whether I’ll buy from the company again, and only because they didn’t make a sincere effort to understand me as a customer. ## **Retention and Understanding Context** At its core, customer retention is about leveraging an understanding of a customer journey to be relevant across every engagement with a customer. Ideally, relevance is expressed across an omnichannel journey; a brand recognizes the context of a customer journey, and a next-best action includes the channel of engagement as one of many parameters. The retailer I mentioned didn’t do any of that. Instead, it sent email blast after email blast – mostly about one sale or another. Why? Probably because it was inexpensive, and because it checked the box for a holiday marketing email campaign. And for a small number of customers, perhaps it was effective, and those customers purchased more lights. But in failing to take a long view approach to retention, the company squandered an opportunity to increase lifetime value for me and likely many other customers. It may have cost almost nothing to send those emails, but true financial accounting should also factor in how many customers were lost, multiplied by average lifetime value. ## **Pay Heed to Customer Intent** How might my experience have been different had the retailer prioritized retention over a mass marketing approach? Instead of being notified of a sale on an item I just purchased, I could have received a link to a tutorial video with tips on how to string lights or showing the best designs and use of color. Perhaps a phone call asking me if I need installation help and pointing me to local resources. Or, maybe the retailer leaves me alone for a while – wow, what a novel concept. It presumably knows that curb appeal is important to me, so maybe it sends me an email early in the fall offering a discount on Halloween yard decorations. Any of those options would have shown a recognition that my customer journey – and my intent – changed as soon as I purchased the lights. At this point, even as a first-time customer, the brand knew several things about me – it had my email and physical addresses, it had a device ID, it had a transaction, a browsing history. A simple demographic analysis will have revealed the average home value in my ZIP code. There was no need, in other words, to overuse/abuse the email just because it was a first-party data source. It had enough information to build a fairly comprehensive customer profile that it could leverage to build a longer (i.e. more profitable) relationship. ## **Retention and the Value Exchange** By treating first-party data as a valuable asset and providing a customer with equal value in return, retention is served because a customer who knows that their data will be used to provide a personal, relevant experience will provide more data, leading in turn to more personalization. The cycle continues, leading to repeat business and higher lifetime value. A [2022 Dynata survey](https://www.redpointglobal.com/press-releases/70-of-consumers-receive-mistargeted-information-from-brands-at-least-once-a-month/) commissioned by Redpoint explores this value exchange in depth. In the survey, 70 percent of consumers said they receive mistargeted information (including irrelevant emails) at least once a month. Interestingly, more than half of consumers (52 percent) said that they are willing to provide additional personal data in exchange for a better customer experience, defined in part as less transactional friction and, instead, more personalized discounts and promotions. Consumers are clear: they will continue to engage with a brand, and indeed provide even *more* valuable, first-party data, in return for a brand demonstrating an understanding that their customer journey differs from another customer. ## **Related Redpoint Orchard Blogs** [What is Customer Lifetime Value (CLV)?](https://www.redpointglobal.com/blog/what-is-customer-lifetime-value-clv/) [Use a Golden Record to Enhance Customer Experience (CX)](https://www.redpointglobal.com/blog/use-a-golden-record-to-enhance-customer-experience-cx/) [A Bountiful Harvest: Continual Identity Resolution and a Pristine, Profitable Golden Record](https://www.redpointglobal.com/blog/a-bountiful-harvest-continual-identity-resolution-and-a-pristine-profitable-golden-record/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Identity Resolution, Retail, Segmentation & Activation, Travel & Hospitality --- ### [Changing Customer Behaviors Open New DTC Doors](https://www.redpointglobal.com/blog/changing-customer-behaviors-open-new-dtc-doors/) **Published:** November 12, 2020 **Author:** Steve Zisk **Content:** The U.S. Department of Commerce study hit like a thunderbolt. In March and April, coinciding with the first eight weeks of the pandemic shutdown, U.S. ecommerce as a percentage of overall retail sales increased more than it had in the previous 10 years. As the chart shows, from 2009 until February, ecommerce rose 10.4 percentage points – and then a dramatic 11 points over the next two months. ![](https://www.redpointglobal.com/wp-content/uploads/2020/06/0602-E-Commerce-Surprise-Murphy-300x191.jpg) But the rise in ecommerce was as a percentage of retail sales – which plunged a record [16.4 percent in April](https://www.npr.org/sections/coronavirus-live-updates/2020/05/15/856253115/retail-wipeout-sales-plunge-a-record-16-4-in-april#:~:text=Retail%20sales%20dropped%20a%20record%2016.4%25%20last%20month.,-Bridget%20Bennett%2FAFP&text=In%20a%20historic%20collapse%2C%20retail,malls%20during%20the%20coronavirus%20pandemic.). While there are encouraging signs of a recovery – retail was up [1.9 percent in September](https://www.marketwatch.com/story/us-retail-sales-surge-19-in-september-in-show-of-strength-for-the-economy-2020-10-16), the fifth straight month of growth – the consumer shift to online, digital-first interactions is likely here to stay. In a recent [McKinsey survey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-global-view-of-how-consumer-behavior-is-changing-amid-covid-19), 73 percent of US shoppers said that they have tried a new shopping behavior since COVID-19, with roughly 80 percent saying they intend to continue the new behavior. Diving deeper, US customers named groceries and household supplies as the categories most likely to purchased online than before – an approximate 50 percent expected growth. ## **Hit Hardest, CPG Companies Need a New Approach** What, then, to make of consumer packaged goods (CPG) companies that rely on retail and channel partners to sell their product – which of course includes grocery and household products in great numbers? With brick-and-mortar retail yet to fully recover, that means fewer eyeballs on front-of-store displays and in-store promotions. It also means relinquishing direct online sales to retail partners transitioning to digital-first models – and relinquishing the trove of customer data that goes with it. One potential avenue for CPG companies to stanch their losses and exposure as consumers transition to online, digital-first experiences is to explore a direct to consumer (DTC) approach and either partially or fully lessen dependence on retail and channel partners for the last mile to the consumer. [Heinz, PepsiCo, Nestlé](https://foodinstitute.com/focus/pivoting-to-dtc/) and their vast product portfolios are among the CPG giants who started DTC channels during the pandemic – primarily as pilot programs to test effectiveness and gather customer data rather than a full-scale transition. Still, one can argue that COVID-19 merely accelerates an existing DTC trend. Even before the retail dip, [nationwide store closures](https://www.forbes.com/sites/walterloeb/2020/07/06/9274-stores-are-closing-in-2020--its-the-pandemic-and-high-debt--more-will-close/?sh=6959ed5b729f) put pressure on CPG companies to at least explore the possibility. Further, digital native start-ups such as Harry’s Razor, Dollar Shave Club, Warby Parker and others proved that a DTC model for CPG products could not only succeed, but thrive by in some cases re-inventing entire business models. ## **DTC and Competing on CX** The imperative to compete on customer experience (CX) is another reason for a DTC acceleration. Beyond a lack of direct customer data, traditional CPG companies don’t really have skin in the game, if you will, when it comes to providing a differentiated CX that has proven to drive revenue growth. As far as the customer is concerned, that’s problematic. Consider a [Harris Poll sponsored by Redpoint](https://www2.redpointglobal.com/webinar-harris-poll-cx-2019-ondemand), where 53 percent of consumers surveyed said they expect a brand to know their buying habits and preferences, and should be able to anticipate their needs. If there is a lesson to be learned from Harry’s Razor and other DTC start-ups, it’s that customer experience is paramount. These companies correctly intuited that consumers value a relationship with a brand; provided with an innovative experience such as selecting eyeglasses online, trying them on at home and keeping what you like, with no shipping costs for returns (Warby Parker), customers willingly break from retail and channel partners in favor of brand loyalty, convenience and personalization. It’s a win-win model where success breeds success; by differentiating on CX, DTC companies are rewarded with first-party customer data, which can be used to further improve the experience and strengthen customer relationships. Compiling first-party customer data that was previously controlled by retail partners, DTC companies have the foundation for developing a single view of the customer that is the basis for providing hyper-personalized experiences. ## **Building Brand Loyalty** A recent partnership between a national retailer and a social media company shows how some traditional CPG companies are exploring innovative DTC options in response to the pandemic and the resulting changing consumer behaviors. Through the social media app, users can virtually access the retail brand’s apparel and dress a personalized digital avatar, trying on different combinations of outfits and making direct purchases within the app. A brand executive said that the inspiration for the partnership was, in part, to drive the brand’s story and engage with a new generation with authenticity. The disruptive (and innovative) DTC channel also lessens the brand’s reliance on retail partners to accomplish those goals – and provides the company with first-party customer data. Prioritizing brand experience through the telling of a brand’s story is a familiar playbook for some notable CPG companies with some DTC presence. On the Yeti website, for instance, there’s just as much real estate devoted to in-depth adventure stories – like [this one](https://stories.yeti.com/story/day-we-went-left) about backcountry snowboarding the New Zealand Alps – as there are product pages for coolers. ## **Delivering Experience Delivers Value** Yeti and other innovative companies are taking a novel approach to building brand affinity and direct relationships with customers that CPG companies have historically struggled with. The approaches help validate the notion that customer experience is king, and part of the experience for a customer is feeling good about the brands one choses to engage with. Going direct to consumer helps traditional CPG companies tell a story – about the company, about the product, and about the customer – intended to strike a chord and deliver meaning, which a customer extrapolates as value. A customer who believes that identifying or associating with a brand says something positive about them will exhibit brand loyalty, which again leads to an exchange of data – and an ongoing cycle of an improved experience, even more customer data and a deepening customer relationship. Rapidly changing consumer behaviors have made it more difficult for brands to engage with customers with personalized, relevant experiences throughout dynamic, omnichannel journeys. Until now, CPG companies could depend on their channel partners to bridge the last mile to the consumer, but the new reality changes that dynamic. Exploring a DTC approach is one way to counter the online migration and dip in retail, while also providing lasting benefits by establishing strong customer relationships, building brand loyalty, and differentiating on customer experience. ## **Related Content** [DTC Evolution as CPG Companies Branch Out](https://www.redpointglobal.com/blog/dtc-evolution-as-cpg-companies-branch-out/) [Cadence of the Customer and the Corner Store Experience](https://www.redpointglobal.com/blog/cadence-of-the-customer-and-the-corner-store-experience/) [Customer Data Insights and the Single Customer View](https://www.redpointglobal.com/blog/customer-data-insights-and-the-single-customer-view/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Management, Data Quality, Journey Orchestration, Omnichannel Marketing, Real-Time Personalization, Retail, Segmentation & Activation, Single Customer View --- ### [5 CDP Pitfalls Retailers Need to Avoid](https://www.redpointglobal.com/blog/5-cdp-pitfalls-retailers-need-to-avoid/) **Published:** September 21, 2022 **Author:** Thomas Kaczmarek **Content:** There’s an old saying about failing to plan is the same as planning to fail. There are many things that can potentially go sideways and you need to account for as many as you can. The same rule of thumb applies to retailers building a customer data platform (CDP) who need to be aware of a host of pitfalls that can derail implementations designed to deliver real-time, omnichannel personalized retail experiences. Here then, without further ado, are the Top Five things retailers need to watch out for when building a CDP to make sure things don’t go horribly wrong. **1. Give Data Quality its Due** One thing that will get things off track in a hurry is if data quality is not given its due as soon as data is ingested from every conceivable source. Retailers should steer clear of a CDP that outsources data quality to a third party, including the cleansing, normalizing and matching of data. This is big. A CDP that outsources or puts off [data quality](https://www.redpointglobal.com/customer-data-management/data-quality/) will make it virtually impossible for marketers to deliver an omnichannel customer experience (CX) because they will not trust the accuracy or completeness of customer data. Also, if data quality is completed downstream, the resulting waiting game means there will not be any real-time visibility into how a customer is moving through a customer journey. ***As every retailer knows, real time is vital for delivering a relevant next-best action the precise moment a customer shows up in a channel**.* **2. Ongoing Identity Resolution** Conversely, another pitfall retailers need to be wary is of a CDP that stops at basic, deterministic matching. Linking a third-party cookie to a device’s browsing history might suffice for basic personalization, but if a basic match is *all* you’re doing you are going to lose out on real-time data integration, being able to effectively integrate all types of data, connect to all sources or build aggregates. Some CDP’s, in other words, treat [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) as a one-time process, but a point-in-time match does little to reveal anything important about how a customer journey unfolds. ***Without such a longitudinal view, retailers lack the context they need to be relevant across all channels, at any point in an omnichannel customer journey**.* **3. A Rules-Based Approach** Retailers also need to be cautious about a CDP that uses a list-based approach to database extractions. In a list-based approach to building an audience, once the list is created it is subject to decay. New data is by definition excluded, meaning a campaign is built around certain attributes of an audience that may no longer exist. ***Redpoint rg1, by contrast, adopts a rules-based approach where an audience is created at the last possible moment before any inflection point, according to dynamic rules in place – a set of logic that is evaluated at any point in a campaign where a list would typically be used.*** A campaign can then be designed for a living, breathing audience that is relevant to an individual at a precise moment of a customer journey. > Some CDP’s treat [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) as a one-time process, but a point-in-time match does little to reveal anything important about how a customer journey unfolds. Consider a direct mail campaign as one example. In a list-based approach, a retailer may send a flyer or coupon to an audience that has shown an affinity for blue quarter-zip pullovers based on browsing history or shopping cart behavior. Once the list is extracted, that mailer will go out to everyone on it – including anyone in the audience who has purchased the pullover in the interim, who has filled a shopping cart with different items, or who has changed a physical or email address and thus will not receive the otherwise relevant piece of content. With [rg1](https://www.redpointglobal.com/rg1/), by contrast, the rules-based approach to database extractions excludes all those possibilities, ensuring a direct mail piece is hyper-relevant to the constitution of the audience at the last possible moment before the mailer is sent. **4. All Customer Data, All Sources** Another snag for retailers interested in delivering an omnichannel CX is a CDP that limits data integration to just two or three channels or sources of customer data. For some retailers, particularly those with channel-specific marketing teams (email, website, etc.), connecting even two channels is a dream scenario. Again, though, as we’ve seen with some of the other pitfalls, while two channels is better than one, it still falls fall short of meeting expectations for an [omnichannel CX](https://www.redpointglobal.com/omnichannel-personalization/). Integrating customer data from the website and email doesn’t help a call center agent fielding a call from a customer trying to resolve a returns problem, for example. Integrating data from every conceivable source of customer data and every type (structured, unstructured, batch, etc.) is another strength of rg1. ***Because every piece of customer data may reveal an important signal about a customer’s behavior, preference or intent, a key to being hyper-relevant is to ensure the accuracy and completeness of a* [*golden customer record*](https://www.redpointglobal.com/single-customer-view/)*****, or Customer 360**.* Ignoring customer data – any customer data – has the potential to introduce irrelevance into any next-best action. **5. Machine Learning** Lastly, there is also a lot that can go wrong when an organization discounts the use of AI or machine learning in delivering an omnichannel CX at scale. Marketing teams accustomed to generalized segmentation often do not appreciate a need to introduce machine learning; segments are broad enough to accommodate manual changes – particularly if a campaign does not aspire to (or is not capable of) a real-time component. Of course, as with list-based vs. a rules-based approach, machine learning is instrumental in accounting for a dynamically changing audience. [Machine learning](https://www.redpointglobal.com/machine-learning) built into rg1 provides marketers with hands-off intelligence that automates the continual creation of new, granular segments. ***In-line, self-training models optimized for a certain outcome (retention, acquisition, lifetime value, etc.) will produce a next-best action for an audience of one precisely tuned for a specific moment in the customer journey and optimized by channel, all without manual intervention.*** To recap, while there might be many things that can go wrong when building a CDP for retail, there is also a lot that can go right when a CDP takes care of data quality at ingest, treats identity resolution as an ongoing process, adopts a rules-based approach to database extractions, connects all sources of customer data and, finally, has built-in machine learning. Retailers can build a very effective CDP when implementing Redpoint’s rg1 for producing consistently relevant, hyper-personalized customer experiences on any channel in any retail environment. For more on how you can meet every retail customer with highly personalized experiences in every interaction and in every channel, [click here.](https://www.redpointglobal.com/retail/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Retail --- ### [A Customer Data Platform (CDP): The Hidden Gem in Your MarTech Stack](https://www.redpointglobal.com/blog/a-customer-data-platform-cdp-the-hidden-gem-in-your-martech-stack/) **Published:** October 27, 2023 **Author:** Redpoint Global **Content:** Asked one time about the staying power of the Grateful Dead despite the band never having had a No. 1 single (and only one Top 10), singer and guitarist Jerry Garcia likened the deep, lasting devotion of the “Deadheads” to …. licorice. “Not everyone likes black licorice,” he said. “But the people that do *really, really* like it.” The quote captures the sentiment of companies that have deployed a customer data platform (CDP), at least according to the results of a recent [survey from the CDP Institute](https://martech.org/are-marketers-cooling-on-cdps/). While not every company has a CDP, those that do are very happy about it. Like discovering a great new band before it becomes popular, a CDP seems to fall into the hidden gem category. In the survey, the percentage of companies that have deployed a CDP saw a slight drop (to 25 percent, from 31 percent in 2022) but of those that have deployed a CDP, 80 percent said that it delivers “significant value.” But the drop in deployments is a tad misleading, as 62 percent of survey respondents said that they have either already started to deploy a CDP, or have plans to do so. And, as the survey acknowledges, other surveys show a continued growth in CDP deployment, including one market analysis that expects the global CDP market to reach [$19.7 billion in 2027](https://www.marketsandmarkets.com/Market-Reports/customer-data-platform-market-94223554.html) – up from $4.8 billion in 2022. ## **How a CDP is Like Licorice … Wait, What?** Asked to select the top three benefits from their CDP, the runaway winner was a **unified view of customer data (77 percent)**, followed by analysis (62 percent) and orchestration (57 percent). Delivering a high-value unified customer profile as the foundation of a personalized customer experience (CX) that drives revenue is the bread and butter of the [Redpoint customer data platform](https://www.redpointglobal.com/rg1/). Why do users of the Redpoint CDP really, really like it? - How about because it combines data from [100 different siloed sources](https://www.youtube.com/watch?v=mM9k9VqJlXQ), giving a travel + hospitality brand new ways to monetize customer data through unique travel experiences? “Redpoint has helped us manage the guest experience from beginning to end. … Having that comprehensive view and putting an optimized campaign or journey in place has significantly helped deliver a better guest experience,” says Andrew Heltzel, Director of Marketing and CRM, Xanterra Travel Collection. - Or because its [advanced identity resolution](https://www.youtube.com/watch?v=D3VtVoyx8Ws) capabilities feature both probabilistic and deterministic matching, ensuring a unified customer profile is precise, allowing a renowned public media company to significantly reduce the number of duplicate accounts and lower costs. “The siloes that existed for years are now gone for us; we can have a single campaign and really measure the effectiveness of that campaign no matter how we reach the donor. … When you find a tool that allows you to find identity resolution as quickly and seamlessly and easily as Redpoint does, you hold onto that and you don’t let go,” says Cate Twohill, Senior Director of Strategic Solutions, Community Development Partnership. To learn more about what customers are saying about the Redpoint CDP, or to see how the platform can help you meet your unique business needs, click [here](https://www.redpointglobal.com/request-demo/?utm_source=website&utm_medium=hero&utm_campaign=oneplatform). *Editor’s Note*: The CDP Institute acknowledges that the pool of survey respondents labeled users (79 organizations) and vendors is not statistically significant to draw far-reaching conclusions. **Blog categories:** Customer Data Platform --- ### [Navigate Digital CX Using First-Party Data as Your Guiding Star](https://www.redpointglobal.com/blog/navigate-digital-cx-using-first-party-data-as-your-guiding-star/) **Published:** November 15, 2023 **Author:** John Nash **Content:** At the outset of World War II, the flagship of a naval force was the super-dreadnought battleship, the lumbering symbol of national might seen as vital for control of the seas. Yet within just a few short years it was nearly obsolete, supplanted by the aircraft carrier as a navy’s true show of strength. The battleship fell victim to a perfect storm of lightning-quick advancements in technology that completely overhauled strategy and tactics. We are seeing a very similar thing happen today in digital customer experiences; old ways of doing things are becoming obsolete faster than anyone anticipated, roiling the waters if you will for companies intent on providing a differentiated, digital-first customer experience. There is, however, a way for companies to chart a course for calmer waters, and that is with a focus on first-party data as the foundation for providing the always-on, connected customer with a real-time, omnichannel experience. ## **A Flight to Digital** The perfect storm that caused the latest CX upheaval started with a flight to digital, which the pandemic greatly accelerated. In response to consumers expressing a lasting preference for digital-first engagements, companies followed suit by continuing to rely heavily on third-party cookies as a proxy for a customer’s online behavior. But then the tide shifted; cookies are going away, ad blockers and privacy controls are now in vogue, and companies once again have to change tack. The paradox is that as it becomes more difficult to develop a deep understanding of a digital-first consumer, more customers are moving online and expect brands to know who they are across an omnichannel customer journey. In a [Harris Poll survey](https://www.redpointglobal.com/resources/harris-poll/), 82 percent of consumers said they are loyal to brands that demonstrate a thorough understanding of them as a unique customer irrespective of channel. This is up 5 percent since the question was posed in 2019 ahead of the pandemic – which upended the traditional notion of what it meant to “understand” a customer. According to research from McKinsey, the flight to digital at the start of the pandemic was all encompassing; digital adoption covered a “decade in days,” impacting not only shopping and online delivery but also telemedicine and online entertainment. In one study, [75 percent of consumers](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-great-consumer-shift-ten-charts-that-show-how-us-shopping-behavior-is-changing) said they had tried a new shopping behavior, with 73 percent expecting the behavior to become permanent. ## **Expectations Solidify Around Privacy & Personalization** With the sudden digital adoption came increased consumer awareness and concern about data privacy. Joint research from Boston Consulting Group and Google found that while [two-thirds of consumers](https://www.bcg.com/publications/2022/consumers-want-data-privacy-and-marketers-can-deliver) want content personalized to their interests, just about half (45 percent) are not comfortable sharing their data for that purpose. When interacting primarily across digital channels, the reluctance of customers to share personal data even though there is a desire for a personalized experience creates a conundrum for brands. How to serve customers with hyper-relevant experiences that reflect a deep understanding of the customer (and generate revenue) without breaking trust. Customers need to know that a brand is only using their personal data in accordance with their expressed intent – including tracking their various devices across the internet. According to Boston Consulting and Google, five of six companies interviewed said they planned to invest in privacy-ready technology and building in-house capabilities as the way forward, revamping their data and tech infrastructure to not rely on third-party cookies, instead placing the protection of consumers at the forefront, and becoming centrally managed with a high degree of data ownership and control. ## **First-Party Data as a Guiding Star** That strategy is, in a nutshell, turning toward first-party data as the new guiding star, and it is the reason why data-driven organizations that differentiate on customer experience turn to Redpoint. The Redpoint CDP perfects an organization’s first-party data in real time, applying advanced identity resolution, data cleansing, governance, matching and merging at the moment data is ingested. When first-party data has been resolved to an identity or household and matched to a unified customer profile using persistent keys, it creates a unified customer profile that combines every possible customer identifier with a complete transactional history and comprehensive data aggregates. Because it persists over time, a unified profile becomes the foundation for developing a deep and contextual understanding of an individual customer across all digital and physical channels. What does this mean from the customer’s perspective? For starters, it leads to consistently relevant experiences because a brand has a cross-channel awareness of a customer’s behaviors and preferences. And when a brand demonstrates a deep understanding of an individual customer with real-time next-best actions across an omnichannel journey, customers intuit that the brand honors them beyond a transactional value. We also know that customers will share more personal data when they do receive a more personalized experience. While in the BCG/Google survey 45 percent are uncomfortable sharing personal data, we see that percentage rise considerably when it is in fact used to improve customer experience. In the Harris Poll, for instance, 66 percent of customers said they will provide brands with more information about themselves in exchange for a more valuable customer experience. Interestingly, 67 percent said they are also fine when the brands they *explicitly approve of* use first-party tracking cookies to improve their experience. ## **The First-Party Data Value Exchange** The first-party data value exchange is about trust. Brand equity is strengthened when consumers trust that their data will be used to deliver a personalized experience, which in turn yields more personal data. Consumer expectations for digital-first engagements and for a brand to know them as a unique customer across an omnichannel journey look like they are here to stay. But the third-party cookie and trying to work around privacy settings or ad blockers was never the right approach, or even the proper motivation, for a digital CX initiative. A good faith effort to provide customers with a digital-first experience that reflects a deep customer understanding starts with first-party data, the lifeline for staying ahead of a perfect storm that may otherwise sink a digital CX effort. **Blog categories:** Customer Data Platform --- ### [What Is Data Enrichment?](https://www.redpointglobal.com/blog/what-is-data-enrichment/) **Published:** July 11, 2018 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) --- ### [Be Consistently Relevant with Open-Time Email](https://www.redpointglobal.com/blog/be-consistently-relevant-with-open-time-email/) **Published:** March 9, 2023 **Author:** Will Stuart-Jones **Content:** In a Dynata Survey sponsored by Redpoint, [70 percent of consumers](https://www.redpointglobal.com/press-releases/70-of-consumers-receive-mistargeted-information-from-brands-at-least-once-a-month/) said they receive mistargeted information at least once a month, with nearly a quarter (24 percent) of those surveyed claiming they receive mistargeted information daily. The problem for brands that send irrelevant communications is that more than half of consumers (51 percent) said that irrelevant messaging negatively impacts their overall customer experience (CX). Think of your own experiences as a consumer, and how you feel when you open an irrelevant email, perhaps one that offers a discount on a product you recently purchased. If it’s from a brand you transact with regularly and one you’re loyal to, you likely feel disrespected; with the money you spend, the brand can’t take the time and effort to show it understands you as a customer? Today’s customer journeys are dynamic. Customers move seamlessly through digital and physical channels, and they expect brands to engage with relevant communications across an omnichannel journey. Consistently relevant email is a big part of delivering a personalized CX in the cadence of an individual customer journey, and that’s where open-time email comes into play. ## **Real-Time Decisioning and Omnichannel Personalization** Open-time email is increasingly becoming an important tool in the marketer’s toolkit. Through open-time email, brands with even the beginnings of cross-channel awareness and a contextual understanding of a customer can leverage that understanding with hyper-relevant emails guaranteed to be in the context of a customer journey. In Redpoint rg1, open-time email is a feature of the platform’s intelligent orchestration capabilities. Unlike most open-time email functionality that might queue up two or three images to switch out based on a customer’s transactional history (where email may be integrated with a point-of-sale system), rg1 bases the real-time decision across an omnichannel understanding. With cross-channel awareness and knowing a customer’s behaviors, actions and preferences across all channels included in a Golden Record, a real-time unified customer profile, rg1 provides the ability to update email content in the cadence of a holistic customer journey. For example, a predictive model may analyze the entirety of a customer’s behaviors to determine a next-best offer based on predictive insight, rather than a simple rule that switches out email A for email B based solely on a transaction. The scenario may include additional factors, such as a time of day to send the email or whether to even send the email at all. Perhaps the email is pulled if a predictive model determines that a next-best action for a customer who abandons a shopping cart over a certain amount should instead receive a call from the call center. ## **Dynamic Journey, Dynamic Email** Another open-time email use case for enhancing a personalized CX is including real-time, location-based content. In a typical open-time email scenario, a real-time decisioning engine integrates with dynamic business logic that will determine the customer and, optimally, the next-best action to be rendered at the time the email is opened. Real-time decisions can consider information accessible via other public APIs, such as those which can determine a customer’s likely location. In the case of location-based content, this might be a third-party travel company or a weather service. Based on a customer’s IP address, content may be updated accordingly, so that an email a customer opens may contain an image and text of the top 10 museums to visit in England vs. Spain, depending on the customer’s location. Or, when there’s a torrential downpour an email embeds an image with an offer for boots and an umbrella. Done well, open-time email can be a highly effective subset of dynamic offer management, improving click-throughs and conversions by helping stop negative customer experiences in their tracks. In a Zippia survey, [89 percent of customers](https://www.zippia.com/advice/customer-experience-statistics/) said they have switched to a competitor following a poor customer experience. With open-time email, contextually relevant information is consistently delivered on the right channel at the right time based on an individual customer’s behaviors and preferences. ## **Related Redpoint Orchard Blogs** [Dynamic Offer Management Can Be Customer-Centric: Here’s How](https://www.redpointglobal.com/dynamic-offer-management-can-be-customer-centric-heres-how/) [Customer Journeys are Dynamic: Your Engagement Technology Should Be as Well](https://www.redpointglobal.com/blog/customer-journeys-are-dynamic-your-engagement-technology-should-be-as-well/) [A Personalized Customer Experience (CX) Delivers Loyalty](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Journey Orchestration, Real-Time Personalization --- ### [Super Bowl Ad Playbook: First-Party Data Creates Opportunity to Score](https://www.redpointglobal.com/blog/super-bowl-ad-playbook-first-party-data-creates-opportunity-to-score/) **Published:** February 13, 2023 **Author:** Steve Zisk **Content:** A [phantom holding penalty](https://www.cnn.com/2023/02/13/sport/holding-call-super-bowl-lvii-chiefs-eagles-spt-intl/index.html), a [slip-and-slide field](https://www.inquirer.com/eagles/eagles-super-bowl-chiefs-state-farm-stadium-field-conditions-20230213.html) and a [high ankle sprain](https://www.espn.com/nfl/story/_/id/35647920/patrick-mahomes-plays-ankle-sprain-leads-chiefs-super-bowl-57-win) are the big on-field trending topics after Kansas City’s 38-35 come-from-behind victory over the Philadelphia Eagles in Super Bowl LVII on Sunday. Off-the-field, social media is buzzing with [Rhianna’s baby bump](https://www.cbsnews.com/news/rihanna-pregnant-at-super-bowl-singer-confirms-she-is-pregnant-with-second-child/), Eagles Coach [Nick Sirianni’s tears](https://www.tmz.com/2023/02/12/eagles-head-coach-nick-sirianni-cries-tears-super-bowl-national-anthem/) during Chris Stapleton’s National Anthem performance, and Rob Gronkowski’s [missed field goal (or was it?)](https://www.boston.com/sports/new-england-patriots/2023/02/12/rob-gronkowskis-missed-field-goal-in-live-super-bowl-commercial-left-many-confused/) during a Fan Duel ad. As for the full field of commercials, which cost a record [$7 million](https://www.sbnation.com/2023/2/11/23594391/super-bowl-ad-cost-2023) for a 30-second spot, advertisers went deep on nostalgia and star power – and sometimes both at once. Michelob ULTRA and Netflix teamed up to produce a spoof of *Caddyshack* with Serena Williams, Tony Romo and Alex Morgan playing starring roles, while clothing retailer Rakuten had Alicia Silverstone reprise her role as Cher Horowitz in *Clueless* to promote a new shopping experience. Streaming service Tubi went all-in with a “rabbit hole” analogy in its first Super Bowl campaign, using anthropomorphic bunnies to sell viewers on the idea of a more [“personal content journey.”](https://www.nexttv.com/news/fox-pushes-tubi-brand-with-two-super-bowl-commercials) (Fox, which aired Super Bowl LVII, [bought Tubi](https://www.nexttv.com/news/fox-completes-acquisition-for-avod-firm-tubi) in 2020 for $440 million.) ## **Shared Data, Shared Audience** Elsewhere in personalization and an enhanced customer experience, a reliance on first-party data as a cornerstone of relevant marketing was evident with a few marque co-branding ads. The *Caddyshack* spin-off was one, where Michelob ULTRA (an AB InBev product) and Netflix joined forces to promote the upcoming golf documentary *Full Swing.* Michelob ULTRA has long positioned itself as a lifestyle brand for health-conscious, active consumers, and in partnering with Netflix to promote a golf documentary it is leveraging its first-party data to expand its reach on new channels. The partnership is similar to the growing trend of brands partnering with media organizations in a [retail media network](https://www.redpointglobal.com/blog/monetize-your-customer-data-with-a-data-clean-room/) data clean room. Astute viewers may recognize the [Bud Light and Game of Thrones](https://www.youtube.com/watch?v=8fhOItB0zUM) partnership in a Super Bowl LIII campaign as a previous partnership between AB InBev and a media organization, in this case HBO. Molson Coors and DraftKings teamed up for an interactive co-branding spot where the brewer’s marque brands Coors Light and Miller Lite battle it out for supremacy only to have Blue Moon – another Molson Coors brand – steal the spotlight in a [surprise ending](https://www.youtube.com/watch?v=VWe63Z6CYfs). The ad [confused](https://www.12news.com/article/sports/nfl/superbowl/blue-moon-super-bowl-commercial/75-8bf5bb1d-1571-4849-bf51-cff89eb2843b) more than a few viewers who wondered why Blue Moon wouldn’t appear until the final seconds, but if you’re Molson Coors and your target audience is beer drinkers, you’re looking at it as a three-for-one special. As for DraftKings, the online daily fantasy sports site contributed by having customers enter a free contest in which they guess the answer to 12 questions for a chance to win $500,000. Questions included an over/under on the number of people in the ad with facial hair (2.5), and the type of flooring in the bar used in the ad (wood/checkered/shag carpet). At least one reviewer included the partnership in its [list of the top five best](https://www.washingtonpost.com/arts-entertainment/2023/02/13/best-super-bowl-commercials-2023/) Super Bowl LVII commercials, along with Ben Affleck working the drive-thru at Dunkin’ and Amy Schumer deleting images of her exes on Google Pixel 7. Beyond the exposure of the 30-second spot, think about the first-party data that DraftKings generates from customers entering the contest, and what that data might be worth to Molson Coors, who might then better optimize its ad spend not just on DraftKings digital channels, but for televised sporting events that draw viewers who participate in daily fantasy contests. ## **A Finger on the Pulse of the Customer** Another ad that brought customer experience to mind was a [Bud Light spot](https://apnews.com/article/kansas-city-chiefs-philadelphia-eagles-nfl-super-bowl-miles-teller-54eadac92cc5b0ef04fa9db39865bb8c) featuring Miles and Keleigh Teller dancing to ubiquitous hold music and making the most of a universally frustrating customer experience. When an entire Super Bowl ad is reserved for calling out a poor experience, you know there’s room for improvement. Experience and first-party data underscored the [T-Mobile broadband ad](https://www.youtube.com/watch?v=jSO-Whn2sCQ) featuring John Travolta, Zach Braff and Donald Faison. Travolta reprised his “Summer Nights” star turn in *Grease*, with the guys belting out re-worked lyrics touting the service’s speeds. The ad focused on upselling existing T-mobile customers, a good practice in an uncertain economy. Left unsaid, of course, is the opportunity for T-mobile to vastly increase the amount of first-party data it has on mobile and broadband customers. One ad that stood out for its refreshing humility was the [100 years of Disney](https://deadline.com/2023/02/walt-disney-celebrates-100-years-storytelling-innovation-with-super-bowl-commercial-1235257164/) 90-second clip that quietly celebrated the iconic brand’s first century. In an age where companies come and go every day, it’s impressive to see one with staying power, which reflects not only the brand’s history of innovation, but also an uncanny ability to truly know its customers and deliver on market demand. As much as Super Bowl ads are known for their attempts to be funny or memorable, what has stood out the past few years, this Super Bowl among them, are the attempts by brands to develop lasting partnerships by leveraging a first-party understanding of the customer. At $7 million a pop, advertisers want real value. Having a deeper understanding of your customers through strategic partnerships helps deliver that value. ## **Related Redpoint Orchard Blogs** [Super Bowl Ads Bet Big on Understanding the Customer](https://www.npr.org/2023/02/13/1156079585/super-bowl-commercials-from-adam-driver-s-to-m-m-candies-the-hits-and-the-misses) [A New Playbook: Super Bowl Ads Go Deep on Empathy](https://www.redpointglobal.com/blog/a-new-playbook-super-bowl-ads-go-deep-on-empathy-and-rising-above/) [Personalization Scores a Big Hit in Super Bowl LIV Commercials](https://www.redpointglobal.com/blog/personalization-scores-a-big-hit-in-super-bowl-liv-commercials/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* --- ### [There’s a Model for That: Machine Learning for the (Business) Win](https://www.redpointglobal.com/blog/theres-a-model-for-that-machine-learning-for-the-business-win/) **Published:** November 4, 2022 **Author:** Ian Clayton **Content:** No matter how the metric is defined, the pressure on marketers to meet key goals and prove ROI is increasingly mission critical. Whether acquisition, retention, higher sales, increasing net promotor scores, lifetime value or converting more loyalty members, marketers tend to be more keyed in on the result than the process. In a [previous blog](https://www.redpointglobal.com/blog/actionable-insights-and-a-consistently-relevant-customer-experience-cx/), we examined how machine learning helps marketers achieve their goals by analyzing data at scale to provide the needed context that will ultimately produce a next-best action. We examined machine learning in the [Redpoint rg1](https://www.redpointglobal.com/rg1/) platform as a key component of the actionable insights layer that bridges the gap between collecting/perfecting data and orchestrating a consistently relevant omnichannel customer experience. With actionable insights, marketers are then ready to intelligently orchestrate next-best actions at scale to create a personalized omnichannel experiences for thousands (or millions) of customers. If you’ll permit me an analogy, the term “orchestration” is accurate. Just as a concertgoer may appreciate the active harmonies, crescendos and tonal melodies, her main appreciation is for how the sound brilliantly comes together, even if she remains blissfully unaware of the multi-layered complexity that brought it all together. The concept applies to marketers working with machine learning. A marketer using machine learning output to select an audience may, for example, may want to know the propensity of a customer to buy a certain product. Perhaps that information is presented in a range from least to most likely to purchase. We explored how the audience insights dashboard presents decision tree logic to show why a customer is placed at one end of the range vs. the other, but ultimately a marketer who trusts that the information is accurate cares primarily about the fact that it is data that can be used to achieve a result. The marketer should not have to care, as it were, whether the information is generated from machine learning or not, only that it is available quickly and provides trustworthy insights. Instead, it’s “here is my data, here is my goal – how do I get there?” ## **Pre-Built Machine Learning Models** Continuing with the theme of not having to concern themselves with the details, marketers do not actively call for a propensity model, for instance. Rather, they’re defining the goal, with the model created through machine learning having been defined in that goal. Marketers, in other words, are not required to create their own models. Because in many instances goals are shared across verticals, rg1 makes it easy to solve business problems by applying and building models as part of the CDP, allowing them to be delivered in different ways according to an industry use case. A healthcare marketing organization, for example, might want to reach out to men over 50 to schedule a preventive screening. Rather than just send an email to everyone over 50, it would be more efficient and produce better outcomes if marketers sent a communication in the patient’s preferred channel, at their preferred time, and using content they are more likely to engage with. A model using the organization’s own data might, for example, suppress people not likely to respond to an email. They’re using an attribute derived from a machine learning algorithm, but presented in language the marketer understands – least likely to most likely being one of many ways to intelligently orchestrate a next-best action. Through learning, the model knows why a customer is more or less likely to respond to SMS vs. email, or it knows the endless permutations that lead to a purchase vs. an abandoned shopping cart. The pre-built models produce the intended results because the algorithms have been trained on countless inputs that account for variables in the data. When a model that is initially fit-for-purpose i*s* used to pursue a goal, the ultimate result can be brought back into the machine learning algorithm in a continual re-training loop for learning and adapting. ## **Bring-Your-Own Model** Another rg1 feature is its compatibility with what’s sometimes referred to as the bring-your-own-model (BYOM) approach to machine learning. During ingestion, a model result is provided as a data source – or the model is called within the rg1 real-time pipeline – to provide insights at the point of requiring them. As an example, if the real-time engine in rg1 is being used to drive a next-best action in a mobile app and the current geolocation is used to drive the model, the result can be gathered immediately to present the result in-line In preparing data for orchestration, the bring-your-own-model approach recognizes that external investments in analytics should not have to be scrapped; data should be able to be taken from anywhere. rg1 can work with an external modeling system, with pre-built models making it easy to prepare data for use downstream, i.e. using it to achieve business goals and leaving all the heavy lifting to machine learning. ## **Related Redpoint Orchard Blogs** [Actionable Insights and a Consistently Relevant Customer Experience (CX)](https://www.redpointglobal.com/blog/actionable-insights-and-a-consistently-relevant-customer-experience-cx/) [Machine Learning Misconceptions, Dispelled](https://www.redpointglobal.com/blog/machine-learning-misconceptions-dispelled/) [How to Avoid Data Distortion & Machine Learning Bias](https://www.redpointglobal.com/blog/how-to-avoid-data-distortion-machine-learning-bias/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Segmentation & Activation --- ### [A Personalized Customer Experience (CX) in Banking](https://www.redpointglobal.com/blog/a-personalized-customer-experience-cx-in-banking/) **Published:** October 31, 2022 **Author:** Mike Ferguson **Content:** Switzerland is a leading global centre for the financial sector, known worldwide for its stability, security and reliability within its banking services. This makes it a “safe haven” for many personal and business investors. But like many other global financial institutions, marketers are challenged with legacy systems that are not designed for the modern marketer who wants to personalise customer experiences. Fragmented legacy data also introduces latency into customer insight, making real-time decisions impossible. Adding to marketers’ challenges, internal data and governance policies restrict or delay adoption of cloud technologies. Many Swiss financial services institutes require that data does not leave the safety of on-premise solutions or private cloud environments and remain strictly in Switzerland. This causes many problems for the modern marketer who wishes to move to a more personalised customer experience by interacting with their customers and prospects through an omnichannel approach. Personalisation and improving customer experience are high on the list of CMO strategies. Personalising customer experiences requires insight into everything that is knowable about your customers and prospects. Including aggregated data from legacy systems such as lifetime value through to real-time streaming data from website activity or call centre interactions. ## **The Role of a CDP in Data Transformation** To mitigate the challenges of latent and siloed data while complying with data protection regulations is why financial institutions are implementing robust enterprise customer data platforms (CDPs) as a single point of control over data, decisions and interactions to improve customer experience. This is where Redpoint Global’s [customer data platform](https://www.redpointglobal.com/customer-data-platform) comes into play. rg1 allows all of your data to be pulled together, cleansed, matched and merged, providing you with a single customer view without any data leaving the safety of your firewalls, ensuring data security and compliance with regulatory requirements. rg1 provides support for GDPR requirements such as Right of Inquiry, Subject Access, Data Lineage and Right to be Forgotten. Solving data quality and integration issues – quickly and affordably – is what Redpoint does better than any other provider or partner. Redpoint’s data quality assurance solution provides data profiling to discover data inconsistencies and anomalies. Redpoint cleanses data with advanced master key management, contextual matching, standardization, normalization, identity/entity resolution, merging/purging, householding, parsing, geocoding, address standardization/correction, de-duplication, validation, migration and enrichment that quickly and dramatically improves data quality. The rg1solution has an open garden approach to connectivity which allows integration with any existing databases or customer touchpoints, negating the need to “rip and replace” your existing marketing automation channels. This ensures you get maximum value from your existing technology investments and allows you to connect to any future technologies or channels as they become available. Thus, future proofing your investment into a CDP. rg1 provides marketing and operations with a single point of control through its intelligent orchestration layer, delivering real-time, personalized engagement across all of your channels. It also enables you to design and coordinate individualized customer journeys across inbound and outbound, and digital and physical touchpoints. It is flexible enough to help you execute either simple one-off campaigns or complex multitouch, multichannel customer journeys. Dynamic rules and content allow you to match each customer’s cadence as their interests, needs and preferences evolve rg1 allows you to have your cake and eat it – providing you with real-time omnichannel orchestration whilst keeping your data within the confines of your country and organisation. Learn more about Redpoint Global by visiting our website [www.redpointglobal.com](https://www.redpointglobal.com) or by contacting Martyn Payne ## **Related Redpoint Orchard Blogs** [Why Banks Strive for a Connected Customer Experience](https://www.redpointglobal.com/blog/why-banks-strive-for-a-connected-customer-experience/) [Banking on Change: Why a Golden Record Satisfied Customer Expectations for a Holistic Experience](https://www.redpointglobal.com/blog/why-banks-strive-for-a-connected-customer-experience/) [New Customer Shockwaves Drive Need for Personalized Customer Experience (CX) in Banking](https://www.redpointglobal.com/blog/new-customer-shockwaves-drive-need-for-personalized-customer-experience-cx-in-banking/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Identity Resolution --- ### [Use a Golden Record to Enhance Customer Experience (CX)](https://www.redpointglobal.com/blog/use-a-golden-record-to-enhance-customer-experience-cx/) **Published:** October 7, 2022 **Author:** Vin DelGuercio **Content:** Like the proverbial dog that catches the car, marketers and business users with access to a customer Golden Record may find themselves wondering what to do with it. After all, it is no small feat to finally have a unified view of a customer, household or another entity across multiple systems and customer accounts that merge each instance of a customer together. We’ve previously covered how to construct a [Golden Record](https://www.redpointglobal.com/blog/the-role-of-a-golden-record-in-providing-a-consistently-relevant-personalized-cx/) and how the various components help marketers deliver a consistently relevant personalized customer experience. Here, we will cover a few ways that a Golden Record can be used to create business value. ## **A Golden Record: More than a Single Customer View** To quickly recap, a true Golden Record is more than what some vendors tout as a single customer view. Because a Golden Record continuously applies data quality and advanced [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) processes as data is being ingested, marketers are assured that the resulting profile is an accurate representation of the customer they intend to interact with. Moreover, they are assured that the accessible Golden Record is representative of a customer at the precise moment in a customer journey that a marketer engages, irrespective of channel or whether it is an inbound or outbound communication. With this understanding, one way to use a Golden Record to create business value is through a person-to-person engagement. Think of a customer dialing into the call center, or a curbside pickup scenario where the store associate represents the last mile to the consumer. A call center agent, or the associate bringing items to the customer’s vehicle will have a contextual understanding that they can leverage at the point of contact to enhance the customer’s experience. A call center agent, for example, will have a customer’s full history across multiple email addresses, phone numbers, devices, loyalty accounts and every other potential identifier, and have this information in one place. Depending on the purpose of the call, the agent may choose to view spend by category, by time dimension or another grouping that the agent can leverage to best achieve the optimal outcome, be it retention, increase lifetime value, conversion or another metric relevant to the individual customer’s journey. Likewise, the in-store associate arriving at the customer’s vehicle might be ready with an upsell offer based on a customer’s browsing history between placing an order and arriving at the store the next day. In any person-to-person encounter, access to a Golden Record provides a business user with a distinct advantage in creating a hyper-relevant experience because the user has a real-time, contextual understanding of the individual customer journey at the point of contact. ## **A Golden Record and a Contextual Understanding** The Golden Record also drives value from a business operations standpoint. Understanding, for example, sales forecasting, merchandising, inventory and other common reporting metrics by aggregating unified profiles provides a far deeper context than analyzing individual sales events. A deeper understanding of who is buying what in the context of multiple customer journeys can help users identify patterns as they relate to not just individuals but wider purchasing groups and households. A user might know, for example, the reason why one product moved off the shelf while another did not. Was there a weather incident in one sales region? Was there a higher level of fraud or coupon abuse? By layering context into purchase patterns, Golden Records provide users with more nuanced reporting, which can be used to drive more intelligent, profitable business decisions. Another way to use a Golden Record to drive value beyond a messaging standpoint is through the management of engagement patterns such as a customer’s channel and frequency preferences. By knowing which channels a customer prefers to engage with a brand, and how often, a brand can optimize engagement on those dimensions, maximizing spend efficiency. Because the Golden Record provides a complete view of those preferences across multiple user accounts, users can leverage that integrated picture to manage an entirety of communications across email, direct mail, SMS and other channels. ## **Next-Best Actions Tied to an Individual, or Account** Another way to use the Golden Record to create business value is through the automation of next-best actions. Real-time website personalization is a perfect example. A customer browsing a brand’s website might see a different image, offer or content than another based not only on the customer’s preferences or interests, but based on how each customer is navigating the website in real time. That’s because a Golden Record applies data quality and identity resolution steps as data is being ingested, which includes data from the online session in progress. In this example, a Golden Record is the difference between displaying static content based on, say, a device ID where the user may not know who it belongs to (an individual, or a member of the household). But because identity resolution is a key component of a Golden Record that resolves identity at an individual level, machine learning models can form an understanding of a browsing event in relation to one person. In a similar vein, because a Golden Record provides a complete view of a customer even when a customer has multiple accounts or multiple identifiers, a next-best action – automated or through an in-person engagement – discerns the optimal action based on *how* the customer is engaging. If, for example, an individual has two loyalty cards, is the recommendation or offer based on purchase data from both cards, or just one of the cards? Or if a customer has multiple travel profiles with an airline, are recommendations triggered based on whether the customer is logged in through a work email vs. a personal email address? In other words, because a Golden Record provides a marketer or business user with everything there is to know about a customer, business value is derived from being able to unify even seemingly disconnected events or disconnected behaviors into a unified profile to support better decisions. In the case of an individual with multiple loyalty cards, this might mean sending only one coupon or reward offer even if you have an email address on file for each of the loyalty accounts. For more on how to drive business value using the Redpoint Golden Record, [click here](https://www.redpointglobal.com/single-customer-view/). ## **Related Redpoint Orchard Blogs** [A Bountiful Harvest: Continual Identity Resolution and a Pristine, Profitable Golden Record](https://www.redpointglobal.com/blog/a-bountiful-harvest-continual-identity-resolution-and-a-pristine-profitable-golden-record/) [Retention Marketing for the Digital-First Customer: Make an Impression with a Golden Record](https://www.redpointglobal.com/blog/retention-marketing-for-the-digital-first-customer-make-an-impression-with-a-golden-record/) [Banking on Change: Why a Golden Record Satisfies Customer Expectations for a Holistic Experience](https://www.redpointglobal.com/blog/banking-on-change-why-a-golden-record-satisfies-customer-expectations-for-a-holistic-experience/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Identity Resolution, Real-Time Personalization --- ### [Test & Learn: Make the Most of Augmenting Data Quality](https://www.redpointglobal.com/blog/test-learn-make-the-most-of-augmenting-data-quality/) **Published:** August 2, 2022 **Author:** Steve Zisk **Content:** In a previous [blog post](https://www.redpointglobal.com/blog/what-is-augmented-data-quality-and-why-does-it-matter/), we explored the purpose for augmenting data quality and outlined how augmentation benefits several parts of the data quality process, with a primary focus on the outcome of delivering a personalized customer experience. To recap, augmenting data quality helps assess the scope and quality of available data across sources to learn what’s needed to transform isolated customer “signals” into a coherent customer understanding. The cleansing, parsing and normalization of data is another data quality area that can be improved by augmentation. With a firm understanding of customer signals and how those signals map to various targets, the next goal of data quality augmentation is to perform identity resolution processes – not just to match records, but to develop a complete, accurate golden record. Once identity resolution is performed successfully – accurate matching and merging, mapping of relationships and entities, de-duplications, etc. – another task for augmentation via intelligent models is to augment the data itself. What this means is augmenting data to help calculate metrics such as [customer lifetime value (CLV)](https://www.redpointglobal.com/blog/what-is-customer-lifetime-value-clv/) or likelihood to churn – or augmenting the metrics themselves. ## **Augmentation vs. Human-Based Rules** It’s worth noting that CLV, churn rate and other metrics likely exist with some element of human-driven rules for discerning the propensity of a customer to churn, to sign up for a loyalty program, to increase their average spend, etc. Augmenting this area of data quality means setting up predictive models and then measuring the quality of the machine learning algorithms against the quality of the human-driven rules. The goal, then, is to understand the precise effect of augmentation – is it really obtaining insights beyond the realm of human-driven rules? This calculation also applies to areas within [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) such as match rules – is augmentation producing a better set of matches than otherwise? In any event, the process to put augmentation through its paces, so to speak, is to run the augmentation workload, look at the matches, the lifetime value aggregates or whatever it is you’re testing, run the human-driven workload and see which produced the better outcome. It may be that each proves valuable, and thus worthwhile to use both. One interesting example of using a combination is to use human oversight to improve on probabilistic matching analysis, with a data steward responsible for examining unresolved matches. The data steward can even be a test bed for additional learning through augmentation, with the decisions the steward makes on grouping together individual records used to train a model in a supervised learning workflow. The caveat to employing a hybrid approach is there must be open, available mechanisms to define your augmentation, which is true of augmentation in general. What this means is that augmentation works best in a situation where the business knows what needs to be trained and what doesn’t, and where a training set includes modeled output such as matches or CLV. The trained model then becomes smarter based on learning from the output. ## **Align Augmentation with Specific Business Outcomes** Any consideration of when or whether to augment data quality should include a firm understanding how any model deployed is operating, as well as a firm understanding of the results. This condition ties back to the first important point of augmenting data quality, that it only be done with a clear purpose, i.e., a defined business outcome. The danger of proceeding without such an understanding is modeling/augmenting areas of data quality without knowing why, which runs the risk of augmenting the wrong things, or augmenting areas of data quality that are not closely tied to an outcome the business is trying to push. Measuring whether a model is making the expected decisions, and how it does so, provides transparency to the process that ensures the augmentation of any area of data quality produces the desired outcomes. Lastly, there is a connecting thread between all areas of augmented data quality that itself might stand to benefit from augmentation. The individual components – knowing what’s needed to transform signals into a customer understanding; pulling together accurate, cleansed and normalized signals to create an accurately matched, clean [golden record](https://www.redpointglobal.com/customer-data-management/data-quality/); and the processes to supplement a golden record with enhanced data – combine to form an overall process where augmentation may help discover data quality trends. Exploring data quality trends, such as what’s happening from a data quality standpoint from individual sources, may help measure whether data is fit for purpose and identity areas for corrective action. Following the proper augmentation guidelines and processes can provide a business with a trust index providing an overall data perspective, essentially a “credit score” for the quality of the data and processes. A trust index, compiled from individual elements of data and individual sources, can itself be augmented with models that convey the readiness of data, how trustworthy it is, where to bolster weak points, etc. Augmenting data quality is a closed loop process that, at its core, asks pertinent questions of whether data is fit for a specific business purpose as the ultimate final measurement. ## **Related Orchard Blogs** [There’s a Model for That: How Automated Machine Learning (AML) Tackles Any Business Use Case](https://www.redpointglobal.com/blog/theres-a-model-for-that-how-automated-machine-learning-aml-tackles-any-business-use-case/) [What is Automated Machine Learning?](https://www.redpointglobal.com/blog/what-is-automated-machine-learning/) [What Does it Mean to be a Good Data Steward?](https://www.redpointglobal.com/blog/what-does-it-mean-to-be-a-good-data-steward/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Identity Resolution --- ### [Redpoint Appoints Chief Information Security Officer](https://www.redpointglobal.com/blog/redpoint-appoints-chief-information-security-officer/) **Published:** May 24, 2022 **Author:** Redpoint Global **Content:** The risk of steep financial penalties and reputational damage make securing customer data a priority for any organization with a brand-consumer dynamic. Security is also top of mind for consumers, who expect brands to safeguard their data, protect their privacy, and honor their permissions for how their personal data is collected, shared, and used. In a recent [KPMG survey](https://advisory.kpmg.us/articles/2021/bridging-the-trust-chasm.html?utm_source=vanity&utm_medium=referral&mid=m-00005652&utm_campaign=c-00107353&cid=c-00107353), 86 percent of consumers said that data privacy is a growing concern, with 68 percent expressing concern with the level of data being collected and 40 percent saying that they do not trust companies to ethically use their data. Redpoint’s commitment to ensuring customer data remains private and secure was bolstered with the announcement today that Ron Sanderson has been promoted to Chief Information Security Officer (CISO). One of the earliest Redpoint employees, Sanderson has spearheaded SOC 2 compliance and HIPAA certification. Most recently serving as the company’s Director of Information Security, Sanderson’s responsibilities include overseeing InfoSec awareness training and leading initiatives to build out Redpoint’s Information Security capabilities from the ground up. As CISO, Sanderson will continue to strengthen Redpoint’s security and privacy approach as the company offers further cloud native offerings to some of the world’s leading retailers, healthcare organizations, and financial institutions. As the KPMG survey makes clear, businesses are hyper-aware of the need to bolster security measures – with 62 percent of business leaders agreeing that their company should do more to strengthen existing data protection measures. And 33 percent agreed that consumers should be concerned about how their personal data is used by their company. For more Redpoint’s approach to data privacy and security, here is the [link to today’s press release](https://www.redpointglobal.com/press-releases/redpoint-global-appoints-ron-sanderson-as-chief-information-security-officer/) announcing Sanderson’s promotion. **Related Content** [Redpoint In Situ, Perfect Data, in Real Time, in Your Own Security Perimeter](https://www.redpointglobal.com/blog/redpoint-in-situ-perfect-data-in-real-time-in-your-own-security-perimeter/) [Marketing Data is Too Important to Cede Control of the Security Perimeter](https://www.redpointglobal.com/blog/marketing-data-is-too-important-to-cede-control-of-the-security-perimeter/) [The Cloud Advantage: Control Your Own Security Perimeter](https://www.redpointglobal.com/blog/the-cloud-advantage-control-your-own-security-perimeter/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* --- ### [Kubernetes Takes Hadoop to the Mat: The Rise of Container Management Software](https://www.redpointglobal.com/blog/kubernetes-takes-hadoop-to-the-mat-the-rise-of-container-management-software/) **Published:** September 29, 2021 **Author:** Kris Tomes **Content:** For a time, Apache Hadoop was almost synonymous with big data. Like Band-Aid for bandage, Google for search and other common genericized trademarks, there was a reason the pairing came very close to taking root before starting to wither on the vine. The Java-based, open-source framework for big data processing was revolutionary when it burst on the scene about 15 years ago. Its distributed data analytics and data storage model that allowed for parallel processing of large datasets, by building out clusters of nodes on multiple machines, really birthed the entire notion of working with big data. This article will look at the rise – and fall – of Hadoop and explore why other options are gaining traction, specifically the emerging popularity of container orchestration platforms to manage containerized, cloud-native applications. ## **The Rise: Why Hadoop Mattered** What is Hadoop, and why did it become synonymous with big data, at least for a time? By distributing data storage and analytics workloads across multiple nodes, parallel processing allowed for – at the time – much faster data processing of large datasets. It promised high availability, lower cost and a safe way to manage data backups. It became the go-to, high-performance big data solution because it could collect and store structured and unstructured data without having to convert data into a single format, and because once required data was extracted, Hadoop could store unprocessed data in perpetuity, in theory with low storage costs because the distribution framework spread the stored data across multiple nodes using commodity hardware. Hadoop remains popular today, but it is no longer a standard-bearer leading the big data charge, having been overtaken by advancements in technology and more flexible, lower-cost options. In addition, enterprise data volumes are increasing at levels unforeseen when Hadoop came along in the early 2000s. Big data and data lakes became possible largely because Hadoop allowed for relatively inexpensive storage of large datasets. While there is a use for this information it isn’t ideal for marketing and some of the more transactional aspects of data management, as stored data is static data. Fast-moving, data-driven enterprises – particularly those that rely on customer data to create differentiated experiences – require dynamic data. In addition, a consolidation of Hadoop providers – Cloudera’s acquisition of Hortonworks, HPE’s acquisition of MapR and IBM abandoning its Hadoop distribution – is further evidence of the shrinking demand for Hadoop distributions and the loss of differentiation in Hadoop’s big data processing. ## **The Fall: Barriers to Success** Container management software to orchestrate and manage containerized, cloud-native applications has been steadily gaining steam. Its popularity, driven mainly by Kubernetes, is chiefly responsible for Hadoop adoption plateauing. Because Kubernetes does not do storage, per se, it is somewhat of an apples to oranges comparison, but in supplying both processing power and operational elegance using containers, vs. the computational power of Hadoop, Kubernetes scores a TKO. While Hadoop revolutionized parallel processing of big data, the implicit promise of cost benefits and, to an extent performance, never really materialized because of the complexity of management. As for cost, while Hadoop certainly made it possible to scale out as many conventional processors as needed to guarantee high performance, the fact is that when implemented in a cloud environment CPUs do not really offer any material cost savings. It is not uncommon for an enterprise to need hundreds of nodes using 30 or more core CPUs. That is an enormous, fixed expense. Performance, too, while unquestionably offering raw speed that simply would not be possible without parallel processing, can sometimes be slow and buggy. With massive datasets, underlying code has a heavy lift to match queries on distributed files. And taking full advantage of Hadoop parallelism with programming in MapReduce, Pig, and Spark is notoriously difficult, generally requiring an expertise incongruent with tools that people are more familiar with, such as SQL and basic BI graphing and querying. Enterprises needed to invest not only in the software solutions but also in specialized staff to manage integrations. Those are the two main reasons why containerization will continue to grow as the favored option for big data processing. Hadoop limitations aside, we will now examine how Kubernetes, and container management software in general, can achieve the cost-savings and processing power that failed to materialize with Hadoop. ## **The Up-and-Comer: Containerization Transforming Data Management** Unlike distributed, parallel processing where clusters of nodes run on dedicated hardware and more clusters means more infrastructure, containerized cloud-native apps are so-called because a container contains a “pre-deployed” instance of the software, built for Kubernetes or another container-orchestration tool, where the traditional infrastructure is inherited in the container. Security, controllability, single-sign-on, interaction with different services, network configuration – those are all controlled by Kubernetes. The chief advantage financially is fewer fixed infrastructure costs because containerized costs are on a consumption basis. When another instance of the software is needed, an enterprise can easily spin up another container. Importantly this can be done automatically, with no IT intervention. Kubernetes will spin containers up and down as needed based on the resource load, and the enterprise only pays for what it uses. In theory, the ability to spin nodes in a cluster up or down as needed in Hadoop was conceivable, but improbable. Scaling a cluster is easier said than done and doesn’t really align with how an enterprise uses a big data platform. If you’re running processing jobs 24/7, you can’t really spin down an entire cluster if you’re still running, say, 50 percent of the cluster at a given time. With containerization, that roadblock disappears. The enterprise can ramp up or down day-by-day or even hour-by-hour, perfectly matching the processing ebbs and flows of the business. This elastic scaling, together with built-in operations oversight and container- and micro-services-based high availability makes Kubernetes a much more governable, transparent, cost-effective source of processing power. ## **Containerization for the Win** Lastly, Hadoop and distributed data analytics and data storage in general helped give rise to the concept of the data lake. Relatively low data storage costs compared to a traditional data warehouse popularized the data lake as a repository for analytics data and big data management. But too often, what happened was businesses just dumped data into a data lake without assessing its value or planning for its use and management. Data lakes have a habit of quickly becoming data swamps, creating enormous data governance issues. With time-sensitive data, however, such as that required by a data-driven enterprise engaging with customers in real time, cloud-based big data storage services offered by AWS, GCP and Azure provide seamless integration with big datasets in containerized applications without the need for persistent data storage. That model, in addition to the ability to spin up and down as needed, makes working with container-management software far more palatable for dynamic organizations with sophisticated data needs, including a need for real-time data to meet the expectations of consumers for personalized, relevant experiences at the moment of interaction. ## **Related Content** [Date Lake vs. Data Swamp: Why Enterprise-Class Businesses Can’t Afford Bad Data](https://www.redpointglobal.com/blog/enterprise-class-businesses-cant-afford-bad-data/) [Cadence, Scope, Flexibility: Understanding a CDP’s Data Architecture Needs](https://www.redpointglobal.com/blog/cadence-scope-flexibility-understanding-a-cdps-data-architecture-needs/) [Wave a Magic Wand: What Can You Accomplish with Perfect Data?](https://www.redpointglobal.com/blog/wave-a-magic-wand-what-can-you-accomplish-with-perfect-data/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Management --- ### [Combat Attrition with Automated Machine Learning (AML)](https://www.redpointglobal.com/blog/combat-attrition-with-automated-machine-learning-aml/) **Published:** June 23, 2021 **Author:** Steve Zisk **Content:** For brands across industries, there can be as many reasons for customer churn as there are customers. But whether it’s in retail, banking, insurance, telecom, media, or even hospitality, a poor customer experience is a universal indicator for attrition. In a [Harris Poll sponsored by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/), 37 percent of consumers said that they will not do business with any company that fails to offer a personalized experience. Furthermore, in the [PwC Future of Customer Experience Survey](https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/future-of-customer-experience.html), 32 percent of respondents said that they will stop doing business with a brand they loved after one bad experience. (For US customers, 59 percent said they will walk away after “several” bad experiences, 17 percent after just one bad experience.) A positive customer experience, then, is widely accepted as the first line of defense against customer attrition, especially with price and product having largely been commoditized. The expectation brands must meet is for a seamless CX across an omnichannel journey that spans digital and physical channels. Because the cost of acquiring a new customer exceeds the cost of retaining an existing one ([5X more](https://www.invespcro.com/blog/customer-acquisition-retention/), according to some studies), companies are incented to provide a differentiated experience that treats each customer as an individual. Much as a [superior CX](https://www.redpointglobal.com/blog/a-superior-customer-experience-cx-transcends-marketing/) comes down to predicting with a fair degree of accuracy relevant experiences for a single customer, predicting churn is about picking up signals at the customer level. One customer’s reasons for leaving may be the same reasons another customer decides to stay, which makes it important to have a granular understanding of each customer. A granular understanding, in parallel with common churn indicators that vary by industry, such as closing a bank account, letting licenses lapse for a SaaS company, or a reduction in monthly spend at a retailer, together form a better predictor of churn than signals taken separately. As with delivering a personalized CX, analyzing churn signals starts with data. But with so many reasons why a customer might leave, and an even greater number of permutations, accurately predicting churn at the individual customer level at scale depends on [automated machine learning (AML)](https://www.redpointglobal.com/machine-learning). ## **AML Reveals Customers’ Intentions – At Scale** While it’s rightly accepted that predicting the behavior of millions, tens of millions or even hundreds of millions of customers is beyond human ability, it’s worthwhile to look at one alternative that many companies still rely on to predict churn – and why it falls short for today’s omnichannel customer journeys. That is, many companies still rely on the common churn predictors – the closed bank account, the reduction in monthly spend, the negative NPS survey, notes from a call center complaint, etc. A good start, perhaps, but as a standalone each fails to account for an entirety of a customer’s behaviors along a customer journey – an analysis of which might lessen the importance of one churn indicator as it relates to the journey. A retail example might be a retailer automating a retention outreach if average monthly spend dips below a certain percentage. Yet without knowing more about a customer, the retailer runs the risk of introducing friction into the customer’s journey. Perhaps the customer has recently lost a job. Or maybe the customer made online purchases from the retailer using a household member’s credit card. From the customer’s perspective, a standard retention email will seem insensitive. ## **Self-Training Models Keep up with the Customer** AML reduces the possibility of introducing friction due to a mistaken interpretation of churn signals with in-line, self-training and continually optimized models that do not require any human intervention. Code-free models and automated algorithmic optimization mean that a model set for a specific business metric – in this case, retention – will run 24/7 testing various permutations to determine the optimal retention action to take for each customer, based on each customer’s churn signals. Organizations can run fleets of models simultaneously, with an evolutionary programming capability determining not only winners and losers, but a winner optimized against the chosen metric for a specific moment in time. A closed savings account may queue a default action barring competing behaviors, but if the owner of the closed account immediately follows it up with a call to customer service to open a new account with a better interest rate, a model will incorporate changes to the data into subsequent production models. New data may be at the customer level, but a change that influences churn rates may also be at the enterprise level. A telecom company with an increase in the number of internet outages may see a significant uptick in churn signals, which self-training models will account for in an updated prediction calculus. AML guarantees that a brand is always in sync with customer intent, and is analyzing churn signals that are precisely aligned with the customer at an exact moment of a customer journey. Because the [Redpoint rg1 platform](https://www.redpointglobal.com/one-platform/) combines AML with a real-time decisioning engine, a brand is empowered to react to churn signals in real time – or at the exact moment an action will optimally drive the intended behavior. Intelligent orchestration capabilities enable brands to react to churn signals in real time at scale, for any number of customers, and for any combination or sequence of behaviors that may predict a single customer’s propensity to churn. In the Harris Poll referenced above, 63 percent of customers said that personalization is now a standard expectation. Asked to define what it means to them, 43 percent said it was a brand recognizing them as the same customer across every channel. A brand that does this well is already safeguarding against churn, but to deliver a personalized omnichannel CX without giving churn its proper due is to risk alienating an existing customer who might otherwise receive a seamless – and frictionless – experience throughout a journey. A perfectly timed, AML-optimized retention outreach that stops churn in its tracks is another arrow in a marketer’s quiver to deliver a superior experience. ## **Related Content** [Retention Marketing for the Digital-First Customer: Make an Impression with a Golden Record](https://www.redpointglobal.com/blog/retention-marketing-for-the-digital-first-customer-make-an-impression-with-a-golden-record/) [To Optimize Revenue Growth, Tune in to Customer Lifetime Value](https://www.redpointglobal.com/blog/to-optimize-revenue-growth-tune-in-to-customer-lifetime-value/) [Algorithmic Optimization and the Magic of AML](https://www.redpointglobal.com/blog/algorithmic-optimization-and-the-magic-of-aml/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* --- ### [A Bountiful Harvest: Farm Market iD Drives Better Agribusiness Decision-Making with Redpoint](https://www.redpointglobal.com/blog/a-bountiful-harvest-farm-market-id-drives-better-agribusiness-decision-making-with-redpoint/) **Published:** June 10, 2021 **Author:** Redpoint Global **Content:** Each year the US agriculture market contributes nearly $1 trillion in economic value to the economy. Even as the industry is being asked to produce more food, it is also faced with less available farmland, increasingly volatile weather, government regulations, trade restrictions and modulating commodity prices. These challenges have given rise to data-driven farm and agribusiness decision making including precision agriculture, a site-specific crop management approach that employs historical, observational and real-time data to optimize field yields, productivity and profitability. Farm Market iD, which is a brand of DTN, provides innovative data and analytics specializing in detailed, accurate, fact-based information and services for the US agriculture market. [Farm Market iD](https://www.farmmarketid.com/) manages trillions of data points covering farms and farmers at the field level, including variables such as soils, crop and real-time satellite imagery and weather conditions. Its proprietary, fact-based database aggregates government records and other proprietary data sources, covering 900 million acres of farmland and nearly 1.5 billion head of livestock. Processing this data at tremendous speed, Farm Market iD creates strategic go-to-market insights for its agribusiness customers, including crop protection, nutrient, seed, farm equipment, financial services and direct marketing companies. The company also offers trend analysis and enhanced marketing capabilities used for everything from research and development to sales strategies. ## **Going Deep with Data: No Soil Left Unturned** To deliver the kind of detailed intelligence its agribusiness customers depend on, Farm Market iD compiles data going back more than 20 years covering more than 30 million farm fields, comprising over 900 million acres. To maximize productivity and profitability, large amounts of disparate data are used to analyze farms, fields, soil and crops at ¼ acre increments for over 900 million acres. In many cases, applying data science on small areas – which may require different types of seed, irrigation, nutrients, etc. – often doubles a farm’s output. Other data sources include real estate data, behavioral-based persona data, Gross Farm Income and crop rotation history. To integrate and process this data, Farm Market iD needed a platform that had the integration, automation, and processing power to manage big data at scale. Farm Market iD chose [Redpoint Data Management](https://www.redpointglobal.com/customer-data-management/master-data-management/) as its data platform, in part because during a proof of concept it processed address hygiene roughly 15 to 20 times faster than a competitor’s 2.5 million records per hour. Because of the Redpoint solution’s ability to process structured and unstructured data, Farm Market iD can integrate all types of data to deliver exceptional levels of information and insight to customers, such as determining crop identity based on infrared signatures from satellites. With Redpoint, Farm Market iD produces a greater depth of information for its agribusiness customers, such as the integration of crop data, field-level geospatial insights, real estate data, statistically modeled insights, consumer demographics, and more. With the ability of Redpoint Data Management to process structured and unstructured data, Farm Marked ID can “layer” all types of information on top of its databases to deliver an exceptional, unprecedented level of information and insight to customers. An imagery layer, for instance, determines crop identity and density based on an infrared signature from satellites. Climate information, elevation data, hailstorm and tornado tracking information are also collected. The solution also has deep capabilities for ETL, geospatial coding, persistent key management, golden record creation, and lights out automation at unparalleled processing speed and scalability. “A great value in Redpoint is time savings. The Redpoint solution allows me to have everything under one application umbrella. We never have to jump from one technology to another,” says Farm Market iD CEO Steve Rao. ## **Reaping New Productivity** The use of Redpoint, Rao says, has freed his team from data flow mechanics, leaving more time for strategy, creative ideation and product development. And because of its ease of use, scalability and performance, it has become a product development tool for the company, allowing it to experiment with new ideas based on client needs, and developing and delivering new products in record time. For example, a new alert system notifies clients – retailers, manufacturers, bankers, etc. – when a farm field changes hands, experiences a severe weather alert, or has experienced a vegetative anomaly, a system made possible by Redpoint’s processing data collected from nearly 900 million acres and 4 million contacts against a variety of timely events. Redpoint’s integrated capabilities, processing speeds and lights-out operations are helping Farm Market iD to use data to change the agriculture industry for the better. ## **Related Content** [Aubuchon Hardware Embraces Cloud-Based Personalization in Next-Gen Marketing Push](https://www.redpointglobal.com/blog/aubuchon-hardware-embraces-cloud-based-personalization-in-next-gen-marketing-push/) [Xanterra, Redpoint and Microsoft: A Piece of “Magic”](https://www.redpointglobal.com/blog/xanterra-redpoint-and-microsoft-a-piece-of-magic/) [Redpoint Global and Lucerna Health Join Forces to Advance Consumerism in Healthcare for Payers and Providers](https://www.redpointglobal.com/blog/redpoint-global-and-lucerna-health-join-forces-to-advance-consumerism-in-healthcare-for-payers-and-providers/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Anonymous to Known, Data Quality, Master Data Management --- ### [Data Lake vs. Data Swamp: Why Enterprise-Class Businesses Can’t Afford Bad Data](https://www.redpointglobal.com/blog/data-lake-vs-data-swamp-why-enterprise-class-businesses-cant-afford-bad-data/) **Published:** May 3, 2021 **Author:** Redpoint Global **Content:** To meet the expectations of the always-on, connected customer for a relevant experience at the precise moment of every interaction requires brands to move at the cadence of the customer. Until recently, when customer journeys were linear, sequential and consisted of a limited number of channels, a basic customer data platform (CDP) or data lake could sufficiently handle the data needs to deliver rudimentary personalization. The same is not true today. Dynamic, omnichannel customer journeys and rapidly changing consumer behaviors far outpace the abilities of basic [CDPs or data lakes](https://www.redpointglobal.com/blog/enterprise-class-businesses-cant-afford-bad-data/) to deliver highly relevant experiences – in real time – at scale. Marketers who need easy access to large data streams may need to rethink their current data practices, many of which likely rest on antiquated notions that a code-centric approach to data warehousing, in which large amounts of data pool into a data lake and ‘stagnate’ – creating inertia that resists large amounts of daily transactional updates because the data must be processed using coded ETL’s as with Databricks. Furthermore, a lack of governance tools and a failure of embedding data quality into the process at the point of ingestion leaves marketers with data unfit for purpose – resulting in poorly executed identity resolution, minimal behavior information and a dearth of transformations (e.g., a year-over-year change in spend). The problem, of course, is that poor data quality inhibits the ability of the enterprise to deliver the personalized experiences customers expect in today’s real-time world. Consider a recent Dynata survey commissioned by Redpoint, where [70 percent of customers](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/) said they will only shop with brands that demonstrate a personal understanding of them. This means the brand knows they are the same customer across all channels and is able to deliver a highly relevant experience at the moment of interaction. ## **Data Lake or Data Swamp?** Meeting this expectation requires access to an enterprise-class dynamic data repository that continuously refreshes and links the totality of an organization’s customer data in a central hub with a single point of operational control: a robust, dynamic CDP. Knowing the key differences between a data lake and a dynamic CDP will help marketers understand why the latter is often the No. 1 revenue-generating solution of the enterprise, and why high-engagement brands in industries such as retail, financial services, telco, travel and hospitality, and healthcare are choosing the Redpoint [rg1 customer experience platform](https://www.redpointglobal.com/one-platform/) as their no-code, data management platform to manage the brand experience. One of the biggest limitations of a data lake and other highly code-dependent systems is the inability to scale – providing personalized experiences for tens or hundreds of millions of customers. By itself, this makes a data lake entirely inadequate for enterprise-wide adoption and CX use cases. It also limits the power of AI and machine learning to provide differentiated experiences; offline, coded models go stale over time. Data scientists build models that, once in production, become outdated as soon as there is new data or as soon as the business decides to optimize a different metric. Because transformations are pushed downstream, marketers must build their own aggregate values, derived attributes and other critical information. And with these data values driving segmentation decisions, audience selections, campaign triggers and real-time personalization decisions, missing it, doing it wrong or inconsistently strips data of much of its value and suboptimizes the overall return on investment. ## **Client Side vs. Server Side** A robust, dynamic CDP also solves for the problem in the marketing cloud world where an enterprise’s technology stack is composed of tools that were standalone packages and acquired and integrated over time. This introduces the potential for errors, data latency, and complexity for marketing programs that need to work across the marketing cloud products. Similarly, many of the [client-side personalization](https://www.redpointglobal.com/blog/real-time-website-personalization-client-side-vs-server-side/) tools that enable building web-based customer profiles may use an enormous volume of web behavior to drive personalization but lack access to other data sources that provide a complete picture of a customer. As a result, users need to take a Lego approach and link building blocks of tools that provide access to other data. The Lego approach isn’t inherently bad; it’s just not scalable nor able to deliver repeatable results. Plus, individuals in the process might think they’re doing a great job—especially because many of the success metrics in that approach are operationally focused versus focused on marketing outcomes or ROI. But it’s virtually impossible to tell if, for example, one marketer’s A/B tests are advancing the overall marketing cause and improving return on investment. This situation conceals the strategic shortcomings hidden behind the technical limitations of the environment and the busy work of employees doing jobs that should have been automated. The Lego approach also creates a significant data security problem, starting all the way back at the data lake and through to the front of the website. Understanding customers is very difficult, for instance, so learning about them over time to improve the relevancy of communications and offers becomes nearly impossible. An enterprise-class CDP can resolve this because it can handle front- and backend data for personalization. ## **Embrace the Data Challenge** The Lego approach is fine for businesses sending out occasional email blasts and doing light personalization on their website. It enables companies to start in limited ways and grow their way to a larger capability. However, ambitious marketers need enterprise-class software for marketing. So, what does enterprise-grade marketing software look like? It embraces the data challenge and provides the appropriate level of precision processing automation, cleansing, matching, de-duplication, governance and data mastering needed to know everything that is knowable about the customer and preparing it for use within seconds of its arrival. It enables companies to build a complete [Golden Record](https://www.redpointglobal.com/blog/what-is-a-golden-record/) that links together all the proxy identities for each possible customer—even unknown customers—and provides a robust long-tail of transactional information that includes everything from granular behavior to KPIs to transformations summaries; everything that is needed to know the customer and properly treat and message the customer with exceptional relevance. In addition, all that data needs to be ingested and processed, and those Golden Records updated, in milliseconds. The result is a marketing data store that has a complete contact graph and an extensive data story that is valid and current up to the millisecond. Nothing short of this level of data perfection is suitable for a large enterprise or ambitious marketing leader who wants to make a boardroom level impact in the organization. Most important, an enterprise-class customer data management platform creates a single-brain approach (i.e., one point of operational control for all channels and messages). It eliminates fragmented communication that goes out based on local brains (distributed in the channels) that only have a local scope of data. Instead, you can control the utilization and orchestration of all channels, easily selecting the optimal messages, timing, and frequency from that single point of operational control. It enables marketers to strategically engineer a holistic engagement journey for individual customers at scale. Think of it from the point of view of a customer. Would you prefer to speak with a committee of representatives where each member has its own style, agenda or cadence or would you rather speak with just one person, consistently over your journey through the brand? In which case would there be a better understanding and intimacy? Which case best reflects the commitment to honoring a first party relationship? That is the difference between marketing with fragmented channels vs. the single-brain approach. Enterprise-class organizations that adopt a robust customer data processing platform are leading the way with an exceptional level of personalized customer engagement. The predictable data security and data perfection of the rg1 platform, for example, enables large enterprises to engineer their customer experiences down to the millisecond – creating the most impactful moment of truth for each and every customer, and at scale. That level of relevance is what customers expect today—and is one of the few points of true differentiation left to build a lasting competitive edge. ## **Related Content** [Embrace Complexity: That’s Where the Magic Happens](https://www.redpointglobal.com/blog/embrace-complexity-thats-where-the-magic-happens/) [Customer Journeys are Dynamic: Your Engagement Technology Should be as Well](https://www.redpointglobal.com/blog/customer-journeys-are-dynamic-your-engagement-technology-should-be-as-well/) [The Sky is the Limit with AI and Machine Learning: Capitalize on the Moment of Interaction](https://www.redpointglobal.com/blog/the-sky-is-the-limit-with-ai-and-machine-learning-capitalize-on-the-moment-of-interaction/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Anonymous to Known, Customer Data Platform, Data Quality, Identity Resolution, Real-Time Personalization --- ### [Tame the Daunting Data Overload with Cognitive Personalization](https://www.redpointglobal.com/blog/tame-the-daunting-data-overload-with-cognitive-personalization/) **Published:** April 27, 2021 **Author:** Rob Fuller **Content:** *Editor’s Note: This is a contributed guest blog from Accenture, a Redpoint partner* The National Retail Federation (NRF) predicts a rosy 2021. In March, it [forecast a year-over-year retail sales growth](https://nrf.com/media-center/press-releases/2021-could-see-record-retail-sales-growth-economy-still-hinges) between 6.5 and 8.2 percent – likely to top 2020’s record growth of 6.7 percent. Online shopping, which increased nearly 22 percent in 2020, is projected to rise between 18 and 23 percent. This forecast was made prior to the newest $1.9 trillion economic stimulus package. With about half of all U.S. adults now vaccinated against COVID-19, businesses hiring, and Americans eager to put the pandemic in the rear view, it seems as if whatever will pass as the “new normal” is on our doorstep. “There will be extraordinary spending. I don’t know how else to put it,” says [NRF chief economist Jack Kleinhenz](https://www.bostonglobe.com/2021/04/18/business/there-will-be-extraordinary-spending-retailers-anticipate-post-pandemic-consumption-boom/). While pent-up consumer demand is a great, retailers must be aware that behaviors have significantly changed, ecommerce adoption has accelerated, and consumers show preference for digital-first, contact-less journeys. These changes in consumer behavior set the expectation that winning share of that pent-up demand is going to take more than just re-opening a shuttered store. Rather, it will take connecting with consistently relevant messages and empathy with how an individual consumer will engage going forward. Retailers must offer more dynamic and unpredictable customer journeys. A daunting data overload in this new environment makes it more and more difficult for marketers to duplicate the traditional shopkeeper experience, where a local proprietor of a mom-and-pop store knew every customer by name and used the familiarity to create a perfect experience every time. ## **A Shopkeeper View Starts with Data** Getting data right (ideally getting to perfect data as Redpoint Global describes it) helps brands recreate the traditional mom-and-pop experience, but there are hurdles to clear before achieving the deep, personal understanding needed to create relevant interactions at scale for tens or hundreds of millions of customers. To truly understand an individual customer journey, data must be tracked, it must be current, and it must be mapped to actual purpose. And to deliver the right customer experience at the right moment, marketers need to be confident that they have the right data, which means that they must first have a sense for what it is they’re actually trying to create. Once the source of the data becomes abstracted, it becomes an enormous challenge for marketers to determine whether the data is fit for purpose – that it truly means what marketers may think it means and that it is representative of the moment they’re trying to create. Marketers also must contend with data governance, and the need to align with brand and corporate objectives. Data overload issues make it hard for marketers to plan a campaign, decide what they need and what will matter in terms of creating the perfect moment for a customer. To discern meaning to the customer data that’s available, marketers often request access to all the data, which adds to the complexity. The better course of action is to decipher the data that is already on hand, figuring out key indicators of purpose and what’s relevant for an individual customer. This is where machine learning comes into play, as the key requirement to tame data overload complexities, filling both reporting and transactional needs. ## **Making Sense of Data with Machine Learning** Machine learning detects patterns that humans can’t detect, and it enables the personalization of experiences at scale. By continually learning and improving, it optimizes existing touchpoints, and the touchpoints marketers are trying to create. From a reporting standpoint, by providing insight into which experiences provide value and where there are gaps in experience, machine learning can then predict what content aspects will resonate the most and make predictions for what additional communications are needed – creating continual understanding and optimization of the customer journey. To advance maturity level with machine learning and other advanced technologies, brands must start thinking about content in terms of data. To better predict customer intent, for example, analysis should include all customer behaviors; details such as which images or words on a page a customer engaged may be important indicators. Message analysis must move beyond basic segmentation analytics to actually understand how a specific message resonates with a customer – not just on a channel basis, but throughout the entire customer journey. Process, data and channel siloes are the main barriers to reaching this state of maturity, but once brands start to think about this maturity model, they will quickly recognize the need to form a single view of the customer. A single customer view is the foundation for cognitive personalization, which bases content on individual behavior; it is messaging not tied to a moment or to a channel, but rather derived from intelligent automation that is fully aligned with and possessed of a deep understanding of a customer’s purpose. Perfect customer data replicates the old-school shopkeeper view, except with machine learning we’re able to do it at scale for the always-on, omnichannel consumer. ## **A CX Partnership that Delivers** To reach a level of sophistication that aligns with customer expectations for consistent and dynamic customer journeys, the right technology platform critical. Accenture Interactive has partnered with Redpoint because the company’s [rg1 platform](https://www.redpointglobal.com/one-platform/) uniquely delivers the level of perfect data integration needed to support the different types of communications and the marketing maturity model I alluded to. Working with Redpoint, we’ve started to help clients layer existing, static experiences with innovative cognitive capabilities that optimize the experience for the customer – and ultimately strengthen loyalty for the brand. The capabilities of a platform like Redpoint allow us to build the types of experiences that are generally too complex and costly to do via traditional approaches. Traditional approaches with heavy data integration needs, even including integrating offline machine learning and analytics, can provide some of the same results but are more costly, rigid, and most importantly not available in the moment of customer interaction. Timing is mission critical in CX. The integrated platform’s inline, automated machine learning and real-time decisioning enables us to do things in the cadence of the customer and help bring up the overall marketing maturity level faster. Delivering a personalized customer experience through an understanding of a customer and optimizing interactions used to be a differentiator that drove loyalty and lift. Now, it’s survival, particularly with the dramatic online shift and other pandemic-related repercussions. The new normal is here, and it demands a new approach. Cognitive personalization through [machine learning for marketing](https://www.redpointglobal.com/machine-learning) gives marketers the confidence that every experience delivered is as close to perfect as possible. The confidence stems from perfect data. ## **Related Content** [What is Automated Machine Learning?](https://www.redpointglobal.com/blog/what-is-automated-machine-learning/) [Algorithmic Optimization and the Magic of AML](https://www.redpointglobal.com/blog/algorithmic-optimization-and-the-magic-of-aml/) [Humanizing a Customer Experience without Humans: Let Machine Learning Take Control](https://www.redpointglobal.com/blog/humanizing-a-customer-experience-without-humans-let-machine-learning-take-control/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Journey Orchestration, Real-Time Personalization, Segmentation & Activation --- ### [There are No Third-Party Shortcuts to Understanding Customers](https://www.redpointglobal.com/blog/there-are-no-third-party-shortcuts-to-understanding-customers/) **Published:** April 15, 2021 **Author:** Dale Renner **Content:** The landscape for how marketers engage with customers is undergoing change at a pace unrivaled since the dawn of the internet. That pace is about to quicken, as third-party cookies become a relic of the past – a shortcut for marketers that has been obstructed to the point they are no longer a viable path to revenue. Marketers are now forced to gain an understanding of customers through methods outside of third-party cookies due to a combination of consumer fatigue, increasingly stringent CCPA and GDPR data regulations, and tech platforms such as Google and Apple steering clear of universal identifiers. There is a palpable fear that sweeping change will make it more difficult to engage digitally with relevant content, which is why prospective replacements for the third-party cookie are garnering so much interest, and why we’re seeing some brands get creative with opt-in requests. To date, the knee-jerk response to uncertainty has been brands and advertisers trying to stay a step ahead of increasing privacy demands by essentially doing the same things – only differently. In many ways, the analogy of re-arranging the deck chairs on the Titanic holds true. The focus is misplaced. Marketers’ eyes may be on the right prize – a relevant customer experience – but the preparation is, in many cases, largely taken from the standpoint of what’s good for the brand rather than the customer. But replacement methods and back-door tracking attempts miss the key point that changes are on the horizon precisely because consumers have made it clear that many of the traditional methods of engagement are no longer acceptable. They are declaring “enough is enough.” ## **Customers Want Brands to Be Relevant** Rather than finding a new way to track an anonymous person across the internet, brands will be far better served focusing on delivering sustained relevance across an entire customer journey. Persistent relevance – a hyper-personalized action that delights a customer at the moment of interaction – comes from building a relationship with a customer that is built on trust. Trust, as I’ve [written before](https://www.redpointglobal.com/blog/the-elevation-of-experience-why-a-deep-customer-understanding-matters-even-more/), is integral to brand promise and is manifested by the experience a customer receives that demonstrates a brand’s deep understanding of each customer. A customer who believes a brand recognizes and values them individually responds with loyalty. In a recent Dynata Research study, commissioned by Redpoint, [70 percent of customers](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/) said that they will only shop with brands that personally understand them. Customers measure value by the relevance of the experience a brand delivers. Achieving and persisting relevance is dependent on a deep understanding of the individual. The key ingredient – perfect data. In return for a superior personalized experience, customers say they are willing to share more about their needs, preferences and behaviors. In a [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/), 54 percent of consumer said they are willing to share personal data for a more personalized experience – with conditions. Namely, 74 percent say it’s important or essential for a brand to share how their personal data is being collected and used. The use of third-party cookies or alternate identifiers simply fail to meet this standard. ## **Relevance Begins with First-Party Data** The key to delivering superior customer experiences is creating, maintaining and effectively utilizing perfect data about each customer. The fundamental building block of perfect data is first-party data. Everything else is just a form of proxy for trying to identify and understand the customer. The notion of elevating first-party customer data may seem intuitive, but it still poses a challenge to many organizations. According to [Gartner](https://www.gartner.com/en/documents/3993169/the-future-of-data-and-analytics-reengineering-the-decis), a lack of the right type of data and poor data quality are the top two data and analysis challenges that prevent organizations from making data-informed decisions, cited by 31 percent and 28 percent of respondents, respectively, as “very challenging.” ## **What Does It All Mean?** A lack of the right type of data is an indictment on the misguided reliance on third-party cookies. Third-party cookies never were anything more than a way to *buy* access to a consumer segment with little attention paid to sustaining relevance. Advertisers may continue to scramble to find new ways to track a customer’s digital footprint and make claims about privacy enhancements along the way, but replacement methods created for the benefit of an advertiser or brand miss the point – and customers take notice. In sharp contrast, first-party data provides the foundation for having perfect data about each person, giving brands the insight to *earn* that customer and persist a relationship. Proper use of first-party customer data reinforces the deepening value exchange and underpins a brand’s ability to use superior experience delivery as a strategic asset and revenue-generating capability. ## **Related Content** [The Elevation of Experience: Why a Deep Customer Understanding Matters Even More](https://www.redpointglobal.com/blog/the-elevation-of-experience-why-a-deep-customer-understanding-matters-even-more/) [Feelings Meet Facts: Infusing Customer Experience with Meaning will Define 2021](https://www.redpointglobal.com/blog/feelings-meet-facts-infusing-customer-experience-with-meaning-will-define-2021/) [Getting Started with Personalization: Include “Unknown” Visitors for Website Personalization](https://www.redpointglobal.com/blog/getting-started-with-personalization-include-unknown-visitors-for-website-personalization/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Anonymous to Known, Data Quality, Real-Time Personalization, Segmentation & Activation --- ### [The Role of a CDP in Multi-Touch Attribution](https://www.redpointglobal.com/blog/the-role-of-a-cdp-in-multi-touch-attribution/) **Published:** April 7, 2021 **Author:** Steve Zisk **Content:** Multi-touch campaign attribution is an imperfect solution to a vexing problem. Though perhaps better than simpler attribution methods, multi-touch attribution by virtue of its over-the-shoulder rear view will never match, with 100 percent accuracy, an action to a reaction. Some form of multi-touch attribution is needed to understand increasingly complex, multi-touch, multi-channel customer journeys. But marketers are far from measuring and improving multi-touch campaigns in an optimal way. Dynamic customer journeys lay bare the shortcomings of first-touch, last-touch, evenly weighted distribution or other traditional attribution methods, which create incentives for doing multi-touch attribution. First, traditional methods use haphazard associations. If a customer makes a purchase on a website directed from a Google search, for example, a last-touch attribution model may not acknowledge or credit the influence of the ad the customer had engaged with on YouTube earlier that day, or that two days prior the customer had browsed the same product on a branded website. Likewise, the seemingly randomized associations may skew the cause-and-effect decisions further downstream, from channels and campaigns to offers, messages, content and any number of factors. When marketing dollars are tied to these factors, which they eventually will be, it is easy to see how marketers end up increasing chaos or contention in their allocations vs. approaching performance marketing. In fixing multi-touch attribution, marketers have the opportunity for a more accurate representation of customer journeys and customer response to interactions, one that measures the effectiveness of a campaign or channel with a higher degree of certainty. ## **Post Hoc Fallacy** Before examining how a [customer data platform (CDP)](https://www.redpointglobal.com/one-platform/) can help with multi-touch attribution, we should recognize some difficulties with multi-touch attribution that are inherent in the “rear-view mirror” approach of trying to understand the causes of a customer action that’s already happened. First, multi-touch attribution is subject to a logical fallacy: Since Y follows X, Y must therefore have been caused by X (in Latin: *post hoc ergo propter hoc*). If a consumer purchases an outfit after seeing an ad, it may be easy to assume that seeing the ad triggered the purchase. But perhaps the buyer’s daughter, thinking her mother would like the outfit, put it in an online shopping cart and shared the link. The “recorded and measured” sequence of events does not in and of itself show the *reasons* for the purchase. Another limitation of multi-touch attribution is that an allocation across touchpoints may fail to account for multiple touches within a specified allocation. A brand that attributes 70 cents of each purchase dollar to an advertising campaign and 30 cents to a direct mail piece may decide to boost spending on advertisement. But if a campaign consists of multiple advertisements, perhaps one is effective, and the others are instead driving customers away. Having a clear understanding that multi-touch attribution is not a perfect solution may enable marketers to better appreciate the broader picture, viewing multi-touch attribution as a vehicle for (and a product of) experimentation and optimization rather than simply for resource allocation. ## **Dive into Experimentation, Optimization** A website product recommendation engine offers a good example for how multi-touch attribution should be used in an experimental context. A rudimentary engine may limit offers to best-sellers, regardless of a customer’s behaviors or preferences. Machine learning models that create special offers for thousands of audiences may result in higher sales, but a data-driven brand will still likely do an enormous amount of a/b testing in order to assess a new recommendation engine’s effectiveness. Similarly, with the advertising example, using multi-touch attribution as a vehicle for testing and optimization may provide a good view of the root causes that contributed to a customer’s action. Rather than a straight-up resource allocation (70/30 advertising vs. direct mail), this broader use of multi-touch may help explain why customer sentiment moves in a particular direction. ## **A CDP & Multi-Touch Attribution Software To The Rescue** Using multi-touch attribution in an experimental/optimization framework is where a CDP comes into play, as long as that CDP offers an experimental framework. Understanding at a basic level if one channel works better than another is a start, but vetting a calculation to understand why it works better, to consider and adopt or discard external forces and variables – that magnifies the power of multi-touch attribution. The Redpoint rg1 customer experience platform provides such an experimental framework to enable marketers to easily set and test an endless number of combinations (messages, offers, channels, business rules), foregoing assumptions by accurately pinning down the entire customer journey. rg1 provides three core capabilities to optimize multi-touch attribution. First, it includes tools for [accurately matching data](https://www.redpointglobal.com/customer-data-management) from anonymous interactions to data from known interactions, and accurately matching offline and non-digital activities with online, digital activities for a more robust picture of every customer interaction throughout an unknown to known journey. Providing complete, accurate, relevant and timely information is an important first step in doing attribution calculations. Second, multi-touch attribution is only as good as a CDPs ability to present cleansed, accurate, normalized, time-stamped and relevant data to the [analytics framework](https://www.redpointglobal.com/machine-learning) – within the CDP or as a separate capability – that does the multi-touch calculations. This is critical to allow for broad experimentation, such as running multiple attribution models simultaneously to truly understand customer behaviors. The third important capability of rg1 is that it collects and shares attribution results. Making attribution calculations, after all, is only half the battle. The other half is making the information available and putting it into the hands of the people who are going to do something with it. If, for example, a business is tracking KPIs based on attribution in a management system, it’s necessary to make the underlying data and the attribution calculations available to the management system at the cadence the system requires to operate. Likewise, when the CMO sees attribution figures that show the success of particular channels, the CMO should have confidence that the dashboard is accurate, up-to-date, and provides the sources and calculations for the attribution. In summary, to engage in multi-touch attribution a [customer data platform cdp](https://www.redpointglobal.com/customer-data-platform) should be able to bring all the data together, make it available to the appropriate analytical engine and calculations being used, and make both the underlying data and the attribution calculations available to the system of record for tracking attribution. ## **With Change Comes Opportunity** Multi-touch attribution is a timely subject because of the phasing out of [third-party cookie](https://www.redpointglobal.com/blog/the-demise-of-the-third-party-cookie-opens-new-methods-of-engagement/)s by Firefox, Safari and – next year – Google. These have been a significant source of attribution for many years for measuring advertising effectiveness, and both marketers and Ad Tech and Martech vendors are scrambling to find replacements or change practices. The demise of the tracking cookie presents opportunity for marketers to take a fresh look at [multi touch attribution tools](https://www.redpointglobal.com/blog/the-role-of-a-cdp-in-multi-touch-attribution/). Rather than just reallocate attribution in a haphazard manner, an examination of the underlying goals for multi-touch attribution may convince an organization of the advantages of adopting an experimental/optimization approach. The right CDP can help. ## **Related Content** [Crocodile Tears: Do Not Lament the “Extinction” of Third-Party Cookies](https://www.redpointglobal.com/blog/crocodile-tears-do-not-lament-the-extinction-of-the-third-party-cookie/) [How to Avoid Data Distortion & Machine Learning Bias](https://www.redpointglobal.com/blog/how-to-avoid-data-distortion-machine-learning-bias/) [Five Reasons You Should Embrace Cross-Channel Marketing](https://www.redpointglobal.com/blog/five-key-reasons-embrace-cross-channel-marketing/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Journey Orchestration, Segmentation & Activation --- ### [Redpoint Global Product Updates: Simplicity, Ease of Use Highlight New Release’s Intelligent Orchestration Capabilities](https://www.redpointglobal.com/blog/redpoint-global-product-updates-simplicity-ease-of-use-highlight-new-releases-intelligent-orchestration-capabilities/) **Published:** March 4, 2021 **Author:** Redpoint Global **Content:** The saying that “it is always the simple that produces the marvelous” is attributed to 19th century British novelist Amelia Barr. The concept underscores many of the enhancements to two of the three components ­­– [Intelligent Orchestration](https://www.redpointglobal.com/orchestration) and [Automated Machine Learning (AML)](https://www.redpointglobal.com/machine-learning) – of the rg1 platform released today. This blog will capture many of the enhancements that will continue, now and in the future, to make things easier, cleaner and faster for both novice and power users of Redpoint. ## **Rules Designer** The Rules Designer enables marketers to define audiences and segments, customize messages by content and channel, and respond to external situations and triggers. This release represents a major UI and functional overhaul to improve ease of use (**see Figure 1**) including a structured, dashboard-style approach that supports simple and comprehensive controls, with one-click functionality to add new criteria or lists, and choose images and other content. Users can also easily bring up visualizations to show how rules interact with one another and drill down to additional views and details. ![New Rules Designer in rgOne](https://www.redpointglobal.com/wp-content/uploads/2021/03/RPI-6.2-Enhancements-Workspace-300x155.jpg)**Figure 1:** New Rules Designer in rg1. An intuitive UI centralizes rules creation in the middle of the page, with other modern touches that make it easier for users to quickly search and drill down into different rules and attributions. ## **Data Connectors** Enhancements to Redpoint’s data connectors similarly stress ease of use, while promoting business value with simple data output for channel teams and orchestration. Simple data extracts are a key piece of the improved functionality, which allows customer teams to maintain existing processes with teams using assets and processes in channel-specific applications, relying on [rg1](https://www.redpointglobal.com/one-platform/) as the central hub for customer data and segmentation. This additional open garden flexibility allows organizations to keep existing teams and processes in place with a future option to migrate to cross-channel teams. Highlights of data extract functionality include simple single-form data connector campaigns to push data to a certain channel. In the web or in the native client, the [data connectors feature](https://www.redpointglobal.com/orchestration/interaction-connections) facilitates a two-click data export process – choose the data and its target. ## **Clustering Enhancements** The new [clustering enhancements](https://www.redpointglobal.com/orchestration/segmentation) continue the simplicity theme, extending the clustered audience capabilities introduced in rg1 in September. Clustered Audiences enable marketers to leverage machine learning to automatically identify customer segments – based on common characteristics – from within a larger list of people. This release adds decision trees (**See Figure 2**) and a word clouds feature that will make it easier for rg1 users to understand and analyze audience clusters. ![AML Clustering Decision Tree in rgOne](https://www.redpointglobal.com/wp-content/uploads/2021/03/RPI-6.2-Enhancements-AML-decision-tree-300x237.jpg)**Figure 2:** An AML Clustering decision tree showing a simple visualization for which attributes are factored in clustering decisions. Adding ways to analyze audience clusters was an enhancement made from direct user feedback; providing more detail than what’s usually represented in a dashboard gives users more insight into how machine learning is making decisions and grouping audiences together – how it determines the when, where and why of any decision and how the data is filtered. It is essentially a tool to help marketers validate that a decision will be useful to their business goals. ## **Real**–**Time Enhancements** Additional performance enhancements to real-time [data orchestration](https://www.redpointglobal.com/blog/no-data-left-behind-the-importance-of-a-closed-loop-cycle-for-intelligent-data-orchestration/) provide users more control over the balance between interaction latency and server /cloud costs. Additional flexibility ensures that a company pays only for the real-time performance it needs. In-event processing and tuning, the elimination of network latency for co-located components, and functionality to meet tight SLAs for proofs of concept and production were all addressed in the today’s release to make it easier for marketers to draw a clearer distinction between cost and performance. ## **Email Service Providers** The big news for ESP support in the release comes with the addition of Luxsci®, a HIPPA-compliant provider – a mandatory consideration for healthcare organizations wanting to go beyond simple marketing messages to interact with patients for care management, prescription compliance, and other highly personalized communications. The addition of Luxsci® continues the expansion of ESP integrations within the rg1 platform. ## **Data Management Pipelines** Lastly, the new [Data Manager](https://www.redpointglobal.com/machine-learning/file-management/) in the AML component of rg1 simplifies sourcing data for training or running models. AML users can now utilize a simple form to create a data pipeline for training AML projects from any database available within the third component of the rg1 platform, [Customer Data Management](https://www.redpointglobal.com/customer-data-management). AML inherits the complete set of database connectors as well as enterprise controls for security, availability, single sign-on, failover and other items and functionality. And by using the Customer Data Management connectors, AML will automatically be able to connect to new data sources based on future updates. Allowing AML to take advantage of everything that Customer Data Management can do with data directly – as well as use cleansed and prepped data from any source – will streamline workflows and reduce the time spent on data prep by data scientists and engineers. ## **Related Content** [The New Birds of a Feather: Real-Time, Dynamic Audience Selection with Automated Machine Learning](https://www.redpointglobal.com/blog/new-birds-of-a-feather-real-time-dynamic-audience-selection/) [The Sky is the Limit with AI and Machine Learning: Capitalize on the Moment of Interaction](https://www.redpointglobal.com/blog/the-sky-is-the-limit-with-ai-and-machine-learning-capitalize-on-the-moment-of-interaction/) [Harness the Power of Digital Technology as a Revenue-Driving Engine](https://www.redpointglobal.com/blog/harness-the-power-of-digital-technology-as-a-revenue-driving-engine/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Real-Time Personalization, Segmentation & Activation --- ### [The Elevation of Experience: Why a Deep Customer Understanding Matters Even More](https://www.redpointglobal.com/blog/the-elevation-of-experience-why-a-deep-customer-understanding-matters-even-more/) **Published:** February 24, 2021 **Author:** Dale Renner **Content:** With the tremendous amount of change over the past year that has altered the way people live, work, shop and socialize, there has been a lot of discussion about customer experience. There is now, I would argue, consensus that elevating customer experience as a central component of a brand’s promise is a vital competitive differentiator. But what do we mean by “experience,” specifically as it pertains to how a customer interacts with a brand? At the widest view, experience encompasses the full value exchange between a customer and a brand, yet far too many companies approach customer experience as simply a set of interactions necessary to deliver a product. A look at the scale of changing consumer behaviors may help understand why experience needs to be thought of in a new light. According to an October [survey from McKinsey](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-global-view-of-how-consumer-behavior-is-changing-amid-covid-19), the only activities that more than two-thirds of US consumers planned to engage in over the next two weeks – or presumably until a return to “normal” – were shopping for groceries/essentials and spending time with family. Activities such as working outside the home, air travel, going to the gym, eating at a restaurant and using public transportation/ride share all polled at less than 40 percent. ## **Transforming the Experience** Just on a surface level, the simple fact that people are having so many fewer in-person experiences is making the ones we do have more valuable. Before engaging in an activity, we now go through a mental checklist of pros and cons. If I choose a non-digital activity, how safe is it? Is a digital experience adding value or should I just go for wherever is cheapest or can deliver fastest? More than ever, consumers put an onus on the brand to prove that it – the brand – is worthy of engaging with us. When viewed in this light, it becomes clear that a personalized customer experience that matches the expectations of today’s always-on consumer is less about knowing their name, knowing which channel they prefer or even selling them a product than it is about establishing a deep understanding of what matters to the customer. Not that any of those things are inconsequential, but the value a customer extracts from their relationship with a brand goes deeper. Why, the customer asks, do you deserve to engage with me? In a recent survey that Redpoint conducted with Dynata, consumers offered that an understanding of who they were – what made them tick – was most important. In the survey, [70 percent of consumers](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/) said that they planned to shop exclusively with brands that personally understand them. Think of that; they didn’t mention price or availability. Rather, a personal understanding – which goes deeper than a collection of personalized interactions. From the customer’s perspective, part of the understanding means that before deciding to engage with a brand, a customer must feel secure knowing that the brand shares their personal values or concerns around social, political or environmental issues. In [Generation (P)urpose](https://www.accenture.com/_acnmedia/PDF-117/Accenture-Generation-P-urpose-PoV.pdf#zoom=40), a 2020 report from Accenture on the importance for companies to bring clarity to their values and beliefs, 80 percent of consumers say that a brand’s purpose is at least as important as customer experience. ## **For a Superior CX, Start from the Outside-In** When purpose is considered as part of what it means to meet the needs and demands of an individual customer, it underscores the responsibility that brands have to understand that customer when delivering their brand promise. A brand may have the capability to deliver a superior experience, but if it breaks its brand promise – which could relate to a social issue, a privacy breach, ignoring a preference, etc. – a superior experience will be for naught. Said another way, brands have had an ability for a long time to engage with customers anywhere at any time. Ubiquitous engagement is not new, but what is new is the increasing customer expectation that engagements are meaningful, that they matter personally and that they reflect a deep understanding. The evolving expectations crystalize why the mindset that incremental efficiency gains to one type of engagement on one channel is outdated. A piecemeal approach takes the view that an engagement technology solution’s features and functions drive outcomes is entirely backward. In a new normal where customers expect a deep understanding to infuse every part of their relationship with a brand, the prevailing mindset must take an outward-in approach that starts with outcomes and works backward to a targeted set of experiences. Is the outcome to sell a product or is it really to form an almost unbreakable bond with the customer defined by shared values, trust and respect? If the latter, a brand will quickly realize the absurdity of trying to drive 10 percent efficiency gains in emails, as an example. Accenture discusses this new way to look at customer experience as the [“business of experience (BX)”](https://www.accenture.com/us-en/insights/interactive/business-of-experience?c=acn_glb_businessofexpergoogle_11656845&n=psgs_1120&gclid=EAIaIQobChMI2sfKk-nf7gIVDKCzCh2_5gH_EAAYAiAAEgKBSvD_BwE&gclsrc=aw.ds), which in the wake of the 2020 disruption takes customer experience from a marketing purview to the boardroom as mission-critical for driving profitable business growth. In their survey of business leaders, 77 percent of CEOs say that after the upending world events and drastic consumer shifts, they will now fundamentally change the way they engage and interact with customers. There has been an awakening, an enlightenment if you will, that the experience a customer derives from a brand is foundational for driving sustained profitable growth. It starts with re-examining everything from the customer’s perspective and values and then determining how best to deliver perfect experiences reflected in highly personalized and sustained engagement. A customer, after all, views a relationship with a brand not as a set of distinct interactions, but as one holistic experience that transcends all channels and marketing itself. While an outcome may differ for each customer, the foundation of the relationship is relevance. It is called the moment of truth – being precise at every moment of interaction and proving to the customer that you are deserving of their time, their attention and their business. ## **Related Content** [Digital Transformation Just Became Accelerated](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) [The New Reality Brings Digital Transformation to the Forefront](https://www.redpointglobal.com/blog/the-new-reality-brings-data-transformation-to-the-forefront/) [Feelings Meet Facts: Infusing Customer Experience with Meaning will Define 2021](https://www.redpointglobal.com/blog/feelings-meet-facts-infusing-customer-experience-with-meaning-will-define-2021/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Identity Resolution, Real-Time Personalization, Single Customer View --- ### [Brexit and Customer Experience: Coping with Chaos](https://www.redpointglobal.com/blog/brexit-and-customer-experience-coping-with-chaos/) **Published:** January 26, 2021 **Author:** Redpoint Global **Content:** Disruption and frustration are apt descriptors of the state of affairs in Britain. Another coronavirus lockdown together with the first month apart from the EU single market and customs union has made for a bumpy beginning to 2021. Both forces – the lockdown and the severing of economic integration with the EU – have contributed to a triple whammy of supply chain interruptions, rising costs and widespread confusion. On a brighter note, here at Redpoint, we’re fond of discussing the importance of a personalised customer experience, which is true in normal times. In fact, a recent survey Redpoint conducted with Dynata underscores this point, where [70 percent of consumers](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/) surveyed said they will shop exclusively with brands that personally understand them. But what does this have to do with Brexit and lockdown? Well, given that these are not normal times, it’s arguably more important than ever for brands to show customers that they are valued. By continuing to provide a seamless, personalised customer experience, companies can help to maintain a semblance of normalcy as Britain tries to right the ship. But there are some monumental hurdles. Many European companies have already announced that they will no longer ship goods to Britain because of the value-added tax (VAT), which for goods valued up to £135 is now collected at point of sale rather than point of import, making EU companies responsible for collecting taxes on behalf of the British government. An online [Dutch bike parts company](https://road.cc/content/news/dutch-bike-part-dealer-shipping-every-country-except-uk-279779), for example, has stated that the UK is now the only country in the world it will not ship to. And, bizarrely, not even Star Trek’s Captain Kirk can avoid it, with namesake [William Shatner tweeting](https://sports.yahoo.com/william-shatner-stop-merchandise-to-uk-122643199.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAJzN6a8kqVecna66TTHeV8ENBTeSoVLh2FzUjDCNzNEXkyBp23QPZfk-bKSXoZbbwcCQ57yJFp_rTqBFvIGjK0UKzWFzwa62L6JafwdFPxhcEbStqgQtIiaTq1fZcGa7awox2MD9qvIifz5TEJNub0wj0uoIn7VaOR0yExnvo14o) that the shatnerstore.com would have to reluctantly suspend orders to the UK. Conversely, British companies exporting to the EU must now certify the origin of all goods for finished products and all the parts used for assembly, thus creating a bureaucracy, tax and paperwork headache. The headache continues for European lovers of tea, with Fortnum & Mason citing “Brexit restrictions” as the reason it has suspended deliveries to the EU. Also, the 11th-hour [Christmas Eve deal](https://fortune.com/2020/12/24/after-years-of-negotiations-the-u-k-has-finally-reached-a-christmas-eve-trade-deal-with-the-eu/) does not cover services, which account for roughly 80 percent of British exports. ![](https://www.redpointglobal.com/wp-content/uploads/2021/01/shatner-tweet-300x192.jpg)William Shatner’s Oct. 16 tweets about the VAT costs that he said would prevent ShatnerStore.com from accepting UK orders. For more details, this [article in the Local](https://www.thelocal.com/20210107/the-small-ways-brexit-has-impacted-daily-lives-of-britons-in-europe) does a fine job detailing the chaos, which ranges from increased shipping costs and food shortages to fishery disputes and even interruption of entertainment streaming. ## **A New Look at Value** To complicate matters for brands that were trying to avoid overburdening customers is the fact they had little time to prepare, given that the deal was announced just one week prior to taking effect. The saving grace, is that consumers in Britain and the EU, knowing the situation, are likely willing to absolve brands of any culpability. They know that Britons are all in the same boat. To receive the benefit of the doubt, however, brands must be perfectly transparent about how they’re dealing with interruptions, cost increases and disruption. Customers are more likely to countenance higher prices, delays or stoppages if they are made aware, up front, about the impact. So to satisfy customer expectations for transparency, brands must shift their focus from the transactional to the intrinsic value of the customer relationship. By demonstrating an understanding and empathy for the customer’s pain points, brands can show that they care about the customer as an individual, beyond the sale. In a [Harris Poll commissioned by Redpoint](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/), nearly half of customers surveyed defined a personalised experience as a brand knowing they’re the same customer across all touchpoints. In addition to personalised recommendations, customised interactions and individualised offers, this means respecting channel and notification preferences and – pertinent to the Brexit situation – knowing the intricate patterns of a customer’s behaviours in order to relay news and updates that are specific to each customer’s situation. Is their size and colour preference impacted by a shipping delay? Will their favourite Bordeaux wine be subject to VAT? By infusing transparency into the customer experience about the Brexit challenges and what they mean for a unique customer, brands may be rewarded with loyalty, despite the nuisance of delays or additional costs. ## **Really Know Your Customers** Knowing everything there is to know about a customer also becomes paramount with markets undergoing dramatic change. Both British *and* European companies will have to adjust now that they’re faced with the prospect of losing each other’s customers. So retention of the customers a company is still able to serve, or acquiring customers to make up for a disappearing market will play an important role in determining which companies will weather the Brexit storm. This need underscores the importance of [advanced identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) to market to existing customers and prospects with pinpoint accuracy, or to formulate a sound diversification strategy if a brand is delving into new markets or products. Identity resolution ensures an accurate recognition of customers – known and anonymous – across all touchpoints, and it is the foundation for providing highly individualised experiences. By connecting customer records across all devices, systems and sources a company is empowered to deliver relevance with every engagement, at the pace of each customer’s unique journey. For a Dutch bike parts company no longer shipping to Britain,t may now become a matter of survival to build a German or French customer base, if it didn’t have one already. With advanced identity resolution, it can begin to assemble a [single customer view](https://www.redpointglobal.com/single-customer-view/) for new customers engaging on the website, mapping individual entities to behaviours and preferences across devices. ## **A Real-Time Pivot** A rapidly changing customer base also highlights the need for companies to be agile, quickly pivoting to capitalize on new opportunities or to stem losses. With the late-arriving Brexit deal leaving little time for advance preparation, an ability to adjust on the fly to new markets, new taxes and supply chain interruptions becomes an even more important capability. As the need for agility pertains to audience segmentation or providing a personalised customer experience with real-time decisioning, it is clear that fixed data models are not up to the task. Organisations forced to take data models offline to rebuild them with a new busines objective in mind lose valuable time; customers may forgive a cost increase in exchange for transparency, but they will be less forgiving of a company that seems to ignore the Brexit fallout – particularly as it affects them personally. That is the hidden cost of a stale data model. Conversely, code-free, self-training models with [automated machine learning](https://www.redpointglobal.com/machine-learning) provides marketers with the agility needed to meet customers with a contextually relevant interaction based on real-time data. With Brexit repercussions resulting in day-to-day and even minute-to-minute change, being able to pivot, in real time, to current conditions is a requirement to satisfy customer expectations for a personalized experience despite fluctuating circumstances beyond a company’s control. ## **In Trying Times, Keep the Customer First** Just a few short weeks into the Brexit fallout, the only true certainty right now is change. Markets and customer bases have been disrupted, and for UK customers the disruption is exacerbated by a new coronavirus shutdown. With the massive scope of change and disruption to even day-to-day life, it’s reasonable to expect that more than a few companies may figuratively shrug their shoulders, hope for the best, and see where the chips fall before trying to pick up the pieces. But there is a way forward. The future might look uncertain now, but with a hyper focus on customer data, organisations will begin to see the light at the end of the tunnel. With a single customer view, automated machine learning and real-time decisioning, it is possible to orchestrate contextually relevant, omnichannel interactions with individual customers. A seamless, personalized customer experience may look different post-Brexit, but it does not have to become a casualty of the go-it-alone breakoff from the EU. All of us Britons, customers and brands, are in this situation together – the pandemic and learning to live with Brexit – so we may as well make the best of it with the tools available. Anyone for tea?! ## **Related Content** [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) [A Personalized Customer Experience (CX) Delivers Loyalty](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty) [Clear the Hurdles for a Personalized Customer Experience](https://www.redpointglobal.com/blog/clear-the-hurdles-for-a-personalized-customer-experience/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality, Identity Resolution, Master Data Management, Real-Time Personalization --- ### [The Year in Marketing 2020: Lessons Learned](https://www.redpointglobal.com/blog/the-year-in-marketing-2020-lessons-learned/) **Published:** December 16, 2020 **Author:** John Nash **Content:** As 2020 draws to a close, there are lessons to be learned for marketers faced with abrupt changing consumer behaviors and an acceleration of digital channels. Three lessons learned immediately spring to mind as I reflect on a rather momentous year that has upended many industries, and has perhaps permanently changed how marketers engage and communicate with customers. **Adapt to Changing Customer Behaviors** First, I believe that the dramatic change in consumer behaviors finally pulls the plug on marketers being able to take for granted static, linear customer journeys. If we apply the 80/20 dynamic, where 80 percent of operational marketing is plowing ahead with traditional marketing approaches and 20 percent reserved for ambitious innovation, this year has taught us that the ratio must be flipped. One reason it must be flipped is in response to the dramatic shift toward an online, digital-first experience. According to the U.S. Department of Commerce, over a two-month period this spring there was an [11 percent increase](https://fred.stlouisfed.org/series/ECOMPCTSA) in ecommerce spending as a percentage of all retail sales, greater than the increase over the previous 10 years combined. The in-store experience that drove many a marketing campaign is, if not disappearing, certainly waning in importance. The National Retail Federation reports that on Black Friday, [44 percent more consumers](https://nrf.com/media-center/press-releases/holiday-shoppers-take-advantage-early-thanksgiving-weekend-deals) shopped only online compared with last year. In addition to a surge in ecommerce, customer expectations for unique, personalized experiences are also intensifying. According to McKinsey, [73 percent of U.S. shoppers](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-global-view-of-how-consumer-behavior-is-changing-amid-covid-19) said that they’ve tried a new shopping behavior this year such as curbside pickup or online grocery delivery, with 80 percent claiming that they will continue the new behavior. Even as customers experiment with new behaviors and a digital-first mindset, they still put the onus on brands to recognize them across channels – just as they did when linear customer journeys consisted of far fewer channel options. In a Redpoint survey conducted with Dynata, [70 percent of consumers](https://www.redpointglobal.com/press-releases/70-percent-of-consumers-say-they-will-exclusively-shop-with-brands-that-personally-understand-them-this-holiday-season/) said that they will shop this holiday season with brands that personally understand them. The totality of these trends points to the absolute necessity for brands to compete on customer experience. The straight-line customer journey of consideration/evaluation/purchase is likely gone for good. Customers can appear anywhere, at any time. Providing a seamless omnichannel experience results in satisfied, loyal customers and ultimately drives revenue. Adapting to changing behaviors by creating unique, personalized experiences should be one of the more important lessons learned for ambitious marketers as we head into the new year. **Be Persistently Curious** With a clear recognition that customer behaviors are changing and the status quo for engaging with customers must likewise evolve, the next lesson learned for marketers is more tactical in nature. How to achieve new goals for creating innovative experiences? I’m reminded of a quote attributed to Louis Pasteur, “Chance favors the prepared mind, and opportunity favors the bold.” An important lesson to be learned from the upheaval of the past year is that now is the time to be curious. Rapidly changing customer behaviors create opportunity for the sheer fact that there is so much more data to observe. > An important lesson to be learned from the upheaval of the past year is that now is the time to be curious. Consider, for example, [1-800-CONTACTS](https://martechtoday.com/1-800-contacts-doubles-down-on-the-digital-customer-experience-245509). Two years ago, the online contact lens retailer set out to enhance a personalized customer experience. With a crush of new customers this year, many of whom were new to wearing contacts, the company suddenly discovered that it had completely new segments. Because it had a platform in place to engage with individual customers at any given moment, the company was prepared to meet the unique needs of new customers. Yes, customer behaviors are changing and perhaps even unrecognizable, but every transaction, every behavior and every preference leave behind a data trail that ambitious marketers are able to mine for clues about how to optimize a unique customer journey. Now is the time to be curious about what makes an individual customer tick. A [single customer view](https://www.redpointglobal.com/single-customer-view/) takes out the guesswork; with customer data from all sources integrated into a single platform and updated in real-time, marketers have what they need to create and experiment with various experiences, to optimize individual customer journeys and to ultimately deliver individual next-best actions for any customer, on any channel. **Goodbye, Third-Party Cookies** The phasing out of the third-party cookie takes us to the third lesson learned. In the same vein as the need to adapt to changing consumer behaviors and the need to explore new opportunities in customer data, the demise of the third-party cookie is a wake-up call for marketers that now is not the time to stand pat. While Safari and Firefox already block third-party cookies, the decision by Google to end support for third-party cookies on [Chrome in January, 2022](https://www.theverge.com/2020/1/14/21064698/google-third-party-cookies-chrome-two-years-privacy-safari-firefox) is significant because of the browser’s global market share of about 70 percent of web traffic. The lesson to be learned is that orchestrating a dynamic customer journey, especially across the web, will depend on severing a reliance on third-party cookies for tracking customers. Instead, organizations must find new ways to gather, share and safeguard customer data for the purpose of creating personalized experiences. Again, this presents opportunity. Already, we’ve seen several brands, [most recently Walgreens](https://adage.com/article/cmo-strategy/walgreens-rolls-out-its-own-retail-media-network/2298531), try to capitalize on the upcoming void by creating their own media networks. Walgreens Advertising Group, and other platforms like it (CVS, Home Depot, Target), offer advertising space on their own channels and partner channels – providing omnichannel marketers with even more digital channels to address ‘known’ consumers and a new competitive landscape. We’ve covered the loss of third-party cookies [in this space](https://www.redpointglobal.com/blog/the-demise-of-the-third-party-cookie-opens-new-methods-of-engagement/) before, especially as it pertains to the need for brands to have customers share more data and the attendant privacy concerns. Building customer trust through transparency about how data is collected, shared and used helps reduce the reliance on third-party cookies, because customers will then be more willing to share personal data. When customers realize that self-identifying and/or sharing data results in a personalized experience, they’ll continue to engage in this value exchange, further reducing the need for brands to rely on third-party cookies. By using emerging technology such as the LiveRamp Identity Link, brands will still be able to reach cohorts of users with relevant messages – even with the user’s identity masked. But even with this new technology, the coming explosion of new digital channels reinforces the need to know everything there is to know about a customer – likes, dislikes, preferences, behaviors, etc. – to know how to effectively spend advertising dollars. The need to adapt to changing consumer behaviors, the need to mine customer data for new opportunities and the need to replace third-party cookies to create personalized customer experiences all speak to the need to re-think how to orchestrate a customer journey. The main marketing takeaway from 2020 is that traditional methods of engaging with a customer are no match for the tremendous amount of change that has occurred. To meet customers with relevant omnichannel experiences, marketers need to take these lessons to heart; by recognizing the changing situation and approaching it with curiosity, marketers will have a great head start in 2021 for creating experiences that matter. **Related Content** [Advanced Personalization: Complex Yes, Complicated No](https://www.redpointglobal.com/blog/advanced-personalization-complex-yes-complicated-no/) [Master Personalization Capabilities Across Anonymous Touchpoints](https://www.redpointglobal.com/blog/master-personalization-capabilities-across-anonymous-touchpoints/) [Now is the Time to Set Ambitious Marketing Goals](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Prescriptive, Predictive & Insight: Tools for a CDP as a Digital Transformation Engine](https://www.redpointglobal.com/blog/prescriptive-predictive-insight-tools-for-a-cdp-as-a-digital-transformation-engine/) **Published:** November 18, 2020 **Author:** Steve Zisk **Content:** There is ample confusion in the marketplace about customer data platforms (CDPs) and digital transformation in general. With traditional tag management vendors identifying as CDPs and cloud vendors pushing into the market, the ability to understand differentiation between solutions is complicated. The Redpoint product marketing team recently had a discussion with an analyst who specializes in personalization and customer experience (CX) best practices about [Redpoint rg1](https://www.redpointglobal.com/rg1/). During this discussion, the analyst identified that a key difference of Redpoint’s approach is its ability to serve as a high-performance engine when it comes to [data management](https://www.redpointglobal.com/customer-data-management/identity-resolution/), [automated machine learning](https://www.redpointglobal.com/machine-learning) and [intelligent orchestration](https://www.redpointglobal.com/orchestration). Regardless of the endpoint, Redpoint could serve to accelerate digital transformation in enterprises in an array of industries. With a consensus that data management is the strength of the platform – encompassing everything from orchestration to real-time decisioning, breadth, depth, quality, [identity resolution](https://www.redpointglobal.com/customer-data-management/identity-resolution/) and latency – the discussion then centered on three key elements for how rg1 stands out from the crowd in the CDP market: driving prescriptive, predictive and insight marketing use cases. ## **Take Prescriptive Capabilities to a New Level with AML** According to the analyst, the Redpoint rg1 automated machine learning solution illustrates the prescriptive capabilities. In a use case such as a retailer using [automated machine learning (AML)](https://www.redpointglobal.com/blog/what-is-automated-machine-learning/) in a dynamic pricing campaign, the prescriptive element showcases the vast divide between manually setting an arbitrary segment (arbitrary by age cut-off, geolocation, income, etc.) and using AML to let data determine the optimal segments and sub-segments. For any retailer that has tested dynamic pricing with an ultimate goal of finding a price that’s fair to the consumer while also maximizing revenue, there’s always a balance between customer needs and company needs. For many companies, human beings manually determine where that dividing line is – setting dynamic pricing rates by age and gender, income level, etc. But the dividing lines tend to be arbitrary. Why, for example, would a 50-and-over male be the cutoff vs. 45-and-over? Or 46? Prescriptive software capabilities – as exemplified by rg1 – ensures that data supports every segment. If a marketer possesses broad and deep data sets – particularly historical data that link purchases to certain behaviors and attributes – it becomes relatively straightforward to let the software determine the optimal dividing lines, based on hard data rather than human intuition. ## **Predicting Customer Behavior is Just a Starting Point** Marketers also care deeply about predictive capabilities – for use cases such as determining a likely outcome based on variables such as which image or content to use, what time of day or day of the week to send an email, email frequency, what content to send on a mobile device vs. email, etc. Predictions, in this sense, extend beyond predicting customer behaviors to include predicting interactions between customer behaviors and other aspects of the system (which product, what channel, etc.). Really, to include any variable in terms of how a marketer engages with a customer. With rg1, data collection is a foundational requirement, and is not limited to simply measuring customer intent. Instead, every interaction is an opportunity to build understanding, with the marketer collecting data across a set of experiments such as A/B and multivariate testing to validate and optimize outcomes. Experiments using both classical rules and predictive models drive and extend machine learning with more data and customer feedback. Marketers quickly discover which combinations or versions are acceptable, and can test items and situations such as an image, a treatment, time of day, a channel – all at once across a wide set of variations. ## **Insight: How Successful Was My Campaign, and Why?** Prescriptive and predictive capabilities in a CDP form a good foundation, but they will not answer all the questions pertinent to a marketer, who of course will want to know *why* a campaign was or was not successful. Why did a prescriptive and/or predictive tool work? In our analyst discussion, he brought up the idea of “insight” – analyzing and visualizing success factors of a campaign, similar to Business Intelligence tools. To reduce operational gaps, rg1 includes an Insight tool to showcase campaign and interaction responses, both current and historical. In this way, rg1 already aligns with the marketers’ need for better “headlights” rather than just the “rearview mirror” of offline BI tools. Insights extend to the prescriptive models discussed earlier as well. When rg1 builds a cluster model, it also defines a decision tree for how a customer/prospect falls into a cluster, allowing a marketer to examine what it means to be in one cluster vs. another and the decision that led to that distinction. And the model and Insight panel can match customer behaviors to predictions across time in multiple campaigns and interactions. This capability will test the efficacy of the tool’s predictive and prescriptive might – and more often than not showcase human limitations and the need for predictive and prescriptive tools in the first place. An Insights dashboard in RPI provides an easy way to drill down into the specific details of a rule, an audience or a piece of content to see how any single insight contributes to the success of a campaign. It provides marketers with an easy way to break down campaign components and analyze one component against another, or against itself across time. Using Insight together with prescriptive and predictive tools and techniques provides marketers with a clear roadmap for determining the kinds of messages to build and how to present those messages to the right audience that will optimize business goals. The roadmap will provide answers as to what image, channel, device, time of day and other variables will most resonate with a target audience – laying out a clear path to follow for all of the core tasks a marketer needs to implement a successful campaign. ## **Not All CDP’s are Digital Transformation Engines** One of the things I was most struck by in this discussion was our shared experiences in conversing with potential CDP customers who recognize the need for a solution but aren’t quite sure about a specific use case for their business. There seems to be a consensus that meeting customer expectations for a superior customer experience requires some level of personalization, which means no longer putting customers into arbitrary segments. Less understood, however, is that audience segmentation only scratches the surface for prescriptive, predictive and insight capabilities. The marketer needs these tools and a deeply experimental mindset to drill down and discern the *potential* of an audience. Identifying customers within an audience is one thing. It’s quite another to identify what the best experience is for each customer in an audience. To do this successfully requires approaching data collection with an open mind. Customer data is not limited to PII and other identifying characteristics. It’s a closed-loop cycle that includes data about the offer that was presented, how it was presented, how the customer engaged with the offer. This approach underscores the need for a solid data foundation as the bare minimum for an enterprise CDP. Prescriptive, predictive and insight tools are key ingredients for a CDP to truly be considered a digital transformation engine. ## **Related Content** [Redpoint Global Moves Up in the Challengers Quadrant of the 2020 of the Gartner Magic Quadrant for Multichannel Marketing Hubs](https://www.redpointglobal.com/redpoint-global-moves-up-in-challengers-quadrant-of-the-2020-gartner-magic-quadrant-for-multichannel-marketing-hubs/) [A CDP Implementation Reference Guide: What You Need to Know](https://www.redpointglobal.com/blog/a-cdp-implementation-reference-guide-what-you-need-to-know/) [How Does Your CDP Stack Up? The Real-Time Difference](https://www.redpointglobal.com/blog/how-does-your-cdp-stack-up-the-real-time-difference/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [Customer Data Insights and the Single Customer View](https://www.redpointglobal.com/blog/customer-data-insights-and-the-single-customer-view/) **Published:** September 28, 2020 **Author:** Steve Zisk **Content:** What are customer insights? A standard definition defines customer insights – or customer intelligence – as an interpretation of trends in human behavior to increase the effectiveness of a product or service for a customer and to increase sales. According to a [survey from McKinsey](https://www.mckinsey.com/business-functions/marketing-and-sales/solutions/periscope/our-insights/articles/the-customer-insights-function-is-ripe-for-a-boost), having a deep understanding of customers, which is often referred to as a 360° customer view, is essential for developing strategies for sustainable growth, creating a superior customer experience (CX) and driving innovation. The research revealed that organizations that leverage customer insights outperform peers by 85 percent in sales growth, which is why more than 200 customer insight professionals in the survey said that customer insights is a key source for competitive differentiation. ## **What is a 360° Customer View?** Digging deeper into what constitutes or defines true customer insights reveals that a company may have some knowledge – or even a wealth of knowledge – about a customer and still lack true customer data insights needed to provide competitive differentiation. A company may, for example, have a record of every customer transaction dating back years, from multiple in-store and ecommerce POS systems. Or, a company may have customer data from multiple channels; a customer’s call center activity, social media and ATM activity, to name a few. Unless the data is fully integrated, breaking down every channel or data siloes, the company will not have a [single view of the customer](https://www.redpointglobal.com/blog/single-view-customer-essential-success/). A true unified customer profile factors the interplay between every piece of customer data; how activity on one channel affects another, or how an in-store transaction might influence online behavior. This is what is meant by a 360° customer view; a unified customer profile that tells a marketer everything there is to know about a customer. A 360° customer view enables true [marketing insights](https://www.redpointglobal.com/blog/why-marketing-must-take-the-lead-on-creating-a-personalized-customer-experience/), empowering marketers to analyze how one customer data set relates to the complete view, make prioritizations according to recency, business goals or what will create the optimal customer experience at a precise moment of a customer journey. A single view of the customer that is updated dynamically in real time is known as the [Golden Record](https://www.redpointglobal.com/single-customer-view/), the foundation for marketers to proactively engage with customers across an omnichannel customer journey, providing a next-best action that is consistently in cadence of each unique journey. ## **How Does a 360° Customer View Work?** To use customer data insights provided by a 360° customer view preclude data or channel siloes. If, for example, a company is using customer lifetime value (CLV) as a metric for an email retention campaign, but bases CLV solely on transactions, transaction frequency and average spend, it misses an opportunity to capture other signals that may drastically alter the calculation. A customer may log a call center complaint, or post a negative social media post. If unstructured data is not persistently updated into a single view of the customer, the company runs the very real risk of introducing friction into the customer experience – especially if the email is locked and loaded and unable to account for dynamic, real-time updates. A single customer view that is updated in real time, with customer data from every conceivable source, is an essential capability to accurately predict probabilities of retention, churn, acquisition or any key indicator tied to a busines outcome. Any customer interaction or engagement at any time or on any channel can change a calculation, which is why data or channel siloes or latency between when the customer data is compiled and it is matched, cleansed and made ready for actionable customer data insights tie the hands of marketers in providing a superior customer experience. ## **Innovative Use Cases for Customer Data Insights** A call center complaint and negative social media post just scratch the surface of the endless possibilities for what constitutes true customer intelligence, and how data-driven marketers can use a unified customer profile – the 360° customer view – to drive competitive differentiation with [ambitious, innovative use cases](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/). In retail, for example, location-based marketing is becoming a popular way to use customer intelligence to create a unique, personalized CX. When geo-fencing insights are incorporated into a 360° customer view and made instantly accessible for a marketer, a retailer knows whether a customer is nearing a location, a competitor’s location or even a specific location inside a store. This knowledge becomes even more valuable when it is combined with other customer insight. Consider a curbside pickup scenario, where a customer has purchased a product online and has arranged a same-day curbside pickup. A geo-fencing capability may let a retailer know to send an SMS alerting the customer of the closest available parking spot, and send an associate out to be ready with the product upon the customer’s arrival. But what if there’s a rainstorm, or heavy traffic at the designated pick-up time? A unified customer profile that includes local weather and traffic conditions may change how that retailer engages with the customer. A personalized CX may account for inclement weather by securing the product in a waterproof container, directing the customer to a covered parking spot, or providing a one-time offer for 10 percent off rain gear, etc. Gridlock near the store may warrant an SMS to let the customer know when a more favorable drive time is expected. Because curbside is an increasingly popular option, which some have called a [“must-have service”](https://www.retaildive.com/news/after-covid-19-is-curbside-delivery-here-to-stay/577937/) that aligns with customer expectations for an increasingly digital and touchless experience, the above scenario illustrates the importance of combining multiple facets of customer data insights to create innovative customer experiences. Behaviors, transactions, preferences – even a customer’s surrounding environment – all contribute to a marketer’s full and complete understanding of a customer, and how that understanding helps to optimize a customer journey. ## **Data Aggregation, Machine Learning and Customer Data Insights** Customer data insights can also be mined from large segments of customers; audience cluster behavior analysis is a useful tool to predict what one customer may do based on trends in a larger data set. A bank customer who posts a social media screed complaining about checking fees may be a key churn indicator. By analyzing the aggregate behavior of thousands of customers who already did churn, a bank might extrapolate more meaning from the single customer’s social post. Perhaps customers who go negative on social are less likely to churn than those who place a call, and are more incentivized to stay with an extra book of checks than a reduced rate. Cluster analysis for the purpose of deriving audience intelligence is made possible by [automated machine learning](https://www.redpointglobal.com/blog/what-is-automated-machine-learning/). Predictive hyper-dimensional [model clustering](https://www.redpointglobal.com/blog/algorithmic-optimization-and-the-magic-of-aml/), with algorithms tuned to optimize and adapt for a specific metric can find nuanced pairings that truly predict a behavior, interest or outcome beyond a shared characteristic such as age, gender, income or geography. Analyzing and understanding macro trends is a beneficial exercise to predicting the behavior of a single customer based on patterns of large segments. Automated machine learning predictions far exceed the capabilities of human in terms of scale and accuracy, providing meaningful customer insights that can be used to deliver a personalized CX across an omnichannel journey. ## **Customer Insights, One Platform & a Single View** For data-driven marketers who understand that true customer insights are a foundation for providing a differentiated customer experience, the days of using intuition or hazarding a guess as to how a customer might behave – or to think a trend applies equally to all customers – are long gone. In [Addressing the Gaps in Customer Experience](https://www.redpointglobal.com/wp-content/uploads/2020/07/Addressing-the-Gaps-in-Customer-Experience.pdf), a Harris Poll sponsored by Redpoint, customers were asked to define what a personalized customer experience means for them and 43 percent said that when it was a company or brand knows they’re the same customer across all touchpoints. That capability comes from having true customer insights, unfettered by data or channel siloes that cloud a single customer view. With actionable customer insights derived from a 360° customer view, brands understand what makes a customer tick – motivations, desires, wants and needs. This is the premise behind the [Redpoint rg1 digital experience platform](https://www.redpointglobal.com/one-platform/), which applies real-time decisioning to the Golden Record to intelligently orchestrate a customer journey in the cadence of the customer. Delivering a superior customer experience depends on knowing everything there is to know about a customer. rg1 generates meaningful customer insights, and then turns them into differentiated customer experiences that drive revenue. ## **Related Content** [Augment Customer Segmentation with a Personalized CX](https://www.redpointglobal.com/blog/augment-customer-segmentation-with-a-personalized-cx/) [Limitless Data Integration Unlocks a Superior Customer Experience](https://www.redpointglobal.com/blog/limitless-data-integration-unlocks-a-superior-customer-experience/) [A Contextual Customer Relationship is the Basis for a Superior Experience](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** 1:1 Personalization, Customer Data Platform, Data Management, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [How to Avoid Data Distortion & Machine Learning Bias](https://www.redpointglobal.com/blog/how-to-avoid-data-distortion-machine-learning-bias/) **Published:** August 12, 2020 **Author:** Steve Zisk **Content:** While there are many sources and types of errors in your marketing data – data biases or distortions – one common trait is that the data inaccurately reflect either the customer you’re engaging with, or the market you are operating in. When data distortion seeps in, it can be hard to eradicate – in large part because data analysis based on flawed data often just reinforces false assumptions, making it less likely to doubt its veracity. Data that is counter-factual, or data that is not representative of the real world, has the potential to damage brand reputation, skew marketing campaigns or otherwise steer marketers down the wrong path. For an organization to rid itself of this pernicious pest, it’s important to understand what data bias is, why it’s important to remove it, and the steps to take to achieve it. ## **What is Data Distortion?** Data distortion is the deviation of data from its true or most accurate representation of the full picture; bad data may interject incorrect or misguided “facts” into useful information or, worse, into models and predictions of customers or business. Internal sources of customer data, as well as data collected on the open market, have a history of being erroneous, incomplete or unrepresentative, omitting key variables and/or simply being out of date. A customer may move and get married; using an old address or marital status after a life change is a common example of data distortion that will erroneously impact a customer record if the information is not updated. Similarly, a purchased data set might contain transactions and behaviors that no longer represent your customers. Imagine using online/offline purchase history from 2019 to predict customer purchase habits in the summer of 2020, when so many people have drastically changed shopping patterns due to Covid-19. Using data collected by some other group for some other purpose, or data collected from some unrepresentative subset of your customers – one timeframe, location, or customer cohort – to represent the breadth and depth of your customers and their affinities, desires and expectations is likely to give you poor results. Categorization decisions made by humans can also cause distortion. Consider a marketing survey that wants to know the age of customers. If responses are presented as a range of options (25-34, 35-50, 51-64, etc.), how did the marketer arrive at those specific cut-offs? Are they random, or meaningful for the purpose of the data collection intended to form the basis of a campaign? If, for example, you’re trying to form meaningful correlations between income and age, it would be very important to know the age at which your customers are retiring for purposes of setting an age range that reflects that reality. Other human-introduced biases may occur when humans capture original data, including situations such as a customer filling out a form. If time constraints prevent the form from being filled out completely, populating the final unanswered questions with a default answer may introduce too many default values into the data collection – which doubles as a [data hygiene problem](https://www.redpointglobal.com/blog/what-is-data-hygiene/). Similarly, if a call center associate is asked to rate the sentiment of a caller, rankings may be skewed by the associate’s own KPIs and measurements on customer satisfaction. Filtering also causes bias if data is filtered to align with assumptions – the reinforcing of false assumptions mentioned above. If, for example, you’re trying to market to your highest value customers, the way you devise a campaign – in terms of channel, type of outreach, tone, message, measurement, etc. – may all be based on your preconceived notion of what a high-value customer looks like. If machine learning models are built to find what you’ve already defined as a high-value customer, they’ll find exactly what you want – whether your presuppositions are right or wrong. Similarly, if you send one group 10 emails and another group five emails, and there’s a greater response from the group that receives 10 and you therefore deem them higher-value, the question becomes how did you determine the groupings in the first place? In the same vein, if analysis reveals results that do not reinforce your expectations, you may re-work the data, change a model, or otherwise massage the results until they better align with the assumptions you were making. ## **What are the Dangers of Data Distortion?** If we think about data distortion in terms of how it affects the customer, the organization and brand reputation from a public/regulatory standpoint, there are two key categorical reasons why biases or distortions should be removed – one consumer oriented and one organizational oriented. From the point of view of the customer, customers clearly have an interest in a brand having an [accurate, relevant picture of who they](https://www.redpointglobal.com/blog/a-contextual-customer-relationship-is-the-basis-for-a-superior-experience/) are. Any inaccurate data that has been collected will distort that picture, especially if it is used to build models, make decisions, create segments, target campaigns and otherwise form the basis for how you will engage with the customer. From the organizational perspective, biased data will similarly skew results with potentially negative outcomes for corporate objectives, whether improving sales, efficiency, customer satisfaction, retention, loyalty or another KPI. Likewise, if campaign measurement introduces errors in measurement, attribution, grouping, or causation – intentional or not – marketers will have an inaccurate picture of a campaign’s effectiveness, and thus will not be acting in the best interest of the goals and KPIs they’re pursuing. A second corporate interest in avoiding data bias pertains to brand reputation, as corporations have a vested interest in collecting accurate data because they want to behave appropriately for their customers. Any inaccurate representations of reality could hinder fair and equitable decisions by the company, which runs counter to making a corporation trustworthy in the eyes of the customers they’re trying to serve. ## **How Do I Remove Data Distortion?** There are five things a marketer can do to ensure machine learning models are free of statistical or sociological biases as described above. The first, broad category for steering clear of data and machine learning bias is to build accurate and careful data collection processes. Make sure your ecommerce site, customer surveys, loyalty memberships, and call center forms all collect data that are consistent, reasonable, and easy for customers and agents to enter. This can help your organization avoid introducing errors or distortions into its own first-party data. Second is to strive to identify and compensate for biases in third-party data (text analytics, facial recognition, credit scores, sociographics) that are used as variables in your models. In other words, do not take for granted that the judgments and values assigned to the data you’re collecting match your own understanding. Third, where possible, build in “explain-ability” in models by documenting training data and decisions, determining primary variables and weights for models, and using less obscure algorithms (such as decision trees) where possible. Next, it’s important to experiment, measure and optimize models to ensure they make measurably good decisions, with a requirement for some independent assessment and tools to define “good” in a value-neutral and explainable way. Finally, you can put in place processes and technology to correct errors and respond to customer inquiries. GDPR and CCPA preference centers, Master Data Management Stewardship software, and Data Quality components for your customer data management all provide means to improve your customer data, help customers fix (and trust) your data, and help your organization turn privacy/compliance challenges into customer engagement opportunities. ## **Don’t Let Data Distortion be Intimidating** Removing errors and biases from machine learning models and the overall way business is conducted aligns an organization well with its internal needs, its expectations for being profitable and for being a “good citizen”. Being cognizant of data collection pitfalls and being proactive in addressing them has many positive payoffs. Because data bias is not usually intentional, it is often perceived as impossible to avoid, as well as to completely remove, by virtue of its being hard to detect. But putting appropriate mitigation measures in place make creating a complete, accurate and representative data set a far less intimidating problem than at first glance; it’s just one of many data management tasks that are necessary to align strategy with execution. Putting those measures in place may be difficult, but it’s certainly less of a hardship than allowing bad data to go unchecked. **RELATED CONTENT** [Why Data Quality Matters for IT and Business Stakeholders](https://www.redpointglobal.com/blog/why-data-quality-matters-for-it-and-business-stakeholders/) [Why Data Veracity is the Foundation for a Personalized Customer Experience](https://www.redpointglobal.com/blog/why-data-veracity-is-the-foundation-for-a-personalized-customer-experience/) [4 Steps to More Efficient Data Preparation](https://www.redpointglobal.com/blog/4-steps-to-more-efficient-data-preparation/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Data Quality --- ### [Embrace Digital Excellence for the “Next Normal”](https://www.redpointglobal.com/blog/embrace-digital-excellence-for-the-next-normal/) **Published:** August 17, 2020 **Author:** Redpoint Global **Content:** A recent article from the McKinsey customer experience (CX) practice delves into what it calls the [“next normal” for customer engagement](https://www.mckinsey.com/business-functions/operations/our-insights/elevating-customer-experience-excellence-in-the-next-normal). It lists three priorities that the authors claim will define CX in the post-pandemic era: digital excellence, dynamic customer insights and safe/contactless engagement. It further postulates that all organizations are actively reorienting business models to be more digital to accomplish these priorities. “The bar for digital excellence, already high before the pandemic,” it states, “has gone through the roof.” While there is no legitimate doubt that digital excellence is indeed a top priority in the wake of the seismic changes to consumer behaviors, there remains ambiguity about its exact meaning. Some may harbor the belief that it refers primarily to shifting resources from in-store to digital channels, or expanding an online presence. I submit to the view that digital excellence refers to providing a customer with a seamless, omnichannel customer journey with relevance and personalization at every touchpoint. Raising the ‘bar’, as it were, means there can be no daylight or disconnect between an experience a customer has with a brand between channels and touchpoints – physical, digital, customer service, acquisition, collections, etc. Because such a holistic, customer-centric experience has proven to drive revenue growth, digital excellence transcends marketing to become a mission-critical enterprise mandate. **Why Digital Excellence Matters** The pandemic has, once and for all, laid bare the misconception that digital excellence was/is not worthy of serious attention, or that a customer data platform (CDP) is a niche offering meant for experimentation in a single channel or on a campaign-by-campaign basis. Organizations that found themselves on the wrong side of the digital divide as customers suddenly disappeared from traditional in-store, physical locations – or flocked to competitors – received a rude awakening. These companies realized almost immediately the very real urgency to provide a seamless omnichannel experience, and that it is intricately tied to revenue. The signs were there pre-pandemic; research from Gartner shows that customer-centric brands achieve up to [20 percent lift](https://www.gartner.com/it/content/1285000/1285024/february_25_customer_centric_web_mmaoz_gphifer.pdf), a direct result of providing that holistic experience by knowing a customer identity across channels and devices, analyzing all relevant customer data from every source (including IoT and sentiment analysis) across a customer’s anonymous and known customer journeys. **A Flight to Quality** Fortunately, there is a way forward. Similar to the Wall Street “flight to quality” phenomenon, where investors shift out of risky assets during a financial downturn, the same applies to organizations that realize nothing less than a single, fully integrated solution that brings together all customer data into one digital platform will suffice for providing customers with the personalized, relevant, omnichannel CX they demand. Organizations want a solution that offers precision, flexibility, and the ability to handle any circumstance or situation that has anything to do with a customer, from simple to complex, touching marketing, customer service, acquisition, operations and everything in between. Everyone is now fully on board with the realization that there is no shortcut for arriving at a single point of operational control that supports a completely integrated, omnichannel experience. This is the premise, and promise, behind the [Redpoint rg1 solution](https://www.redpointglobal.com/one-platform/), which takes [customer experience](https://www.redpointglobal.com/blog/a-personalized-customer-experience-delivers-loyalty) to the next level while also minimizing risk, reducing the number of vendors and martech sprawl, and with integration flexibility that maintains the desired level of security and data protection according to industry needs. **Prepare for the ‘Next Normal’** A single point of control is required to orchestrate a holistic omnichannel experience, particularly with customers shifting behaviors and moving to more unpredictable, digital-first journeys. According to the McKinsey article, even those companies that had started on a digital transformation path and had developed an omnichannel strategy were forced with the arrival of the pandemic to “throw out their playbooks and improvise to keep pace” with new unique and compelling experiences. rg1 shortens this learning curve because the “playbook” (the platform) is both rules-based and in real time. There are no lists, or machine learning models to “throw out” because the rules-based platform dynamically updates a customer [Golden Record](https://www.redpointglobal.com/single-customer-view/) in real time. Knowing everything there is to know about a customer at the precise moment of an engagement supports the unique and compelling experience because it will always be relevant and in cadence with a journey. Self-trained and code-free automated machine learning algorithms further negate the need to improvise. Redpoint Automated Machine Learning (AML) embraces an [evolutionary programming approach](https://www.redpointglobal.com/blog/algorithmic-optimization-and-the-magic-of-aml/) that automates algorithmic optimization directly against a metric a marketer is trying to push during development. This “fitness function” guarantees that a model will be highly relevant and effective in optimizing the metric the marketer intends to move. There is no need to haphazard a guess as to which action might most delight a customer depending on where they are in a journey. Customer journeys are dynamic, and creating a unique, compelling experience at scale requires a platform with [built-in AML capabilities](https://www.redpointglobal.com/machine-learning) that will optimize business goals and help marketers orchestrate a next-best action for a customer throughout an omnichannel journey. The always-on, continuously connected customer perceives a relationship with a brand as a holistic experience, which runs counter to a brand’s traditional method of engaging with customers on a channel-by-channel basis. The sudden change in customer behaviors will expose any gap between the experience a customer expects and what a brand is able to deliver. Anything less than excellence – any uneven channel experience – will drive a customer away. This is why ambitious marketers rely on Redpoint. Traditional marketing technologies just aren’t up to the task of providing a hyper-personalized, relevant unified experience throughout an omnichannel journey at scale. With Redpoint, that’s a starting point. **Related Content** [Now is the Time to Set Ambitious Marketing Goals](https://www.redpointglobal.com/blog/now-is-the-time-to-set-ambitious-marketing-goals/) [Digital Transformation Just Became Accelerated](https://www.redpointglobal.com/blog/digital-transformation-just-became-accelerated/) [Top 10 Benefits of Rules-Based Orchestration](https://www.redpointglobal.com/blog/top-10-benefits-of-rules-based-orchestration/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Quality, Omnichannel Marketing, Real-Time Personalization, Single Customer View --- ### [What is Data Hygiene?](https://www.redpointglobal.com/blog/what-is-data-hygiene/) **Published:** July 24, 2020 **Author:** Steve Zisk **Content:** Imagine you’re a passenger in a Boeing 767 at an altitude of 41,000 feet when the aircraft runs out of fuel, in large part because of a data hygiene error. If you suddenly stalled out in the clouds midway between Montreal and Edmonton and you knew it was because a refueling calculation that incorrectly converted pounds instead of liters, proper data hygiene would become very important to you. I’m referring of course to the infamous [“Gimli Glider”](https://en.wikipedia.org/wiki/Gimli_Glider) incident of 1983, where this very situation unfolded, before fortunately ending without a single fatality as the pilot glided the aircraft to a safe landing. The tired “garbage in, garbage out” saying is usually met with a roll of eyes, but the Gimli example shows us that poor data hygiene does have potentially catastrophic consequences. As marketers, we’re fortunate that negative outcomes fall short of life and death implications, but revenue loss is certainly at play when poor data hygiene results in missed opportunities, customer dissatisfaction or regulatory fines. **Data Hygiene: A Breakdown** What, then, is superior data hygiene that ultimately supports a superior customer experience? It’s ensuring that there are rules for converting measurements and transaction amounts (US and Canadian dollars, for example) into an apples-to-apples comparison. It’s having rules for address, email, name and phone number standardizations – and for any data that makes up a unique customer record. And it’s having rules that prioritize customer data – rules, in other words, that stipulate how “noise” (the volume of customer data from every system) become “signals” (identifying what’s relevant, and why). Exceptional data hygiene also recognizes that customer data is used for a wide range of purposes for the benefit of both a business and the customer, and that those purposes may sometimes be in conflict. A business aims to use customer data to build accurate, relevant and reliable models and use those models to intelligently orchestrate an extraordinary omnichannel customer experience. A customer, meanwhile, wants to own, protect and manage their identity, and know that their customer record with a brand accurately reflects who they are, and puts them in the best light possible. **How to Get Data Hygiene Right** Within these occasionally dueling purposes, there are synergies. Both the business and the customer want data looking right, even if it is for different reasons. To ensure that it is right – to check off all the data hygiene buckets, if you will – three things have to happen. First, is to separate the noise from the signals. Making a decision about the relevance of data is part of it, but this component also entails ensuring accuracy in attaching relevant data to the right customer. Second, it’s important to clean up existing data. This includes correcting obvious errors, as well as making sure that data is formatted correctly so marketers can use it for the intended purpose. Matching identities, ensuring time stamps are accurate, and ensuring apples-to-apples comparisons are all part of this important data hygiene operation. Third, superior data hygiene requires solving for identity resolution beyond a simple rule for matching (or not matching) a Robert Smith with a Bob Smith at the same canonical address. Rather, it entails making sure that the data collected is appropriately matched to the correct aspects of a person, such as an accurate determination of who is using a shared device, which address is outdated due to a life change, or how frequently someone visits a brand’s sister site . **Data Hygiene: Beyond the Basics** In addition to the obvious data hygiene exercises such as fixing misspellings, matching street names to a canonical address (Avenue of the Americas vs. Sixth Avenue or Suite 120 vs. Apt. 120, etc.), and rules for standardizing each element of an identity (phone number, IP address, email address, postal address, etc.) there are also more complicated rules in play. These include parsing and processing accurate signals from something as complex as the body of an email, the body of a social post or a web form. Simple or complex, rules must be in place for determining what’s a match or not a match under any number of circumstances, with a prioritization mechanism tuned for accuracy. Data hygiene rules may also differ by industry, as they relate to a customer or segment of customers, or even as they relate to a specific business use case. As an example, cleansing a name and matching an identity for the purposes of billing may be vastly different from what you’re allowed to do for the purposes of marketing. Similarly, the healthcare industry has more stringent data hygiene requirements regarding the collecting, storing and sharing of medical records that it does for the purposes of sending a welcome email in an onboarding campaign. **Data Hygiene is Not a One and Done Deal** Lastly, data hygiene is a 24/7 process. Filtering, cleansing and matching all have to happen simultaneously and in concert as customer data is collected in real time from various sources. Consider for example a web behavior signal that alerts a CDP that a customer is on a web page for an hour. Length of time on page may be an important signal that triggers a certain offer vs. a different offer for a different time threshold. A marketer must trust the accuracy of the information, which is why rules for matching, understanding and correlating billions of bytes of data into meaningful signals is a continuous process. Because data hygiene is continuous, it also must operate within the parameters of imperfect human behaviors. The classic example for this is a call center operator whose job performance is measured in part by handle time, who might want to keep call length short. This may potentially interfere with good data hygiene, such as ensuring the proper spelling of a name or email address, or that a form is filled out correctly. Human error, miscommunication and mistakes – although hopefully less severe than the Gimli Glider incident – have real-world consequences for marketers striving to deliver a superior customer experience. Conversely, data hygiene done well results in confidence in the relevance and accuracy of data signals that deliver better customer engagement, better permissions-based marketing and more accurate and timely interactions. **RELATED CONTENT** [No Data Left Behind: Analytics, Orchestration and Making Data Work for You](https://www.redpointglobal.com/blog/no-data-left-behind-analytics-orchestration-and-making-data-work-for-you/) [Why Data Veracity is the Foundation for a Personalized Customer Experience](https://www.redpointglobal.com/blog/why-data-veracity-is-the-foundation-for-a-personalized-customer-experience/) [What is Data Lineage and Why is it Important?](https://www.redpointglobal.com/blog/what-is-data-lineage-and-why-is-it-important/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* **Blog categories:** Customer Data Platform, Data Governance & Security, Data Management, Data Quality --- ### [The Demise of the Third-Party Cookie Opens New Methods of Engagement](https://www.redpointglobal.com/blog/the-demise-of-the-third-party-cookie-opens-new-methods-of-engagement/) **Published:** April 17, 2020 **Author:** Steve Zisk **Content:** ![](https://www.redpointglobal.com/wp-content/uploads/2020/04/04-17-blog-300x206.jpg)Google’s announcement that Chrome will join Firefox and Safari in [phasing out third-party cookies](https://www.theverge.com/2020/1/14/21064698/google-third-party-cookies-chrome-two-years-privacy-safari-firefox) by the end of 2021 appears to be the final nail in the coffin in the much anticipated demise of the cookie long used by advertisers, publishers, and marketers to track a user’s activity across websites. Some of the clamor over what the decision ultimately means for marketers has sparked discussion about the effect it will have on first-party cookies, used by a domain primarily to enhance the user experience on that domain. The short answer is, it won’t. The first-party cookie will still do all the heavy lifting for a website to provide a consistent experience; recognizing a loyalty member at log-in, remembering multi-page form information, associating a shopping cart with a specific browser, etc. Most consumers recognize and appreciate a first-party cookie’s value in providing a personalized experience. Because third-party cookies have evolved mostly into a tool for targeted advertising, their impending demise will likely not be mourned by most consumers, especially those frustrated by retargeted ads that seemingly follow them all over the web. That use case explains why third-party cookies are colloquially known as “tracking” cookies; managed and placed by a third-party domain, the cookie can be accessed by any website that loads the browser’s information, enabling the advertiser to track users and collect information as they move across the web. **No Third-Party Cookies? Now What?** The end of the tracking use case is one of the main consequences of the inglorious phase-out of the third-party cookie. This will end not only following a specific user – attached to a browser – as they move from website to website, but also the classic marketing use case of trying to reach look-alike audiences. Both will cease because the lack of third-party cookies will make it impossible to track your own users on someone else’s website. There are, however, replacement methods being offered that innovative marketers can use to fill the gap created by the loss of the third-party cookie. The first is for a brand or company to establish a relationship with customers based on trust. A trusted brand practices authentication, it asks and abides by customer preferences, it offers intuitive opt-in options, and it is completely transparent about how it collects information, what it uses the information for, and its policies for complying with GDPR, CCPA, and other data privacy regulations. The second replacement method is the use of emerging technology – the LiveRamp Identity Link, or Google’s Privacy Sandbox – that do not rely on third-party cookies, but rather appropriate bits of information that can be shared at an aggregate level, essentially masking a user’s identity while reaching a cohort of users with relevant messages. A tangible result of this – as compared with using a third-party cookie – is that a customer who places, say, fishing gear in a shopping cart, will no longer see an ad for the same tackle box track them across multiple websites. Instead, they would see generalized yet relevant ads based on surfing history, or purchase history. Redpoint helps our clients with both of these methods through [native capabilities in our platform](https://www.redpointglobal.com/wp-content/uploads/2019/06/SB-RedPoint-Digital-Acquisition-Platform-0519.pdf): privacy workflows and management, a privacy-first anonymous customer store, opt-in and opt-out controls for campaigns, channels, and engagement, and selection rules to control ad usage and suppression. We also partner with LiveRamp, PossibleNOW, and others for preferences, web and mobile privacy controls, and intelligent ad tech. **Take a Good, Hard Look at Where to Find Value** The two replacement methods pre-date the death of the third-party cookie because the writing has been on the wall for some time that consumers have steadily been pushing back against a laissez faire approach toward data privacy. We’re not in a static place, in other words. Forward-looking brands have been hyper-sensitive about the “creep factor” resulting from ads following consumers around the web, and wary about the unauthorized access and/or use of their customer data by third parties. Open ad networks are most susceptible to the long-term ramifications when third-party cookies disappear, but replacement technologies will cushion the blow. While it’s true that the value, reach, and specificity of ad placement through an ad network will diminish, the needle won’t drop to zero. What this means is that brands placing marketing and advertising dollars with an ad network will have to be more judicious about ROI from adtech, martech, ecommerce, mobile, and other methods of customer engagement. Companies who have long used the third-party as a crutch to track a user across the internet might view a heightened need to consider how they reach their customers as a kind of nuisance. Retargeted ads may not be pretty, but they’re easier than devoting the necessary time and resources to engage with a customer across an omnichannel journey. Delivering a personalized customer experience in the context of a unique customer journey does not rely on third-party cookies. Rather, it is based on having a single view of the customer – of which a device ID is just one component. A third-party cookie is not a true ally for a brand interested in recognizing a customer’s preferences, patterns, and behaviors across all channels, which is the foundation for a superior customer experience that drives new revenue. **RELATED CONTENT** [Combat Message Fatigue with Personalization](https://www.redpointglobal.com/blog/combat-message-fatigue-with-personalization/) [Getting Started with Personalization: Include “Unknown” Visitors for Website Personalization](https://www.redpointglobal.com/blog/getting-started-with-personalization-include-unknown-visitors-for-website-personalization/) [Advanced Personalization: Complex Yes, Complicated, No](https://www.redpointglobal.com/blog/advanced-personalization-complex-yes-complicated-no/) ###### *Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on [LinkedIn](https://www.linkedin.com/company/1191453/), [Twitter](https://twitter.com/RedPointGlobal), and [Facebook](https://www.facebook.com/RedPointGlobal/).* [![](https://www.redpointglobal.com/wp-content/uploads/2020/04/0416-SB-RedPoint-Digital-Acquisition-Platform-238x300.jpg)](https://www.redpointglobal.com/wp-content/uploads/2019/06/SB-RedPoint-Digital-Acquisition-Platform-0519.pdf) **Blog categories:** 1:1 Personalization, Data Management, Data Quality, Journey Orchestration --- ## Resources ### [The State of Customer Data Readiness, 2026](https://www.redpointglobal.com/resources/customer-data-readiness-report/) **Published:** August 17, 2026 **Author:** teresa keegan **Resources categories:** Customer Data Readiness **Resources tags:** Research Report --- ### [Introducing Redpoint Identity Studio](https://www.redpointglobal.com/resources/introducing-redpoint-identity-studio/) **Published:** July 6, 2026 **Author:** teresa keegan **Resources categories:** Identity Resolution **Resources tags:** Video --- ### [Redpoint Identity Studio](https://www.redpointglobal.com/resources/redpoint-identity-studio/) **Published:** June 1, 2026 **Author:** teresa keegan **Resources categories:** Identity Resolution **Resources tags:** Product Brief --- ### [How to Choose and Succeed with A CDP](https://www.redpointglobal.com/resources/cdp-selection-guide/) **Published:** May 4, 2026 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** eBook / White Paper --- ### [Redpoint Data Readiness Hub - Professional Plan](https://www.redpointglobal.com/resources/data-readiness-hub-professional-plan/) **Published:** May 18, 2026 **Author:** teresa keegan **Resources categories:** Customer Data Readiness **Resources tags:** Product Brief --- ### [Redpoint and Snowflake: A Winning Combination](https://www.redpointglobal.com/resources/redpoint-and-snowflake-a-winning-combination/) **Published:** December 14, 2023 **Author:** teresa keegan **Resources categories:** Cloud Data, Customer Data Plaform **Resources tags:** Solution Brief --- ### [The New Patient Journey: How Hyper-Personalization Fuels Acquisition and Retention for Health System Growth](https://www.redpointglobal.com/resources/the-new-patient-journey-how-hyper-personalization-fuels-acquisition-and-retention-for-health-system-growth/) **Published:** March 2, 2026 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** 1:1 Personalization, Healthcare **Resources tags:** eBook / White Paper --- ### [Personalization, For Real: The 3 R's that Turn Promise Into Practice](https://www.redpointglobal.com/resources/customer-experience-personalization-guide/) **Published:** May 5, 2026 **Author:** teresa keegan **Resources categories:** Real-Time Personalization **Resources tags:** eBook / White Paper --- ### [Closing the Context Gap: The New Rules of Enterprise Growth](https://www.redpointglobal.com/resources/closing-the-context-gap-the-new-rules-of-enterprise-growth/) **Published:** May 1, 2026 **Author:** teresa keegan **Resources categories:** Customer Data Readiness **Resources tags:** Infographic, Product Brief --- ### [Redpoint for Direct Marketers](https://www.redpointglobal.com/resources/redpoint-for-direct-marketers/) **Published:** March 28, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Data Management, Retail **Resources tags:** Solution Brief --- ### [Data Readiness in the AI Era: Elevating Customer Data for Exceptional Results](https://www.redpointglobal.com/resources/data-readiness-in-the-ai-era-elevating-customer-data-for-exceptional-results/) **Published:** June 5, 2025 **Author:** teresa keegan **Resources tags:** Video --- ### [Closing the Context Gap in Member Data to Drive Senior Adherence, Satisfaction, and Loyalty](https://www.redpointglobal.com/resources/closing-the-context-gap-in-member-data-to-drive-senior-adherence-satisfaction-and-loyalty/) **Published:** January 2, 2026 **Author:** teresa keegan **Resources categories:** Healthcare, Single Customer View **Resources tags:** Product Brief --- ### [Do You Really Know Your Retail Bank Customers?](https://www.redpointglobal.com/resources/do-you-really-know-your-retail-bank-customers/) **Published:** February 11, 2026 **Author:** teresa keegan **Resources categories:** Financial Services **Resources tags:** Infographic, Solution Brief --- ### [Data Contract Checklist: Your Blueprint for Trustworthy Data](https://www.redpointglobal.com/resources/data-contract-checklist-your-blueprint-for-trustworthy-data/) **Published:** April 1, 2026 **Author:** teresa keegan **Resources categories:** Customer Data Readiness **Resources tags:** Solution Brief --- ### [eCom Ops Podcast | From SEO to AEO: Why Clean Data is the Real AI Strategy](https://www.redpointglobal.com/resources/ecom-ops-podcast-from-seo-to-aeo-why-clean-data-is-the-real-ai-strategy/) **Published:** March 12, 2026 **Author:** teresa keegan **Resources categories:** Customer Data Readiness **Resources tags:** Video --- ### [Customer Data Readiness: Best Practices for Data Leaders](https://www.redpointglobal.com/resources/data-readiness-for-ai-best-practices-for-data-leaders/) **Published:** July 14, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** AI & Machine Learning **Resources tags:** Infographic, Solution Brief --- ### [2026 Predictions: Data Readiness for Personalization and AI](https://www.redpointglobal.com/resources/2026-predictions-data-readiness-for-personalization-and-ai/) **Published:** February 3, 2026 **Author:** teresa keegan **Resources categories:** Customer Data Readiness **Resources tags:** Video --- ### [Video: Predict & Adapt - How Machine Learning Can Optimize the Entire Customer Journey](https://www.redpointglobal.com/resources/video-predict-adapt-how-machine-learning-can-optimize-the-entire-customer-journey/) **Published:** March 25, 2021 **Author:** wpengine **Resources tags:** Video --- ### [Back to Basics | Identity Resolution: The Significance in CX Execution](https://www.redpointglobal.com/resources/back-to-basics-identity-resolution-the-significance-in-cx-execution/) **Published:** April 16, 2024 **Author:** teresa keegan **Resources tags:** Video **Resource Type:** Wistia Video --- ### [The Member-Centric Blueprint: Personalize, Harmonize, and Optimize Engagement & Outcomes](https://www.redpointglobal.com/resources/the-member-centric-blueprint/) **Published:** March 28, 2025 **Author:** teresa keegan **Resources categories:** Healthcare **Resources tags:** Video --- ### [The Personalization Framework: Building Blocks for Exceptional Customer Experiences](https://www.redpointglobal.com/resources/webinar-the-personalization-framework-building-blocks-for-exceptional-customer-experiences/) **Published:** July 30, 2024 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** 1:1 Personalization **Resources tags:** Video --- ### [Back to Basics | Data Observability: The Crucial Role to Effective CDP Operations](https://www.redpointglobal.com/resources/back-to-basics-data-observability-the-crucial-role-to-effective-cdp-operations/) **Published:** April 16, 2024 **Author:** teresa keegan **Resources tags:** Video **Resource Type:** Wistia Video --- ### [Charting the Path to a Unified Digital Member Experience](https://www.redpointglobal.com/resources/charting-the-path-to-a-unified-digital-member-experience/) **Published:** March 28, 2025 **Author:** teresa keegan **Resources categories:** Healthcare **Resources tags:** Video --- ### [Activating First-Party Data in Advertising](https://www.redpointglobal.com/resources/activating-first-party-data-in-advertising/) **Published:** September 13, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Advertising **Resources tags:** Solution Brief --- ### [Harnessing AI for Customer Engagement in Financial Services](https://www.redpointglobal.com/resources/harnessing-ai-for-customer-engagement-in-financial-services/) **Published:** August 18, 2025 **Author:** teresa keegan **Resources categories:** Financial Services **Resources tags:** Video --- ### [Healthcare Personalization at Scale: Rising to the Challenge](https://www.redpointglobal.com/resources/webinar-healthcare-personalization-at-scale-rising-to-the-challenge/) **Published:** July 27, 2024 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Healthcare **Resources tags:** Video --- ### [What is Data Readiness?](https://www.redpointglobal.com/resources/what-is-data-readiness/) **Published:** June 3, 2025 **Author:** teresa keegan **Resources categories:** Data Management **Resources tags:** Infographic, Solution Brief --- ### [Solving Marketing Challenges with a Customer Data Platform](https://www.redpointglobal.com/resources/webinar-solving-marketing-challenges-with-a-customer-data-platform/) **Published:** October 15, 2024 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [Transform Your CAHPS Scores with Personalized, Omnichannel Member Engagement](https://www.redpointglobal.com/resources/transform-your-cahps-scores-with-personalized-omnichannel-member-engagement/) **Published:** January 31, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** 1:1 Personalization, Healthcare **Resources tags:** Solution Brief --- ### [The Patient360 Data Activation Framework](https://www.redpointglobal.com/resources/the-patient360-data-activation-framework/) **Published:** June 26, 2025 **Author:** teresa keegan **Resources categories:** Healthcare, Single Customer View **Resources tags:** Infographic, Solution Brief --- ### [The Healthcare ROI Gamechanger](https://www.redpointglobal.com/resources/the-healthcare-roi-gamechanger/) **Published:** April 23, 2024 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Healthcare **Resources tags:** eBook / White Paper --- ### [The Customer Experience Gap](https://www.redpointglobal.com/resources/the-customer-experience-gap/) **Published:** October 12, 2021 **Author:** teresa keegan **Resources categories:** Customer Experience **Resources tags:** Infographic --- ### [The AI-CX Connection: Why Data Readiness is the Payer Advantage](https://www.redpointglobal.com/resources/the-ai-cx-connection-why-data-readiness-is-the-payer-advantage/) **Published:** July 25, 2025 **Author:** teresa keegan **Resources categories:** AI & Machine Learning, Healthcare **Resources tags:** Video --- ### [The 3 R's of Personalization: Right Time](https://www.redpointglobal.com/resources/the-3-rs-of-personalization-right-time/) **Published:** February 18, 2025 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** 1:1 Personalization **Resources tags:** Video --- ### [The 3 R's of Personalization: Relevance](https://www.redpointglobal.com/resources/the-3-rs-of-personalization-relevance/) **Published:** February 18, 2025 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** 1:1 Personalization **Resources tags:** Video --- ### [Solving the CDP Puzzle: The Missing Pieces to Maximize Value](https://www.redpointglobal.com/resources/solving-the-cdp-puzzle-the-missing-pieces-to-maximize-value/) **Published:** May 12, 2025 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [Setting the Stage for the Second-Generation Healthcare Customer Experience](https://www.redpointglobal.com/resources/setting-the-stage-for-the-second-generation-healthcare-customer-experience/) **Published:** March 19, 2021 **Author:** teresa keegan **Resources categories:** Healthcare, Pharmaceutical **Resources tags:** eBook / White Paper --- ### [Scalable Without Sacrifice - A CDP Engineered for Enterprises](https://www.redpointglobal.com/resources/scalable-without-sacrifice-a-cdp-engineered-for-enterprises/) **Published:** September 15, 2021 **Author:** wpengine **Resources categories:** Customer Data Plaform, Data Quality **Resources tags:** eBook / White Paper --- ### [Redpoint Data Readiness Hub](https://www.redpointglobal.com/resources/redpoint-data-readiness-hub-customer-data-that-delivers/) **Published:** July 16, 2025 **Author:** teresa keegan **Resources categories:** Data Quality **Resources tags:** Product Brief, Solution Brief --- ### [Redpoint Data Readiness for Salesforce](https://www.redpointglobal.com/resources/redpoint-data-readiness-for-salesforce/) **Published:** March 21, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Data Quality **Resources tags:** Solution Brief --- ### [Redpoint Data Readiness for Data Processors](https://www.redpointglobal.com/resources/redpoint-data-readiness-for-data-processors/) **Published:** February 10, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Data Management **Resources tags:** Product Brief, Solution Brief --- ### [Redpoint Data Readiness for Adobe](https://www.redpointglobal.com/resources/redpoint-data-readiness-for-adobe/) **Published:** February 24, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Identity Resolution **Resources tags:** Solution Brief --- ### [Real-Time Decisioning Puts the Customer in Charge of the Journey](https://www.redpointglobal.com/resources/real-time-decisioning-puts-the-customer-in-charge-of-the-journey/) **Published:** December 11, 2020 **Author:** wpengine **Resources categories:** Data Quality, Real-Time Personalization **Resources tags:** eBook / White Paper --- ### [Pulse Check: Is Your Consumer to Patient Engagement Strategy Built for the Digital Age?](https://www.redpointglobal.com/resources/pulse-check-is-your-consumer-to-patient-engagement-strategy-built-for-the-digital-age/) **Published:** June 30, 2025 **Author:** teresa keegan **Resources categories:** Customer Data Readiness, Healthcare **Resources tags:** Video --- ### [Optimizing Customer Engagement Through Connected Data](https://www.redpointglobal.com/resources/optimizing-customer-engagement-through-connected-data/) **Published:** May 5, 2021 **Author:** wpengine **Resources categories:** Data Observability, Data Quality **Resources tags:** eBook / White Paper --- ### [From Enrollment to Engagement: A Member-Centric Approach to Retention](https://www.redpointglobal.com/resources/member-centric-approach-to-retention/) **Published:** February 19, 2025 **Author:** teresa keegan **Resources categories:** Customer Experience, Healthcare **Resources tags:** eBook / White Paper --- ### [Mastering Identity, Maximizing Impact: A Roadmap for Data Readiness](https://www.redpointglobal.com/resources/mastering-identity-maximizing-impact-a-roadmap-for-data-readiness/) **Published:** September 30, 2025 **Author:** teresa keegan **Resources categories:** Customer Data Readiness **Resources tags:** Video --- ### [Forrester Report: The Capabilities CDPs Need to Deliver Value for B2C Marketers](https://www.redpointglobal.com/resources/forrester-report-the-capabilities-cdps-need-to-deliver-value-for-b2c-marketers/) **Published:** May 27, 2020 **Author:** Teresa Keegan **Resources categories:** Customer Data Plaform **Resources tags:** Analyst Report --- ### [Enterprise-Class Businesses Can't Afford Bad Data](https://www.redpointglobal.com/resources/enterprise-class-businesses-cant-afford-bad-data/) **Published:** June 10, 2021 **Author:** wpengine **Resources categories:** Data Quality **Resources tags:** eBook / White Paper --- ### [Empowering Health Plans with AI: A New Era of Member Engagement](https://www.redpointglobal.com/resources/empowering-health-plans-with-ai-a-new-era-of-member-engagement/) **Published:** February 5, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** AI & Machine Learning, Healthcare **Resources tags:** Solution Brief --- ### [Embracing the Power of AI: The Ultimate Health Plan Roadmap](https://www.redpointglobal.com/resources/embracing-the-power-of-ai-the-ultimate-health-plan-roadmap/) **Published:** April 4, 2025 **Author:** teresa keegan **Resources categories:** AI & Machine Learning, Healthcare **Resources tags:** eBook / White Paper --- ### [Drive Engagement with Annual Wellness Visits and Maximize Risk Adjustment Accuracy](https://www.redpointglobal.com/resources/drive-engagement-with-annual-wellness-visits-and-maximize-risk-adjustment-accuracy/) **Published:** March 11, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Healthcare **Resources tags:** Solution Brief --- ### [Data Observability and Measurement](https://www.redpointglobal.com/resources/data-observability-and-measurement/) **Published:** March 6, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Data Observability **Resources tags:** Product Brief --- ### [Customer Data with Redpoint Data Management](https://www.redpointglobal.com/resources/customer-data-with-redpoint-data-management-deep-dive/) **Published:** February 10, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Data Management **Resources tags:** Product Brief --- ### [Customer Data Readiness: A Best Practices Approach for Data Leaders](https://www.redpointglobal.com/resources/customer-data-readiness-a-best-practices-approach-for-data-leaders/) **Published:** September 17, 2025 **Author:** teresa keegan **Resources categories:** Customer Data Readiness **Resources tags:** eBook / White Paper --- ### [Bridge the Data Gap: Maturity Framework for Smarter Patient Engagement](https://www.redpointglobal.com/resources/bridge-the-data-gap-maturity-framework-for-smarter-patient-engagement/) **Published:** June 25, 2025 **Author:** teresa keegan **Resources categories:** Data Quality, Healthcare **Resources tags:** eBook / White Paper --- ### [Harris Poll: Addressing the Gaps in Customer Experience](https://www.redpointglobal.com/resources/addressing-the-gaps-in-customer-experience/) **Published:** October 27, 2020 **Author:** teresa keegan **Resources categories:** Customer Experience **Resources tags:** Analyst Report --- ### [2022 Healthcare Perspectives on Consumer Engagement](https://www.redpointglobal.com/resources/2022-healthcare-perspectives-on-consumer-engagement/) **Published:** November 9, 2022 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Healthcare **Resources tags:** Research Report --- ### [The CDP Reckoning: What Will the Future Demand of Your Customer Data?](https://www.redpointglobal.com/resources/the-cdp-reckoning-what-will-the-future-demand-of-your-customer-data/) **Published:** December 12, 2025 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [QKS Group | SPARK Matrix™: Data Quality and Observability Tools, 2025](https://www.redpointglobal.com/resources/qks-group-spark-matrix-data-quality-and-observability-tools-2025/) **Published:** December 4, 2025 **Author:** teresa keegan **Resources categories:** Data Observability, Data Quality **Resources tags:** Analyst Report --- ### [Automated Data Ingestion & Data Quality](https://www.redpointglobal.com/resources/automated-data-ingestion-and-data-quality/) **Published:** October 31, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Data Ingestion, Data Quality **Resources tags:** Product Brief --- ### [Unification & Identity Resolution](https://www.redpointglobal.com/resources/unification-and-identity-resolution/) **Published:** October 31, 2025 **Author:** teresa keegan **Resources categories:** Identity Resolution **Resources tags:** Product Brief --- ### [Eliminating Provider Ghost Networks with a Data-First Approach](https://www.redpointglobal.com/resources/eliminating-provider-ghost-networks-with-a-data-first-approach/) **Published:** November 21, 2025 **Author:** teresa keegan **Resources categories:** Customer Data Readiness, Healthcare **Resources tags:** Solution Brief --- ### [Increasing Annual Wellness Visits](https://www.redpointglobal.com/resources/increasing-annual-wellness-visits/) **Published:** October 17, 2025 **Author:** teresa keegan **Resources categories:** Healthcare **Resources tags:** eBook / White Paper --- ### [Set Every Patient on the Right Care Path](https://www.redpointglobal.com/resources/get-every-patient-on-the-right-care-path/) **Published:** June 12, 2024 **Author:** Kenneth Murphy **Excerpt:** Redpoint: A different kind of Data Readiness Hub for health systems and provider organizations. **Resources categories:** Healthcare **Resources tags:** Solution Brief --- ### [Put Your Members at the Center with the Redpoint Data Readiness Hub](https://www.redpointglobal.com/resources/put-your-members-at-the-center-with-the-redpoint-data-readiness-hub/) **Published:** April 18, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Customer Data Plaform, Healthcare **Resources tags:** Overview, Solution Brief --- ### [Beyond the Exam Room: Building a 360° View of the Healthcare Consumer](https://www.redpointglobal.com/resources/beyond-the-exam-room-building-a-360-view-of-the-healthcare-consumer/) **Published:** September 30, 2025 **Author:** Red P. Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Healthcare --- ### [Unforgettable Journeys Await: Elevate Your Travel Experience with Redpoint](https://www.redpointglobal.com/resources/unforgettable-journeys-await-elevate-your-travel-experience-with-redpoint/) **Published:** June 29, 2025 **Author:** teresa keegan **Resources categories:** Travel & Hospitality **Resources tags:** Infographic, Solution Brief --- ### [Video: Six Key Customer-Data Trends You Can't Afford to Ignore](https://www.youtube.com/watch?v=Ps0c9aiAqO4) **Published:** January 21, 2022 **Author:** wpengine **Resources tags:** Video --- ### [Video: The Power of a Highly Personalized Customer Journey](https://www.youtube.com/watch?v=FbQLsW5Fp0I&t=2s) **Published:** February 9, 2022 **Author:** wpengine **Resources tags:** Video --- ### [Video: The Golden Record - Part 1: How Do I Build a Golden Record?](https://www.youtube.com/watch?v=70_C5iAvGF0) **Published:** February 15, 2022 **Author:** wpengine **Resources tags:** Video --- ### [Video: The Golden Record - Part 2: What to Do with the Golden Record](https://www.youtube.com/watch?v=NW0zEmCo3LM) **Published:** February 16, 2022 **Author:** wpengine **Resources tags:** Video --- ### [Video: Facts Not Fiction - Beyond the CDP](https://youtu.be/ojO3lge1EHA) **Published:** March 29, 2022 **Author:** wpengine **Resources tags:** Video --- ### [Video: AI Demystified - Extracting the Value from Your Customer Data](https://www.youtube.com/watch?v=aroFPaEjZq4) **Published:** March 30, 2022 **Author:** wpengine **Resources categories:** AI & Machine Learning **Resources tags:** Video --- ### [Video: The Challenges of Data Quality](https://www.youtube.com/watch?v=GHPApJccKJ8) **Published:** April 1, 2022 **Author:** wpengine **Resources tags:** Video --- ### [Video: Are You Really Listening to Your Customer?](https://www.youtube.com/watch?v=SuYtJsxFIv4) **Published:** April 14, 2022 **Author:** wpengine **Resources categories:** 1:1 Personalization **Resources tags:** Video --- ### [The Redpoint Single Customer View](https://www.youtube.com/watch?v=AVbehj559OA) **Published:** November 2, 2022 **Author:** wpengine **Resources categories:** Single Customer View **Resources tags:** Video --- ### [The Redpoint Healthcare Golden Record](https://www.youtube.com/watch?v=Soq8ZaZd2kI) **Published:** November 2, 2022 **Author:** wpengine **Resources categories:** Healthcare **Resources tags:** Video --- ### [Video: Data-Driven Answers to Achieve Omnichannel Success](https://www.youtube.com/watch?v=z7soTTOQMHE) **Published:** November 9, 2022 **Author:** wpengine **Resources tags:** Video --- ### [Video: Getting Started with Data Quality](https://www.youtube.com/watch?v=uiDGn0PGbwQ&t=1s) **Published:** November 9, 2022 **Author:** wpengine **Resources tags:** Video --- ### [Video: Keys to Identity Resolution - Connecting the Known and Unknown](https://youtu.be/xnDNRDSn-OE) **Published:** November 10, 2022 **Author:** wpengine **Resources categories:** Identity Resolution **Resources tags:** Video --- ### [The Importance of Composability in a MarTech Stack](https://www.redpointglobal.com/resources/the-importance-of-composability-in-a-martech-stack/) **Published:** July 30, 2024 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Composability **Resources tags:** Video --- ### [Doing More with Less: A Retailer’s Guide to Personalized CX](https://www.redpointglobal.com/resources/webinar-doing-more-with-less-a-retailers-guide-to-personalized-cx/) **Published:** September 26, 2024 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Retail **Resources tags:** Video --- ### [Data or Die: Data Quality is Indispensable for AI-Driven CX](https://www.redpointglobal.com/resources/webinar-data-or-die-data-quality-is-indispensable-for-ai-driven-cx/) **Published:** November 20, 2024 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** AI & Machine Learning **Resources tags:** Video --- ### [Deconstructing Data | AI, Data Quality, & Consumer Perspectives](https://www.redpointglobal.com/resources/deconstructing-data-ai-data-quality-consumer-perspectives/) **Published:** December 12, 2024 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** AI & Machine Learning, Data Quality **Resources tags:** Video --- ### [2023 Healthcare Consumer Perspectives on Digital Engagement and AI](https://www.redpointglobal.com/resources/2023-healthcare-consumer-perspectives-on-digital-engagement/) **Published:** September 21, 2023 **Author:** Teresa Keegan **Resources categories:** AI & Machine Learning, Healthcare **Resources tags:** Research Report --- ### [Segmentation for the Modern Marketer](https://www.redpointglobal.com/resources/segmentation-for-the-modern-marketer/) **Published:** February 28, 2023 **Author:** Redpoint Global **Resources categories:** Segmentation & Activation **Resources tags:** eBook / White Paper --- ### [The Evolution of Customer Retention: How Personalized CX Drives Loyalty and Lifetime Value](https://www.redpointglobal.com/resources/the-evolution-of-customer-retention-how-a-personalized-cx-drives-loyalty-and-lifetime-value/) **Published:** May 5, 2023 **Author:** Redpoint Global **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** 1:1 Personalization, Customer Data Plaform, Customer Experience, Identity Resolution, Omnichannel Marketing, Single Customer View **Resources tags:** eBook / White Paper --- ### [Data-Driven Personalization Capability Maturity Model](https://www.redpointglobal.com/resources/data-driven-personalization-capability-maturity-model/) **Published:** June 3, 2024 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** 1:1 Personalization, Customer Experience, Real-Time Personalization **Resources tags:** eBook / White Paper --- ### [The Definitive CDP Guide: Defining and Applying a Use-Case Driven Agile Approach](https://www.redpointglobal.com/resources/the-definitive-cdp-guide-defining-and-applying-a-use-case-driven-agile-approach/) **Published:** August 9, 2024 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources tags:** Analyst Report, eBook / White Paper --- ### [Data-Driven Personalization Capability Maturity Model: A Retail Roadmap for CX Impact](https://www.redpointglobal.com/resources/data-driven-personalization-capability-maturity-model-a-retail-roadmap-for-cx-impact/) **Published:** October 4, 2024 **Author:** Teresa Keegan **Resources categories:** 1:1 Personalization, Retail **Resources tags:** eBook / White Paper --- ### [Member-Centric Strategy for CAHPS Improvement](https://www.redpointglobal.com/resources/member-centric-strategy-for-cahps-improvement/) **Published:** December 18, 2024 **Author:** teresa keegan **Resources categories:** Customer Experience, Healthcare **Resources tags:** eBook / White Paper --- ### [How to Pragmatically Drive Business Value with Data-Driven Capability Maturity Models](https://www.redpointglobal.com/resources/how-to-pragmatically-drive-business-value-with-data-driven-capability-maturity-models/) **Published:** June 17, 2024 **Author:** Teresa Keegan **Content:** How to Pragmatically Drive Business Value with Data-Driven Capability Maturity Models **Resources tags:** Video **Resource Type:** Wistia Video --- ### [The Redpoint Approach to HIPAA, HiTrust and SOC 2 Compliance](https://www.redpointglobal.com/resources/the-redpoint-approach-to-hipaa-hitrust-and-soc-2-compliance/) **Published:** September 15, 2022 **Author:** Teresa Keegan **Resources categories:** Healthcare **Resources tags:** Solution Brief --- ### [The Retail Imperative to Transform Customer Experiences](https://www.redpointglobal.com/resources/solution-brief-the-retail-imperative-to-transform-customer-experiences/) **Published:** August 21, 2018 **Author:** teresa keegan **Resources categories:** Retail **Resources tags:** Solution Brief --- ### [Retailers Need a Single Customer View](https://www.redpointglobal.com/resources/infographic-retailers-need-a-single-customer-view/) **Published:** August 18, 2023 **Author:** teresa keegan **Resources categories:** Retail **Resources tags:** Infographic, Solution Brief --- ### [Improve Outcomes with Personalized Healthcare Experiences](https://www.redpointglobal.com/resources/improve-outcomes-with-personalized-healthcare-experiences/) **Published:** September 15, 2022 **Author:** Teresa Keegan **Resources categories:** Healthcare **Resources tags:** Solution Brief --- ### [Improve Member Experiences with the Redpoint CDP](https://www.redpointglobal.com/resources/improve-member-experiences-with-the-redpoint-cdp/) **Published:** September 15, 2022 **Author:** teresa keegan **Resources categories:** Healthcare **Resources tags:** Solution Brief --- ### [Achieving Plan Member Engagement in Consumer-Driven Healthcare](https://www.redpointglobal.com/resources/achieving-plan-member-engagement-in-consumer-driven-healthcare/) **Published:** September 4, 2020 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Healthcare **Resources tags:** Solution Brief --- ### [Redpoint Blueprints: Redpoint + Tealium](https://www.redpointglobal.com/resources/video-redpoint-blueprints-redpoint-and-tealium/) **Published:** July 20, 2024 **Author:** teresa keegan **Resources categories:** Composability **Resources tags:** Video **Resource Type:** Wistia Video --- ### [Redpoint Blueprints: Redpoint + Snowflake](https://www.redpointglobal.com/resources/video-redpoint-blueprints-redpoint-and-snowflake/) **Published:** July 20, 2024 **Author:** teresa keegan **Resources categories:** Cloud Data, Customer Data Plaform **Resources tags:** Video **Resource Type:** Wistia Video --- ### [Redpoint Blueprints: Redpoint + Databricks](https://www.redpointglobal.com/resources/video-redpoint-blueprints-redpoint-and-databricks/) **Published:** July 20, 2024 **Author:** teresa keegan **Resources categories:** Composability **Resources tags:** Video **Resource Type:** Wistia Video --- ### [Redpoint Blueprints: Redpoint + Braze](https://www.redpointglobal.com/resources/video-redpoint-blueprints-redpoint-and-braze/) **Published:** July 20, 2024 **Author:** teresa keegan **Resources categories:** Composability **Resources tags:** Video **Resource Type:** Wistia Video --- ### [Technology is Turbocharging the Healthcare Customer Experience](https://www.redpointglobal.com/resources/technology-is-turbocharging-the-healthcare-customer-experience/) **Published:** December 4, 2020 **Author:** teresa keegan **Resources categories:** Healthcare, Pharmaceutical **Resources tags:** eBook / White Paper --- ### [Redpoint Blueprints: Redpoint + Snowflake + Twilio](https://www.redpointglobal.com/resources/redpoint-snowflake-and-twilio/) **Published:** July 11, 2024 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Overview, Solution Brief --- ### [Redpoint Blueprints: Redpoint + Snowflake + The Trade Desk](https://www.redpointglobal.com/resources/redpoint-snowflake-and-the-trade-desk/) **Published:** July 11, 2024 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Solution Brief --- ### [Redpoint Blueprints: Redpoint + Snowflake + Tealium](https://www.redpointglobal.com/resources/redpoint-snowflake-and-tealium/) **Published:** July 11, 2024 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Solution Brief --- ### [Redpoint Blueprints: Redpoint + Snowflake + Meta](https://www.redpointglobal.com/resources/redpoint-snowflake-and-meta/) **Published:** July 16, 2024 **Author:** teresa keegan **Resources tags:** Solution Brief --- ### [Redpoint Blueprints: Redpoint + Snowflake + Databricks](https://www.redpointglobal.com/resources/redpoint-snowflake-and-databricks/) **Published:** July 18, 2024 **Author:** teresa keegan **Resources categories:** Composability **Resources tags:** Solution Brief --- ### [Redpoint Blueprints: Redpoint + Snowflake + Braze](https://www.redpointglobal.com/resources/redpoint-snowflake-and-braze/) **Published:** July 18, 2024 **Author:** teresa keegan **Resources categories:** Composability **Resources tags:** Solution Brief --- ### [Redpoint Blueprints: Redpoint + Snowflake + Adobe Experience Platform](https://www.redpointglobal.com/resources/redpoint-snowflake-and-adobe-experience-platform/) **Published:** July 1, 2024 **Author:** Kenneth Murphy **Resources categories:** Customer Data Plaform **Resources tags:** Solution Brief --- ### [Redpoint Orchestration](https://www.redpointglobal.com/resources/redpoint-orchestration/) **Published:** February 6, 2024 **Author:** teresa keegan **Resources categories:** Orchestration **Resources tags:** Product Brief --- ### [Redpoint for Financial Services](https://www.redpointglobal.com/resources/redpoint-for-financial-services/) **Published:** December 12, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Customer Data Plaform, Financial Services **Resources tags:** Solution Brief --- ### [Redpoint Blueprints: Redpoint + Snowflake](https://www.redpointglobal.com/resources/redpoint-and-snowflake/) **Published:** July 16, 2024 **Author:** teresa keegan **Resources tags:** Solution Brief --- ### [Redpoint AI](https://www.redpointglobal.com/resources/redpoint-ai/) **Published:** June 12, 2024 **Author:** Kenneth Murphy **Excerpt:** By putting the most accurate, real-time customer profile data into the hands of marketers, Redpoint AI gives you the power to create and scale AI-driven personalized customer experiences that drive revenue. Redpoint AI offers the most advanced, reliable means to analyze, build, predict, and optimize the customer experience for today’s always-on, continuously connected customer. **Resources categories:** AI & Machine Learning **Resources tags:** Solution Brief --- ### [Harris Poll: Revisiting the Gaps in Customer Experience](https://www.redpointglobal.com/resources/harris-poll/) **Published:** September 22, 2021 **Author:** teresa keegan **Resources categories:** Customer Experience **Resources tags:** Analyst Report --- ### [Deliver on Healthcare Professionals' Expectations for Seamless Digital and In-Person Relationships](https://www.redpointglobal.com/resources/deliver-on-healthcare-professionals-expectations/) **Published:** September 22, 2022 **Author:** teresa keegan **Resources categories:** Healthcare **Resources tags:** Solution Brief --- ### [Composability without Compromise](https://www.redpointglobal.com/resources/composability-without-compromise/) **Published:** October 3, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Composability **Resources tags:** Solution Brief --- ### [CDP Institute Report: RealCDP Audit 2023](https://www.redpointglobal.com/resources/cdp-institute-report-realcdp-audit-2023/) **Published:** March 20, 2023 **Author:** Redpoint Global **Resources categories:** Customer Data Plaform **Resources tags:** Analyst Report --- ### [CDP Institute Report: RealCDP Audit 2022](https://www.redpointglobal.com/resources/cdp-institute-report-realcdp-audit-2022/) **Published:** January 25, 2022 **Author:** Redpoint Global **Resources categories:** Customer Data Plaform **Resources tags:** Analyst Report --- ### [CDP Institute Report: Data Quality for Marketers](https://www.redpointglobal.com/resources/cdp-institute-report-data-quality-for-marketers/) **Published:** January 4, 2022 **Author:** wpengine **Resources categories:** Customer Data Plaform, Data Quality **Resources tags:** Analyst Report --- ### [CDP Institute Report: CDP Vendor Comparison](https://www.redpointglobal.com/resources/cdp-institute-report-cdp-vendor-comparison/) **Published:** January 18, 2021 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Analyst Report --- ### [Calculating the ROI of a Customer Data Platform](https://www.redpointglobal.com/resources/calculating-the-roi-of-a-customer-data-platform/) **Published:** March 23, 2021 **Author:** wpengine **Resources categories:** Customer Data Plaform **Resources tags:** eBook / White Paper --- ### [A Guide to Selecting the Right Customer Data Platform (CDP)](https://www.redpointglobal.com/resources/a-guide-to-selecting-the-right-customer-data-platform-cdp/) **Published:** November 10, 2020 **Author:** wpengine **Resources categories:** Customer Data Plaform **Resources tags:** eBook / White Paper --- ### [The 3 Rs of Great Personalization: Recognize, Relevance, Right Time](https://event.on24.com/eventRegistration/EventLobbyServlet?target=reg20.jsp&eventid=4778762&sessionid=1&key=3B2BB4029FC9FDB17DFE28FE67396C1C&groupId=5828167) **Published:** February 17, 2025 **Author:** teresa keegan --- ### [The 3 Rs of Personalization: Recognize](https://event.on24.com/wcc/r/4778762/3B2BB4029FC9FDB17DFE28FE67396C1C) **Published:** February 18, 2025 **Author:** teresa keegan **Resources categories:** 1:1 Personalization **Resources tags:** Video --- ### [Enhance Customer Experience with Timely & Welcomed Triggered Actions](https://www.redpointglobal.com/resources/enhance-customer-experience-with-timely-welcomed-triggered-actions/) **Published:** February 3, 2023 **Author:** Teresa Keegan **Resources categories:** 1:1 Personalization, Orchestration, Real-Time Personalization **Resources tags:** eBook / White Paper --- ### [The Role of Identity Resolution](https://www.redpointglobal.com/resources/the-role-of-identity-resolution/) **Published:** December 30, 2022 **Author:** Teresa Keegan **Resources categories:** Identity Resolution **Resources tags:** eBook / White Paper --- ### [Cognizant & Redpoint: A Quick Conversation on MDM v CDP](https://event.on24.com/wcc/r/4353516/DD7D2CBF0A0830982AABE38F58BD6ADA#new_tab) **Published:** October 23, 2023 **Author:** Teresa Keegan **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [Redpoint CDP for Retail](https://www.redpointglobal.com/resources/redpoint-cdp-for-retail/) **Published:** October 9, 2024 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Retail **Resources tags:** Solution Brief --- ### [Redpoint Real-Time Interactions](https://www.redpointglobal.com/resources/redpoint-real-time-interactions/) **Published:** February 6, 2024 **Author:** teresa keegan **Resources categories:** Real-Time Personalization **Resources tags:** Product Brief --- ### [Segmentation & Activation](https://www.redpointglobal.com/resources/segmentation-and-activation/) **Published:** March 6, 2024 **Author:** teresa keegan **Resources categories:** Segmentation & Activation **Resources tags:** Solution Brief --- ### [Identity Resolution: See Your Customers Clearly](https://www.redpointglobal.com/resources/cdp-institute-report-identity-resolution-see-customers-clearly/) **Published:** December 30, 2022 **Author:** Teresa Keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Identity Resolution **Resources tags:** Research Report --- ### [Redpoint Data Management](https://www.redpointglobal.com/resources/redpoint-data-management/) **Published:** March 6, 2024 **Author:** teresa keegan **Resources categories:** Data Management **Resources tags:** Product Brief --- ### [Redpoint Global - Buy Your Second CDP First](https://www.redpointglobal.com/resources/video-redpoint-global-buy-your-second-cdp-first/) **Published:** August 22, 2024 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Video **Resource Type:** Wistia Video --- ### [Redpoint Blueprints: Redpoint + Snowflake + Salesforce Marketing Cloud](https://www.redpointglobal.com/resources/redpoint-snowflake-and-salesforce-marketing-cloud/) **Published:** July 11, 2024 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Solution Brief --- ### [The Rise of First-Party Data](https://www.redpointglobal.com/resources/the-rise-of-first-party-data/) **Published:** November 1, 2022 **Author:** wpengine **Resources categories:** Customer Data Plaform, Customer Experience, Data Quality, Identity Resolution, Single Customer View **Resources tags:** eBook / White Paper --- ### [Strengthen HCP Relationships and Improve Patient Outcomes](https://www.redpointglobal.com/resources/strengthen-hcp-relationships-and-improve-patient-outcomes/) **Published:** July 3, 2024 **Author:** Kenneth Murphy **Resources categories:** Pharmaceutical **Resources tags:** Solution Brief --- ### [The Redpoint CDP](https://www.redpointglobal.com/resources/the-redpoint-cdp/) **Published:** February 6, 2024 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Product Brief --- ### [Boost Medicare Advantage Member Retention Now](https://www.redpointglobal.com/resources/boost-medicare-advantage-member-retention-now/) **Published:** January 31, 2025 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** 1:1 Personalization, Healthcare **Resources tags:** Solution Brief --- ### [Webinar Series | Back to Basics](https://www.redpointglobal.com/cdp-back-to-basics/) **Published:** September 24, 2024 **Author:** teresa keegan **Content:** **Join us for a series of mini-webinars diving into the essential elements of a Customer Data Platform (CDP). Learn how the significance of each CDP component is a crucial building block and why data integrity is the key to success in the realm of customer data and customer experiences.** **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [On-Demand: Back to Basics, Episode 1- Understanding the Core Elements of a CDP](https://event.on24.com/wcc/r/4544523/CE314B45C2ECDA81C6085670897CECBD?partnerref=rpgweb#new_tab) **Published:** May 7, 2024 **Author:** Teresa Keegan **Content:** **Join us for a series of mini-webinars diving into the essential elements of a Customer Data Platform (CDP). Learn how the significance of each CDP component is a crucial building block and why data integrity is the key to success in the realm of customer data and customer experiences.** **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [On-Demand: Back to Basics, Episode 2 - The Importance of Data Quality in Your CDP](https://event.on24.com/wcc/r/4551509/EFB080958CEC25E2F17689E58054BBDA?partnerref=rpgweb#new_tab) **Published:** May 7, 2024 **Author:** Teresa Keegan **Content:** **Join us for a series of mini-webinars diving into the essential elements of a Customer Data Platform (CDP). Learn how the significance of each CDP component is a crucial building block and why data integrity is the key to success in the realm of customer data and customer experiences.** **Resources tags:** Video --- ### [On-Demand: Back to Basics, Episode 5 - Segmentation & Effective Activation](https://event.on24.com/wcc/r/4595606/CFA289D8EBC1F011DE7D4F978D2965CC?partnerref=rpgweb#new_tab) **Published:** May 7, 2024 **Author:** Teresa Keegan **Content:** **Join us for a series of mini-webinars diving into the essential elements of a Customer Data Platform (CDP). Learn how the significance of each CDP component is a crucial building block and why data integrity is the key to success in the realm of customer data and customer experiences.** **Resources tags:** Video --- ### [DATAcated & Redpoint Talk Snowflake and Transforming Data Management](https://redpointglobal.wistia.com/medias/ebqkjwtpgi#new_tab) **Published:** July 2, 2024 **Author:** Teresa Keegan **Content:** DATAcated interviewed Beth Scagnoli, VP of Product Management at Redpoint Global, at the 2024 Snowflake Data Summit. **Resources categories:** Data Management **Resources tags:** Video --- ### [Redpoint + Braze + Snowflake: A Winning Combination](https://redpointglobal.wistia.com/medias/sa5gscxtjb#new_tab) **Published:** June 19, 2024 **Author:** teresa keegan **Content:** The combination of Redpoint, Snowflake, and Braze gives you a highly scalable, highly, flexible engagement, toolkit that can drive a lot of value for your organization. --- ### [Video: Every CDP Does Everything?](https://youtu.be/Zu6e3HivnoU#new_tab) **Published:** April 9, 2021 **Author:** wpengine **Resources tags:** Video --- ### [Snowflake and Redpoint: Don't Duplicate Your Data](https://www.youtube.com/watch?v=XWgMzaPwN8Y#new_tab) **Published:** June 3, 2024 **Author:** teresa keegan **Resources categories:** Customer Data Plaform, Data Management **Resources tags:** Video --- ### [CDP Success Story: DSW and Redpoint](https://youtu.be/xsgETOyy9h0#new_tab) **Published:** May 24, 2024 **Author:** teresa keegan **Resources categories:** Retail **Resources tags:** Video --- ### [Monetize Access to Your Customer Data with a Retail Media Network](https://www.redpointglobal.com/resources/monetize-access-to-your-customer-data-with-a-retail-media-network/) **Published:** November 28, 2023 **Author:** teresa keegan **Content:** [![The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/wp-content/uploads/2025/03/define-data-400x284.jpg)](https://www.redpointglobal.com/data-the-defining-difference/)## [The Data Readiness Series | Data: The Defining Difference](https://www.redpointglobal.com/data-the-defining-difference/) **Resources categories:** Retail **Resources tags:** Solution Brief --- ### [Customer-Centric Engagement in Healthcare](https://www.redpointglobal.com/resources/solution-brief-customer-centric-engagement-in-healthcare/) **Published:** May 9, 2018 **Author:** teresa keegan **Resources categories:** Healthcare, Pharmaceutical **Resources tags:** Solution Brief --- ### [Redpoint CDP x Snowflake: The Framework to Support an Evolving MarTech Stack](https://event.on24.com/wcc/r/4414778/B97632EF49033C94E63EF573FD11F360/?utm_source=website&utm_medium=hero&utm_campaign=solutions&utm_term=Snowflake#new_tab) **Published:** December 7, 2023 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [Unlocking Business Flexibility With Agile Customer Data Platforms](https://event.on24.com/wcc/r/4479913/1DED217539F24D0B80134F44E3347A51) **Published:** February 26, 2024 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [Video: Deep Dive into Data Matching & Merging](https://www.redpointglobal.com/resources/video-deep-dive-into-data-matching-merging/) **Published:** April 30, 2021 **Author:** wpengine **Resources categories:** Data Quality **Resources tags:** Video --- ### [Video: Deep Dive into Data Quality](https://www.redpointglobal.com/resources/video-deep-dive-into-data-quality/) **Published:** April 23, 2021 **Author:** wpengine **Resources categories:** Data Quality **Resources tags:** Video --- ### [Data Quality with Redpoint](https://youtu.be/kPBJhCyKHcU#new_tab) **Published:** October 17, 2023 **Author:** wpengine **Resources categories:** Data Quality **Resources tags:** Video --- ### [Identity Resolution with Redpoint](https://youtu.be/gOY2iU1mlPU#new_tab) **Published:** October 17, 2023 **Author:** teresa keegan **Resources categories:** Identity Resolution **Resources tags:** Video --- ### [What Can a Customer Data Platform (CDP) Offer](https://youtu.be/X1LN-JnjfBA#new_tab) **Published:** December 19, 2023 **Author:** Teresa Keegan **Resources tags:** Video --- ### [Customer Testimonial: CDP Community - Cate Twohill](https://www.youtube.com/watch?v=D3VtVoyx8Ws&t=3s#new_tab) **Published:** September 29, 2020 **Author:** teresa keegan **Resources categories:** Media & Entertaintment **Resources tags:** Video --- ### [Video: How Customer Data Platforms Deliver Best-in-Class Customer Experiences](https://youtu.be/_hMhniJTSvk#new_tab) **Published:** March 21, 2020 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [Video: CDP Use Cases - What to Consider Before Investing in a CDP](https://event.on24.com/wcc/r/4050535/9994DBF0BACD355407BE801DD7A7A554?partnerref=rpgwebresources#new_tab) **Published:** December 7, 2022 **Author:** wpengine **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [Consumerism - The Heart of Healthcare](https://youtu.be/442e0WY_Uas#new_tab) **Published:** July 15, 2022 **Author:** teresa keegan **Resources categories:** Healthcare **Resources tags:** Video --- ### [Delivering Timely Interactions at America's Top Satellite TV Provider](https://www2.redpointglobal.com/video-transcend-2019-dish-case-study#new_tab) **Published:** December 15, 2019 **Author:** teresa keegan **Resources categories:** Media & Entertaintment **Resources tags:** Video --- ### [Video: The Customer Journey Impact on Customer Experience](https://event.on24.com/wcc/r/4248160/BD0A3A722E952171CBC5DA0DA265AC73?partnerref=rpgwebresources#new_tab) **Published:** December 7, 2022 **Author:** Teresa Keegan **Resources categories:** Customer Experience **Resources tags:** Video --- ### [Best Practices at Each Stage of a Customer Data Platform](https://event.on24.com/wcc/r/4413189/5C29B41ACD411B4A83F4121A1BA88FA8?partnerref=rpresources#new_tab) **Published:** February 20, 2024 **Author:** teresa keegan **Resources categories:** Customer Data Plaform **Resources tags:** Video --- ### [Video: Identity Resolution Series - What is Identity Resolution?](https://event.on24.com/wcc/r/4050473/0C6630AE3F8585725D9BF1D2DFD9B298?partnerref=rpgwebresources#new_tab) **Published:** November 9, 2022 **Author:** wpengine **Resources categories:** Identity Resolution **Resources tags:** Video --- ### [Video: Identity Resolution Series - CDPs Need Identity Resolution](https://event.on24.com/wcc/r/4050487/4DA0DE22222EEE26AAE1698FBEAE4FBA?partnerref=rpgwebresources#new_tab) **Published:** November 9, 2022 **Author:** wpengine **Resources categories:** Identity Resolution **Resources tags:** Video --- ### [Video: Identity Resolution Series - Use Cases of Identity Resolution](https://event.on24.com/wcc/r/4050489/EDE67F4C71696935FA0C0282DAD90457?partnerref=rpgwebresources#new_tab) **Published:** November 10, 2022 **Author:** wpengine **Resources categories:** Identity Resolution **Resources tags:** Video --- ### [Video: Identity Resolution Series - Best Practices to Create Accurate Golden Records](https://event.on24.com/wcc/r/4050492/9EEF1B412808E03698316F17E46370BB?partnerref=rpgwebresources#new_tab) **Published:** November 10, 2022 **Author:** wpengine **Resources categories:** Identity Resolution **Resources tags:** Video --- ### [Video: Start with Customer Understanding and Empathy for CX Success - Go Beyond the Data](https://event.on24.com/wcc/r/4050503/6D1CC78508CA948577A9A9F9D9E34FDA?partnerref=rpgwebresources#new_tab) **Published:** November 10, 2022 **Author:** wpengine **Resources categories:** Customer Experience **Resources tags:** Video --- ### [Video: The Power of Personalisation](https://event.on24.com/wcc/r/4050506/CE308AA835D4E061388EB5B3EAFD418F?partnerref=rpgwebresources#new_tab) **Published:** November 10, 2022 **Author:** wpengine **Resources categories:** 1:1 Personalization **Resources tags:** Video --- ### [Video: Personalized Marketing Through Triggered Actions](https://event.on24.com/wcc/r/4050545/DEBB9E07D3FB7F85E1DC9DE0D25FB47C?partnerref=rpgwebresources#new_tab) **Published:** December 7, 2022 **Author:** wpengine **Resources categories:** 1:1 Personalization, Customer Experience **Resources tags:** Video --- ### [Video: Steps to Deliver Highly Personalized Experiences with Quality Data](https://event.on24.com/wcc/r/4247924/098B4F666CB0B4E42D61D33E1EA1082A?partnerref=rpgwebresources#new_tab) **Published:** December 7, 2022 **Author:** Teresa Keegan **Resources categories:** 1:1 Personalization, Data Quality **Resources tags:** Video --- ### [Video: The Customer Data Value Exchange](https://event.on24.com/wcc/r/4247904/364D42992CF3B7AEC0357CECD7543EA6?partnerref=rpgwebresources#new_tab) **Published:** December 7, 2022 **Author:** Teresa Keegan **Resources tags:** Video --- ### [Video: Martech Strategies: Turning Trends Into Actions](https://event.on24.com/wcc/r/4248148/92B7D847B12D032B55742ACDCB443D4C?partnerref=rpgwebresources#new_tab) **Published:** December 7, 2022 **Author:** Teresa Keegan **Resources tags:** Video --- ### [Video: Data-Powered Strategies for Improving Consumer Experience](https://event.on24.com/wcc/r/4072979/1C73936D93A8D2138E57237E99FD5A8C#new_tab) **Published:** February 3, 2023 **Author:** wpengine **Resources categories:** Healthcare **Resources tags:** Video --- ### [Return on Engagement: Why Email Matters for Remarkable Omnichannel Experience](https://event.on24.com/wcc/r/4300412/63CBAF91376EC1994CC1F2EB0C9EA18D?partnerref=rpgwebresources#new_tab) **Published:** July 26, 2023 **Author:** teresa keegan **Resources categories:** Healthcare **Resources tags:** Video --- ### [Video: Avoid an Identity Crisis - Know Your Customers, Post Third-Party Cookies](https://www.redpointglobal.com/resources/video-avoid-an-identity-crisis-know-your-customers-post-third-party-cookies/) **Published:** January 19, 2022 **Author:** wpengine **Resources categories:** 1:1 Personalization **Resources tags:** Video --- ### [Video: AI & Machine Learning to Increase Productivity & Profitability](https://www.redpointglobal.com/resources/video-ai-machine-learning-to-increase-productivity-profitability/) **Published:** February 18, 2021 **Author:** wpengine **Resources categories:** AI & Machine Learning **Resources tags:** Video --- ### [Powering Data-Led Customer Experiences for an International Home Improvement Company](https://www.redpointglobal.com/resources/data-lead-cx-for-international-home-improvement-company/) **Published:** March 6, 2024 **Author:** teresa keegan **Resources tags:** Case Study --- ### [Video: What is Master Data Management](https://www.redpointglobal.com/resources/video-what-is-master-data-management/) **Published:** August 13, 2021 **Author:** wpengine **Resources tags:** Video --- ### [Video: The Data & Analytics Behind a Personalized CX](https://www.redpointglobal.com/resources/video-the-data-analytics-behind-a-personalized-cx/) **Published:** February 25, 2021 **Author:** wpengine **Resources tags:** Video --- ### [Video: Strategies for Improving Member/Patient Engagement](https://www.redpointglobal.com/resources/video-strategies-for-improving-member-patient-engagement/) **Published:** September 23, 2020 **Author:** wpengine **Resources tags:** Video --- ### [Video: Solving Business Challenges with Analytics & Machine Learning](https://www.redpointglobal.com/resources/video-solving-business-challenges-with-analytics-machine-learning/) **Published:** February 18, 2021 **Author:** wpengine **Resources tags:** Video --- ### [Video: Real-Time Consumer Experience, More than Meets the Eye](https://www.redpointglobal.com/resources/video-real-time-consumer-experience-more-than-meets-the-eye/) **Published:** May 6, 2021 **Author:** wpengine **Resources tags:** Video --- ### [Video: Meet Rising Consumer Expectations to Attract & Retain Modern Shoppers](https://www.redpointglobal.com/resources/video-meet-rising-consumer-expectations-to-attract-retain-modern-shoppers/) **Published:** August 10, 2021 **Author:** wpengine **Resources tags:** Video --- ### [Video: How One Company Used Machine Learning to Drive Business Value](https://www.redpointglobal.com/resources/video-how-one-company-used-machine-learning-to-drive-business-value/) **Published:** February 20, 2021 **Author:** wpengine **Resources tags:** Video --- ### [Video: How Personalization is Driving Our Most Beloved Customer Experiences](https://www.redpointglobal.com/resources/video-how-personalization-is-driving-our-most-beloved-customer-experiences/) **Published:** March 31, 2021 **Author:** wpengine **Resources tags:** Video --- ### [Video: Delivering a Highly Personalized Customer Experience](https://www.redpointglobal.com/resources/video-delivering-a-highly-personalized-customer-experience/) **Published:** September 10, 2021 **Author:** wpengine **Resources tags:** Video --- ### [Video: CX Strategies - Breaking Down Marketers’ Perceptions vs Consumer Expectations](https://www.redpointglobal.com/resources/video-cx-strategies-breaking-down-marketers-perceptions-vs-consumer-expectations/) **Published:** January 21, 2022 **Author:** wpengine **Resources tags:** Video --- ### [Video: Can You Really Know Your Customers Without Cookies?](https://www.redpointglobal.com/resources/video-can-you-really-know-your-customers-without-cookies/) **Published:** May 19, 2021 **Author:** wpengine **Resources tags:** Video --- ## Events ### [2026 Predictions: Data Readiness for Personalization and AI](https://www.redpointglobal.com/resources/2026-predictions-data-readiness-for-personalization-and-ai/) **Published:** March 23, 2026 **Author:** teresa keegan **Events categories:** Past Events, Webinar --- ## Landing Pages categories ### [Thank You](https://www.redpointglobal.com/landing_pages_category/thank-you/) --- ### [Social LP](https://www.redpointglobal.com/landing_pages_category/social-lp/) --- ### [Email LP](https://www.redpointglobal.com/landing_pages_category/email-lp/) --- ### [Request a Demo](https://www.redpointglobal.com/landing_pages_category/request-a-demo/) --- ### [Wistia Video - Resource](https://www.redpointglobal.com/landing_pages_category/wistia-video-resource/) --- ## Partners categories ### [ecosystem](https://www.redpointglobal.com/partners_category/ecosystem/) --- ## Integrations categories ### [In nav](https://www.redpointglobal.com/integration_category/in-nav/) --- ### [Category](https://www.redpointglobal.com/integration_category/category/) --- ### [Database](https://www.redpointglobal.com/integration_category/category/database/) --- ### [ESP](https://www.redpointglobal.com/integration_category/category/esp/) --- ### [Push](https://www.redpointglobal.com/integration_category/category/push/) --- ### [CRM](https://www.redpointglobal.com/integration_category/category/crm/) --- ### [Data Onboarding](https://www.redpointglobal.com/integration_category/category/data-onboarding/) --- ### [File Storage](https://www.redpointglobal.com/integration_category/category/file-storage/) --- ### [Cache](https://www.redpointglobal.com/integration_category/category/cache/) --- ### [Queue Providers](https://www.redpointglobal.com/integration_category/category/queue-providers/) --- ### [Web Analytics](https://www.redpointglobal.com/integration_category/category/web-analytics-service/) --- ### [Survey](https://www.redpointglobal.com/integration_category/category/survey/) --- ### [Misc](https://www.redpointglobal.com/integration_category/category/misc/) --- ### [External Content Provider](https://www.redpointglobal.com/integration_category/category/external-content-provider/) --- ### [Web Adapter](https://www.redpointglobal.com/integration_category/category/web-adapter/) --- ### [SMS](https://www.redpointglobal.com/integration_category/category/sms/) --- ### [Type](https://www.redpointglobal.com/integration_category/type/) --- ### [Destination](https://www.redpointglobal.com/integration_category/type/destination/) --- ### [Source](https://www.redpointglobal.com/integration_category/type/source/) --- ### [Data Processors](https://www.redpointglobal.com/integration_category/category/data-processors/) --- ### [Unica LP](https://www.redpointglobal.com/integration_category/category/unica-lp/) --- ## Case Studies categories ### [Top-Four](https://www.redpointglobal.com/case_studies_category/top-four/) --- ### [Full Page Case Study](https://www.redpointglobal.com/case_studies_category/full-page-case-study/) --- ### [Full Page - Fully Editable](https://www.redpointglobal.com/case_studies_category/full-page-fully-editable/) --- ## Case Studies tags ### [Travel & Hospitality](https://www.redpointglobal.com/case_studies_tag/travel-hospitality/) --- ### [Retail](https://www.redpointglobal.com/case_studies_tag/retail/) --- ### [Financial Services](https://www.redpointglobal.com/case_studies_tag/financial-services/) --- ### [Media & Entertainment](https://www.redpointglobal.com/case_studies_tag/media-entertainment/) --- ### [Healthcare Payers](https://www.redpointglobal.com/case_studies_tag/healthcare-payers/) --- ### [Healthcare Providers](https://www.redpointglobal.com/case_studies_tag/healthcare-providers/) --- ### [Pharmaceutical](https://www.redpointglobal.com/case_studies_tag/pharmaceutical/) --- ## Blog categories ### [Single Customer View](https://www.redpointglobal.com/blog_category/by-topic/single-customer-view/) --- ### [Customer Data Platform](https://www.redpointglobal.com/blog_category/by-topic/customer-data-platform/) --- ### [Data Quality](https://www.redpointglobal.com/blog_category/by-topic/data-quality/) --- ### [Identity Resolution](https://www.redpointglobal.com/blog_category/by-topic/identity-resolution/) --- ### [Omnichannel Marketing](https://www.redpointglobal.com/blog_category/by-topic/omnichannel-marketing/) --- ### [Data Management](https://www.redpointglobal.com/blog_category/by-topic/data-management/) --- ### [Real-Time Personalization](https://www.redpointglobal.com/blog_category/by-topic/real-time-personalization/) --- ### [Anonymous to Known](https://www.redpointglobal.com/blog_category/by-topic/anonymous-to-known/) --- ### [Master Data Management](https://www.redpointglobal.com/blog_category/by-topic/master-data-management/) --- ### [Data Governance & Security](https://www.redpointglobal.com/blog_category/by-topic/data-governance-security/) --- ### [1:1 Personalization](https://www.redpointglobal.com/blog_category/by-topic/one-to-one-personalization/) --- ### [Segmentation & Activation](https://www.redpointglobal.com/blog_category/by-topic/segmentation-activation/) --- ### [Journey Orchestration](https://www.redpointglobal.com/blog_category/by-topic/journey-orchestration/) --- ### [AI & Machine Learning](https://www.redpointglobal.com/blog_category/by-topic/ai-machine-learning/) --- ### [Data Observability](https://www.redpointglobal.com/blog_category/by-topic/data-observability/) --- ### [Data Ingestion](https://www.redpointglobal.com/blog_category/by-topic/data-ingestion/) --- ### [Composability](https://www.redpointglobal.com/blog_category/by-topic/composability/) --- ### [By Industry](https://www.redpointglobal.com/blog_category/by-industry/) --- ### [Retail](https://www.redpointglobal.com/blog_category/by-industry/retail-by-industry/) --- ### [Financial Services](https://www.redpointglobal.com/blog_category/by-industry/financial-services/) --- ### [Healthcare](https://www.redpointglobal.com/blog_category/by-industry/healthcare/) --- ### [Travel & Hospitality](https://www.redpointglobal.com/blog_category/by-industry/travel-hospitality/) --- ### [By Topic](https://www.redpointglobal.com/blog_category/by-topic/) --- ### [Data Readiness](https://www.redpointglobal.com/blog_category/by-topic/data-readiness/) --- ### [Agentic AI](https://www.redpointglobal.com/blog_category/by-topic/agentic-ai/) --- ### [Data Product](https://www.redpointglobal.com/blog_category/data-product/) --- ## Blog tags ### [Data quality](https://www.redpointglobal.com/blog_tag/data-quality/) --- ### [customer data platform](https://www.redpointglobal.com/blog_tag/customer-data-platform/) --- ### [CDP](https://www.redpointglobal.com/blog_tag/cdp/) --- ### [Data-in-Place](https://www.redpointglobal.com/blog_tag/data-in-place/) --- ### [Data Observability](https://www.redpointglobal.com/blog_tag/data-observability/) --- ### [Golden Record](https://www.redpointglobal.com/blog_tag/golden-record/) --- ### [GenAI](https://www.redpointglobal.com/blog_tag/genai/) --- ### [Composable](https://www.redpointglobal.com/blog_tag/composable/) --- ### [identity resolution](https://www.redpointglobal.com/blog_tag/identity-resolution/) --- ### [Data readiness](https://www.redpointglobal.com/blog_tag/data-readiness/) --- ### [Agentic AI](https://www.redpointglobal.com/blog_tag/agentic-ai/) --- ## Events categories ### [Webinar](https://www.redpointglobal.com/events_category/webinar/) --- ### [Past Events](https://www.redpointglobal.com/events_category/past-events/) --- ## Resources categories ### [Customer Data Plaform](https://www.redpointglobal.com/resources_category/by-topic/customer-data-plaform/) --- ### [Data Quality](https://www.redpointglobal.com/resources_category/by-topic/data-quality/) --- ### [Data Ingestion](https://www.redpointglobal.com/resources_category/by-topic/data-ingestion/) --- ### [Data Observability](https://www.redpointglobal.com/resources_category/by-topic/data-observability/) --- ### [Identity Resolution](https://www.redpointglobal.com/resources_category/by-topic/identity-resolution/) --- ### [Segmentation & Activation](https://www.redpointglobal.com/resources_category/by-topic/segmentation-activation/) --- ### [Real-Time Personalization](https://www.redpointglobal.com/resources_category/by-topic/real-time-personalization/) --- ### [Orchestration](https://www.redpointglobal.com/resources_category/by-topic/orchestration/) --- ### [Financial Services](https://www.redpointglobal.com/resources_category/by-industry/financial-services/) --- ### [Retail](https://www.redpointglobal.com/resources_category/by-industry/retail/) --- ### [Healthcare](https://www.redpointglobal.com/resources_category/by-industry/healthcare/) --- ### [Pharmaceutical](https://www.redpointglobal.com/resources_category/by-industry/pharmaceutical/) --- ### [Media & Entertaintment](https://www.redpointglobal.com/resources_category/by-industry/media-entertaintment/) --- ### [Travel & Hospitality](https://www.redpointglobal.com/resources_category/by-industry/travel-hospitality/) --- ### [1:1 Personalization](https://www.redpointglobal.com/resources_category/by-topic/one-to-one-personalization/) --- ### [Omnichannel Marketing](https://www.redpointglobal.com/resources_category/by-topic/omnichannel-marketing/) --- ### [Single Customer View](https://www.redpointglobal.com/resources_category/by-topic/single-customer-view/) --- ### [Composability](https://www.redpointglobal.com/resources_category/by-topic/composability/) --- ### [Cloud Data](https://www.redpointglobal.com/resources_category/by-topic/cloud-data/) --- ### [AI & Machine Learning](https://www.redpointglobal.com/resources_category/by-topic/ai-machine-learning/) --- ### [By Industry](https://www.redpointglobal.com/resources_category/by-industry/) --- ### [By Topic](https://www.redpointglobal.com/resources_category/by-topic/) --- ### [Customer Experience](https://www.redpointglobal.com/resources_category/by-topic/customer-experience/) --- ### [Data Management](https://www.redpointglobal.com/resources_category/by-topic/data-management/) --- ### [Advertising](https://www.redpointglobal.com/resources_category/by-topic/advertising/) --- ### [Customer Data Readiness](https://www.redpointglobal.com/resources_category/by-topic/customer-data-readiness/) --- ## Resources tags ### [Analyst Report](https://www.redpointglobal.com/resources_tag/analyst-report/) --- ### [Infographic](https://www.redpointglobal.com/resources_tag/infographic/) --- ### [Research Report](https://www.redpointglobal.com/resources_tag/research-report/) --- ### [Solution Brief](https://www.redpointglobal.com/resources_tag/solution-brief/) --- ### [Video](https://www.redpointglobal.com/resources_tag/video/) --- ### [eBook / White Paper](https://www.redpointglobal.com/resources_tag/ebook-white-paper/) --- ### [Overview](https://www.redpointglobal.com/resources_tag/overview/) --- ### [Product Brief](https://www.redpointglobal.com/resources_tag/product-brief/) --- ### [Case Study](https://www.redpointglobal.com/resources_tag/case-study/) --- ## Customers categories ### [Healthcare](https://www.redpointglobal.com/customers_category/healthcare/) --- ### [Retail](https://www.redpointglobal.com/customers_category/retail/) --- ### [Home Page](https://www.redpointglobal.com/customers_category/home-page/) --- ### [Finance](https://www.redpointglobal.com/customers_category/finance/) --- ### [Travel & Hospitality](https://www.redpointglobal.com/customers_category/travel-hospitality/) --- ### [Media & Entertainment](https://www.redpointglobal.com/customers_category/media-entertainment/) --- ### [Unica Campaign](https://www.redpointglobal.com/customers_category/unica-campaign/) --- ## Integration Type ### [ecosystem](https://www.redpointglobal.com/integration_type/ecosystem/) --- ## Landing Page Type ### [Free Form](https://www.redpointglobal.com/landing_page_type/free-form/) --- ### [Lovable](https://www.redpointglobal.com/landing_page_type/lovable/) --- ## Blog Type ### [In Nav](https://www.redpointglobal.com/blog_type/in-nav/) --- ## Resource Type ### [Wistia Video](https://www.redpointglobal.com/resource_type/wistia-video/) ---