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Data Ingestion

A core CDP capability, data ingestion refers to bringing data about a customer from multiple systems into the platform.


For a robust, enterprise-grade CDP, data ingestion means ingesting all forms of customer data – first-party, second-party, third-party, batch, streaming, social. All forms and types of data represent signals about a customer, and thus all forms and types should be collected.


One factor to consider about a CDP’s approach to data ingestion is the cadence, particularly when thinking about outcomes or use cases that may require a real-time interaction. Continuous, real-time data ingestion from sources and systems that are themselves in real time ensures that data moving through the platform is in the cadence of the customer.


Segmentation is the act of dividing customers into cohesive yet distinct and granular groups that can be targeted with relevant messaging, content and offers delivered in the context of an individual customer journey.


Segmentation is a basic marketing strategy for providing customers with relevant experiences. In a marketing use case, if a cohort (women, 25-34) has expressed a strong affinity for a certain product, basic automated segmentation is the process of carving that audience out of your customer database to market to the audience differently than you would another segment. A limitation of basic segmentation is that the segmentation method – age, gender, income, geo, etc. – may not capture what matters or is meaningful to a customer, nor does a static segment (created at a point in time) capture how a customer proceeds through a customer journey, either across channels or in terms of what is relevant to the customer at the moment of interaction. The limitations of basic segmentation can be traced to the fact that a list-based segment begins to decay the moment it is created. Marketers extract and divide an audience, turn the segment into a list and push the list into a channel, where they execute a campaign. But this process does not account for the fact that customer intent, preferences and even life stages are always in flux.


The Redpoint CDP performs dynamic segmentation, using a rules-based approach that accounts for the fact that customer journeys are dynamic across multiple online and offline channels. The Redpoint CDP continually updates data from various sources and adjusts audience segments in real-time, moving with a customer or group of customers across omnichannel customer journeys. Built once, segments change on the fly as real-time data is ingested, optimized according to a pre-determined rule that aligns with the business objective.

Dynamic audience segmentation using rules instead of lists ensures that segments are accurate and up-to-date at the moment of engagement, independent of channel. Some of the conditions and variables that move customers into and out of a universal segment include time-based or geo-based triggers, or any change to a unified customer profile.

Dynamic segmentation is customer-centric, focused only dividing an audience according to the attributes that are important and meaningful to understanding the customer at the moment of interaction, as determined by the underlying data.Segmentation in the Redpoint CDP is also intelligent, i.e., it provides marketers with the ability to create complex segments without requiring code to segment an audience any way a marketer would want – multiple resolution levels, aggregates on demand, nesting, data flows, hooks into third-party systems during audience creation, unmatched metadata management, etc. Complex selections can be done without a single line of SQL.

Artificial Intelligence

Artificial Intelligence (AI) is a branch of computer science that focuses on building and managing technology that can learn to autonomously make decisions and carry out actions on behalf of a human being. AI can include any type of software or hardware component that supports machine learning, computer vision and natural language processing (NLP).


For use in a CDP, AI is used to improve a process (e.g., identity resolution, A/B testing, segmentation, etc.) to ultimately deepen customer understanding and enable a more personalized customer experience. CDPs can also leverage generative AI (GenAI) to allow marketers to directly interact with AI through large language models (LLMs) and natural language processing to iteratively build out and refine segments.


Redpoint AI improves the quality of data stored in the Redpoint CDP, and that AI-ready data is then used to train AI models and enhance customer profiles. As the hub of an AI cycle where better data yields better results, Redpoint AI enables the continuous refinement and optimization of customer data management processes that leads to more accurate and actionable insights for personalized marketing and CX initiatives. Redpoint AI does this through:

Data Cleaning and Enhancement

AI algorithms are used to analyze and cleanse incoming customer data streams, identifying and correcting errors, inconsistencies, and missing information. This includes tasks such as data deduplication, normalization, validation, and enrichment. By leveraging AI-driven data cleaning techniques, organizations can ensure that the data stored in the CDP is accurate, complete, and up-to-date, laying a solid foundation for effective customer data management and analysis.

AI-Driven Insights

The cleansed and enhanced customer data stored in the CDP is then used to train AI models and generate insights into customer behavior, preferences, and trends. This includes predictive analytics models that forecast future customer behavior, segmentation algorithms that group customers based on common attributes, and personalization engines that recommend targeted offers and content to individual customers. By applying AI to the enriched customer data, organizations can uncover valuable insights that drive more informed decision-making and personalized customer interactions.

Feedback Loop

The insights generated by AI models are fed back into the CDP to further enhance customer profiles and improve data quality over time. For example, predictive analytics models may identify patterns or anomalies in customer behavior that were not previously captured, leading to updates and refinements in customer profiles. Similarly, segmentation algorithms may identify new customer segments or refine existing ones based on emerging trends or changes in customer preferences. By continuously updating and enriching customer profiles with AI-driven insights, organizations can ensure that their CDP remains adaptive and responsive to evolving customer needs and market dynamics.


Iterative Improvement

The AI virtuous cycle operates in an iterative manner, with each iteration leading to incremental improvements in data quality, insights generation, and customer engagement. As organizations collect more data, train more AI models, and refine their customer data management processes, the AI virtuous cycle becomes more effective at driving business outcomes such as increased revenue, improved customer satisfaction, and enhanced brand loyalty. By embracing the AI virtuous cycle, organizations can harness the power of AI to unlock the full potential of their customer data and deliver personalized and impactful customer experiences at scale.