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 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, 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.
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 [email protected] and [email protected] 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.
