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 the meaning of a CDP 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 the Meaning of a CDP? 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, 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, ready to drive innovative CX use cases, 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 now takes a back seat to other functionality. A CDP that deprioritizes data quality – 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)
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.
Timeliness as it relates to the meaning of a CDP is understood to mean making sure that data is ready for business use up to and including real time. 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?
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.
