The CDP Institute 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, so the intent here is not to point out why Redpoint rg1 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 Redpoint rg1 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.