Customer Data Platforms (CDPs) can be confusing. It’s a term that’s been adopted by so many solutions over the past few years that it can be very overwhelming for organizations looking to implement a new CDP. One of the widely accepted classifications of types of CDP encompasses what are essentially four different variations as defined below. Each CDP type builds upon the previous – for example an Analytics CDP should meet the definition of a Data CDP and also include the added functionality defined for Analytics CDP. A Campaign CDP would build upon that further and provide application of personalization, and so on. Let's dive in:
The most crucial thing to understand is that all the other columns are irrelevant if you don’t have good data. Data should be at the heart of any CDP solution, although there are many so-called CDPs in the market that only provide campaign or delivery functionality. These solutions will ultimately fail because they don't include the critical foundational component of data.
Before we dive deeper into each type of CDP, let’s consider the purpose and benefits of a CDP.
A CDP at its core is a solution that captures and combines customer data, organizes it by individual, and makes the data available to every aspect of the business as needed. The major claim to fame of a CDP is that the process of collecting huge amounts of data is automated across channel, device, and users, then structured, deduplicated, and verified to ensure you can use the data effectively. In the ever-evolving marketing landscape, global marketers are still struggling to do exactly that. According to Nielsen’s 2022 Global annual marketing report 36% of global marketers claim data access, identity resolution, and driving insights from data are extremely, or very difficult. The solution to these challenges is to prioritize data strategies, starting with investment in first-party data via a robust CDP.
The benefits are many. Gartner says that “CDPs enable a unified view of the customer, create more efficient marketing through better targeting and coordinated orchestration, provide better visibility into the full customer journey, and enable personalization which improves the customer experience.” Similarly, according to Forrester: “CDPs enable organizations to drive seamless, targeted, and rich multichannel digital customer experiences.”
What does all of this have in common? At the end of the day, a CDP is a data capture solution that powers improved customer understanding and, as a result, better experiences. But the heart of it all is…data. If you don’t have good data, everything else is irrelevant. Best-of-breed data capture capabilities are the building block of a successful MarTech stack because the data captured is the foundation of all the segmentation, insights, personalization, and delivery outcomes that come after. That’s why the CDP Institute structures their CDP definitions progressively, starting with core data functionality.
What are the 4 types of CDP?
As mentioned, CDP vendors can be grouped into four categories based on the functionality provided by their systems. Each category includes functions provided by the previous categories.
These systems gather customer data from source systems and can also be defined as data ingestion and integration or assembly solutions. They link data to customer identities and store the results in a database that’s made available to external systems. CDPs can use data from many sources, including first-party data, zero and second-party data, and third-party cookie data (while it lasts). This is considered the minimum set of functions required to be considered a CDP. While all CDPs need data storage capabilities, not all systems that store data are CDPs. Data CDPs can also extract audience segments and send them to external systems, and often employ specialized technologies for customer data management and access. Some began as tag management or web analytics systems and are often strongest in those areas.
These systems provide data assembly plus analytical applications. The applications always include customer segmentation and sometimes extend to machine learning, predictive modeling, revenue attribution, and journey mapping. Data unification and identity resolution can (and should) be part of an analytics solution, stitching and enriching data to build a comprehensive single customer view. These detailed, individual-level analytics also enable highly detailed revenue attribution and marketing optimization. Analytics CDPs often automate the distribution of data to other systems.
These systems are often referred to as engagement CDPs and provide data assembly, analytics, and decisioning. What distinguishes them from segmentation is they can specify different actions for different individuals within a segment. These decisions may be personalized messages, outbound marketing campaigns, real-time interactions, or product or content recommendations. They often include orchestrating customer actions across channels. Think of it as a combination of a data & analytics CDP with a decisioning system.
These systems provide data assembly, analytics, decisioning, and message delivery or activation, meaning they can connect to some or all your engagement channels from email to website, mobile apps, CRM, advertising, and more. Products in this category often started as delivery systems and added CDP functions later. They’re typically more like a marketing cloud than a true CDP, and often not as strong on the data capture and data assembly side of things as they are on delivery. Many delivery CDPs only enable activation in limited channels, undermining the true value of a CDP which is in feeding multiple relevant downstream applications throughout the organization.
As you can see in the various types of CDP, the core functionality is capturing data in a way that it can be used across the organization. If your customer data platform doesn’t fully encompass the first pillar (data CDP) the rest of it doesn’t matter. As our CEO so aptly puts it “You can buy the prettiest and most renowned tools for data activation, but they won’t deliver unless you have the right data. You won’t get that from web analytics or assembly CDPs. It’s like buying a car and skipping the costs for an engine.”
An effective CDP will seamlessly capture, structure, contextualize, and enrich your customer data from any source and connect it to the applications that need itinstant occurrence to facilitate relevant, one-to-one marketing in real-time.deliver highly engaging, hyper-personalized brand experiences that convert and retain customers. To truly stand out against the competition, remember that the key to all of these functions is
Nielsen’s Chief Marketing and Communications Officer hit the nail on the head: “With real-time changes in consumer attitudes, behavior and media engagement, the right data is more important than ever. Robust and accurate data must be marketers’ north star for understanding and engaging the consumer and for measurement and attribution that enables the highest ROI.“