Customer Data Platform - CDP and Data Management Platform - DMP: Key Differences, Benefits, and how they co-existed
Better Together In Powering A More Successful MarTech Stack
Organizations that want to stay ahead of the curve and compete in today’s fast-paced consumer market know that customers must be at the center of everything they do. This means providing the right experience at the right time, which requires real-time data and the correlation of that data across relevant marketing touchpoints and channels that can be tied back to an individual.
Brands are looking at Customer Data Platforms (CDP) and Data Management Platforms (DMP) to help capture, correlate, and manage their customer data, but many are confused as to exactly what each technology is and which solution is better suited for their organization based on the feature set of the platform at hand, and the answer could very well be both.
What’s the difference between a CDP and a DMP? Which benefits does one offer that the other doesn’t? Do they work together or in parallel? Should an organization have both in their martech stack? Here we explain the key differences between each solution so you know what the best choice is for you.
What is a CDP?
A Customer Data Platform (CDP) is a service to capture, correlate, and activate customer data across multiple channels, devices, and technologies. They provide a single view of the customer and allow for powerful data enablement across multiple teams, tools, skill sets, and features. CDPs create centralized data to be used by all facets of an organization in real time.
What is a DMP?
A Data Management Platform (DMP) aggregates website behavioral data and categorizes it into taxonomies which are used to build segments. Segments can consist of first, second, and third-party data, which can be used for analysis and distribution into other adtech systems, primarily DSPs, to enable the buying and selling of programmatic advertising. The purpose of DMPs is to help brands drive more visitors to their websites through top of funnel targeting with the goal of generating more leads into the sales funnel. Examples of DMPs are Salesforce Krux and Oracle Bluekai.
Why are CDPs So Hot Right Now?
DMPs were formed to understand digital behavior across the web, and by nature are designed to communicate with other technologies that utilize third-party cookies, like DSPs. Following marketing cloud acquisition, DMPs tried to take on the role of integrators and identity resolution tools, but because they can only manage third-party cookies, true identity resolution could not be accomplished and data activation across multiple channels and tools couldn’t be done efficiently.
Enter the CDP
As true identity resolution requires having multiple identifiers unified into a single profile and the ability to ingest data from all customer data sources CDPs entered the space to achieve what DMPs alone could not. With the rise of global privacy regulations, a single view of the customer and storage of that single view has become a top priority for organizations.
Key Differences Between a CDP and a DMP
Anonymous in nature
Resolves identity down to a specific person
|ID Management||Limited to the storage of third-party identifiers, natively in platform||Ability to store and persist all identifiers associated to a person, natively in platform|
|Visitor Matching||Heavily reliant on probabilistic identifiers||Based on deterministic identifiers|
|Data Ingestion||Capable of both offline and digital data ingestion, however data needs to have already been matched to a third-party identifier within the DMP prior to ingestion||Capable of ingesting both online and offline data, with no previous data matching in the CDP required|
|Data Enrichment||Based on third-party data blending and/or look-alike modeling||Based on the first-party dataset captured and correlated in platform, option to augment with third-party data sets|
|Data Activation||Primarily other third-party based AdTech platforms||All tools and channels, based on visitor identifiers|
Better Together: How CDPs and DMPs Work Together
When talking about CDPs and DMPs it’s important not to think of each solution in an either/or capacity, but rather as two solutions that are complementary to each other within a martech stack.
Due to the nature of data ingestion and ID resolution addressed above, CDPs are better qualified to be your single source of truth as it pertains to your customer data. Data should be analyzed and modeled in the CDP for a true understanding of behavior across all touchpoints. CDPs should then be the point of customer data orchestration to all tools including the DMP.
The DMP, utilizing the granular insights from the CDP, will create better look-alike audiences which will improve engagement and ultimately ROI throughout your marketing stack.
What Is First, Second, and Third-Party Data?
First-party data is the data that your organization collects directly from your customer base from your digital properties or anywhere your customer data is stored.
Second-party data is another organization’s first-party data which has been shared directly with your organization.
Third-party data is any information collected by an entity that does not have a direct relationship with the customer the data is being collected on. Often times, third-party data is generated on a variety of websites and platforms and is then aggregated together by a third-party data provider such as a DMP.
What is a Cookie?
A cookie (when used in web terms) is informational data that’s stored on a customer’s browser after they visit a website so their preferences and behaviors are remembered and used in future requests and interactions.
First-party cookies are used to collect first-party data. First-party cookies can only be accessed by the domain they are set on and thus cannot be accessed across domains.
Second-party cookies do not exist. Second-party data is collected by first-party cookies.
Third-party cookies are set by a domain other then the domain the visitor is currently viewing. The nature of third-party cookies allows them to collect data across domains but not be accessible by the current domain being viewed.