CDP vs. DMP: The 3 Main Differences & What’s Right for You

If you’re looking for a better understanding of the differences between CDP and DMP, you’re in the right place. In this post, we’ll define both CDP and DMP and explain how they are often confused for one another. Then we’ll dive into the three main differences: data usage, customer identity data, and data retention. We’ll help you decide which solution is best for your needs.

What are CDPs and DMPs?

CDP stands for Customer Data Platform. It’s a software platform that collects customer data from various sources (websites, CRMs, etc.) and stores it in a central repository. The CDP then analyzes the data and provides insights to help marketers gain better understanding of their customers.

DMP stands for Data Management Platform. A DMP is a centralized system used to store and manage large amounts of data from multiple sources including websites, mobile apps, analytics tools, marketing platforms, etc. It helps businesses capture insights about their target audience so they can better understand how to engage with them.

CDPs and DMPs are often confused with each other because they both provide similar capabilities: storing, managing, and analyzing data. However, there are three key differences between them, as described in the following sections.

1. Data usage: 1st- party data vs. 2nd and 3rd party data

CDPs are typically used to store and manage 1st-party data, which refers to the customer data that a company collects from its own website, mobile app, CRM system, etc. On the other hand, DMPs are typically used to store and manage 2nd- and 3rd-party data, which refers to the customer data collected from external sources such as advertising networks and market research firms.

1st-party data has different value and use than 2nd- and 3rd-party data. With 1st-party data, companies can gain insights about their own customers and use that information to target them more effectively. With 2nd- and 3rd-party data, companies can understand the behaviors of potential customers outside of their own customer base, allowing them to target more broadly. As an example, a clothing retailer might use 2nd- and 3rd-party data to identify people who are likely to be in the market for a new wardrobe while 1st-party data could be used to personalize recommendations for existing customers.

2. Customer identity data

The second key difference between CDPs and DMPs is that CDPs are typically used to store a company’s customer profile data while DMPs are not. A customer profile contains all the available information about a particular customer such as name, gender, age, address, purchase history, etc. This type of data provides businesses with valuable insights about their customers which can be used for better segmentation and personalization.

In contrast, DMPs rely on anonymous data which does not contain any customer identity information. This data can be used for segmentation and targeting but it is not as powerful as 1st-party profile data since it does not provide any insights about the customer’s behavior or preferences.

3. Data retention

The third key difference between CDPs and DMPs is that CDPs are designed to store data for long-term use while DMPs are not. This means that a company using a CDP can access historical customer data over time, allowing them to gain deeper insights about their customers. On the other hand, DMPs are designed to store data for only a specific period of time which means that data older than that set time frame cannot be accessed.

As an example, a company using a CDP might be able to look back over the past year and gain insights about their customer’s purchase frequency, location, demographics, etc. On the other hand, a company using a DMP might only be able to access data from the last 30 days. Moreover, the DMP data is anonymous so the company cannot gain insights about individual customers. That said, DMPs are great for providing companies with a snapshot of their target audience over time.

CDP vs DMP: how to know which is best for you

The choice of whether to use a CDP or DMP depends on your data needs and the goals you want to achieve. For example, if you’re looking for insights about your own customers then a CDP is likely the best option as it provides access to 1st-party profile data and allows for long-term storage. On the other hand, if you’re looking for insights about potential customers outside of your customer base then a DMP might be a better fit since it provides access to 2nd- and 3rd-party data but does not allow access to historical customer data.

You may want to use a DMP to create marketing campaigns for new audiences, or audiences that are unfamiliar to you. It can be helpful to use third-party data sources–such as Facebook–to gain access to audiences that you do not already know.

A CDP is best, however, for unifying and sorting your first-party data. Your CDP will enable you to create hyper-personalized marketing experiences, because you will have your customer data from various channels and platforms housed under one roof. CDPs also allow you to hyper personalize campaigns for your users. For example, you can target customers based on purchase history, pages they viewed and more.

Final Thoughts

Ultimately, choosing between CDPs and DMPs comes down to understanding your business’s needs and deciding which tool will best help you achieve your goals. By taking the time to evaluate your needs and understanding the key differences between CDPs and DMPs, you can make an informed decision about which tool is right for you.

Many businesses find it extremely useful to be able to retain customer data in one single source, as a source of truth for future marketing campaigns, and a reference point for measuring accuracy of campaigns throughout the customer lifecycle. Zeta Global’s CDP+ is a customer data management platform designed to deliver personalized experiences at scale.

In addition to offering valuable insights into 1st party data, Zeta’s CDP+ also allows access and incorporation of 3rd party data, enabling you to combine insights from all data sources in one location, with a low-code interface that works with existing technology to provide total data transparency and control.