CDP vs DMP: What's the difference?

Customer data management has undergone some pretty radical changes recently, introducing some surprising results for its handlers. Primarily, the shift is in the tools used to store and handle customer data. Technology is moving quickly in this area, and rapid updates address new privacy measures as well as the escalating needs of firms consuming this data.

These new requirements for customer data management and the availability of new types of data have led to the evolution of overlapping storage systems with similar but distinct purposes. This article will help illustrate the difference between two of these tools — customer data platforms (CDPs) and data management platforms (DMPs) — and explain how modern developments influence the use of CDPs versus DMPs.

Visualizing the head-to-head comparison of a CDP vs DMP


As customer data storage needs have increased in complexity, specialized data systems have become necessary to manage different needs. Although CDP and DMP systems are both designed to handle customer data for companies to analyze their customer base, they serve different purposes.

At the highest level, the difference between CDP and DMP storage could be characterized as the difference between marketing and advertising. While CDPs offer a deep and lasting view of existing users and customers, a DMP collates transient demographic information about target markets and audiences. Therefore a CDP is most useful to teams looking to understand and improve their product, whereas a DMP helps teams design and optimize advertising.

Let’s dive into each tool in more depth.

What is a CDP?

A Customer Data Platform (CDP)is a specialized software platform designed to collect customer data from various sources, unify that data to build complete customer profiles, and activate that data to enrich the quality of customer interactions across multiple touchpoints. The complete customer view – often called a single customer view or customer 360 – is the central component of the CDP.

Unlike many other CDPs that are solely touted as being marketing tools, RudderStack's CDP is designed to empower every business team.

When developing your product or brand, you will have access to a wide spectrum of information on new and existing customers. This information could come from any number of sources, like page views, purchasing patterns, or app usage statistics. Refining your product or outreach to appeal to those customers requires some means of consolidating and organizing these individual sources of customer information.

A CDP allows you to solve problems like customer duplication, tracking, and personalization for customers across many channels, while closely controlling data to meet privacy regulations. It also enables identity resolution, an important process for improving data quality and refining customer experiences.

The data input into a CDP is usually first-party data (more on this later), which means it has higher value and imputes greater responsibility to your brand. CDPs require a high level of customer data security, corresponding to the sensitivity of their stored data.

For more information read: What is a Customer Data Platform?

How does a CDP work?

  • Event Streaming: RudderStack's warehouse native event collection and integrations allow you to easily ingest clean, first-party event data, send it across your data stack, and store it in your warehouse. Our state of the art, high-performance SDKs allow you to collect everything from web, server, mobile and cloud.
  • Data Unification: CDPs help by eliminating the complex modeling work for identity resolution and customer 360, empowering every team with a single source of truth.
  • Accelerates Critical Data Projects: From building powerful AI & ML models on clean customer data to shipping projects that are in compliance with GDPR, HIPPA and more; CDPs are a must for any data team.
  • Data Activation: CDPs should have a data activation component. Activate profiles, warehouse tables, or no-code audiences in downstream tools with Reverse ETL. However data activation is not limited to Reverse ETL, like many vendors may claim. Read: what is data activation to learn more.

What is a DMP?

The first steps in brand advertising involve sifting through customers as a statistical distribution, rather than individual contacts with detailed data points. A DMP collects data from a wide band of sources; however, it doesn’t allow you to identify individual customers. Instead, it combines anonymized data from large-scale customer data vendors to give a view into potential advertising targets and a reflection of conversion rates for existing campaigns.

DMPs are often thought of as “cookie jars,” a reference to the fact that most of the data suitable for a DMP comes from browser cookies that track website usage. This means DMPs can offer only limited insight into your brand or product. They may contain data about clickthrough rates in relation to particular ad campaigns, but it couldn’t generate something like a sales funnel or user experience analysis that would result from tracking individual customer interactions.

The data in a DMP is much less valuable on an individual basis than that in a CDP. This is primarily because most data used by DMPs is available from vendors, and is therefore replaceable. But data stored in DMPs also has a high rate of expiry. This is not only due to privacy controls and vendor terms, but also because anonymized market data frequently changes as a result of seasonal effects or large-scale market changes.

How does a DMP work?

Data Management Platforms (DMPs) are instrumental in gathering and securing data from mobile applications and websites. Typically, they are more widely employed as third-party tools for enrichment, enhancing audience targeting capabilities. This allows for expanded reach across various advertising platforms. The primary advantage of a DMP is its ability to provide access to otherwise unavailable data.

Additionally, these platforms offer the capability to link directly with Demand-Side Platforms (DSPs). This connection facilitates communication with ad exchanges, ad networks, and publishers, streamlining the process of real-time bidding for ad placements. For marketers, this means the ability to build an audience within a DMP and seamlessly integrate it with a DSP.

What are the Differences Between a CDP and a DMP?

Both Customer Data Platforms (CDPs) and Data Management Platforms (DMPs) are valuable tools for building and targeting specific audiences. However, they serve different purposes. DMPs are particularly focused on data onboarding, which involves integrating and managing data from various sources. On the other hand, CDPs are designed to activate and utilize your first-party data, which is directly collected from your interactions with customers.

To clarify the distinctions between these platforms, here's a simple table outlining their primary differences:

FeatureCDP (Customer Data Platform)DMP (Data Management Platform)
Data sourcesPrimarily first part dataPrimarily closed third party data sets
Data scaleWide and deep spectrum of customer dataAnonymized, based on cookies
Typical purposeUnify customer data, gain insights and take actionAchieve better targeting for advertising campaigns
Most common usersWidespread usage by anyone who consumes customer data internallyPaid advertising teams
User TargetingHighly personalized customer journeysBroad audience targeting
IntegrationsIntegrates with various downstream destinations accross multiple business teams to activate data across channelsMostly integrates with ad networks, exchanges, and platforms for specific ad targeting and/or buying
Use CasesCollection and integration, ID resolution, data quality, compliance, personalization, data enrichment, AI & ML, warehouse activation and analyticsA primary application is in programmatic advertising, where they enhance audience targeting, facilitate media purchasing, and enable real-time bidding (RTB)
Privacy and ComplianceSupport continuous compliance across the data lifecycle without tedious integration work due to handling PIIMust adhere to privacy regulations, though their emphasis is on handling anonymized data, which is subject to distinct compliance obligations
Data Types Personally Identifiable Information (PII): CDPs collect detailed data that directly identifies individuals, such as: email addresses, phone numbers, physical Addresses, customer namesAnonymized Data: This is the core data type used by DMPs and includes non-personally identifiable information (PII)

When to use a CDP or a DMP

With the parameters of each of these customer data systems now defined, it’s time to consider what your data needs are specifically. Data is a resource for solving problems, whether used to model financial forecasts, develop product features, or find new sales leads.

To determine the ideal tooling to solve your problem, first define your question or goal, identify the data that you have access to or could gather, and decide how you will use that data to approach the problem. While your data and company start to spin up, a CDP fills the getting-to-know-the-data role quite well. A DMP can be introduced later to act on insights from the CDP, and maximize advertising conversions. In all likelihood, any company of sufficient scale will want to use both CDPs and DMPs, to continue gathering customer data while also expanding the customer base.

Defining the data space

With so much talk about data, it is important to understand the different types of data and their uses. We’ve already mentioned first-party and third-party data, but a good understanding of what these are is crucial to understanding how the customer data space is developing. In any case, it’s worth getting a deeper familiarity with different types of customer data when contemplating your storage system.

First-party data

This is the personal, internal customer data collected by your business with the consent of the customer.

You have sole access to your first-party data, and this gives you unique insight into your market share that no other competitor could gain. On the other hand, with modern privacy regulations like GDPR and CCPA, this type of data confers responsibility on anyone who collects and maintains it. These two factors combined make properly protecting and controlling access to first-party data crucial to its usage.

Second-party data

This is a rare category of data. Second-party data is any first-party data collected by another entity and then shared with your firm as part of a contract. You will have the same legal and ethical exposure from handling the data, while gaining less value from it.

While it’s unlikely that this data of this type could be leveraged for marketing or advertising capacity, any contract where you handle second-party data will almost certainly require the same data controls you would want to have for your own first-party data, and it therefore has similar resource demands.

Third-party data

The ‘third party’ in this data is typically a massive advertising vendor like Meta or Google, which has enough cookie-tracking volume to provide highly specific demographic information as a service. Other vendors specialize in refining third-party data, and can provide high value resources for subscription – recognizable vendors of this category include Clearbit or Zoominfo. This data is much less valuable, so systems controlling it can be as simple as vendor-provided web accounts. However, if you do want to bring third-party data into your own ecosystem to supplement analysis work, requirements for controls and security are less rigorous.

Remember the price tag

While first-party data is generally cheap to collect — the only additional costs to storing it are server space and infrastructure overhead —  third-party data can be quite expensive. The contents of a DMP are mostly valuable at tremendous scale, which means vendors operate by large volumes and correspondingly large sales. DMP providers often follow a similar strategy, such that the software itself is intended for big companies with deep warchests.

Third-party data remains a strong advertising tool, but its startup costs can be prohibitive to smaller-scale projects.

The CDP value model

Personally identifiable information (PII) is at the core of CDP functionality. It allows you to create a historically-tracked picture of your customers. As your data scales up, the resolution and value of this data store compounds on itself, tracking the journey of individual customers for a given version of the product. As more data is collected over time, historical resolution across versions provides additional avenues for potential analysis.

In this way, a CDP acts as a value multiplier for your customer data. Even non-customer data can be augmented as a CDP spools up — most CDPs are designed to maximize this functionality by absorbing information across a huge range of data sources. When choosing a CDP, make sure your candidates support ingestion capabilities that match your existing data-generating tools.

The DMP value model

Unlike the temporally-organized CDP, a DMP delivers a short-term data stream that is constantly updated, reflecting current market conditions. Rather than using a DMP to store and accrue value from customer data, it offers a way to quickly convert semi-public third-party data into market share via advertising.

Modern developments in data storage

Both government legislation and user expectations about data privacy have shifted in recent years, with a profound effect on how customer data must be handled. GDPR, CCPA, and iOS 14.5 changes are notable examples of a large trend towards privacy rights biting into the world of cookie tracking that is the core of DMP functionality.

As a result of the shrinking supply of quality third-party data, many companies find themselves increasingly reliant on their own first-party data to do the heavy lifting of demographic research that was available from major third-party data vendors.

Responding to these ongoing market changes, many CDP providers are pivoting towards products that could plug new holes in their data systems. This has led to CDP models that can “bundle” the function of several parts of the data stack into a CDP — including the use cases of a DMP.

Although these unifying changes are still quite new, CDPs are beginning to segregate back into old-school, modular systems highly targeted to customer data, as discussed in this article, and more monolithic systems that bundle a great deal of the data stack.

Final thoughts on CDPs vs DMPs

When you’re weighing which software to add to your datastack or deciding which strategy to adopt for your data storage, it pays to review the situation you want to address and take stock of your data and problem set before choosing a tool. It’s also helpful to stay apprised of new developments in the data systems you are researching.

You can keep up to date with data storage on the Rudderstack blog (try our internal search feature!), or you can review other articles in our learning center to build a foundation of knowledge in the field.

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