Machine learning model training
What is Behavioral Analytics?
What is Diagnostic Analytics?
The Difference Between Data Analytics and Statistics
Data Analytics vs. Business Analytics
What is Data Analytics?
The Difference Between Data Analytics and Data Visualization
Data Analytics vs. Data Science
Quantitative vs. Qualitative Data
Data Analytics Processes
Data Analytics vs. Data Analysis
Data Analytics Lifecycle
Data Analytics vs Business Intelligence
What is Descriptive Analytics?
What Is Google Analytics 4 and Why Should You Migrate?
Google Analytics 4 and eCommerce Tracking
GA4 Migration Guide
Understanding Data Streams in Google Analytics 4
GA4 vs. Universal Analytics
Understanding Google Analytics 4 Organization Hierarchy
Benefits and Limitations of Google Analytics 4 (GA4)
What are the New Features of Google Analytics 4 (GA4)?
What Is Customer Data?
Collecting Customer Data
Types of Customer Data
The Importance of First-Party Customer Data After iOS Updates
CDPs vs. DMPs
What is an Identity Graph?
Customer Data Analytics
Customer Data Management
A complete guide to first-party customer data
What Is a Customer Data Platform?
Customer Data Protection
Difference Between Big Data and Data Warehouses
Data Warehouses versus Data Lakes
A top-level guide to data lakes
Data Warehouses versus Data Marts
Best Practices for Accessing Your Data Warehouse
What are the Benefits of a Data Warehouse?
Data Warehouse Architecture
What Is a Data Warehouse?
How to Move Data in Data Warehouses
Data Warehouse Best Practices — preparing your data for peak performance
Key Concepts of a Data Warehouse
Data Warehouses versus Databases: What’s the Difference?
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
How to Create and Use Business Intelligence with a Data Warehouse
How do Data Warehouses Enhance Data Mining?
Data Security Strategies
How To Handle Your Company’s Sensitive Data
How to Manage Data Retention
Data Access Control
Data Security Technologies
What is Persistent Data?
Data Sharing and Third Parties
What is Consent Management?
What is PII Masking and How Can You Use It?
Data Protection Security Controls
Data Security Best Practices For Companies
We'll send you updates from the blog and monthly release notes.
CDPs vs. DMPs
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.
The customer data storage question
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.
CDP — a first-party data system
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 a deeper dive into CDPs, read our article What Is a Customer Data Platform? In the Learning Center.
DMP — third-party demographic insight
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.
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.
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.
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.
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.
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.