Is a CDP a clean room?

What is a CDP?

A customer data platform (CDP) is software that creates a unified customer database for collecting, analyzing, and managing customer data from multiple sources (, 2021; CDP Institute, 2020; Octolis, 2022). The main functions of a CDP include:

Data Collection – A CDP ingests customer data from different channels like website, mobile apps, CRM, email, offline sources, and more. It brings customer data together into a single customer view.

Identity Resolution – CDPs use identity resolution and mapping to connect data from disparate sources to unique individual customer profiles. This allows a single view of each customer.

Segmentation – CDPs segment unified customer profiles based on various attributes like demographics, behaviors, and interests. This allows for personalized marketing.

Activation – CDPs activate the segmented customer data and profiles by sharing with other systems for targeting, personalization, analytics, reporting and more.

In summary, a CDP is packaged software that collects customer data from all sources, resolves identities, creates unified profiles, segments customers, and activates the data across other systems.

What is a Clean Room?

A clean room is a secure data environment that enables different organizations to safely share and analyze data without exposing sensitive information (Digiday). The main purpose of a clean room is to enable privacy-preserving data analysis by facilitating collaboration between parties that want to gain mutual benefits from data sharing, while maintaining data privacy and security.

Clean rooms work by keeping data separate through access control, aggregation, and anonymization. Each party uploads their data into a secure enclave where it is anonymized and aggregated before being shared. The clean room allows analysis on the combined data set without revealing individual-level information (TechTarget).

This privacy-preserving approach allows organizations to analyze a broader set of data to extract insights while protecting sensitive customer information. Clean rooms are often used for marketing analytics, real-world evidence studies in healthcare, and financial crime detection while maintaining compliance obligations (Clearcode).

CDP Data Collection

CDPs collect various types of data from first, second, and third party sources to create unified customer profiles. This includes:

  • First-party data like customer identifiers, contact information, transaction history, and behavioral data collected directly from the customer. Sources include websites, mobile apps, CRM systems, email platforms, and loyalty programs. (Gartner)
  • Second-party data provided by partners through co-marketing efforts, data sharing agreements, etc. This may include additional customer attributes or transaction data.
  • Third-party data purchased from data brokers or firms. This includes demographic, interest, and intent data used to enrich profiles.

By consolidating data from these sources into a unified customer view, CDPs enable deeper insights and more coordinated experiences across touchpoints.

CDP Data Storage

CDPs store both structured and unstructured customer data from many different sources. Structured data refers to highly organized information like customer names, emails, addresses stored in tables. Unstructured data is information like call center notes, social media posts, that don’t fit neatly into rows and columns.

CDPs use data models and schemas to organize the incoming structured and unstructured data. Data models describe the entities (e.g. customers, products) and relationships in the data. Schemas outline the fields and data types for structured data stored in the CDP. For example, a customer schema may specify name as text, email as text, address as text. The schemas allow the CDP to understand the meaning of the structured data it ingests.

According to DELTA Solutions, “CDP data storage, processing and analysis components” include both a “data warehouse — high-performance repository for BI and SQL analytics” for structured data as well as an “operational database — Structured and unstructured data storage optimized for high-ingestion loads” ( The data warehouse stores structured data needed for analytics while the operational database stores incoming structured and unstructured customer data.

CDP Identity Resolution

One of the key capabilities of CDPs is identity resolution, which involves creating unified customer profiles by connecting data about individuals from various sources. This allows marketers to gain a comprehensive view of each customer by consolidating all their data and interactions into a single profile.

Identity resolution is challenging because customers interact with businesses through many different channels and devices, resulting in fragmented data. CDPs use identity mapping and linking techniques to connect the dots between different identifiers like cookies, email addresses, account IDs, and device IDs that all relate to the same individual. Sophisticated probabilistic and deterministic matching algorithms help determine when two identifiers belong to the same person.

By building unified customer profiles, CDPs create the foundation for personalized experiences across channels. Marketers can leverage the detailed single view of each customer to deliver targeted messages and offers tailored to their interests and needs.

However, as noted in this Hightouch article, some limitations around accessing CDP identity resolution models may introduce blind spots in understanding your customers. Thoughtful oversight is required to maximize the accuracy of profile-building through CDP identity mapping.

CDP Segmentation

One of the key capabilities of a CDP is creating audience segments for targeted marketing and personalization. CDPs ingest behavioral data, demographic data, transactional data and more to build unified customer profiles. These profiles enable segmentation based on various attributes.

Segments can be created based on:

  • Behavioral data – web visits, purchases, content views, etc.
  • Demographic data – age, gender, location, etc.
  • Predictive data – propensity scores, lifetime value, etc.

According to Dissecting a CDP’s Segmentation Engine (, CDPs utilize rule-based and machine learning approaches to build segments. Rules can define specific criteria while machine learning models can detect patterns.

Segments are ultimately used to deliver personalized experiences across channels. CDPs enable real-time segmentation and activation to customer data platforms like email, web, mobile apps, call centers and more.

Clean Room Data Security

Clean rooms utilize stringent security measures to protect sensitive customer data. This includes encryption, rigid access controls, and auditing procedures. All raw data remains within each company’s firewall, and is not directly shared.

Instead of exchanging raw data, statistical models are developed independently by each party. These models capture valuable insights without exposing private information. The models are then brought together in the clean room environment for matching and analysis.

By keeping data separated, clean rooms enable collaborative analytics between organizations in a secure way. Sensitive personal information stays protected within each company’s own secure environment.

According to Diconium, “The ‘Clean Room’ minimizes the risk of unauthorized access and tampering as much as possible through encryption, authentication, access control and complete documentation of all transactions.”

This approach allows companies to gain insights from a wider dataset while limiting privacy risks. Customers maintain control of their personal data. Overall, clean room protocols provide the benefits of data sharing without compromising critical security protections.


Comparing CDP and Clean Room

A key difference between a CDP and a clean room is that a CDP is a software platform, while a clean room is a technique or process for securely analyzing data. As the name suggests, a customer data platform (CDP) is software that helps companies collect, manage, and activate first-party customer data from various sources.

In contrast, a clean room is not a software platform itself, but rather a controlled environment that enables companies to securely match their first-party data with second-party or third-party data. As described by ClearCode, “A clean room is a technique allowing analysts from multiple companies to run computations on sensitive shared datasets without directly exposing the data.”

So while a CDP manages customer data, a clean room focuses on securely analyzing combined data sets. A CDP consolidates customer data from sources like your website, mobile apps, CRM, email systems, and more to create unified customer profiles. This gives you a single view of customers that can be used for segmentation, personalization and other use cases.

A clean room, on the other hand, doesn’t store customer data. Rather, it allows data scientists to run queries and analysis on securely matched data from multiple parties, without exposing raw data. As Epsilon explains, clean rooms enable “privacy-protected analysis” of combined first and third-party data.

In summary, a CDP is software for managing customer data, while a clean room is a technique for securely analyzing datasets from multiple sources.

Using CDPs and Clean Rooms Together

CDPs and clean rooms serve complementary purposes and can provide powerful insights when used together. Clean rooms enable privacy-preserving analysis by keeping data separate while allowing models and algorithms to run against the combined data.

As Epsilon explains, “With a clean room, advertisers can work directly with publishers to run match rates, build lookalike models, and measure performance by match status across channels — all without sharing underlying data. This allows publishers to maintain privacy promises to users while still providing advertisers with valuable insights from their data.” (

Meanwhile, CDPs provide a unified customer data set in one location to power personalized experiences across channels. The CDP’s data set fuels customer analytics and segmentation. When combined with a clean room’s privacy-preserving analysis capabilities, marketers gain enhanced insights without compromising user data privacy.

As Epsilon summarizes, “Ultimately, the biggest win when using a CDP and clean room together is the visibility into the customer journey across owned and paid channels.” (


In summary, while CDPs and clean rooms share some similarities in how they collect and store customer data, they serve quite different purposes. CDPs are marketing tools focused on unifying disparate customer data to enable segmentation and personalized experiences across channels. In contrast, clean rooms are analytic environments that allow brands to collaborate with external partners to gain insights while maintaining data privacy.

Some key differences are:

  • CDPs perform identity resolution to stitch together data on individual customers. Clean rooms keep data separate between partners.
  • CDPs enable real-time segmentation and activation. Clean rooms focus on analytics and insights.
  • CDPs consolidate first-party data. Clean rooms allow controlled sharing of data assets.

That said, CDPs and clean rooms can complement each other when used together. For example, brands can leverage their CDP for audience segmentation and then collaborate with partners in a clean room environment to enrich insights on those segments. The combined strengths of both approaches can drive more personalized and optimized customer experiences.