A guide to data clean rooms in advertising & marketing

It’s no secret that marketers face mounting pressure to maximize the value of every impression and interaction. To enable this in a privacy-first way, data clean room technology in advertising is quickly becoming the cornerstone of effective data collaboration. Clean rooms enable brands and publishers to combine insights without sharing raw customer data, thus protecting privacy while improving performance.

This guide explains how data clean rooms enable effective ad campaigns with first-party data, what makes them different from other adtech/martech tools, and how marketers can use them to plan, activate, and measure campaigns more effectively. Whether you’re exploring what a clean room in marketing is or evaluating which solution fits your stack, this article offers a clear, practical overview.

What is a data clean room in advertising and marketing?

A data clean room for advertising purposes is a secure, privacy-preserving environment that allows multiple parties — such as brands, agencies, publishers, and retailers — to analyze and combine their data sets. In the best-case scenario, the technology enables this without ever exposing raw, personally identifiable information (PII). Clean room environments have become vital in a post–third-party cookie world, where advertisers still need to understand audiences and measure performance while respecting data privacy.

Also used in non-advertising verticals, a clean room acts as a neutral meeting place where sensitive data can be used for analysis under strict governance and permission-based access. Each participant maintains full control of their own information — a principle known as data sovereignty — ensuring that no data is ever copied, exported, or viewed in plain text by other parties. Data clean rooms thus also enforce strong data governance and data segregation by design.

During collaboration in a secure clean room, all processing happens under encryption-in-use, powered by privacy-enhancing technologies like confidential computing and trusted execution environments (TEEs). This guarantees that even the clean room provider or cloud providers cannot access the underlying information. Data segregation keeps each contributor’s data isolated, while audit trails and privacy laws compliance (audit trails and fine grained controls) safeguard every operation.

When analysis is complete, only aggregated data or approved audience insights leave the clean room — raw or user-level data never gets transferred from one party to another. In short, a true clean room enables secure data collaboration that helps advertisers plan, measure, and activate campaigns responsibly in a privacy-first advertising ecosystem.

Types of data clean rooms

The term “data clean room” is often used loosely across adtech/martech. In practice, there are several types — but not all meet the standard of a true privacy-preserving environment.

Walled garden clean rooms (e.g., Google Ads Data Hub, Amazon Marketing Cloud) allow analysis within a single platform’s ecosystem but lack full neutrality.

Independent or neutral clean rooms like Decentriq enforce encryption-in-use and verifiable privacy guarantees, offering true collaboration without data exposure. 

Cloud provider-native clean rooms run across infrastructure from cloud providers, but to qualify as truly neutral clean rooms, they must not be tied to any one specific:

• cloud provider
• publisher
• agency
• ID

While every approach supports privacy-safe advertising to some degree, only those that combine neutrality, an intuitive, no-code user experience, and technology-guaranteed privacy (not trust alone) meet the definition of a true data clean room.

Why marketers use data clean rooms

Marketers use data clean rooms to unlock deeper, privacy-safe insights from their first-party data and partnerships. As third-party data quality diminishes, advertisers need ways to connect, analyze, and activate audiences responsibly without compromising trust or compliance. 

According to Gartner, “Among the top three most commonly reported barriers to realizing value from data for analytics leaders are compliance with data privacy and regulations (45%) and limited cross-functional collaboration (44%).” Data clean rooms directly address both issues by creating a neutral, secure space for brands and partners to collaborate without exposing raw data.

One major benefit is smarter audience planning and analysis. Clean rooms reveal where audiences overlap between brands and publishers, highlighting untapped opportunities and helping marketers reduce wasted impressions across multiple channels. These privacy-safe insights drive better campaign design and more efficient media investment.

Clean rooms also enable secure audience activation. By running lookalike modeling and segmentation directly inside a clean room, brands can build lookalike audiences or refine targeting strategies with precision. This process (sometimes called “clean room audience targeting”) respects user consent while maintaining data governance and transparency.

Finally, clean rooms transform how marketers measure and prove impact. By connecting ad exposure data with conversion or transaction data, combining this data at the most granular level, then returning aggregated insights, teams can evaluate performance without exposing individual-level details. The result is more accurate attribution models, clearer ROI signals, and the confidence to optimize future campaigns.

In short, data clean rooms allow marketers to continue making data-driven decisions while fully embracing a privacy-first advertising ecosystem.

You may be interested in reading our article on cookieless advertising, where we dig deeper into the concept of privacy-first advertising.

How data clean rooms work in advertising

To understand how data clean rooms work in advertising, it helps to visualize them as a secure bridge between advertisers, publishers, and technology partners. Each party contributes valuable insights into an environment where collaboration can happen safely. For the purposes of this article, we’ll outline the typical collaboration process for a Decentriq data clean room. 

1. Data encryption

The process begins with encrypting the data before it enters the clean room environment. Each participant encrypts their data such that no third party can ever decrypt it — only the TEE running inside the clean room has this ability. This ensures it remains protected even before ingestion and throughout computation.

2. Data ingestion and preparation

Once encrypted, the next step is secure data ingestion and preparation. Participants connect their protected data sets to the clean room using permission-controlled channels. These data sets might include CRM records, conversion logs, or campaign data from advertising platforms.

3. Matching and analysis

After ingestion, the clean room performs matching and analysis under strict governance policies. Depending on which use case the collaborators are interested in, this step identifies audience overlap, measures exposure, or generates performance models while maintaining complete data segregation between parties. Only queries that meet predefined privacy thresholds are allowed to run.

4. Data insights

Once data analysis is complete, the environment produces privacy-safe audience segments or summary reports. No user-level data or personally identifiable information (PII) is ever transferred from one party to another.

5. Data activation

Finally, these insights can be activated across channels. Advertisers use the aggregated results to inform media planning, frequency management, and audience targeting in connected environments like web, mobile apps, or connected TV (CTV).

Through this step-by-step process, data clean rooms enable collaboration that’s both analytically powerful and privacy-preserving.

How clean rooms differ from CDPs and DMPs

While customer data platforms (CDPs) and data management platforms (DMPs) manage audience information, clean rooms are fundamentally different. CDPs unify and activate a brand’s owned data. DMPs aggregate and model third-party data as well as curate first-party audiences based on web and mobile user visits.

Clean rooms, on the other hand, enable secure collaboration between these and other data sets (in the most secure instance, without exposing them), thus acting as the privacy-first connective tissue of the entire advertising ecosystem.

Advertising use cases for data clean rooms

While each use case differs by goal and data type, the common thread is secure collaboration that yields actionable, aggregated insights without exposing raw data. Below are eight practical ways marketers are using data clean rooms today.

1. Audience insights for better targeting

One of the most common uses of data clean rooms is to uncover audience overlap between advertisers and media partners. By securely combining first-party customer data with publisher audiences, marketers can identify shared segments that are most likely to respond to specific messages or offers.

Inside a clean room, each participant uploads encrypted, pseudonymized data. Matching occurs using privacy-preserving computation, never plain-text identifiers. The result is an aggregated view of audience intersections that informs targeting strategy: for example, identifying which high-value customer segments align with premium inventory or particular channels.

This approach enables smarter campaign planning while respecting consent and data protection laws. The main pitfall comes from poor data quality or misaligned identifiers, which can reduce match rates. Advertisers can mitigate this by using consistent, privacy-safe identifiers—such as hashed email addresses, hashed phone numbers, or device IDs so the clean room can accurately match records across partners.

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IKEA leveraged insights uncovered through data collaboration with willhaben to enable data-driven segment selection for high-quality audience targeting:

2. Improving media planning and reducing waste

Marketers are also using clean rooms to make more efficient media investments. By securely comparing ad exposure data across partners, they can see which environments drive incremental reach rather than duplication.

In this scenario, advertisers and publishers contribute impression-level data. Within the clean room, encrypted computation models overlap and deduplicate audiences across platforms. Outputs are aggregated reports that show total reach, frequency, and unique audience contribution per channel.

Because only aggregated metrics leave the clean room, no personal data changes hands. The result is better allocation of spend and less media waste. The challenge lies in ensuring interoperability across publisher systems, which is one reason advertisers increasingly seek neutral clean rooms like Decentriq that can handle multi-party collaboration.

3. Activating privacy-safe audiences

A growing number of advertisers are turning to data clean rooms for secure audience activation. Instead of sharing raw user data with ad platforms, they can define and activate audience segments directly from aggregated results (namely a trained lookalike model) within the clean room.

Here, first-party CRM data is matched with publisher or platform identifiers to create privacy-compliant targeting lists. These segments can then be pushed for activation across connected advertising ecosystems — without either side viewing user-level data.

This method ensures compliance with consent requirements and helps brands maintain control over how their data is used. A potential pitfall is limited support from certain activation partners; integration depth varies, so choosing a clean room that integrates with leading DSPs and walled gardens is key.

4. Measuring campaign effectiveness and attribution

Clean rooms are increasingly vital for measurement and attribution. Advertisers can combine exposure data from publishers with conversion data from their own systems to evaluate campaign performance in a privacy-safe way.

Within the clean room, encrypted datasets are linked to determine how ad exposure correlates with conversions. The computation produces aggregated attribution reports, revealing performance by channel, audience, or creative type without ever revealing individual identities.

This allows marketers to move beyond click-based attribution and toward true incrementality. The main challenge lies in aligning attribution windows and identifiers across partners, so careful configuration is essential.

5. Enhancing customer experience through personalization

Data clean rooms enable more relevant and cohesive experiences across channels by allowing brands and publishers to collaborate on privacy-safe personalization. By analyzing shared segments or behavioral patterns, advertisers can deliver consistent messages to customers without direct data sharing.

For instance, a streaming service and a consumer brand could collaborate to identify lifestyle-based affinities and co-create campaigns aligned to user interests. Since outputs remain aggregated, no personal preferences or identities are exposed.

While personalization drives engagement, marketers must still ensure compliance with privacy preferences. Using consent-based inputs is non-negotiable.

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Discover how Laboratoires Pierre Fabre collaborated with the three publishers to uncover rich insights about their customer personas:

6. Building first-party data partnerships

First-party data partnerships are one of the most strategic outcomes of clean room adoption.

Inside the clean room, both parties’ encrypted data sets are analyzed under strict governance, ensuring that sensitive details like transaction-level information remain protected. The result is an aggregated view of customer overlap, performance metrics, and new opportunities for collaboration.

This model turns data from a potential liability into a shared strategic asset. The key challenge is aligning objectives and ensuring both sides trust the governance model. This is where Decentriq’s verifiable privacy guarantees are particularly valuable.

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Retailer x CTV partnership: Read about how OBI combined its rich CRM and retail media data with Ad Alliance’s premium publisher inventory to achieve more relevant and impactful targeting for its supplying brand GARDENA.

8. Experimentation and incrementality testing

Data clean rooms are increasingly being used to conduct controlled experiments that assess the true incremental impact of campaigns. Marketers can run holdout or geo-split tests entirely inside the clean room, comparing exposed and unexposed groups while preserving privacy.

In this workflow, campaign and conversion data are ingested and grouped according to test design parameters. The clean room computes lift metrics such as incremental conversions or revenue per region based on aggregated data. Because no individual user identifiers are exported, the process remains compliant and secure.

Experimentation allows advertisers to isolate causal effects and make more confident media investment decisions. The key to success lies in designing experiments with sufficient sample sizes and ensuring that test and control groups are properly segmented before ingestion.

Together, these eight use cases illustrate what clean rooms make possible: from smarter audience insights and measurement to activation, collaboration, and experimentation. Each relies on the same foundation — privacy-enhancing technologies that turn data collaboration into a competitive advantage for advertisers.

Challenges of using data clean rooms for marketing and advertising

Despite their advantages, data clean rooms also present challenges. Identity and matching limits can reduce overlap precision, especially where consented identifiers are scarce. Consent constraints and fragmented policies across partners may limit data availability.

Operationally, some clean rooms can be complex to deploy. Integrating multiple systems requires coordination between marketing teams, engineers, and data scientists. Data volume or data sparsity can also affect results. Costs vary, particularly for managed solutions that require ongoing compliance support.

Interoperability remains another issue, as not all clean rooms are compatible. Vendor lock-in can limit cross-environment collaboration. Cloud providers may also differ in privacy guarantees, but only those supporting encryption-in-use ensure true confidentiality.

Decentriq helps overcome these challenges through a neutral clean room platform that combines verifiable privacy, seamless integrations, and technology-backed trust rather than faith in the operator.

How to choose the right data clean room platform for you

Selecting the right data clean room platform is critical to maximize privacy compliance and marketing effectiveness. The vendor landscape includes independent, neutral providers like Decentriq and InfoSum, as well as walled garden solutions from major tech platforms. When evaluating a solution, marketers should check for several key capabilities:

• Encryption-in-use: Ensures data remains secure while being processed.
• Neutrality: The clean room is agnostic to any identity, cloud platform to which its connected, agency using it, or publisher its connected to.
• Integrations: Compatibility with ad platforms and analytics tools for activation.
• User-friendly UX: Enabling no-code collaborations

By considering these factors, brands can choose a platform that delivers both secure data collaboration and actionable marketing insights.

To help you understand the landscape better, we have compared the most popular data clean room providers on the market here.

Not all data clean rooms are created equal

As data clean rooms have grown in popularity, the term itself has become diluted and is often applied to environments that fall short of the privacy and neutrality standards marketers actually need. Many so-called clean rooms still rely on the trust of a single party or cloud provider, leaving gaps in governance, neutrality, and protection.

A true data clean room delivers verifiable privacy through encryption-in-use technology, ensuring no provider or intermediary can ever access the underlying data. It supports collaboration among multiple parties while preserving full data sovereignty. Every query runs under strict controls in a permission-based environment, with audit trails that guarantee compliance and prevent misuse.

This is where Decentriq stands apart. Built on confidential computing, Decentriq enables secure data collaboration without exposing any raw data, even to the platform operator. Brands, publishers, and partners can analyze transaction data, model audiences, and measure outcomes across the entire advertising ecosystem, all while maintaining full ownership of their own data clean room.

By combining privacy-enhancing technologies with true neutrality and interoperability, Decentriq offers marketers a way to realize the full potential of data-driven advertising — without compromising privacy, trust, or performance.

Improve your marketing and ad effectiveness with a data clean room

As privacy expectations rise, marketers need solutions that protect consumers while still delivering measurable results. Data clean rooms offer that balance through a secure environment for data collaboration, analysis, and activation.

By adopting a data clean room solution, advertisers can unlock deeper insights, improve media efficiency, and uphold compliance across every campaign. To learn how Decentriq can help you achieve these goals, visit our advertiser data solution page or request a demo.