The deprecation of third-party cookies, increased data privacy regulations and compliance requirements have challenged marketers to shift their marketing and advertising programs towards a first-party data strategy. Now, marketers are exploring new ways to optimize ad spend, create better loyalty programs, and provide the best digital experiences by building direct relationships with customers.
Enter the data clean room. It’s not exactly a new tool for data management, but it helps resolve some of the biggest data-oriented challenges marketers face today. Interest keeps growing: in 2023, the IAB’s State of Data report found that 80 percent of advertisers with media budgets of $1 billion or more were expected to use data clean rooms.
What is a Data Clean Room?
A data clean room is a secure and anonymous private data exchange. It’s a database where a company matches its first-party data with aggregated data from a second-or-third-party data source, like a publisher or a trusted partner. Once the data sources are matched up, one or both parties can analyze the combined data to be leveraged for various applications.
Here’s how it works:
- First-party data is combined with aggregated data from a third-party source, like a publisher, without sharing personally identifiable information (PII) or raw data. There is no way for either party to reverse the data to get the PII.
- Each data provider sets the permissions for what and how its data is analyzed. There are strict privacy controls in a data clean room, and each party specifies what data the other party can access.
- This data typically lives in the data clean room environment. Only aggregate data is queried.
In most cases, all data is brought into a central location, but there are some examples where distributed data clean rooms keep the data in its original location, and its owner allows controlled analytics to the other party.
Types of Data Clean Rooms
Data clean rooms help organizations process and analyze data from different partners in a secure and compliant way. Different data clean rooms serve various data goals.
Walled Gardens
The most prominent examples of data clean rooms today are walled garden data clean rooms that come from big ad media publishers, like Google Ads Data Hub and Amazon Marketing Cloud.
When you work with a data clean room from one of these walled gardens, you analyze the performance of your ads from the individual publisher’s platform – they do not provide a cross-platform perspective. One drawback of these data clean rooms is you cannot analyze performance across publishers. There may also be restrictions or limitations on how you can use the data.
AdTech Vendors
AdTech vendors or agencies also provide data clean rooms. However, in certain cases, there may be no way to know if the data clean room’s attribution model methodology is valid or accurate. If you choose to work with someone else’s data clean room, you need to ensure it provides the security necessary to house your first-party data and that your data is appropriately pseudonymized to safeguard the privacy of customer information.
Private Data Clean Rooms
Today, many companies and independent vendors are building their own private data clean rooms. There, they can work with multiple partner datasets to create an omnichannel view of their customer data to analyze for various purposes, like optimizing advertising spend or executing personalized marketing campaigns.
Why Are Data Clean Rooms Important?
Consumers are now more aware of how brands use their personal information. Privacy laws also continue to come into place to protect consumer privacy. Regulations like the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are the two most well-known privacy laws, but they are just the start to a broader data regulation environment.
Publishers like Google and Apple have also restricted third-party cookies and have implemented tools that allow consumers to control how their personal data is shared.
Access to first-party data that comes from a consumer’s direct interaction with a brand helps marketers understand a lot about their customers, but it doesn’t always tell the entire story. Second-and-third party data from partners, publishers, and ad networks help fill in the missing pieces.
Data clean rooms provide access to third-party data that privacy laws and the end of cookies are taking away
Data clean rooms give this access in a secure, compliant environment, allowing marketers to:
- Understand how customers are interacting with brands
- Find wasted ad spend or avoid duplicated effort across channels
- Identify lookalike audiences
- Build new segments for targeting
- Determine customer lifetime value (CLTV).
Depending on the type of data clean room, marketers may be also able to build custom audiences that can be sent directly to an ad platform, whether that’s a publisher, ad network, a demand-side platform (DSP), or a customer data platform.
How To Use A Data Clean Room to Improve Data Strategy
There are many use cases for data clean rooms, but the most well-known is between a publisher and an advertiser.
We’ve already talked about the walled gardens of Google and Amazon, which publishers own their own data clean rooms. Advertisers bring in their first-party data and then analyze the combined data to understand ad performance. .
If an advertiser works with multiple publishers, they have to perform their analysis separately for each publisher and then manually bring that data together to give them a more holistic view of their ad spend. The same is true if an advertiser wanted to work with a data clean room from an AdTech vendor.
However, there are data clean rooms run by agencies that bring in third-party data from multiple ad networks, publishers, and demand-side platforms, giving advertisers a complete picture of ad spend (though this still would not include data from the walled garden publishers).
Retailers and Consumer Packaged Goods (CPGs)
Another use case for data clean rooms is for CPG companies. Since CPG companies do not sell their products to consumers directly, they have limited transaction data. They do, however, have first-party data from direct-to-consumer interactions, marketing, advertising, and loyalty programs.
The retailers that sell CPG products have additional transaction data from their own marketplaces or platforms. So, if the two parties combined their data in a data clean room, CPG companies could better understand how their marketing campaigns were driving purchases from the retailer. They could also analyze the combined data to improve targeting and segmentation of their campaigns and offers to specific high-performing segments through a retailer’s media network.
Strategic Partnerships
While airlines, hotels, and car rental services do not provide the same services, these services are complementary and often are purchased together. If these parties were to combine their data, they could better understand what their target markets want. By analyzing the shared dataset, they may find opportunities to co-market or deliver loyalty programs that provide more value for both the customers and involved partners.
Working Together: Data Clean Rooms and CDPs
A customer data platform (CDP) is at the heart of your first-party data strategy. It’s where you bring together first, second and third-party customer data to build a single customer view that’s required to create personalized, relevant experiences at scale.
A data clean room is an extension of a first-party data strategy. A brand can connect its CDP to a data clean room to allow first-party data to be anonymized and analyzed alongside third-party sources. It can also receive data from the data clean room in the form of segments or targeted audiences it can then share with connected marketing platforms for activation.
A CDP does not provide the same environment as a data clean room. But it does give data providers and organizations centralized control of their data and its use. Together, a data clean room and a CDP allows organizations to manage, process and analyze data in way that’s safe, efficient and compliant. Teams evaluating platforms for this combination can go deeper with CDP Training from Treasure AI.
How to choose a data clean room
The type of clean room you choose determines which questions you can answer, so evaluate it before any data moves — not after the first campaign. Five criteria separate a clean room worth using from one that will stall your analysis:
- Matching methodology. Ask how identifiers are resolved, what match rate to expect for your audience, and whether the methodology is documented well enough to defend the results internally.
- Governance controls. You should be able to see, in advance, who can run which queries, who approves them, and what outputs are permitted to leave the room.
- Query flexibility. Prebuilt dashboards answer the vendor’s questions quickly; direct query access answers yours. Decide which you need before you commit.
- Interoperability. Results that cannot leave the room cannot reach your CDP or your activation platforms. Check what export formats exist and what restrictions attach to them.
- Privacy mechanics. Pseudonymization and aggregation thresholds should be described in documentation you can actually read, not promised in a sales deck.
Where each type of clean room fits:
| Your situation | Best fit | Why it wins | Watch out for |
|---|---|---|---|
| Most of your media runs through one large publisher | Walled garden clean room | The deepest campaign data for that platform, with matching handled for you | No cross-publisher view, and export rules limit what leaves |
| You need to verify an AdTech partner’s reporting | Vendor or agency clean room | Neutral ground where both parties’ numbers face the same checks | The attribution methodology may not be inspectable |
| You collaborate with several partners on shared planning | Private clean room | You set the governance rules, and partners stay interchangeable | You also own the setup, matching quality, and ongoing cost |
Whichever type you pick, get written answers on matching, governance, and data egress before onboarding. Renegotiating those terms after your data is inside rarely works.
Where data clean room projects go wrong
Most clean room disappointments trace back to preparation, not to the technology. Four failure modes account for most of them.
Weak match rates
A clean room can only join records it can recognize. If your first-party data carries inconsistent email formats, stale phone numbers, or duplicate profiles, overlap will come back low and the platform gets blamed. Fix identity resolution inside your own environment before onboarding; the clean room cannot repair records it cannot match.
Governance defined after onboarding
When partners wait until the data is loaded to debate who may query what, the project stalls in approval limbo. Write the access rules — permitted queries, approval steps, output formats, retention — and get sign-off from every party before ingestion day.
Aggregation thresholds hide small segments
Clean rooms return results only for cohorts above a minimum size, and they do not always announce when a result was suppressed. A segment that comes back empty may simply fall below the threshold rather than genuinely not exist. Design analyses around minimum cohort sizes, and lengthen the measurement window or combine channels when a cell returns empty.
Incomparable per-publisher metrics
Each publisher counts reach, views, and conversions under its own definitions, so exporting several reports into one spreadsheet produces columns that look aligned and are not. Agree on metric definitions with every partner before the analysis starts, or normalize the definitions centrally before comparing.
What you can measure in a data clean room
Because analysis runs on joined data that no party can browse row by row, a clean room makes a specific set of measurements possible:
- Audience overlap. How much of a partner’s audience you already reach, before you commit budget. Overlap is not automatically bad — it shows where incremental reach actually lives.
- Deduplicated reach and frequency. How many distinct people a campaign touched across you and the partner, and how often each was exposed, without either side exporting user lists.
- Conversion paths. Which sequences of exposures precede purchase, measured on joined data that never leaves the room.
- Lift and incrementality. Holdout comparisons that show whether the campaign caused the outcome or the outcome would have happened anyway.
The output is always aggregate: segment counts, lift estimates, overlap percentages. Those aggregates are what feed activation — audiences pushed to an ad exchange or a publisher’s platform, and insights returned to your CDP for the next planning cycle. If a proposed analysis requires row-level records to leave the room, it is not a clean room use case; it is a data share, and it deserves the scrutiny that distinction implies.
FAQ
Do data clean rooms replace a CDP?
No — a data clean room is for analyzing data with partners, while a CDP unifies and activates your own customer data. A clean room hosts a specific collaboration and holds joined data only while that analysis runs. A CDP builds your single customer view continuously and prepares the first-party data that makes clean room matching work. Used together, the room produces aggregate audiences and insights that flow back into the CDP for activation and the next planning cycle.
How do data clean rooms fit into AI-driven advertising?
They give AI-driven campaign systems a governed way to query audience data without exposing raw records. As agentic advertising shifts planning and buying toward autonomous systems, those systems still need access to performance and audience data. Clean rooms expose only the aggregated queries such agents need, and an agentic CDP prepares the first-party data behind them — keeping automated decisions inside guardrails you define.
Related Articles
- Using a Data Clean Room to Enhance Data Privacy — How data clean rooms enable privacy-safe collaboration
- First-Party Data Strategy: Start with Trust — Building trust-based first-party data collection
- CDP vs ESP: Which Does Your Marketing Stack Need? — Comparing CDP and email service provider capabilities
- Data Privacy Governance Best Practices — Practical framework for privacy-first data management