How Clean Rooms Are Reshaping Data Sharing

Nothing can excite media executives and send consultants scrambling to make the case for millions in new CapEx investment like when a next-generation AdTech platform comes along.

So when clean rooms — the latest and greatest AdTech innovation to allow brands, retailers, publishers, and ad platforms to collaborate on customer data — burst onto the scene, confusion and scrambling were just what took place.

What is a "clean room"? How is it different from other industry acronyms like DMPs and CDPs? And should your organization’s next big RFP be for one?

If these questions are keeping you on your toes, you’ve come to the right place.

We’ve put together this guide to address all your clean room questions.

What is a clean room?

A clean room is a space where brands, retailers, media companies, and ad marketplaces can share and join pseudonymized first-party customer datasets to create combined audiences.

The clean room’s participants can then query those audiences for aggregated attribution, measurement, and insight without exposing emails, phone numbers, and addresses of individuals and households.

Essentially, clean rooms are privacy-friendly ways to combine your first-party data with second-party data from your partners with the goal of monitoring campaign performance and getting a deeper understanding of your customers.

There are two types of clean rooms you need to know about: walled-garden clean rooms and neutral clean rooms.

Walled-garden clean rooms

Walled-garden clean rooms like Google Ads Data Hub and Amazon Marketing Cloud forbid the uploading of personal identifiers and apply additional measures, like salting hashed attributes, to obscure and protect individual customer information.

Before uploading a customer dataset to the clean room, each participant is required to delete first and last names, hash emails and phone numbers, and strip down addresses to ZIP codes or hashed standardized strings.

This way, customer profiles can still be stitched together and segmented into queryable audiences while still keeping individual customer identities hidden and safe.

This type of clean rooms is owned by media companies or big tech platforms, and their focus is to keep customer data in. Unlike neutral rooms, walled gardens never allow row-level exports; only aggregates or opaque, in-platform-only IDs are returned.

Neutral clean rooms

Neutral clean rooms such as Amazon’s AWS Clean Rooms or Databricks Clean Rooms Software-as-a-Service (SaaS) are licensed to organizations who prefer to stand up a clean room of their own.

The data sharing contracts between a neutral clean room’s participants dictate what they can and cannot do with the combined datasets — including which tables are matchable, queryable or exportable.

Although the default for any clean room is aggregate-only, row-level exports or persistent joins of customer data can be allowed so long as the data sharing contracts between its participants permit it.

Why they’re called that way

The term "clean room" actually comes from the semiconductor manufacturing industry.

In the world of semiconductor manufacturing, clean rooms are sterile, dust-free spaces with controlled humidity, temperature, and air pressure where microchip circuits are made.

In advertising, clean rooms are also controlled environments, but for exchanging data, enriching profiles, and building segments under strict rules for data privacy.

How clean rooms work

Imagine a frozen-food brand that runs commercials during a hit cooking show.

After the flight, the media team wants to know:

• Incremental reach and frequency by age/gender household buckets
• Whether viewers of that show buy the brand’s entrées more than non-viewers

The TV programmer holds the exposure logs but, for privacy and policy reasons, never shares row-level viewing data.

By signing a data-sharing contract and meeting inside a clean room, the brand and programmer can combine datasets — letting the brand ask the questions that matter and get answers grounded in real-world performance.

If a retailer also joins the clean room, hashed IDs from its loyalty program let the partners trace the commercials to incremental sales lift by ZIP code.

Notice how “clean” the exchange is:

• The brand never hands over e-mails, phone numbers, IP addresses, mobile-ad IDs (MAIDs), or ZIP codes.
• The programmer never releases raw impression logs, yet its pseudonymous IDs still match to the brand’s and the retailer’s files.
• The retailer never shares receipt-level SKU data, but all parties can still tie TV exposure to sales lift.

Of course, this is an oversimplified example. It assumes that each participant has datasets that can be pseudonymized and matched. Even so, it shows exactly how a clean room works, and why enterprises are eager to use one.

Clean rooms vs. DMPs

Clean rooms are spaces for audience analysis that rely on pseudonymized first-party data like emails and phone numbers.

On the other hand, Data Management Platforms (DMPs) are platforms for audience activation that rely on third-party cookies and device IDs. (Although these days, many brands also pump hashed first-party data into their DMPs.)

DMPs were built for the open, cookie-based web before the advent of GDPR and the Intelligent Tracking Prevention (ITP) feature of Apple’s Safari browser.

The first DMP came to market in 2007, when the AdTech startup BlueKai launched an exchange for audience data. Two years later, in 2009, the advisory Forrester Research coined the term "Data Management Platform." The term quickly trended.

DMPs collect third-party cookie IDs and device IDs, and stitch them together into audiences that advertisers can retarget across the web. But data protection regulations and browser vendors phasing out third-party cookies have undercut that model.

Clean rooms flip that model on its head.

Rather than collecting cookie and device IDs from websites and mobile apps, clean rooms enable the creation of audiences based on hashed first-party data brought by the owners of that data themselves.

In other words, DMPs are outside-in; clean rooms are inside-out. Clean room participants need to have an individual’s email address, phone number, or ZIP code to be able to pseudonymize it and match it with other participants’ data.

DMPs are not necessarily dead yet. They continue to power web retargeting, frequency capping, and lookalike audiences inside many Demand-Side Platforms (DSPs).

However, DMP audiences have shrunk in size since only Chrome supports third-party cookies of all major browsers (though Google will eventually phase out third-party cookies from its browser as well) and the use of third-party cookies itself now requires consent.

Clean rooms vs. CDPs

Customer Data Platforms (CDPs) collect first-party data from your touchpoints — websites, apps, email campaigns, point of sale (POS), internet of things (IoT) — and unify it to stitch together rich profiles so you can segment, advertise, and market to your customer base.

Clean rooms are where you upload your pseudonymized first-party data so it can be combined with the pseudonymized first-party data of your partners.

This data is combined, allowing all parties to match, segment, and analyze shared audiences. While you cannot directly advertise from within the clean room, the insights gained can be used to inform and activate marketing campaigns on external platforms.

Do you need one?

If you advertise across multiple channels and need to blend first-, second-, and even third-party datasets to measure campaign impact, then yes, a clean room can give you the edge.

With the right partners, a clean room lets you see which channels, campaigns, and tactics actually drive results, maximizing ROAS whether your ad budget is ten or five hundred million.

But clean rooms aren’t plug-and-play. To get real value, you’ll need a robust first-party dataset — and relationships with partners willing to share theirs.

Be sure to come prepared with both, or you may find it tough to make a clean room work for you.

And if you have doubts, get in touch. We’ll help you make the right call.