Data clean rooms and retail media, explained

A data clean room is a controlled environment where two or more parties can analyze combined data without either side seeing the other's raw records. In retail media, clean rooms are often used so a retailer and an advertiser can match their data, measure a campaign and see aggregated results, while each party's underlying data stays protected.

The idea has become common in parts of the industry. Whether it's relevant to a particular campaign depends on what data is involved and what questions need answering.

How does a data clean room work?

Each party brings data into the clean room under agreed rules. The clean room runs approved analyses on the combined data and returns only aggregated outputs, such as totals or counts above a minimum size. Neither party can pull out the other's individual records.

A typical use in retail media: a retailer has purchase data from its loyalty program, and an advertiser has data about who saw its ads. The clean room matches the two to estimate how many exposed customers later bought the product, returning only aggregate results. The data clean room overview describes common designs.

Clean rooms depend on rules: who can run which queries, what minimum group sizes apply, and what outputs can leave the room. Those rules are what make the arrangement privacy-protective.

Clean rooms are usually operated by a technology provider, a retailer or a neutral third party. Each setup has its own rules and technical controls, so two clean rooms can differ considerably in what they allow.

When are clean rooms used in retail media?

Mostly when shopper-level data is involved. Retail media networks built on loyalty programs, online accounts or app logins hold data tied to individual customers. Matching that data with an advertiser's data raises privacy questions, and clean rooms offer a way to do it under controls.

They're less central when the data is already aggregated. If campaign measurement uses store-level sales, comparing advertised and unadvertised stores, there are no individual records to match. The analysis happens at the store and product level from the start.

That's the case for data at the store, UPC and transaction level, which describes what sold where without tracking individual shoppers. For those comparisons, the questions are about store selection and fair comparison rather than individual matching.

What should advertisers ask about clean rooms?

What data goes in, from each side, and at what level of detail.

What analyses are allowed, and who approves them.

What outputs come out, and what minimum sizes apply to protect privacy.

Who operates the clean room, and how access is controlled and audited.

How long data stays in the environment, and how it's deleted.

The FTC's privacy and security guidance offers background on the broader obligations businesses face when handling consumer data. Rules vary by state.

Does every campaign need a clean room?

No. A clean room is a tool for a specific problem: analyzing combined shopper-level data from different parties under privacy controls. If your campaign's measurement relies on aggregated store-level data, the problem it solves may not arise.

Clean rooms also add cost and setup time. For campaigns where aggregated store-level comparisons answer the main questions, that effort may not be necessary. Where shopper-level questions matter, the setup can be worthwhile.

What every campaign does need is clarity about data. What's being used, at what level, by whom and for what purpose. Ask any network to explain that plainly. For NRS Digital Media campaigns, ask the NRS Digital Media team what data and measurement your campaign can use. More about the network is on the NRS Digital Media site.