# Retail media and clean rooms: sharing data without giving it away
A snack manufacturer wants to know if its in-store display and app ads actually sold more chips last quarter. The retailer holds the answer inside its loyalty card and point-of-sale (POS) data. Neither side wants to hand over raw files: the brand doesn't want to expose its media spend and audience logic, the retailer won't release shopper-level purchase records. The deal happens anyway, inside a "clean room." Nobody sees the other's raw data. Both walk away with a lift number.
This setup is now the default structure for retail media deals, and it's worth understanding exactly how it works and where it can still fail.
A data clean room is a controlled environment where two or more parties bring their data, run agreed-upon queries against it, and only see the aggregated output, never each other's underlying records.
Technically, this relies on tools like hashed matching (turning identifiers such as email or loyalty ID into irreversible codes so both sides can match records without revealing them), differential privacy (adding statistical noise so no individual can be re-identified from results), and k-anonymity thresholds (a rule that no output can be shown unless it represents at least *kk* people, commonly 25 to 100 depending on the retailer).
Major implementations: Amazon Marketing Cloud, Walmart Connect's clean room (built partly on Publicis Epsilon's tech), Kroger Precision Marketing (via 84.51°), and Google's Ads Data Hub. Snowflake and Databricks also offer neutral clean room infrastructure that brands and retailers can jointly license instead of trusting either party's platform.
Three forces pushed the industry here simultaneously.
Cookie deprecation. Google has been phasing third-party cookies out of Chrome through 2025 to 2026 (timeline repeatedly delayed and revised, this is Google's own stated plan, not a fixed law), killing the old way brands tracked shoppers across the web.
Privacy law. In the EU, the GDPR (General Data Protection Regulation, enforced by national Data Protection Authorities) requires a lawful basis to process personal data and gives shoppers rights to access, correct, or delete it. In the US, there's no single federal privacy law, but state laws like the CCPA/CPRA (California Consumer Privacy Act, updated by the California Privacy Rights Act, enforced by the California Privacy Protection Agency) give similar rights and impose obligations when data is "sold" or "shared," a term broad enough to cover many ad-targeting arrangements.
Retailer leverage. Retailers like Kroger, Walmart, Tesco (Clubcard), and Carrefour realized their first-party loyalty data is more valuable and more durable than any cookie ever was. Retail media is now one of the fastest-growing ad segmentssegmentsDividing a market into distinct groups of customers who share similar needs, characteristics or behaviours, so each group can be served with a tailored approach.View full definition → globally; eMarketer estimates (as of 2024 to 2025 reporting) put US retail media spend in the tens of billions annually, with Amazon holding the largest single share.
Clean rooms let retailers monetize that data without a "sale" in the legal sense, since raw personal data never leaves their environment.
Say the snack brand ran a four-week retail media campaign. The workflow:
1. Brand uploads its campaign exposure log (which loyalty IDs, hashed, saw the ad) into the clean room.
2. Retailer's POS data (same hashed loyalty IDs, purchase history) sits in the same environment.
3. A pre-agreed SQLSQLSales Qualified Lead: a prospect the sales team has validated as ready for direct outreach and a proposal, having passed clear qualification criteria.View full definition →-style query joins the two on the hashed ID and buckets shoppers into "exposed" vs "control."
4. Only the aggregate result returns: incremental sales lift, spend efficiency, category halo effect.
A simplified version of the query logic:
SELECT
campaign_group,
COUNT(DISTINCT hashed_loyalty_id) AS shoppers,
SUM(basket_value) AS total_sales
FROM matched_exposure_and_pos
WHERE k_anon_check >= 25
GROUP BY campaign_group;That k_anon_check >= 25 line is the governance control: if a segment (say, a niche flavor SKU in a small store cluster) has fewer than 25 matched shoppers, the query returns nothing rather than a number that could be reverse-engineered to identify individuals.
Worked example: exposed group of 50,000 shoppers spends an average of $4.20 on the category post-campaign; matched control group of 50,000 spends $3.80. Lift = ($4.20 - $3.80) / $3.80 = 10.5%. That's the only number the brand's media team sees. They never see which 50,000 people, what else was in their basket, or their names.
Clean rooms reduce risk, they don't eliminate the need for oversight. Three checks matter most:
Query auditing. Someone should log every query run against the clean room and review for "creep": a brand asking overly narrow questions designed to isolate small, identifiable segmentssegmentsDividing a market into distinct groups of customers who share similar needs, characteristics or behaviours, so each group can be served with a tailored approach.View full definition → (a classic re-identification attack vector).
Consent lineage. The loyalty data feeding the clean room has to trace back to a valid consent or legitimate-interest basis under GDPR, or a proper CCPA opt-out mechanism. If the underlying consent was defective, aggregating it in a clean room doesn't fix that. Governance teams should be able to answer "what did the shopper agree to" for any data reaching the room. The UK ICO's guidance on anonymisation and pseudonymisation is a solid free reference on where "anonymised enough" legally starts.
Vendor and cross-border checks. Many clean room providers are US hyperscalers (Amazon, Google, Microsoft, Snowflake, Databricks). A European retailer running EU shopper data through a US-headquartered clean room needs a valid transfer mechanism, typically Standard Contractual Clauses (SCCs), since the 2020 *Schrems II* ruling struck down the previous EU-US Privacy Shield framework. This is a live legal question, not a solved one, especially after the 2023 EU-US Data Privacy Framework, which itself faces ongoing legal challenges.
Knowledge check
1. What is the core structural feature that makes a data clean room acceptable to both a brand and a retailer who don't trust each other with raw data?
2. Why would a retailer use a k-anonymity threshold (e.g., no output shown unless it represents at least 25-100 people) in a clean room query?
3. A brand wants to know exactly which individual loyalty card holders saw its app ad and later bought chips, so it can build a personalized retargeting list. Why would a clean room NOT be the right tool for this goal?
4. Select ALL correct answers about why retail media clean room deals have become the industry default.
Select all the correct answers.
5. Select ALL correct answers about the technical mechanisms that make clean rooms privacy-safe.
Select all the correct answers.
Clean rooms solve the "don't hand over raw data" problem. They don't solve everything:
🎬 [VIDEO: "What Are Data Clean Rooms? (And Why Retailers Love Them)" - youtube.com - search for recent explainers from IAB Tech Lab or major ad-tech conference talks (LiveRamp, Snowflake, InfoSum) for a visual walkthrough of the matching and query flow described above]