Quick answer
A data clean room is a controlled environment where two or more parties can join and analyse their first-party data, usually matched on hashed identifiers, under rules that let each side see only aggregated results rather than the other’s raw, person-level records.
What is a data clean room?
A data clean room is a secure environment where two companies (say, a brand and a retailer, or an advertiser and a publisher) can combine their customer data to answer shared questions, without either side seeing the other's raw records. Each side contributes data; the room matches it, runs only approved queries, and returns aggregated results.
Clean rooms grew as third-party cookies declined and privacy rules tightened, because they let partners measure and target with first-party data instead.
How it works
- Each party uploads data to the clean room provider (or the room runs where the data already sits, in a cloud warehouse).
- Identifiers are matched, usually on hashed emails or phone numbers, sometimes through an identity provider.
- Rules limit queries: which columns can be joined, which outputs are allowed, and a minimum audience size per result.
- Results are aggregated: counts, rates, sums. No row-level data leaves the room.
- Optional activation: a matched audience is sent to an ad platform without either party seeing who is in it.
Typical use cases
| Use case | Question |
|---|---|
| Overlap analysis | How many of our customers also shop with this retailer? |
| Campaign measurement | Did people who saw the publisher's ads buy more in our store? |
| Audience building | Target the retailer's shoppers who have not bought our brand yet |
| Reach and frequency | How many unique people did our campaign reach across both platforms? |
A sample overlap query
A brand and a retailer compare customer lists (sample numbers):
| Count | |
|---|---|
| Brand customers (hashed emails) | 200,000 |
| Retailer loyalty members | 1,500,000 |
| Matched | 36,000 (18% of the brand list) |
The brand can then see, for example, average basket size of matched customers by region, but any region with fewer than 50 matched people is suppressed, so no small group can be singled out.
Clean room vs CDP vs data sharing
| Data clean room | CDP | Direct data sharing | |
|---|---|---|---|
| Whose data | Two or more companies | One company | Two companies |
| Raw records visible to the partner | No | Not applicable | Yes |
| Typical output | Aggregates, matched audiences | Unified profiles, segments | Full files |
Privacy limits
- Hashing is not anonymisation. A hashed email is pseudonymous; under GDPR it is still personal data, so you still need a lawful basis and must disclose the sharing.
- Small results can identify people. Aggregation thresholds and limits on repeated queries matter.
- Match rates vary. Different email addresses per person and typos lower matches; reported figures should state the match rate.
This is general information, not legal advice.
What a small team can do instead
Most small and mid-size teams do not need a clean room. Clean first-party analytics, consistent UTM tagging and aggregated reports shared with partners cover most measurement questions. TapCub supports this path: the website script is anonymous with no cookie by default; in identified mode, emails and phone numbers are hashed before lookup; and reports and dashboards can be shared as aggregates without exposing person-level data. See also identity resolution and hashing.
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Solutions Engineer
Covers SDKs, API integrations, and data implementation, with an emphasis on validation, system interoperability, and troubleshooting.