Clean Room (Data)

Clean Room (Data): A Practical Guide for UK Marketers

What is a Clean Room?

A clean room is a secure, neutral environment where two or more parties can collaborate on data insights without either party directly accessing the other's raw customer data. Think of it as a locked meeting room where data is analysed behind glass – results are shared, but the underlying personal information never leaves each organisation's control.

In the UK marketing context, clean rooms have become essential tools for maintaining GDPR compliance while still enabling sophisticated audience targeting, campaign measurement, and customer insights. Rather than exchanging customer lists or uploading raw data to a third party, clean rooms let you work with encrypted or hashed data that's processed in a controlled environment.

Why Clean Rooms Matter for UK Marketers

The regulatory landscape has fundamentally changed how you can use customer data. GDPR, the Online Safety Bill, and upcoming Digital Markets Act regulations create real friction in data sharing. Clean rooms solve this by:

• Maintaining data ownership: Your customer data stays with you
• Reducing compliance risk: No unauthorised data transfers or exposure
• Enabling partnerships: Work with media owners, agencies, and data providers without sharing personal information
• Improving measurement: Match campaigns to outcomes without raw-data handovers

For Connect Media Group's clients, this means you can still run sophisticated audience targeting and attribution campaigns while staying compliant with UK and EU regulations.

How Clean Rooms Work in Practice

The basic process:

1. Data preparation: You hash or encrypt your customer identifiers (email addresses, phone numbers) using a consistent algorithm
2. Upload to clean room: Send only hashed data and approved attributes (e.g., purchase history, engagement level) to the clean room platform
3. Matching: The platform matches your hashed identifiers with other parties' data (e.g., a publisher's audience segment) without revealing actual identities
4. Analysis: You gain insights on matched audiences – size, overlap, attributes – without seeing the other party's customer names
5. Activation: Use aggregated insights to inform targeting, suppress audiences, or refine campaigns

Real example: A fashion retailer wants to understand overlap with a magazine publisher's subscribers. Instead of swapping customer lists, they: - Hash their email database and upload it to a clean room - The publisher hashes their subscriber list separately - The clean room matches hashed IDs and shows: "15,000 overlap customers exist, and they're 40% more likely to purchase premium items" - The retailer uses this insight to refine targeting without ever seeing subscriber names

Key Use Cases for UK Marketers

Audience expansion and lookalike modelling Identify similarities between your best customers and a partner's audience to inform targeting strategy, without exposing individual customer names.

Campaign measurement and attribution Match campaign interactions with conversion data to measure ROI, even when data lives in different systems. A publisher can help you understand which of their readers converted, without sharing individual reader identity.

Media buying optimisation Work with media owners and agencies to understand which audience segments perform best, informing media mix decisions and budget allocation.

Customer data platform (CDP) enrichment Enrich your first-party data with insights from partners – purchase propensity, category interest, lifecycle stage – without importing raw third-party customer records into your CDP.

Choosing and Setting Up a Clean Room

What to look for:

• GDPR and UK PECR compliance: Ensure the platform is built with UK regulations in mind, not just generic privacy
• Transparency: You should understand exactly how data is hashed, matched, and stored
• Encryption standards: Look for AES-256 encryption at rest and in transit
• Audit trails: The platform should log all access and processing for compliance verification
• Supported integrations: Can it connect to your CDP, DMP, or analytics platforms?
• Ease of use: Can your team onboard data without months of technical setup?

Practical setup steps:

1. Audit your data: Identify which customer attributes and identifiers you're comfortable sharing (often transaction data, not personal details)
2. Choose hash type: Decide on a consistent hashing algorithm (e.g., SHA-256) so matching works across partners
3. Define data governance: Establish rules about what insights can be derived and how results are shared
4. Test with one partner: Start with a trusted partner to validate workflow before scaling
5. Document the process: Create internal documentation on data flows, access controls, and compliance steps

Best Practices and Common Pitfalls

Do: - Start with small, low-risk projects to build confidence - Involve your legal and compliance teams from the start - Use consistent hashing methods across all partners - Aggregate results before sharing (never share individual-level matches) - Set clear data retention policies – how long does matched data stay in the clean room? - Document consent: Ensure customers have agreed to their data being used this way

Don't: - Assume all clean rooms are equally secure – vet vendors thoroughly - Skip data governance conversations with partners - Share raw results that could re-identify individuals - Upload data without understanding the hashing and matching logic - Forget that clean rooms are a tool, not a compliance guarantee – proper consent and legal basis still matter

Common pitfall: Marketers sometimes assume a clean room means they don't need customer consent. In reality, you still need a valid legal basis (consent, legitimate interest, etc.) to use customer data in a clean room. The clean room is about how you share it, not whether you're allowed to use it.

Real-World Example: Retail Campaign

An online retailer (Client A) wants to target high-value customers across a media network (Partner B):

1. Client A prepares a file: customer ID (hashed), total spend, category preferences
2. Partner B prepares a file: audience member ID (hashed), publisher segment, engagement level
3. Both upload to a clean room (e.g., Criteo, The Trade Desk, or Havas's platform)
4. Clean room matches ~50,000 overlapping hashed IDs
5. Results: "Matched audience skews 60% towards 'premium segment' and shows 3x average engagement"
6. Client A uses this insight: Budget 40% of media spend to this segment, knowing it performs better
7. Neither party has exposed customer identities

Integration with Your Existing Stack

Clean rooms work best when integrated with your CDP or DMP:

• CDP integration: Export hashed audiences from your CDP to a clean room, match with partners, and reimport insights
• DMP connection: Use clean room results to refine segments and audience targeting within your DMP
• Analytics flow: Connect clean room match results to your BI tool to track performance over time

Ask your clean room vendor about pre-built connectors for tools like Segment, mParticle, or Adobe Experience Platform.

Measuring Success

Track these metrics:

• Match rate: What percentage of your audience matches with partners? (typically 30–60%)
• Insight quality: Do clean room insights lead to better campaign performance?
• Time to insight: How quickly can you move from data upload to actionable results?
• Cost per matched record: Are you paying fairly for the service relative to scale?

Summary

Clean rooms represent a pragmatic solution to a real tension: the need for data-driven marketing and the UK's strict privacy rules. By using clean rooms, you can collaborate with partners, measure campaigns, and build audiences – all without exposing raw customer data. Start small, choose a reputable vendor, and ensure your legal and compliance teams are aligned. Done right, clean rooms let you have your data privacy and your marketing insights too.