Digital clean room solutions have become a practical way to keep audience targeting and measurement effective while meeting stricter privacy expectations. As third-party cookies fade and regulators scrutinize data handling, marketers need environments that let teams collaborate on insights without exposing raw user-level data. This review compares leading options, key capabilities, and selection criteria—so you can choose wisely before your next campaign depends on it.
What is a digital clean room for privacy-safe targeting
A digital clean room is a controlled analytics environment that allows two or more parties (for example, a brand and a publisher, or a brand and a walled garden) to run queries on their combined datasets without sharing underlying, row-level personal data. Clean rooms typically support privacy-safe targeting through:
- Secure data ingestion and isolation (often in a dedicated tenant or segregated project).
- Identity resolution or matching using privacy-preserving joins (hashed identifiers, tokens, or platform IDs).
- Aggregation and thresholding to prevent small-segment “re-identification” risk.
- Query controls (approved SQL templates, restricted functions, and governance workflows).
- Output controls that limit what can be exported (for example, only aggregated results or approved audience segments).
For privacy-safe targeting, the goal is not to “get back” user-level tracking. The goal is to enable useful activation and measurement—such as suppressing existing customers, building lookalike-style cohorts, and measuring incremental lift—while reducing data leakage and improving compliance posture.
Two clean room types matter in practice. Walled-garden clean rooms operate inside major media platforms and help you analyze exposure and conversions within that ecosystem. Independent clean rooms (often built on cloud data warehouses) aim to connect multiple partners and channels, typically with more control over data models and governance.
Clean room architecture and security controls
When reviewing vendors, treat architecture as the foundation of trust. In 2025, most credible clean room solutions combine cloud-native security with purpose-built privacy controls. Key elements to evaluate:
- Isolation model: Dedicated environment vs shared compute. Dedicated projects and strict network boundaries reduce accidental exposure.
- Encryption and key management: Encryption in transit and at rest is table stakes; customer-managed keys and hardware security modules strengthen assurance for sensitive datasets.
- Access governance: Role-based access control, least-privilege defaults, and granular permissions for datasets, queries, and outputs. Look for approval workflows for new queries and exports.
- Auditability: Immutable logs for data access, query execution, and output generation. Audits should be easy to map to internal policies and external compliance requirements.
- Privacy safeguards: Minimum aggregation thresholds, differential privacy options, noise injection where appropriate, and protection against “difference attacks” (repeated queries designed to infer an individual).
Expect your legal, privacy, and security teams to ask follow-up questions: Where is data processed? Who can see what? What stops a partner from reconstructing identities? The best vendors answer these directly with documentation, third-party certifications, and clear contractual terms (including data retention and breach responsibilities).
Also verify operational realities: how long onboarding takes, whether your teams can use standard SQL and BI tools, and what happens when a partner’s data schema changes. These details determine whether privacy-safe targeting becomes routine—or stays a pilot.
Top digital clean room vendors and platforms
The “best” clean room depends on your media mix, data maturity, and partner ecosystem. Below is a practical review of commonly adopted approaches in 2025, focusing on strengths, limitations, and ideal use cases rather than marketing claims.
Google Ads Data Hub (ADH)
- Strengths: Deep analysis of Google media exposure and conversion signals with strict privacy controls; strong for reach, frequency, and conversion analysis within Google environments.
- Limitations: Primarily designed around Google’s ecosystem; cross-platform stitching depends on what you can legally and technically bring in and how outputs can be activated.
- Best for: Brands with significant Google spend that need robust measurement and audience insights within Google’s constraints.
Amazon Marketing Cloud (AMC)
- Strengths: Strong for retail media analytics and understanding Amazon Ads performance; useful for path-to-purchase insights tied to Amazon signals.
- Limitations: Most actionable for Amazon-centric strategies; activation and data portability are governed by Amazon’s policies.
- Best for: Retail and CPG advertisers prioritizing Amazon Ads measurement and optimization.
Meta Advanced Analytics (clean room capabilities within Meta’s environment)
- Strengths: Designed for privacy-safe analysis of Meta campaign performance; helpful for conversion and audience insights without exporting raw user data.
- Limitations: Constrained to Meta’s ecosystem; methodology, thresholds, and outputs follow platform rules.
- Best for: Meta-heavy advertisers who want consistent measurement and experimentation workflows inside Meta.
Snowflake (data clean room framework and partner ecosystem)
- Strengths: Strong for multi-party collaboration across an ecosystem of publishers and data providers; flexible data modeling; integrates with enterprise governance and security controls.
- Limitations: Requires disciplined data engineering and governance; outcomes depend on how well you implement templates, policies, and partner agreements.
- Best for: Enterprises wanting a scalable, partner-friendly clean room approach across many datasets and channels.
Databricks (lakehouse-based clean room patterns)
- Strengths: Strong for advanced analytics and ML use cases where you want to build privacy-aware models on governed datasets; good fit for organizations already standardized on a lakehouse.
- Limitations: More build-oriented; you may need accelerators, reference architectures, and strong internal engineering to deliver marketing-friendly workflows.
- Best for: Teams investing in ML-driven measurement, uplift modeling, and experimentation with tight governance.
InfoSum (non-movement data collaboration)
- Strengths: “Data never leaves” approach can reduce sharing risk; strong partner network in some markets; useful for collaboration where parties are cautious about exporting data.
- Limitations: Capabilities and flexibility vary by partner integrations; some workflows can be less familiar for teams expecting standard warehouse analytics.
- Best for: Privacy-forward collaborations between brands and publishers where minimizing data movement is a priority.
Habu (clean room software layer)
- Strengths: Emphasizes usability, workflows, and deployment across cloud environments; can accelerate partner collaboration with packaged templates and governance features.
- Limitations: Success depends on partner adoption and the underlying cloud/data architecture; evaluate roadmap and ecosystem fit.
- Best for: Organizations that want a productized layer to operationalize clean room use cases without building everything from scratch.
Practical selection tip: if most of your spend sits inside one major platform, start with that platform’s clean room for measurement depth. If your strategy depends on many publishers, retail media networks, and first-party data, prioritize an independent approach that supports multi-party collaboration and repeatable governance.
Privacy-safe measurement and audience targeting use cases
Clean rooms earn their keep when they improve decisions, not when they merely satisfy a compliance checkbox. In 2025, the most common and high-impact use cases include:
- Customer suppression: Exclude existing customers from acquisition campaigns by matching privacy-safe identifiers and activating suppression lists under controlled rules.
- Overlap and reach analysis: Estimate deduplicated reach across partners and understand how exposure overlaps without exporting user-level data.
- Conversion and path analysis: Analyze which sequences of impressions correlate with conversions, using aggregation thresholds and approved query logic.
- Incrementality testing: Run holdouts or geo/segment experiments and measure lift with controls that reduce bias and leakage.
- Cohort discovery for targeting: Build audience cohorts based on aggregated behaviors or attributes, then activate those cohorts where permitted.
- Frequency management insights: Identify overexposure risks and performance drop-offs at higher frequency levels.
Readers often ask: “Can a clean room replace a DMP?” In most organizations, it does not replace every DMP function, but it does replace the assumption that you need to centralize and expose user-level profiles to collaborate. Clean rooms work best when paired with:
- A strong first-party data program (clear consent, accurate customer data, and robust preference management).
- A warehouse or lakehouse foundation for governed analytics.
- Server-side conversion signals where appropriate and compliant, to improve measurement reliability.
Another follow-up: “Will a clean room improve campaign performance?” It can—if you use it to remove waste (suppression), reduce overfrequency, and run disciplined incrementality tests. If you only run descriptive reports, you may improve reporting but not outcomes.
Evaluation criteria and implementation checklist
To choose a clean room solution confidently, score vendors against business goals and risk tolerance. The criteria below reflect what tends to matter most for privacy-safe targeting programs that must scale beyond a single pilot.
1) Data governance and legal fit
- Does the vendor support your consent model and data processing terms?
- Can you enforce retention, purpose limitation, and deletion requirements?
- Are partner contracts and data sharing agreements supported with clear roles and responsibilities?
2) Partner ecosystem and interoperability
- Which publishers, retail media networks, and data providers can you collaborate with today?
- Can you support multiple identifiers and match methods while minimizing leakage?
- Do outputs integrate with your activation stack (DSPs, CDPs, ad servers) under policy controls?
3) Privacy controls you can prove
- Configurable thresholds, query governance, and protections against re-identification attempts.
- Auditable logs and administrative controls your security team will accept.
- Clear documentation of what users can and cannot do—without relying on “trust us” claims.
4) Usability for marketing and analytics teams
- Are there templates for common analyses (incrementality, reach, overlap, frequency)?
- Can analysts use SQL and connect approved BI tools without workarounds?
- Is the workflow fast enough for campaign pacing decisions, not just quarterly reviews?
5) Cost and operational complexity
- Compute and storage costs, plus vendor licensing and support.
- Data engineering effort to onboard sources and maintain schemas.
- Time-to-value: pilot in weeks, scale in months, not endless integration cycles.
A practical implementation approach is to start with two partner collaborations and two use cases: customer suppression and incrementality. These deliver measurable value and force you to operationalize governance, identity strategy, and output approvals. Once those work reliably, expand to multi-partner reach analysis and cohort discovery.
EEAT best practices for clean room programs
Clean rooms sit at the intersection of advertising performance and privacy risk. To align with Google’s EEAT expectations for helpful, trustworthy content and operations, build your program around demonstrable expertise and transparent governance.
Show expertise with documented methodology
- Use standardized query templates with peer review and version control.
- Publish internal “measurement playbooks” that define KPIs, attribution boundaries, and experiment design.
- Explain limitations up front (for example, thresholds, missing channels, delayed reporting windows).
Demonstrate experience with repeatable workflows
- Run recurring analyses on a schedule and track business outcomes (waste reduction, lift, CAC improvements).
- Maintain a backlog of hypotheses and test results, not just dashboards.
- Build a cross-functional operating cadence between marketing, analytics, privacy, and security.
Build authority through governance and validation
- Use formal approval gates for new datasets, new joins, and new export types.
- Validate results with triangulation: compare clean room outputs to platform reports, first-party sales data, and controlled experiments.
- Prefer vendors with credible security posture documentation and third-party attestations relevant to your risk profile.
Earn trust with privacy-by-design
- Minimize data fields to what you need for a defined purpose.
- Train users on safe querying patterns and prohibited behaviors (like repeated slicing to reach small cells).
- Maintain clear data lineage so you can answer “where did this metric come from?” during audits.
These practices answer the question executives inevitably ask: “Are we improving marketing performance without increasing privacy risk?” A well-run clean room program produces defensible measurement and controlled activation that you can explain to regulators, partners, and customers.
FAQs about digital clean room solutions
Do clean rooms allow user-level targeting?
Clean rooms are designed to avoid exposing user-level data between parties. Targeting typically happens through approved audience cohorts or platform-specific activation mechanisms, with strict controls on what can be exported and minimum size thresholds.
What data do you need to start using a clean room?
You typically need first-party customer data with appropriate consent (often hashed email or other identifiers where permitted), conversion events, and campaign exposure data from a partner or platform. Start with a limited set of fields tied to one or two defined use cases.
How is identity matching handled in a privacy-safe way?
Many solutions use hashed identifiers, tokenization, or platform IDs to perform joins. Strong implementations restrict join types, enforce thresholds, and prevent exporting matched row-level datasets. Your legal basis and consent model still determine what identifiers you can use.
Can a clean room help with incrementality measurement?
Yes. Clean rooms often support holdout-based measurement and controlled experiment analysis using aggregated outputs. The key is disciplined test design, consistent conversion definitions, and guardrails that prevent biased slicing or leakage.
How do you choose between a walled-garden clean room and an independent clean room?
If you need deep measurement within a single platform, use that platform’s clean room. If you need consistent analysis across many partners and data sources, an independent clean room approach—often warehouse-based—offers broader interoperability, with more responsibility on your team for governance and implementation.
What are the main risks to watch for?
The biggest risks are re-identification via small segments, uncontrolled query patterns, exporting overly granular outputs, and weak governance around partner access. Choose solutions with strong privacy controls, auditing, and enforceable policies, and back them with training and internal approvals.
Digital clean rooms can make privacy-safe targeting and measurement practical in 2025, but only when architecture, governance, and use cases align. Start with the partners and channels that drive the most spend, then prove value with suppression and incrementality. Choose a solution with enforceable privacy controls, clear audit trails, and workable activation paths. Done well, clean rooms reduce waste while keeping collaboration defensible.