Data Clean Room Adoption & Trust Assessment

Welcome! This survey explores how marketing professionals view data clean rooms for measurement purposes. It should take approximately 8–10 minutes to complete. Your participation is entirely voluntary, and you may stop at any time. There are no right or wrong answers — we are interested in your honest opinions and experiences based on your organization's current practices. All responses are confidential and will be reported only in aggregate for research purposes.

Which of the following best describes your involvement in measurement or data clean room decisions at your organization?

• I am a primary decision-maker
• I influence or recommend decisions
• I am involved but do not make or recommend decisions
• I am not involved in these decisions

Which of the following best describes your familiarity with and current stance on data clean rooms for marketing measurement?

• Currently use a data clean room
• Piloting or evaluating a data clean room
• Familiar but not planning to use one
• Not familiar with data clean rooms

For context: A data clean room is a secure environment where multiple parties can combine and analyze data under strict privacy controls. Raw, user-level data is never directly shared — only approved queries producing aggregated outputs are permitted, supporting use cases such as measurement and audience analysis.

What are the main reasons your organization is not currently planning to use a data clean room? Select all that apply.

• Insufficient internal resources or skills
• Legal or privacy risk concerns
• Total cost of ownership
• Limited access to platforms or partners
• Complexity of setup and operations
• Unclear measurement improvement over current methods
• Data sharing restrictions with partners
• Other (please specify)

Which of the following measurement use cases does your organization currently run in a data clean room? Select all that apply.

• Incrementality or lift testing
• Attribution or contribution analysis
• Reach and frequency deduplication
• Audience overlap or sizing
• Marketing mix modeling (MMM) calibration or validation
• Data enrichment for measurement
• Cross-publisher cohort analysis
• Other (please specify)

Thinking about the past 6 months, how much do you trust the measurement results produced through data clean rooms?

How accurate do you believe data clean room outputs are today for attribution or contribution analysis?

Which of the following would most increase your trust in clean-room-based measurement? Select up to three.

• Transparent query templates and documentation
• Ability to reproduce results independently
• Third-party audit or certification
• Open-source or inspectable methods
• Publisher- or platform-level verification
• Use of randomized holdouts or gold-standard tests
• Clear privacy guarantees and controls

Please rank the following obstacles to reliable clean-room measurement for your organization, from biggest obstacle (1) to smallest.

1. Interoperability across platforms or partners
2. Data latency or limited query flexibility
3. Legal/privacy risk or policy uncertainty
4. Cost of tools and services
5. Internal skills or bandwidth
6. Limited transparency into methods
7. Vendor or partner lock-in

Over the next 12 months, how do you expect your organization's investment in data clean-room-based measurement to change?

• Increase significantly
• Increase somewhat
• No change
• Decrease somewhat
• Decrease significantly
• Unsure

Based on your responses throughout this survey, what is the single biggest change that would increase your trust or comfort with data clean room measurement?

Which of the following best describes your organization?

• Brand or advertiser
• Agency
• Publisher or platform
• Ad tech or measurement provider
• Consulting or other services
• Other (please specify)

Thank you for completing this survey! Your insights will help shape how the industry approaches data clean room measurement. All responses will be reported only in aggregate, and your individual answers will remain confidential.

Based on the definition above, how interested are you in exploring or evaluating a data clean room for your organization this year?

• Very interested
• Somewhat interested
• Not very interested
• Not interested at all

How comfortable would you be sharing your organization's first-party data in a clean room operated by a major platform (e.g., Google, Meta, Amazon)?

How accurate do you believe data clean room outputs are today for incrementality or lift testing?

Please rank the following outcomes you value most from data clean rooms, from most valuable (1) to least valuable.

1. Improved measurement accuracy
2. Cross-partner interoperability
3. Privacy protection and compliance
4. Speed and query flexibility
5. Cost efficiency
6. Control and reproducibility
7. Access to partner or platform data

We'd like to understand your experience with data clean rooms in a bit more depth. Please share your thoughts on what has shaped your current views, and we may ask a couple of follow-up questions.

Which of the following best describes your primary role?

• Marketing or Media
• Analytics or Measurement
• Data or Engineering
• Privacy, Legal, or Compliance
• Executive or Leadership
• Other (please specify)

How comfortable would you be sharing your organization's first-party data in a clean room operated by an independent third-party provider?

How accurate do you believe data clean room outputs are today for reach and frequency measurement?

In which region is your organization primarily based?

• North America
• Europe
• Asia-Pacific
• Latin America
• Middle East or Africa
• Multiple regions

How comfortable would you be sharing your organization's first-party data in a clean room operated and managed by your own organization?

Approximately how many employees does your organization have?

• 1–49
• 50–249
• 250–999
• 1,000–4,999
• 5,000+

What is your organization's approximate annual media spend?

• Less than $1M
• $1M–$9.9M
• $10M–$49.9M
• $50M–$199.9M
• $200M+
• Prefer not to say

How many years of experience do you have in marketing or analytics?

• 0–2 years
• 3–5 years
• 6–10 years
• 11+ years