Welcome! This brief survey asks about your familiarity, trust, comfort, and perceived value of data clean rooms for marketing measurement. Please answer based on your organization’s current practices. Your responses are confidential and reported in aggregate.
Which best describes your familiarity 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
- Not familiar with data clean rooms
Quick definition: A data clean room is a secure environment where parties can bring their data together and analyze it with strict privacy controls. Raw, user-level data is not directly shared; approved queries produce aggregated outputs for use cases like measurement.
Based on this definition, how interested are you in learning more or evaluating a data clean room this year?
- Very interested
- Somewhat interested
- Not very interested
- Not interested
If you’re familiar but not planning to use a clean room, what are the main reasons? Select all that apply.
- Insufficient internal resources or skills
- Legal or privacy risk
- Total cost of ownership
- Limited access to platforms or partners
- Complexity of setup/operations
- Unclear measurement improvement
- Data sharing restrictions with partners
Which measurement use cases do you run in a clean room today? Select all that apply.
- Incrementality/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
Thinking about the last 6 months, how much do you trust measurement results produced via data clean rooms?
Attention check: To confirm you’re paying attention, please select “Neutral” below.
- Strongly disagree
- Disagree
- Neutral
- Agree
- Strongly agree
How comfortable would you be sharing first-party data in a clean room operated by each of the following?
How accurate do you believe clean-room outputs are today for each task?
Which 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
Rank the obstacles to reliable clean-room measurement for your organization (1 = biggest obstacle).
Allocate 100 points across the outcomes you value most from clean rooms (higher = more valuable).
Which best describes your organization?
- Brand/advertiser
- Agency
- Publisher/platform
- Ad tech/measurement provider
- Consulting/other services
- Other
What is your primary role?
- Marketing/Media
- Analytics/Measurement
- Data/Engineering
- Privacy/Legal/Compliance
- Executive/Leadership
- Other
Where is your primary region?
- North America
- Europe
- Asia-Pacific
- Latin America
- Middle East/Africa
- Multiple regions
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 annual media spend?
- Less than $1M
- $1M–$9.9M
- $10M–$49.9M
- $50M–$199.9M
- $200M+
- Prefer not to say
Do you personally influence decisions about measurement or clean-room use?
Years of experience in marketing/analytics:
- 0–2 years
- 3–5 years
- 6–10 years
- 11+ years
Over the next 12 months, how do you expect your organization’s investment in clean-room-based measurement to change?
- Increase significantly
- Increase somewhat
- No change
- Decrease
- Unsure
What is the single biggest change that would increase your trust or comfort with clean-room measurement?
Max 600 chars
AI Interview: 2 Follow-up Questions on Data Clean Rooms
Thank you for participating! Your feedback is valuable.