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Marketing Data Clean Room Adoption & Trust Survey Template

Assess marketers' awareness, trust, comfort, and value of data clean rooms for measurement. Includes Likert scales plus adoption, ROI, and barrier questions.

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AI-Powered Questions

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Comprehensive insights and recommendations

Sample Survey Items

Q1
Chat Message
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.
Q2
Multiple Choice
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
Q3
Chat Message
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.
Q4
Multiple Choice
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
Q5
Multiple Choice
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
Q6
Multiple Choice
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
Q7
Opinion Scale
Thinking about the last 6 months, how much do you trust measurement results produced via data clean rooms?
Range: 1 – 10
Min: Do not trust at allMid: NeutralMax: Completely trust
Q8
Multiple Choice
Attention check: To confirm you’re paying attention, please select “Neutral” below.
  • Strongly disagree
  • Disagree
  • Neutral
  • Agree
  • Strongly agree
Q9
Matrix
How comfortable would you be sharing first-party data in a clean room operated by each of the following?
RowsVery uncomfortableUncomfortableNeutralComfortableVery comfortable
A large platform (walled garden)•••••
A neutral third-party provider•••••
A publisher consortium or alliance•••••
Your company (self-managed clean room)•••••
Q10
Matrix
How accurate do you believe clean-room outputs are today for each task?
RowsVery lowLowModerateHighVery highNot sure
Incrementality/lift estimates••••••
Multi-touch attribution or contribution••••••
Audience overlap estimates••••••
Reach and frequency deduplication••••••
MMM calibration inputs••••••
Q11
Multiple Choice
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
Q12
Ranking
Rank the obstacles to reliable clean-room measurement for your organization (1 = biggest obstacle).
Drag to order (top = most important)
  1. Interoperability across platforms/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
Q13
Constant Sum
Allocate 100 points across the outcomes you value most from clean rooms (higher = more valuable).
Total must equal 100
  • Improved measurement accuracy
  • Cross-partner interoperability
  • Privacy protection and compliance
  • Speed and query flexibility
  • Cost efficiency
  • Control and reproducibility
  • Access to partner/platform data
Min per option: 0Whole numbers only
Q14
Multiple Choice
Which best describes your organization?
  • Brand/advertiser
  • Agency
  • Publisher/platform
  • Ad tech/measurement provider
  • Consulting/other services
  • Other
Q15
Multiple Choice
What is your primary role?
  • Marketing/Media
  • Analytics/Measurement
  • Data/Engineering
  • Privacy/Legal/Compliance
  • Executive/Leadership
  • Other
Q16
Multiple Choice
Where is your primary region?
  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East/Africa
  • Multiple regions
Q17
Multiple Choice
Approximately how many employees does your organization have?
  • 1–49
  • 50–249
  • 250–999
  • 1,000–4,999
  • 5,000+
Q18
Multiple Choice
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
Q19
Multiple Choice
Do you personally influence decisions about measurement or clean-room use?
  • Yes
  • No
Q20
Multiple Choice
Years of experience in marketing/analytics:
  • 0–2 years
  • 3–5 years
  • 6–10 years
  • 11+ years
Q21
Multiple Choice
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
Q22
Long Text
What is the single biggest change that would increase your trust or comfort with clean-room measurement?
Max 600 chars
Q23
AI Interview
AI Interview: 2 Follow-up Questions on Data Clean Rooms
AI InterviewLength: 2Personality: Expert InterviewerMode: Fast
Q24
Chat Message
Thank you for participating! Your feedback is valuable.

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