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Experimentation Maturity & Data Trust Assessment

Measures A/B testing ease-of-use, guardrail adoption, result trust, and decision confidence among product and engineering teams. Use it to identify friction points, governance gaps, and training needs to scale experimentation.

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AI-Powered Questions

Intelligent follow-up questions based on responses

Automated Analysis

Real-time sentiment and insight detection

Smart Distribution

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Detailed Reports

Comprehensive insights and recommendations

Template Overview

24

Questions

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This professionally designed survey template helps you gather valuable insights with intelligent question flow and automated analysis.

Sample Survey Items

Q1
Chat Message
Welcome! This survey explores your experimentation practices and confidence in results. It takes approximately 5–7 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 experience. All responses are confidential, anonymized, and reported only in aggregate to improve experimentation practices.
Q2
Multiple Choice
How often are experiments (e.g., A/B tests, feature experiments) part of your work?
  • Regularly (monthly or more)
  • Occasionally (quarterly)
  • Rarely (yearly or less)
  • Never
Q3
Multiple Choice
Which platforms or approaches do you currently use for experimentation? Select all that apply.
  • In-house experimentation framework
  • Feature flag platform (e.g., LaunchDarkly, Flagsmith)
  • Third-party A/B tool (e.g., Optimizely, VWO, AB Tasty)
  • SQL / notebooks only (no dedicated tool)
  • Dashboarding tool (e.g., internal BI)
  • None of the above
  • Other (please specify)
Q4
Dropdown
In the last 3 months, approximately how many experiments did you help design, run, or analyze?
  • 0
  • 1–2
  • 3–5
  • 6–10
  • 11–20
  • More than 20
Q5
Opinion Scale
How easy or difficult is it to set up a standard A/B test using your current tools and processes?
Range: 1 7
Min: Very difficultMid: NeutralMax: Very easy
Q6
Opinion Scale
How easy or difficult is it to analyze a completed experiment and interpret its results?
Range: 1 7
Min: Very difficultMid: NeutralMax: Very easy
Q7
Multiple Choice
Which of the following quality controls are currently enforced in your experimentation workflow? Select all that apply.
  • Pre-launch checklist
  • Blocking deployment on missing instrumentation
  • Automated SRM (sample ratio mismatch) alerting
  • Sequential testing / alpha spending
  • Max exposure or blast-radius limits
  • Quality gates for key metrics
  • Post-experiment QA template
  • None of the above
  • Other (please specify)
Q8
Multiple Choice
What is the primary decision rule your team uses to determine whether an experiment's results are conclusive?
  • Fixed p-value threshold (e.g., 0.05)
  • Bayesian decision rule
  • Business threshold / minimum detectable effect
  • Case-by-case judgement
  • No standard rule / not sure
  • Other (please specify)
Q9
Opinion Scale
How much do you trust your organization's experiment results to inform product decisions?
Range: 1 7
Min: Not at allMid: NeutralMax: Completely
Q10
Opinion Scale
How confident are you in acting on an experiment's outcome to make a product or business decision?
Range: 1 7
Min: Not at all confidentMid: NeutralMax: Extremely confident
Q11
Long Text
What, if anything, most undermines your trust in experiment results today? Please share specifics.
Max chars
Q12
Ranking
Rank the following blockers to reliable experimentation from biggest (top) to smallest (bottom).
Drag to order (top = most important)
  1. Data quality / instrumentation issues
  2. Metric definitions ambiguity
  3. Sample contamination / overlap
  4. Insufficient traffic / power
  5. Engineering constraints / time
  6. Organizational pressure to ship
Q13
Ranking
Rank the following phases of a typical experiment by how much effort they require (most effort at top).
Drag to order (top = most important)
  1. Planning and design
  2. Instrumentation and data validation
  3. Implementation and rollout setup
  4. Running and monitoring
  5. Analysis and interpretation
  6. Decision and rollout
  7. Documentation and communication
Q14
Long Text
What one change would most improve your experimentation workflow?
Max chars
Q15
Chat Message
The next two questions are for those who do not currently run experiments. If you do run experiments, please skip ahead.
Q16
Multiple Choice
What are the main reasons you do not currently run experiments? Select all that apply.
  • Not enough traffic to test
  • Missing instrumentation / metrics
  • Tooling is hard to use
  • Unclear process or approvals
  • Lack of statistical support
  • Feature timelines too tight
  • We prioritize other methods (e.g., user research)
  • Other (please specify)
Q17
Long Text
What resources, tools, or support would help you start running experiments confidently?
Max chars
Q18
AI Interview
Based on your responses, we'd like to explore your experimentation experience in a bit more depth. Please share your thoughts openly—an AI moderator may ask a follow-up question or two.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 7
Q19
Multiple Choice
What is your primary role?
  • Product manager
  • Engineer
  • Data scientist / analyst
  • Designer / UX
  • Growth / marketing
  • Other (please specify)
Q20
Multiple Choice
Which team are you primarily part of?
  • Core product
  • Platform / infrastructure
  • Growth / monetization
  • Data / analytics
  • Other / cross-functional
Q21
Multiple Choice
How many years have you been involved in running or analyzing experiments?
  • Less than 1 year
  • 1–2 years
  • 3–5 years
  • 6–9 years
  • 10+ years
Q22
Multiple Choice
Where are you primarily located?
  • Americas
  • EMEA
  • APAC
  • Prefer not to say
Q23
Multiple Choice
Approximately how many employees are in your company?
  • 1–49
  • 50–249
  • 250–999
  • 1,000–4,999
  • 5,000+
  • Prefer not to say
Q24
Chat Message
All set—thank you for sharing your perspective! Your responses will help us identify ways to improve experimentation practices across the organization.

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