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.
What's Included
AI-Powered Questions
Intelligent follow-up questions based on responses
Automated Analysis
Real-time sentiment and insight detection
Smart Distribution
Target the right audience automatically
Detailed Reports
Comprehensive insights and recommendations
Template Overview
24
Questions
AI-Powered
Smart Analysis
Ready-to-Use
Launch in Minutes
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)
Data quality / instrumentation issues
Metric definitions ambiguity
Sample contamination / overlap
Insufficient traffic / power
Engineering constraints / time
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)
Planning and design
Instrumentation and data validation
Implementation and rollout setup
Running and monitoring
Analysis and interpretation
Decision and rollout
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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