Quickly assess A/B testing ease, guardrails, data trust, and decision confidence. Uncover friction, governance gaps, and training needs to scale experiments.
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
Sample Survey Items
Q1
Chat Message
Welcome! This brief survey asks about your experimentation practices and confidence in results. It should take about 5–7 minutes. Please answer based on your recent experience.
Q2
Multiple Choice
How often are 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 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
Q4
Numeric
In the last 3 months, how many experiments did you meaningfully contribute to?
Accepts a numeric value
Whole numbers only
Q5
Rating
Setting up a standard A/B test is…
Scale: 10 (star)
Min: Very hardMax: Very easy
Q6
Opinion Scale
Analyzing a completed experiment is…
Range: 1 – 10
Min: Very difficultMid: NeutralMax: Very easy
Q7
Matrix
How are the following guardrails handled today?
Rows
Not in place
Informal only
Documented process
Automated enforcement
Don’t know
Traffic allocation control
•
•
•
•
•
Sample ratio mismatch (SRM) checks
•
•
•
•
•
Minimum sample size / power guidance
•
•
•
•
•
Exposure and assignment consistency
•
•
•
•
•
Peeking / early stopping protections
•
•
•
•
•
Holdout or AA test support
•
•
•
•
•
Metric definition versioning
•
•
•
•
•
Q8
Multiple Choice
Which controls are enforced today? Select all that apply.
Pre-launch checklist
Blocking deployment on missing instrumentation
Automated SRM alerting
Sequential testing / alpha spending
Max exposure or blast-radius limits
Quality gates for key metrics
Post-experiment QA template
None of the above
Q9
Multiple Choice
What primary decision rule do you use to call results?
Fixed p-value threshold (e.g., 0.05)
Bayesian decision rule
Business threshold / minimum detectable effect
Case-by-case judgement
No standard / not sure
Q10
Multiple Choice
Attention check: To confirm you’re paying attention, please select “Occasionally.”
Never
Rarely
Occasionally
Often
Q11
Rating
I trust our experiment results to inform product decisions.
Scale: 10 (star)
Min: Strongly disagreeMax: Strongly agree
Q12
Long Text
What most undermines trust in results today? Please share specifics.
Max 600 chars
Q13
Opinion Scale
How confident are you acting on an experiment’s outcome, typically?
Range: 1 – 10
Min: Not confidentMid: SomewhatMax: Very confident
Q14
Ranking
Rank the biggest blockers to reliable experimentation (top = biggest).
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
Q15
Constant Sum
Allocate 100 points across where effort typically goes for one experiment.
Total must equal 100
Planning and design
Instrumentation and data validation
Implementation and rollout setup
Running and monitoring
Analysis and interpretation
Decision and rollout
Documentation and communication
Min per option: 0Whole numbers only
Q16
Long Text
What one change would most improve our experimentation workflow?
Max 600 chars
Q17
Multiple Choice
Why don’t you run experiments currently? 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)
Q18
Long Text
What would help you start running experiments confidently?
Max 600 chars
Q19
Multiple Choice
What is your primary role?
Product manager
Engineer
Data scientist / analyst
Designer / UX
Growth / marketing
Other
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
Long Text
Anything else you’d like us to know about your experimentation experience?
Max 600 chars
Q25
AI Interview
AI Interview: 2 Follow-up Questions on Experimentation UX & Trust
AI InterviewLength: 2Personality: Expert InterviewerMode: Fast
Reference questions: 18
Q26
Chat Message
All set—thanks for sharing your perspective.
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