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Experimentation & A/B Testing Maturity Assessment

Assesses experimentation program maturity across culture, process, tooling, governance, and outcomes. Designed for product, growth, and data teams to benchmark capabilities and identify improvement priorities.

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AI-Powered Questions

Intelligent follow-up questions based on responses

Automated Analysis

Real-time sentiment and insight detection

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Detailed Reports

Comprehensive insights and recommendations

Template Overview

32

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 to the Experimentation & A/B Testing Maturity Assessment. This survey evaluates how your team and organization approach experimentation — covering process, tooling, governance, and outcomes. Your responses will help benchmark maturity and identify areas for improvement. • Participation is voluntary and you may stop at any time. • There are no right or wrong answers — we are interested in your honest perspective. • All responses are confidential and will be reported only in aggregate. • Estimated completion time: 10–12 minutes. Please proceed to begin.
Q2
Multiple Choice
Which function best describes your primary role?
  • Product management
  • Growth / performance marketing
  • Lifecycle / CRM
  • Brand / creative marketing
  • Data / analytics
  • Engineering
  • Design / UX
  • Other (please specify)
Q3
Dropdown
Approximately how many people on your team are directly involved in experimentation?
  • 1
  • 2–5
  • 6–10
  • 11–20
  • 21–50
  • 51+
Q4
Multiple Choice
In the last 90 days, approximately how many experiments did your team launch?
  • 0
  • 1–2
  • 3–5
  • 6–10
  • 11–20
  • 21+
Q5
Multiple Choice
What are the primary objectives your experiments target? (Select up to 5)
  • Conversion rate
  • Retention / churn
  • Engagement
  • Monetization / revenue
  • Activation / onboarding
  • Acquisition / traffic
  • Feature adoption
  • Pricing / packaging
  • Brand / creative effectiveness
  • Learning about user behavior
  • Other (please specify)
Q6
Opinion Scale
Over the past 6 months, how would you rate the overall rigor of your team's experiment hypotheses?
Range: 1 7
Min: Not at all rigorousMid: NeutralMax: Extremely rigorous
Q7
Opinion Scale
Our team documents a clear hypothesis for every experiment before launch.
Range: 1 7
Min: Strongly disagreeMid: NeutralMax: Strongly agree
Q8
Opinion Scale
We have a clear prioritization framework for deciding which experiments to run.
Range: 1 7
Min: Strongly disagreeMid: NeutralMax: Strongly agree
Q9
Opinion Scale
Experiment designs and analysis plans are peer-reviewed before launch.
Range: 1 7
Min: Strongly disagreeMid: NeutralMax: Strongly agree
Q10
Opinion Scale
Learnings from experiments are shared broadly and inform future decisions across teams.
Range: 1 7
Min: Strongly disagreeMid: NeutralMax: Strongly agree
Q11
Multiple Choice
Which test or study types does your team run regularly? (Select all that apply)
  • A/B or split tests
  • Multivariate tests (MVT)
  • Holdout / control tests
  • Quasi-experiments / observational studies
  • Multi-armed bandits
  • Sequential tests
  • UX / usability studies
  • Surveys / concept tests
  • Feature-flag rollouts / experiments
  • Other (please specify)
Q12
Dropdown
What is the typical runtime for a single experiment, from launch to decision?
  • Same day
  • 1–3 days
  • 4–7 days
  • 1–2 weeks
  • 3–4 weeks
  • Over 4 weeks
  • Varies widely
Q13
Ranking
Rank the following phases by where your team spends the most effort in a typical experiment (most effort first).
Drag to order (top = most important)
  1. Ideation / prioritization
  2. Design, UX, and copy
  3. Instrumentation and data quality
  4. Implementation / engineering
  5. QA and launch
  6. Monitoring during run
  7. Analysis and interpretation
  8. Documentation and sharing
  9. Rollout and follow-up
Q14
Multiple Choice
Which experimentation tools or platforms does your team currently use? (Select all that apply)
  • Optimizely
  • VWO
  • AB Tasty
  • Statsig
  • Eppo
  • Amplitude Experiment
  • LaunchDarkly or Flagsmith
  • Google Optimize (legacy)
  • In-house / custom platform
  • None currently
  • Other (please specify)
Q15
Multiple Choice
How are experiment datasets integrated with your analytics and data warehouse?
  • Fully integrated with analytics and warehouse
  • Partial integration; some manual pulls required
  • Isolated within the experimentation tool only
  • I don't know
Q16
Multiple Choice
Do you have a defined and versioned metrics catalog for experiments?
  • Yes, centrally defined and versioned
  • Yes, team-specific only
  • In progress
  • No
Q17
Multiple Choice
How does your team typically determine sample size and test duration?
  • Fixed-horizon power analysis
  • Sequential testing / alpha spending
  • Heuristics or benchmarks
  • Vendor tool auto-calculates
  • We usually don't calculate this
  • I don't know
  • Other (please specify)
Q18
Ranking
When deciding whether to ship a winning variant, rank these factors by importance to your team (most important first).
Drag to order (top = most important)
  1. Effect size vs. baseline
  2. Statistical significance or credible interval
  3. Impact on guardrail metrics
  4. Estimated business value
  5. Implementation cost / complexity
  6. Qualitative feedback / UX signals
Q19
Multiple Choice
Which risk controls does your team typically apply to experiments? (Select all that apply)
  • Guardrail metrics monitored
  • Kill switches / instant rollback
  • Ethics / privacy review when needed
  • Traffic allocation caps
  • Country / segment exclusions
  • QA and instrumentation checklist
  • None of the above
  • Other (please specify)
Q20
Multiple Choice
Is there an experimentation council or governance body at your organization?
  • Yes, org-wide
  • Yes, within my business unit
  • No, but being considered
  • No
Q21
Multiple Choice
Where are experiment plans and results typically documented? (Select all that apply)
  • Central system of record
  • Team wiki or docs
  • Within the testing tool
  • Spreadsheets
  • Not consistently documented
  • Other (please specify)
Q22
Opinion Scale
Overall, how would you rate the maturity of experimentation in your organization today?
Range: 1 7
Min: Very immature / ad-hocMid: NeutralMax: Best-in-class
Q23
Dropdown
Typically, how many business days elapse between a test ending and a final decision being made?
  • Same day
  • 1–2 days
  • 3–5 days
  • 6–10 days
  • 11–20 days
  • Over 20 days
  • We don't track this
Q24
Multiple Choice
In the last 6 months, approximately what share of completed experiments led to a production rollout?
  • 0–10%
  • 11–25%
  • 26–40%
  • 41–60%
  • 61–80%
  • 81–100%
  • We don't track this
Q25
Long Text
What are the biggest blockers or challenges to effective experimentation in your organization right now?
Max chars
Q26
AI Interview
Based on your survey responses, we'd like to explore your experimentation challenges and aspirations in a bit more depth.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 6
Q27
Dropdown
What is your seniority level?
  • Individual contributor
  • Manager
  • Director
  • VP
  • C-level
  • Other
Q28
Dropdown
Approximately how many employees are in your company?
  • 1–10
  • 11–50
  • 51–200
  • 201–1,000
  • 1,001–5,000
  • 5,001–10,000
  • 10,001+
Q29
Dropdown
Which industry best describes your organization?
  • Consumer software
  • B2B / SaaS
  • E-commerce / retail
  • Financial services / fintech
  • Media / entertainment
  • Healthcare / life sciences
  • Gaming
  • Telecom
  • Travel / hospitality
  • Other (please specify)
Q30
Dropdown
Where are you primarily based?
  • North America
  • Latin America
  • Europe
  • Middle East
  • Africa
  • Asia
  • Oceania
Q31
Dropdown
How many years have you worked with experimentation or A/B testing?
  • Less than 1
  • 1–3
  • 4–6
  • 7–10
  • 11+
Q32
Chat Message
Thank you for completing the Experimentation Maturity Assessment! Your responses will be analyzed in aggregate to produce benchmarking insights. If you opted in, results will be shared with participants once the analysis is complete. If you have any questions, please contact the research team at the email provided in your invitation.

Frequently Asked Questions

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