Standard : Experiment Success Rate
Description
Experiment Success Rate measures the percentage of experiments that confirm their original hypothesis or lead to a clear decision. It shows how effectively teams are learning from discovery work.
A balanced success rate is healthy — too high may indicate overly safe experiments, too low may show poor hypothesis design.
How to Use
What to Measure
- Number of experiments run in a period (A/B tests, prototypes, MVPs).
- Number that yield a clear positive decision or learning outcome.
Experiment Success Rate (%) = (Successful Experiments ÷ Total Experiments) × 100
Example: 12 experiments run, 6 confirm hypotheses → 50% success rate.
Instrumentation Tips
- Use an experimentation backlog or log.
- Define success criteria upfront for each experiment.
- Track both positive and negative learnings as outcomes.
Why It Matters
- Learning velocity: Measures how quickly insights are generated.
- Risk reduction: Validates assumptions before scaling.
- Confidence building: Aligns stakeholders on evidence-based decisions.
Best Practices
- Keep experiments small and fast.
- Use statistical rigor for quantitative tests.
- Document learnings in a shared knowledge base.
Common Pitfalls
- Only counting positive results as “success.”
- Running experiments without a clear hypothesis.
- Ignoring negative learnings that inform future strategy.
Signals of Success
- Increased volume of experiments with steady or rising success rate.
- Roadmap decisions consistently informed by experiment results.
- Reduced build-and-pray delivery outcomes.
- [[Opportunity Validation Rate]]
- [[Learning Velocity]]
- [[A/B Test Coverage]]