Standard : Number of Experiments Run
Description
Number of Experiments Run measures how many structured experiments a team conducts in a given period (e.g. prototypes, A/B tests, concierge tests). It indicates how frequently teams are testing assumptions and learning.
Higher volume generally signals a culture of experimentation, provided each experiment is meaningful and tied to a hypothesis.
How to Use
What to Measure
- Count of all discovery or growth experiments started and completed in a given period.
- Include both successful and unsuccessful experiments.
Experiment Count = Total Experiments Conducted in Period
Example: Team runs 12 experiments in Q1 → Experiment Count = 12.
Instrumentation Tips
- Maintain an experiment backlog or register.
- Track start and end dates to measure cadence.
- Capture experiment outcomes for later analysis.
Why It Matters
- Learning velocity: Indicates how fast the team is generating insights.
- Risk reduction: Validates ideas before large-scale investment.
- Innovation culture: Reinforces data-driven decision-making.
Best Practices
- Keep experiments small, fast, and cheap.
- Tie each experiment to a clear hypothesis.
- Document learnings and share them with the wider organisation.
Common Pitfalls
- Counting vanity experiments that don't test real assumptions.
- Not completing experiments or failing to extract learnings.
- Over-investing in a single large test instead of iterating.
Signals of Success
- Steady or increasing experiment volume per quarter.
- Faster insight-to-action cycles.
- Roadmap adjustments driven by experiment results.
- [[CoE/Product/Measures/Discovery Effectiveness/Experiment Success Rate]]
- [[Learning Velocity]]
- [[A/B Test Coverage]]