Experimentation Metrics Every Team Should Track
Mike Johnson
Engineering Lead
Tracking the right metrics helps you understand whether your experimentation program is actually working. Here's the comprehensive list.
Velocity Metrics
Experiments Launched Per Month
What it measures: How many experiments your team starts each month.Why it matters: Higher velocity means faster learning. Low velocity means missed opportunities.
Benchmarks:
Experiments Completed Per Month
What it measures: How many experiments reach statistical significance.Why it matters: Launched experiments only matter if they conclude. Track completion rate.
Target: 80%+ of launched experiments should complete.
Average Experiment Duration
What it measures: Days from launch to decision.Why it matters: Long experiments tie up resources and delay learning.
Target: Most experiments should conclude within 2-4 weeks.
Time to First Experiment
What it measures: Days from feature spec to experiment launch.Why it matters: Measures how embedded experimentation is in your development process.
Target: New features should be experimentable within one sprint of completion.
Quality Metrics
Win Rate
What it measures: Percentage of experiments that beat the control.Why it matters: Indicates hypothesis quality. Very high win rates suggest you're testing safe ideas.
Healthy range: 25-40% win rate. Higher suggests you're not taking enough risks.
Inconclusive Rate
What it measures: Experiments that end without statistical significance.Why it matters: Too many inconclusive experiments waste resources.
Target: Under 20% inconclusive rate.
Prediction Accuracy
What it measures: How often team members correctly predict outcomes.Why it matters: Indicates how well your team understands users.
Track over time: Accuracy should improve as teams learn from experiments.
Impact Metrics
Decision Influence Rate
What it measures: How often experiment results change product decisions.Why it matters: Experiments that don't influence decisions are wasted effort.
Target: 70%+ of experiments should influence a decision (ship, don't ship, or iterate).
Cumulative Metric Lift
What it measures: Total improvement in key metrics from experiments.Why it matters: Shows the ROI of your experimentation program.
Track: Conversion rate, revenue, engagement improvements from experiments over time.
Bad Ship Prevention
What it measures: Features that would have shipped but were stopped by experiment results.Why it matters: Preventing a bad feature can be worth more than shipping a good one.
Track: Count of losing experiments that changed launch decisions.
Culture Metrics
Team Participation Rate
What it measures: Percentage of team members who engage with experiments.Why it matters: Experimentation culture requires broad participation, not just the data team.
Target: 60%+ of product team actively following experiments.
Cross-Team Experiment Visibility
What it measures: How aware teams are of other teams' experiments.Why it matters: Prevents duplicate experiments and spreads learnings.
Measure: Survey or prediction participation from outside experiment owner's team.
Hypothesis Source Diversity
What it measures: Who generates experiment hypotheses.Why it matters: Best hypotheses come from diverse perspectives.
Target: Hypotheses from PMs, engineers, designers, support, and customers.
Engagement Metrics (with Gamification)
Prediction Participation
What it measures: Percentage of experiments receiving predictions.Why it matters: Shows engagement with the experimentation program.
Target: 70%+ of experiments should receive predictions.
Average Predictions Per Experiment
What it measures: How many team members bet on each experiment.Why it matters: Higher participation means broader engagement.
Target: Average of 5+ predictions per experiment for teams of 20.
Leaderboard Engagement
What it measures: How often team members check standings.Why it matters: Active leaderboard checking indicates program engagement.
Track: Weekly active users on leaderboard page.
Building Your Dashboard
Start with these five essential metrics:
- Experiments per month (velocity)
- Win rate (quality)
- Decision influence rate (impact)
- Team participation (culture)
- Prediction accuracy (engagement/learning)
Add more metrics as your program matures. The goal is insight, not data overload.
Tracking Tools
Experimentation platforms: Amplitude Experiment, Statsig, LaunchDarkly provide velocity and quality metrics.
Gamification platforms: ExperimentBets tracks participation, predictions, and leaderboard engagement.
Custom dashboards: Use Looker, Mode, or similar for combined views.
Spreadsheets: Start simple. A well-maintained spreadsheet beats an unused dashboard.
Related Articles
The Complete Guide to Building an Experimentation Culture
Everything you need to know about creating a data-driven team that embraces testing. From mindset shifts to practical implementation, this comprehensive guide covers it all.
Read moreBest Practices for Experimentation Culture: 15 Rules That Work
The proven rules that high-performing teams follow to build and maintain strong experimentation cultures. Actionable practices you can implement today.
Read more7 Signs Your Experimentation Program is Failing
Most A/B tests get ignored. The data team runs them, shares the results, and nothing happens. Here are the warning signs that your experimentation program needs help.
Read more