Standard : Experiment Velocity (Try–Learn–Improve Cycle Rate)
Description
Experiment Velocity measures the number of structured improvement experiments a team runs within a given timeframe. It reflects how often a team intentionally tests new ways of working, learns from the outcome, and adapts accordingly.
High experiment velocity indicates a curious, learning-oriented culture. Rather than sticking with habits or top-down mandates, the team tries new ideas, evaluates them, and integrates what works.
How to Use
What to Measure
- Count the number of explicitly framed experiments initiated and completed per sprint, month or quarter.
- An experiment typically includes:
- A hypothesis: "We believe that doing X will improve Y."
- A defined timeframe or sprint
- A method to observe or measure the outcome
- A review of what was learned and the decision to adopt, adapt or discard
Experiment Velocity = Number of Completed Experiments / Time Period
Example:
- A team runs 6 experiments over 3 sprints → 2 experiments per sprint
Optional:
- Track % of successful experiments adopted
- Monitor learning outcomes even from "failed" tests
Instrumentation Tips
- Maintain an "experiment board" with status and outcomes
- Use structured templates for planning and reviewing experiments
- Include a lightweight retro on each experiment
- Log outcomes in team wiki or improvement log for future reference
Benchmarks
| Experiment Velocity |
Interpretation |
| 2+ per sprint |
High-velocity learning team |
| 1 per sprint |
Healthy experimentation culture |
| 1–2 per month |
Some learning, may be ad hoc |
| <1 per month |
Low experimentation, likely inertia or fear |
Benchmarks may vary with team maturity and workload. Focus on quality over quantity.
Why It Matters
Accelerates learning
Frequent experiments help teams rapidly test assumptions and evolve better ways of working.
Builds autonomy and mastery
Teams feel empowered to change their environment and own the outcomes.
Strengthens adaptability
Regular learning makes teams better prepared for change and uncertainty.
Reduces risk of stagnation
Avoids long periods of unchallenged, ineffective habits or processes.
Best Practices
- Keep experiments small, safe to fail, and timeboxed
- Align experiments to current challenges or friction points
- Share learnings across teams to reduce duplication
- Track hypotheses and outcomes to improve future rigour
- Encourage psychological safety to support “learning over being right”
Common Pitfalls
- Treating every change as an experiment without clear learning intent
- Skipping outcome review, so learning is lost
- Always measuring success as “adoption” rather than learning
- Avoiding experiments due to fear of failure or judgment
Signals of Success
- Teams propose and run experiments unprompted
- Experiments lead to measurable improvements or insight
- Learning from experiments is documented and reused
- Stakeholders and leadership support a test-and-learn culture
- [[CoE/Agile/Measures/Continuous Improvement/Retrospective Action Completion Rate]]
- [[Improvement Initiative Throughput]]
- [[Learning Investment Ratio]]
- [[Innovation Adoption Rate]]
Aligned Industry Research
Lean Startup (Eric Ries)
Pioneered the idea of build-measure-learn cycles as the foundation of innovation.
Team Topologies
Supports platform and enabling teams in helping others experiment safely and effectively.
Continuous Discovery Habits (Teresa Torres)
Reinforces the value of weekly small bets and regular learning cycles within product teams.