DLG section: Post experiment learnings & next steps
Awesome, we have successfully conducted the experiment. Now it's time to gauge the results and note learnings.
1. Problem statement & overview
2. Hypothesis, goal setting & success metrics
3. Experimentation design: Part 1
4. Experimentation design: Part 2
5. Post experiment learnings & next steps
6. Stakeholder management
Experiment Results
After running the experiment, collect and analyse the results to determine its impact.
Key Analysis Questions:
- Which variation performed the best?
- Did the test achieve statistical significance?
- What were the key trade-offs observed?
Example Analysis Framework:
Variation | Conversion Rate | Lift % | Statistical Significance |
|---|---|---|---|
Control | 10% | - | - |
Variation A | 12% | +20% | 95% |
Variation B | 11% | +10% | 88% |
Release Decision
Based on the results, make a decision on whether to scale, iterate, or abandon the experiment.
Possible Outcomes:
- Scale: The experiment was successful with a statistically significant improvement.
- Iterate: Some improvements were seen but require further testing.
- Kill: The experiment showed no meaningful impact or negative trade-offs.
Learnings
Every experiment should provide insights, regardless of its success.
Key Learnings to Document:
- What worked and why?
- What didn’t work and why?
- Unexpected behaviours or insights from user interactions.
Next Steps
Based on the learnings, determine actionable next steps.
Actionable Next Steps Framework:
If the experiment was successful: Plan a full-scale rollout, monitor long-term performance.
If the experiment was inconclusive: Run further tests with refined hypotheses.
If the experiment was unsuccessful: Identify alternative solutions based on insights gathered.
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