Users complete experiments but don't learn from them systematically
After each experiment, prompt users to capture what they learned. Over time, this builds a knowledge base that makes the product indispensable.
When to use
Your product involves iterative work where past insights inform future decisions.
Hypothesis template
If we prompt users to capture [learnings] after each experiment, knowledge base usage will increase by [target]% and the product becomes harder to leave.
Method
The problem: Users run experiments, see results, and move on. The insights are lost. When they need to make a decision later, they can't remember what they learned.
How to do it:
- After completing an experiment, show a prompt: "What did you learn?"
- Offer structured capture: "What worked?", "What surprised you?", "What would you do differently?"
- Make it quick - 3 fields, 1 minute to fill out
- Save learnings and make them searchable
- Surface relevant past learnings when starting new experiments
- Track: learning capture rate, learning recall usage, retention of users with learnings vs without
Key insight: The more learnings a user captures, the harder it is to leave. Their knowledge base is unique and can't be exported. This creates natural lock-in through value, not barriers.
Success metrics
- •Learning capture rate
- •Knowledge base size per user
- •Learning recall frequency
- •Retention of users with learnings vs without
Prerequisites
- A learning capture UI
- Search/retrieval for past learnings
- Integration with the experiment workflow
Common pitfalls
- •Making capture feel like a chore (keep it under 1 minute)
- •Not surfacing learnings when they're relevant
- •Not making learnings searchable
- •Capturing too much detail (3 fields max)
Source: Common product pattern. Knowledge capture creates compounding value that increases switching costs naturally.
Suggested ICE scores
Ready to run this experiment?
Sign up free to use this playbook with step-by-step guidance and track your results.