Product Discovery

# Product Discovery Run structured discovery to identify high-value opportunities and de-risk product bets. ## When To Use Use this skill for: - Opportunity Solution Tree facilitation - Assumption mapping and test planning - Problem validation interviews and evidence synthesis - Solution validation with prototypes/experiments - Discovery sprint planning and outputs ## Core Discovery Workflow 1. Define desired outcome - Set one measurable outcome to improve. - Establish baseline and target horizon. 2. Build Opportunity Solution Tree (OST) - Outcome -> opportunities -> solution ideas -> experiments - Keep opportunities grounded in user evidence, not internal opinions. 3. Map assumptions - Identify desirability, viability, feasibility, and usability assumptions. - Score assumptions by risk and certainty. Use: ```bash python3 scripts/assumption_mapper.py assumptions.csv ``` 4. Validate the problem - Conduct interviews and behavior analysis. - Confirm frequency, severity, and willingness to solve. - Reject weak opportunities early. 5. Validate the solution - Prototype before building. - Run concept, usability, and value tests. - Measure behavior, not only stated preference. 6. Plan discovery sprint - 1-2 week cycle with explicit hypotheses - Daily evidence reviews - End with decision: proceed, pivot, or stop ## Opportunity Solution Tree (Teresa Torres) Structure: - Outcome: metric you want to move - Opportunities: unmet customer needs/pains - Solutions: candidate interventions - Experiments: fastest learning actions Quality checks: - At least 3 distinct opportunities before converging. - At least 2 experiments per top opportunity. - Tie every branch to evidence source. ## Assumption Mapping Assumption categories: - Desirability: users want this - Viability: business value exists - Feasibility: team can build/operate it - Usability: users can successfully use it Prioritization rule: - High risk + low certainty assumptions are tested first. ## Problem Validation Techniques - Problem interviews focused on current behavior - Journey friction mapping - Support ticket and sales-call synthesis - Behavioral analytics triangulation Evidence threshold examples: - Same pain repeated across multiple target users - Observable workaround behavior - Measurable cost of current pain ## Solution Validation Techniques - Concept tests (value proposition comprehension) - Prototype usability tests (task success/time-to-complete) - Fake door or concierge tests (demand signal) - Limited beta cohorts (retention/activation signals) ## Discovery Sprint Planning Suggested 10-day structure: - Day 1-2: Outcome + opportunity framing - Day 3-4: Assumption mapping + test design - Day 5-7: Problem and solution tests - Day 8-9: Evidence synthesis + decision options - Day 10: Stakeholder decision review ## Tooling ### `scripts/assumption_mapper.py` CLI utility that: - reads assumptions from CSV or inline input - scores risk/certainty priority - emits prioritized test plan with suggested test types See `references/discovery-frameworks.md` for framework details.