Product Discovery

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.