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| Criterion |  Product Discovery Opportunity Solution Tree |  Decision Making Decision Matrix |  Product Strategy DIBB |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | When discovery keeps swinging between goals, ideas, and learning loops, it creates clear steering logic. It connects the desired outcome, opportunities, and experiments into a legible structure. | When several options collide with several criteria, comparison quickly becomes subjective. A Decision Matrix makes the trade-off visible and brings weighting, criteria, and options into a shared logic. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Medium | Medium | Low | Low |
Timedifferent | 1-2 h Setup, laufend | 45-90 min | 1-2 h | 30-60 min |
Participantsdifferent | 2-6 | 2-8 | 2-8 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Opportunity Solution Tree, Experiment Backlog, Learning Log | Decision Matrix, Scoring Rationale, Selected Option | DIBB document, Belief list, Bet list, Learning report | Completed Experiment Canvas, Success Metric |
Tagsno overlap | DiscoveryOutcomesExperimentsOpportunity | DecisionCriteriaScoringTradeoffs | StrategyDecisionAssumptionsHypothesis | ExperimentsValidationDiscoveryHypothesis |