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| Criterion |  Product Discovery Opportunity Solution Tree |  Product Strategy DIBB |  Product Discovery Hypothesis Prioritization Canvas |  Product Discovery Opportunity Scoring |
|---|---|---|---|---|
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. | 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 many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | When opportunities need ranking by their effect, it makes importance and satisfaction directly comparable. It separates problem, assumption, solution, and evidence. The result is captured as an opportunity-score table and top outcomes. |
Complexitydifferent | Medium | Low | Medium | Medium |
Timedifferent | 1-2 h Setup, laufend | 1-2 h | 60-90 min | 60-120 min |
Participantsdifferent | 2-6 | 2-8 | 3-8 | 3-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Opportunity Solution Tree, Experiment Backlog, Learning Log | DIBB document, Belief list, Bet list, Learning report | Prioritization Canvas, Hypothesis Backlog | Opportunity Score Table, Top Outcomes |
Tagsno overlap | DiscoveryOutcomesExperimentsOpportunity | StrategyDecisionAssumptionsHypothesis | ExperimentsPrioritizationDiscoveryHypothesis | PrioritizationDiscoveryOutcomesNeeds |