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| Criterion |  Decision Making Force Field Analysis |  Product Discovery Fake Door Test |  Product Discovery Hypothesis Prioritization Canvas |  Product Strategy DIBB |
|---|---|---|---|---|
Purposedifferent | In change initiatives, supporting and restraining forces sit at the table at the same time. Force Field Analysis makes these tensions explicit and shows where change can be pushed forward by strengthening or relieving. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. | 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. | 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. |
Complexitydifferent | Low | Medium | Medium | Low |
Timedifferent | 45-90 min | 1-5 Tage | 60-90 min | 1-2 h |
Participantsdifferent | 3-12 | Nutzertraffic | 3-8 | 2-8 |
Formatdifferent | Workshop | Async | Workshop | Workshop + async |
Outputdifferent | Force Field Map, Change Levers, Risk Notes | Click Data, Interest Signal, Learning Decision | Prioritization Canvas, Hypothesis Backlog | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | ChangeDecisionStrategy | ValidationExperimentsDemandDiscovery | ExperimentsPrioritizationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis |