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| Criterion |  Decision Making Force Field Analysis |  Product Discovery Fake Door Test |  Growth A/B Testing |  Product Discovery Smoke Test |
|---|---|---|---|---|
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 two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | Low | Medium | High | Low |
Timedifferent | 45-90 min | 1-5 Tage | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 3-12 | Nutzertraffic | 1-6 | Nutzertraffic |
Formatdifferent | Workshop | Async | Async | Async |
Outputdifferent | Force Field Map, Change Levers, Risk Notes | Click Data, Interest Signal, Learning Decision | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ChangeDecisionStrategy | ValidationExperimentsDemandDiscovery | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |