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| Criterion |  Growth Funnel Analysis |  Decision Making Force Field Analysis |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses. | 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 only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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. |
Complexitydifferent | Medium | Low | Low | High |
Timedifferent | 1-3 h | 45-90 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-5 | 3-12 | Nutzertraffic | 1-6 |
Formatdifferent | Async | Workshop | Async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Force Field Map, Change Levers, Risk Notes | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | ChangeDecisionStrategy | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |