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| Criterion |  Growth Funnel Analysis |  Product Strategy Impact Mapping |  Product Discovery Fake Door 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. | When a goal needs to be connected to several possible paths, it shows chains of impact instead of feature lists. It connects business goal, behavior change, and measures. | 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. |
Complexitydifferent | Medium | Medium | Medium | High |
Timedifferent | 1-3 h | 45-90 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-5 | 3-8 | Nutzertraffic | 1-6 |
Formatdifferent | Async | Workshop | Async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Impact Map, Outcome Hypotheses, Delivery Options | Click Data, Interest Signal, Learning Decision | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | OutcomesStrategyBehaviorPlanning | ValidationExperimentsDemandDiscovery | ExperimentsGrowthAnalyticsValidation |