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| Criterion |  Growth A/B Testing |  Growth Funnel Analysis |  Product Discovery Fake Door Test |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | 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 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 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 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 | High | Medium | Medium | Low |
Timedifferent | 1-4 Wochen | 1-3 h | 1-5 Tage | 1-5 Tage |
Participantsdifferent | 1-6 | 1-5 | Nutzertraffic | Nutzertraffic |
Formatsame | Async | Async | Async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Funnel report, Drop-off analysis, Optimization hypotheses | Click Data, Interest Signal, Learning Decision | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | AnalyticsConversionGrowth | ValidationExperimentsDemandDiscovery | ValidationExperimentsDemandGrowth |