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| Criterion |  Operations Change Analysis |  Growth Funnel Analysis |  Growth A/B Testing |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | For a deviation after a change, the method isolates the influence of the altered condition. It narrows down which differences are relevant and which are just accompanying noise. | 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 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 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. |
Complexitydifferent | Medium | Medium | High | Medium |
Timedifferent | 45-120 min | 1-3 h | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 2-6 | 1-5 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Async | Async |
Outputdifferent | Change Matrix, Cause Hypotheses, Validation Questions, Action List | Funnel report, Drop-off analysis, Optimization hypotheses | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ChangeRoot causeTroubleshootingComparison | AnalyticsConversionGrowth | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |