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| Criterion |  Operations Change Analysis |  Product Discovery Smoke Test |  Growth Funnel Analysis |  Growth A/B Testing |
|---|---|---|---|---|
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 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 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. |
Complexitydifferent | Medium | Low | Medium | High |
Timedifferent | 45-120 min | 1-5 Tage | 1-3 h | 1-4 Wochen |
Participantsdifferent | 2-6 | Nutzertraffic | 1-5 | 1-6 |
Formatdifferent | Workshop + async | Async | Async | Async |
Outputdifferent | Change Matrix, Cause Hypotheses, Validation Questions, Action List | Interest Metrics, Conversion Signal, Learning Note | Funnel report, Drop-off analysis, Optimization hypotheses | Experiment results, Decision log, Learning summary |
Tagsno overlap | ChangeRoot causeTroubleshootingComparison | ValidationExperimentsDemandGrowth | AnalyticsConversionGrowth | ExperimentsGrowthAnalyticsValidation |