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| Criterion |  Operations ABC Analysis |  Growth A/B Testing |  Growth Funnel Analysis |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | With many similar objects of differing economic significance, the method prioritizes effort by effect. It separates what needs regular steering from what rarely needs attention. | 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 only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | Low | High | Medium | Low |
Timedifferent | 30-60 min | 1-4 Wochen | 1-3 h | 1-5 Tage |
Participantsdifferent | 1-5 | 1-6 | 1-5 | Nutzertraffic |
Formatsame | Async | Async | Async | Async |
Outputdifferent | ABC Classification, Focus Rules, Control List | Experiment results, Decision log, Learning summary | Funnel report, Drop-off analysis, Optimization hypotheses | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | PrioritizationOperationsPortfolio | ExperimentsGrowthAnalyticsValidation | AnalyticsConversionGrowth | ValidationExperimentsDemandGrowth |