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| Criterion |  Growth A/B Testing |  Operations Failure Mode and Effects Analysis |  Growth Funnel Analysis |  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. | For a process, product, or service with noticeable failure risks, the method assesses possible failure modes in advance. It directs attention to combinations of occurrence, effect, and detectability. | 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 | High | High | Medium | Low |
Timedifferent | 1-4 Wochen | 2-6 h | 1-3 h | 1-5 Tage |
Participantsdifferent | 1-6 | 3-10 | 1-5 | Nutzertraffic |
Formatdifferent | Async | Workshop | Async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | FMEA Table, Risk Priority, Mitigation Actions | Funnel report, Drop-off analysis, Optimization hypotheses | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | RiskQualityOperationsRoot cause | AnalyticsConversionGrowth | ValidationExperimentsDemandGrowth |