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| Criterion |  Growth A/B Testing |  Operations PDCA Cycle |  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 an improvement that still has to prove itself in everyday work, the method runs it through small learning loops. It connects planning, checking, and standardization into a repeatable learning mode. | 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 | Low | Medium | Low |
Timedifferent | 1-4 Wochen | 1 h bis mehrere Wochen | 1-3 h | 1-5 Tage |
Participantsdifferent | 1-6 | 1-8 | 1-5 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Funnel report, Drop-off analysis, Optimization hypotheses | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | Continuous improvementLeanExperiments | AnalyticsConversionGrowth | ValidationExperimentsDemandGrowth |