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| Criterion |  Growth A/B Testing |  Operations DMAIC |  Product Discovery Smoke Test |  Growth Funnel Analysis |
|---|---|---|---|---|
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 problem with fluctuating performance, the method brings analysis and improvement into a disciplined sequence. It creates a framework in which numbers, causes, and control come together. | 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. |
Complexitydifferent | High | High | Low | Medium |
Timedifferent | 1-4 Wochen | 2-12 Wochen | 1-5 Tage | 1-3 h |
Participantsdifferent | 1-6 | 3-10 | Nutzertraffic | 1-5 |
Formatdifferent | Async | Workshop + async | Async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Project Charter, Measurement Plan, Cause Analysis, Control Plan | Interest Metrics, Conversion Signal, Learning Note | Funnel report, Drop-off analysis, Optimization hypotheses |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | Continuous improvementQualityProcess improvement | ValidationExperimentsDemandGrowth | AnalyticsConversionGrowth |