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| Criterion |  Operations PDCA Cycle |  Growth Funnel Analysis |  Growth Growth Experiment |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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 growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | 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 | Low | Medium | Medium | High |
Timedifferent | 1 h bis mehrere Wochen | 1-3 h | 1-2 Wochen | 1-4 Wochen |
Participantsdifferent | 1-8 | 1-5 | 1-6 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Funnel report, Drop-off analysis, Optimization hypotheses | Experiment card, Result summary, Next bet | Experiment results, Decision log, Learning summary |
Tagsno overlap | Continuous improvementLeanExperiments | AnalyticsConversionGrowth | MarketingGrowthExperimentsLearning | ExperimentsGrowthAnalyticsValidation |