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| Criterion |  Operations PDCA Cycle |  Growth Funnel Analysis |  Product Discovery Experiment Canvas |  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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | 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 | Low | High |
Timedifferent | 1 h bis mehrere Wochen | 1-3 h | 30-60 min | 1-4 Wochen |
Participantsdifferent | 1-8 | 1-5 | 1-5 | 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 | Completed Experiment Canvas, Success Metric | Experiment results, Decision log, Learning summary |
Tagsno overlap | Continuous improvementLeanExperiments | AnalyticsConversionGrowth | ExperimentsValidationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |