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| Criterion |  Operations PDCA Cycle |  Product Discovery Pretotyping |  Growth A/B Testing |  Product Discovery Fake Door Test |
|---|---|---|---|---|
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. | Pretotyping clarifies whether a customer problem and solution idea create enough real demand. It separates problem, assumption, solution, and evidence, and captures the result as a pretotyping sketch, test setup, and conversion data. | 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. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitydifferent | Low | Low | High | Medium |
Timedifferent | 1 h bis mehrere Wochen | Stunden bis wenige Tage | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-8 | 1-4 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Pretotyping sketch, Test setup, Conversion data, Go or no-go decision | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | Continuous improvementLeanExperiments | ValidationDemandMVP | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |