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| Criterion |  Product Discovery Context Canvas |  Operations PDCA Cycle |  Growth A/B Testing |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | When an environment of users, systems, and constraints is hard to grasp, it puts the context compactly on the table. It creates a clear reading of influence, boundary, and dependency. | 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 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 | 45-90 min | 1 h bis mehrere Wochen | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 3-10 | 1-8 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop | Workshop + async | Async | Async |
Outputdifferent | Context canvas, Open questions, Shared context notes | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ContextFramingRequirementsCanvas | Continuous improvementLeanExperiments | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |