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| Criterion |  Knowledge Modeling Event Modeling |  Product Discovery Pretotyping |  Growth A/B Testing |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | Event Modeling connects business workflows with commands, events, and views into one coherent mental model. It helps design behavior, UI, and technical slices from the same underlying logic. | 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 | Medium | Low | High | Medium |
Timedifferent | 2-6 h | Stunden bis wenige Tage | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 2-8 | 1-4 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Event Model, UI Flow, Commands, Read Models | 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 | EventsBlueprintDomain-Driven DesignBehavior | ValidationDemandMVP | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |