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| Criterion |  Product Discovery Pretotyping |  Knowledge Modeling Event Modeling |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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. | 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. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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 | Stunden bis wenige Tage | 2-6 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-4 | 2-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Pretotyping sketch, Test setup, Conversion data, Go or no-go decision | Event Model, UI Flow, Commands, Read Models | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | ValidationDemandMVP | EventsBlueprintDomain-Driven DesignBehavior | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |