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| Criterion |  Knowledge Modeling Event Modeling |  Decision Making Decision Tree |  Growth A/B Testing |  Product Discovery Smoke 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. | For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch. | 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 only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | Medium | Medium | High | Low |
Timedifferent | 2-6 h | 30-90 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 2-8 | 1-6 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Event Model, UI Flow, Commands, Read Models | Decision Tree, Option Map, Assumption List | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | EventsBlueprintDomain-Driven DesignBehavior | DecisionTreeOptions | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |