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| Criterion |  Decision Making Decision Tree |  Systems Thinking Intervention Mapping |  Growth A/B Testing |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | 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. | Intervention Mapping translates a need for change into a planned, evaluable program. The method connects target group, determinants, actions, and measurement into a traceable chain. | 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 | High | High | Low |
Timedifferent | 30-90 min | 1-5 Tage | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-6 | 4-12 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Logic Model, Change Objectives, Intervention Components, Evaluation Plan | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | DecisionTreeOptions | ChangeSystems thinkingCapability | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |