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| Criterion |  Decision Making Decision Tree |  Product Discovery Smoke Test |  Decision Making Decision Matrix |  Growth A/B Testing |
|---|---|---|---|---|
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. | 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 several options collide with several criteria, comparison quickly becomes subjective. A Decision Matrix makes the trade-off visible and brings weighting, criteria, and options into a shared logic. | 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 | Medium | Low | Medium | High |
Timedifferent | 30-90 min | 1-5 Tage | 45-90 min | 1-4 Wochen |
Participantsdifferent | 1-6 | Nutzertraffic | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Interest Metrics, Conversion Signal, Learning Note | Decision Matrix, Scoring Rationale, Selected Option | Experiment results, Decision log, Learning summary |
Tagsno overlap | DecisionTreeOptions | ValidationExperimentsDemandGrowth | DecisionCriteriaScoringTradeoffs | ExperimentsGrowthAnalyticsValidation |