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| Criterion |  Product Strategy ICE Scoring |  Decision Making Decision Tree |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When ideas need sorting quickly, it condenses impact, confidence, and effort into one compact score. It connects customer value, product logic, and decision priorities. The result is captured as an ICE table and top-ideas list. | 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 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 | 30-60 min | 30-90 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 2-8 | 1-6 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | ICE Table, Top Idea List | Decision Tree, Option Map, Assumption List | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | PrioritizationScoringGrowthDecision | DecisionTreeOptions | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |