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| Criterion |  Growth Funnel Analysis |  Decision Making Decision Tree |  Product Strategy ICE Scoring |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses. | 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 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. | 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 | Medium | Low | High |
Timedifferent | 1-3 h | 30-90 min | 30-60 min | 1-4 Wochen |
Participantsdifferent | 1-5 | 1-6 | 2-8 | 1-6 |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Decision Tree, Option Map, Assumption List | ICE Table, Top Idea List | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | DecisionTreeOptions | PrioritizationScoringGrowthDecision | ExperimentsGrowthAnalyticsValidation |