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| Criterion |  Decision Making Decision Tree |  Growth Funnel Analysis |  Product Discovery Experiment Canvas |  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 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. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | 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 | 30-90 min | 1-3 h | 30-60 min | 1-4 Wochen |
Participantsdifferent | 1-6 | 1-5 | 1-5 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Funnel report, Drop-off analysis, Optimization hypotheses | Completed Experiment Canvas, Success Metric | Experiment results, Decision log, Learning summary |
Tagsno overlap | DecisionTreeOptions | AnalyticsConversionGrowth | ExperimentsValidationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |