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| Criterion |  Decision Making Decision Tree |  Growth Hooked Model |  Product Discovery MVP Test 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. | The Hooked Model helps clarify engagement loops, user behavior, and experiments. It makes repeat behavior measurable and captures the result as a hooked loop, trigger map, and reward design. | When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. | 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 | Medium | High |
Timedifferent | 30-90 min | Multiple workshops over several weeks | 45-75 min | 1-4 Wochen |
Participantsdifferent | 1-6 | 2-8 | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Hooked loop, Trigger map, Reward design, Ethics check | Test Matrix, Test Plan | Experiment results, Decision log, Learning summary |
Tagsno overlap | DecisionTreeOptions | GrowthBehaviorRetention | ExperimentsValidationDiscoveryOptions | ExperimentsGrowthAnalyticsValidation |