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| Criterion |  Growth Hooked Model |  Growth A/B Testing |  Decision Making Decision Tree |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | 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 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. | 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. |
Complexitydifferent | Medium | High | Medium | Low |
Timedifferent | Multiple workshops over several weeks | 1-4 Wochen | 30-90 min | 1-5 Tage |
Participantsdifferent | 2-8 | 1-6 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Hooked loop, Trigger map, Reward design, Ethics check | Experiment results, Decision log, Learning summary | Decision Tree, Option Map, Assumption List | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | GrowthBehaviorRetention | ExperimentsGrowthAnalyticsValidation | DecisionTreeOptions | ValidationExperimentsDemandGrowth |