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| Criterion |  Growth Hooked Model |  Decision Making Decision Tree |  Product Discovery Experiment Canvas |  Growth A/B Testing |
|---|---|---|---|---|
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. | 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 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 | Multiple workshops over several weeks | 30-90 min | 30-60 min | 1-4 Wochen |
Participantsdifferent | 2-8 | 1-6 | 1-5 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Hooked loop, Trigger map, Reward design, Ethics check | Decision Tree, Option Map, Assumption List | Completed Experiment Canvas, Success Metric | Experiment results, Decision log, Learning summary |
Tagsno overlap | GrowthBehaviorRetention | DecisionTreeOptions | ExperimentsValidationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |