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| Criterion |  Decision Making Constraint Analysis |  Growth Hooked Model |  Growth A/B Testing |  Decision Making Decision Tree |
|---|---|---|---|---|
Purposedifferent | When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. | 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. |
Complexitydifferent | Low | Medium | High | Medium |
Timedifferent | 30-90 min | Multiple workshops over several weeks | 1-4 Wochen | 30-90 min |
Participantsdifferent | 2-8 | 2-8 | 1-6 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Hooked loop, Trigger map, Reward design, Ethics check | Experiment results, Decision log, Learning summary | Decision Tree, Option Map, Assumption List |
Tagsno overlap | ConstraintsDecisionPlanningOptions | GrowthBehaviorRetention | ExperimentsGrowthAnalyticsValidation | DecisionTreeOptions |