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| Criterion |  Growth Hooked Model |  Decision Making Decision Tree |  Decision Making Delphi Method |  Product Strategy DIBB |
|---|---|---|---|---|
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 knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. |
Complexitydifferent | Medium | Medium | High | Low |
Timedifferent | Multiple workshops over several weeks | 30-90 min | 1-4 Wochen | 1-2 h |
Participantsdifferent | 2-8 | 1-6 | 6-30 Experten | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | Hooked loop, Trigger map, Reward design, Ethics check | Decision Tree, Option Map, Assumption List | Expert Forecast, Consensus Range, Assumption Notes | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | GrowthBehaviorRetention | DecisionTreeOptions | ForecastingExpertsDecisionStrategy | StrategyDecisionAssumptionsHypothesis |