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| Criterion |  Decision Making Decision Tree |  Growth Growth Experiment |  Decision Making Constraint Analysis |  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. | When growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | 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. | 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 | 30-90 min | 1-2 Wochen | 30-90 min | 1-4 Wochen |
Participantsdifferent | 1-6 | 1-6 | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Experiment card, Result summary, Next bet | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Experiment results, Decision log, Learning summary |
Tagsno overlap | DecisionTreeOptions | MarketingGrowthExperimentsLearning | ConstraintsDecisionPlanningOptions | ExperimentsGrowthAnalyticsValidation |