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| Criterion |  Decision Making Constraint Analysis |  Innovation Design Sprint |  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. | When the product question is unclear and time pressure is high, the method takes a team from an open idea to a testable solution. It connects problem understanding, decision, and learning in one compact format, lowering the risk of spending a lot of energy on a mere guess. | 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 | High | High | Medium |
Timedifferent | 30-90 min | 4-5 Tage | 1-4 Wochen | 30-90 min |
Participantsdifferent | 2-8 | 5-8 | 1-6 | 1-6 |
Formatdifferent | Workshop + async | Workshop | Async | Workshop + async |
Outputdifferent | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Prototype, Test Findings, Decision Rationale | Experiment results, Decision log, Learning summary | Decision Tree, Option Map, Assumption List |
Tagsno overlap | ConstraintsDecisionPlanningOptions | Design sprintPrototypeValidationInnovation | ExperimentsGrowthAnalyticsValidation | DecisionTreeOptions |