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| Criterion |  Growth A/B Testing |  Innovation Design Sprint |  Decision Making Constraint Analysis |  Decision Making Decision Tree |
|---|---|---|---|---|
Purposedifferent | 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. | 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 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. | 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 | High | High | Low | Medium |
Timedifferent | 1-4 Wochen | 4-5 Tage | 30-90 min | 30-90 min |
Participantsdifferent | 1-6 | 5-8 | 2-8 | 1-6 |
Formatdifferent | Async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Prototype, Test Findings, Decision Rationale | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Decision Tree, Option Map, Assumption List |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | Design sprintPrototypeValidationInnovation | ConstraintsDecisionPlanningOptions | DecisionTreeOptions |