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| Criterion |  Innovation Design Sprint |  Growth A/B Testing |  Decision Making Decision Tree |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | 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. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | High | High | Medium | Low |
Timedifferent | 4-5 Tage | 1-4 Wochen | 30-90 min | 1-5 Tage |
Participantsdifferent | 5-8 | 1-6 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop | Async | Workshop + async | Async |
Outputdifferent | Prototype, Test Findings, Decision Rationale | Experiment results, Decision log, Learning summary | Decision Tree, Option Map, Assumption List | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | Design sprintPrototypeValidationInnovation | ExperimentsGrowthAnalyticsValidation | DecisionTreeOptions | ValidationExperimentsDemandGrowth |