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| Criterion |  Growth A/B Testing |  Innovation Design Sprint |  Decision Making Constraint Analysis |  Product Discovery Smoke Test |
|---|---|---|---|---|
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. | 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 | Low | Low |
Timedifferent | 1-4 Wochen | 4-5 Tage | 30-90 min | 1-5 Tage |
Participantsdifferent | 1-6 | 5-8 | 2-8 | Nutzertraffic |
Formatdifferent | Async | Workshop | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Prototype, Test Findings, Decision Rationale | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | Design sprintPrototypeValidationInnovation | ConstraintsDecisionPlanningOptions | ValidationExperimentsDemandGrowth |