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| Criterion |  Decision Making Constraint Analysis |  Product Discovery Smoke Test |  Product Discovery MVP Test Matrix |  Growth A/B Testing |
|---|---|---|---|---|
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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. | 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 | Low | Low | Medium | High |
Timedifferent | 30-90 min | 1-5 Tage | 45-75 min | 1-4 Wochen |
Participantsdifferent | 2-8 | Nutzertraffic | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop | Async |
Outputdifferent | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Interest Metrics, Conversion Signal, Learning Note | Test Matrix, Test Plan | Experiment results, Decision log, Learning summary |
Tagsno overlap | ConstraintsDecisionPlanningOptions | ValidationExperimentsDemandGrowth | ExperimentsValidationDiscoveryOptions | ExperimentsGrowthAnalyticsValidation |