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| Criterion |  Operations Change Analysis |  Decision Making Constraint Analysis |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | For a deviation after a change, the method isolates the influence of the altered condition. It narrows down which differences are relevant and which are just accompanying noise. | 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 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 | Medium | Low | Low | High |
Timedifferent | 45-120 min | 30-90 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 2-6 | 2-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Change Matrix, Cause Hypotheses, Validation Questions, Action List | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | ChangeRoot causeTroubleshootingComparison | ConstraintsDecisionPlanningOptions | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |