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| Criterion |  Decision Making Pareto Analysis |  Growth A/B Testing |  Operations Failure Mode and Effects Analysis |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | In many problem situations, effect is distributed unequally across many causes. A Pareto Analysis separates the few drivers with high leverage from the long remainder and focuses attention on what measurably moves the needle. | 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 a process, product, or service with noticeable failure risks, the method assesses possible failure modes in advance. It directs attention to combinations of occurrence, effect, and detectability. | 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 | Low | High | High | Low |
Timedifferent | 30-60 min | 1-4 Wochen | 2-6 h | 1-5 Tage |
Participantsdifferent | 1-6 | 1-6 | 3-10 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Workshop | Async |
Outputdifferent | Pareto Chart, Top Causes, Focus List | Experiment results, Decision log, Learning summary | FMEA Table, Risk Priority, Mitigation Actions | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | PrioritizationQualityFocusRoot cause | ExperimentsGrowthAnalyticsValidation | RiskQualityOperationsRoot cause | ValidationExperimentsDemandGrowth |