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| Criterion |  Growth A/B Testing |  Product Strategy ICE Scoring |  Decision Making Pareto 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 ideas need sorting quickly, it condenses impact, confidence, and effort into one compact score. It connects customer value, product logic, and decision priorities. The result is captured as an ICE table and top-ideas list. | 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 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 | Low | Low | Low |
Timedifferent | 1-4 Wochen | 30-60 min | 30-60 min | 1-5 Tage |
Participantsdifferent | 1-6 | 2-8 | 1-6 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | ICE Table, Top Idea List | Pareto Chart, Top Causes, Focus List | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | PrioritizationScoringGrowthDecision | PrioritizationQualityFocusRoot cause | ValidationExperimentsDemandGrowth |