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| Criterion |  Growth A/B Testing |  Growth Funnel Analysis |  Product Strategy ICE Scoring |  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 many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses. | 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. | 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 | Medium | Low | Low |
Timedifferent | 1-4 Wochen | 1-3 h | 30-60 min | 1-5 Tage |
Participantsdifferent | 1-6 | 1-5 | 2-8 | Nutzertraffic |
Formatdifferent | Async | Async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Funnel report, Drop-off analysis, Optimization hypotheses | ICE Table, Top Idea List | Interest Metrics, Conversion Signal, Learning Note |
Tags1 shared | ExperimentsGrowthAnalyticsValidation | AnalyticsConversionGrowth | PrioritizationScoringGrowthDecision | ValidationExperimentsDemandGrowth |