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| Criterion |  Growth A/B Testing |  Growth North Star Metric |  Growth Funnel 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 product teams track many metrics, the view of the actual customer outcome easily gets lost. A North Star Metric bundles growth, usage, and value contribution into one signal that makes the system's direction visible. | 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 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 | Medium | Low |
Timedifferent | 1-4 Wochen | 1-2 h | 1-3 h | 1-5 Tage |
Participantsdifferent | 1-6 | 3-8 | 1-5 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | North Star metric, Input metric tree, Measurement cadence | Funnel report, Drop-off analysis, Optimization hypotheses | Interest Metrics, Conversion Signal, Learning Note |
Tags1 shared | ExperimentsGrowthAnalyticsValidation | GrowthMetricsAlignmentRetention | AnalyticsConversionGrowth | ValidationExperimentsDemandGrowth |