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| Criterion |  Growth A/B Testing |  Growth Funnel Analysis |  Growth Growth Experiment |  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 growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | 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 | 1-3 h | 1-2 Wochen | 1-5 Tage |
Participantsdifferent | 1-6 | 1-5 | 1-6 | Nutzertraffic |
Formatdifferent | Async | Async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Funnel report, Drop-off analysis, Optimization hypotheses | Experiment card, Result summary, Next bet | Interest Metrics, Conversion Signal, Learning Note |
Tags1 shared | ExperimentsGrowthAnalyticsValidation | AnalyticsConversionGrowth | MarketingGrowthExperimentsLearning | ValidationExperimentsDemandGrowth |