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| Criterion |  Growth A/B Testing |  Product Discovery Smoke Test |  Growth Funnel Analysis |  Growth Flywheel |
|---|---|---|---|---|
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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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. | Growth often breaks down where a reinforcing mechanism builds up too much friction. A Flywheel shows the cycle of value, repetition, and reinforcement meant to sustain growth. |
Complexitydifferent | High | Low | Medium | Medium |
Timedifferent | 1-4 Wochen | 1-5 Tage | 1-3 h | 60-120 min |
Participantsdifferent | 1-6 | Nutzertraffic | 1-5 | 3-8 |
Formatdifferent | Async | Async | Async | Workshop |
Outputdifferent | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note | Funnel report, Drop-off analysis, Optimization hypotheses | Flywheel Map, Friction Points, Growth Levers, Experiment Backlog |
Tags1 shared | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth | AnalyticsConversionGrowth | GrowthRetentionConversion |