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| Criterion |  Growth Funnel Analysis |  Agile Ideal Days |  Growth A/B Testing |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | 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 effort needs to be thought of as real working time, it offers a simple common denominator for comparison. It sorts work by value, risk, and delivery ability. The result is captured as Ideal Day Estimates, Assumption Notes, and Capacity Caveats. | 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. |
Complexitydifferent | Medium | Low | High | Low |
Timedifferent | 1-3 h | 15-60 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-5 | 2-9 | 1-6 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | AnalyticsConversionGrowth | EstimationEffortAgile | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |