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| Criterion |  Growth Funnel Analysis |  Growth A/B Testing |  Delivery Cost of Delay |  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 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. | Helps clarify scope, order, and delivery flow in concrete terms. It makes work, constraints, and sequence manageable. The result is captured as a Cost of Delay table and prioritization sequence. | 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 | High | High | Low |
Timedifferent | 1-3 h | 1-4 Wochen | 90-180 min | 1-5 Tage |
Participantsdifferent | 1-5 | 1-6 | 3-8 | Nutzertraffic |
Formatdifferent | Async | Async | Workshop | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Experiment results, Decision log, Learning summary | CoD Table, Prioritization Sequence | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | AnalyticsConversionGrowth | ExperimentsGrowthAnalyticsValidation | PrioritizationDeliveryEconomicsDecision | ValidationExperimentsDemandGrowth |