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| Criterion |  Growth Funnel Analysis |  Engineering Kanban |  Growth A/B Testing |  Product Discovery Fake Door 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 work sits in too many parallel streams, throughput suffers and no one sees the bottlenecks. Kanban makes the flow of work visible and limits overload so a system becomes calmer and more predictable. | 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 is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitydifferent | Medium | Medium | High | Medium |
Timedifferent | 1-3 h | Ongoing | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-5 | 2-12 | 1-6 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Kanban board, WIP policies, Flow metrics | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | AnalyticsConversionGrowth | FlowVisual managementDelivery | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |