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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Funnel Analysis |  Engineering Kanban |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication. | 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. |
Complexitydifferent | High | Medium | Medium | High |
Timedifferent | 30-90 min Setup, danach laufend | 1-3 h | Ongoing | 1-4 Wochen |
Participantsdifferent | 1-8 | 1-5 | 2-12 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Funnel report, Drop-off analysis, Optimization hypotheses | Kanban board, WIP policies, Flow metrics | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | AnalyticsConversionGrowth | FlowVisual managementDelivery | ExperimentsGrowthAnalyticsValidation |