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| Criterion |  Growth Funnel Analysis |  Delivery Monte Carlo Forecasting |  Growth A/B Testing |  Product Discovery Assumption Mapping |
|---|---|---|---|---|
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. | 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 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 assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. |
Complexitydifferent | Medium | High | High | Medium |
Timedifferent | 1-3 h | 30-90 min Setup, danach laufend | 1-4 Wochen | 45-60 min |
Participantsdifferent | 1-5 | 1-8 | 1-6 | 2-8 |
Formatdifferent | Async | Workshop + async | Async | Workshop + async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Forecast Percentiles, Throughput Dataset, Risk Communication | Experiment results, Decision log, Learning summary | Assumption map, Test backlog, Risk ranking |
Tagsno overlap | AnalyticsConversionGrowth | ForecastingFlowDelivery | ExperimentsGrowthAnalyticsValidation | AssumptionsRiskExperimentsValidation |