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| Criterion |  Delivery Monte Carlo Forecasting |  Product Discovery Smoke Test |  Growth Funnel Analysis |  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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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. |
Complexitydifferent | High | Low | Medium | High |
Timedifferent | 30-90 min Setup, danach laufend | 1-5 Tage | 1-3 h | 1-4 Wochen |
Participantsdifferent | 1-8 | Nutzertraffic | 1-5 | 1-6 |
Formatdifferent | Workshop + async | Async | Async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Interest Metrics, Conversion Signal, Learning Note | Funnel report, Drop-off analysis, Optimization hypotheses | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | ValidationExperimentsDemandGrowth | AnalyticsConversionGrowth | ExperimentsGrowthAnalyticsValidation |