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| Criterion |  Delivery Monte Carlo Forecasting |  Product Discovery Smoke Test |  Growth Funnel Analysis |  Product Discovery Fake Door Test |
|---|---|---|---|---|
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 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 | High | Low | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 1-5 Tage | 1-3 h | 1-5 Tage |
Participantsdifferent | 1-8 | Nutzertraffic | 1-5 | Nutzertraffic |
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 | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ForecastingFlowDelivery | ValidationExperimentsDemandGrowth | AnalyticsConversionGrowth | ValidationExperimentsDemandDiscovery |