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| Criterion | ![]() Delivery Monte Carlo Forecasting | ![]() Delivery Cost of Delay | ![]() 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. | Helps clarify scope, order, and delivery flow in concrete terms. It makes work, constraints, and sequence manageable. The result is captured as a Cost of Delay table and prioritization sequence. | 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 | High | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 90-180 min | 1-5 Tage |
Participantsdifferent | 1-8 | 3-8 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | CoD Table, Prioritization Sequence | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ForecastingFlowDelivery | PrioritizationDeliveryEconomicsDecision | ValidationExperimentsDemandDiscovery |
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