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| Criterion |  Delivery Monte Carlo Forecasting |  Decision Making Delphi Method |  Product Discovery Smoke Test |  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 knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together. | 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 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 | High | Low | High |
Timedifferent | 30-90 min Setup, danach laufend | 1-4 Wochen | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-8 | 6-30 Experten | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Async | Async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Expert Forecast, Consensus Range, Assumption Notes | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | ForecastingExpertsDecisionStrategy | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |