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| Criterion |  Decision Making Delphi Method |  Delivery Monte Carlo Forecasting |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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. | 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 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 | 1-4 Wochen | 30-90 min Setup, danach laufend | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 6-30 Experten | 1-8 | Nutzertraffic | 1-6 |
Formatdifferent | Async | Workshop + async | Async | Async |
Outputdifferent | Expert Forecast, Consensus Range, Assumption Notes | Forecast Percentiles, Throughput Dataset, Risk Communication | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingExpertsDecisionStrategy | ForecastingFlowDelivery | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |