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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Funnel Analysis |  Decision Making Delphi Method |  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 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 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 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 | Medium | High | High |
Timedifferent | 30-90 min Setup, danach laufend | 1-3 h | 1-4 Wochen | 1-4 Wochen |
Participantsdifferent | 1-8 | 1-5 | 6-30 Experten | 1-6 |
Formatdifferent | Workshop + async | Async | Async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Funnel report, Drop-off analysis, Optimization hypotheses | Expert Forecast, Consensus Range, Assumption Notes | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | AnalyticsConversionGrowth | ForecastingExpertsDecisionStrategy | ExperimentsGrowthAnalyticsValidation |