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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Funnel Analysis |  Agile NoEstimates |  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 estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | 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 | Medium | High |
Timedifferent | 30-90 min Setup, danach laufend | 1-3 h | laufend | 1-4 Wochen |
Participantsdifferent | 1-8 | 1-5 | 2-12 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Funnel report, Drop-off analysis, Optimization hypotheses | Throughput Data, Flow Forecast, Slicing Rules | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | AnalyticsConversionGrowth | EstimationForecastingFlow | ExperimentsGrowthAnalyticsValidation |