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| Criterion |  Growth Funnel Analysis |  Decision Making Three-Point Estimation |  Delivery Monte Carlo Forecasting |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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. | A single estimate for effort, time, or risk often looks too smooth for the real uncertainty behind it. It separates options, evaluation criteria, and open risks. The result is captured as a Three-Point Estimate, Risk Range, and Assumption Notes. | 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 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 | Medium | Medium | High | High |
Timedifferent | 1-3 h | 10-30 min je Item | 30-90 min Setup, danach laufend | 1-4 Wochen |
Participantsdifferent | 1-5 | 1-8 | 1-8 | 1-6 |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Three-Point Estimate, Risk Range, Assumption Notes | Forecast Percentiles, Throughput Dataset, Risk Communication | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | EstimationUncertaintyForecasting | ForecastingFlowDelivery | ExperimentsGrowthAnalyticsValidation |