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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Funnel Analysis |  Growth Hooked Model |  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. | The Hooked Model helps clarify engagement loops, user behavior, and experiments. It makes repeat behavior measurable and captures the result as a hooked loop, trigger map, and reward design. | 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 | Multiple workshops over several weeks | 1-4 Wochen |
Participantsdifferent | 1-8 | 1-5 | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Funnel report, Drop-off analysis, Optimization hypotheses | Hooked loop, Trigger map, Reward design, Ethics check | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | AnalyticsConversionGrowth | GrowthBehaviorRetention | ExperimentsGrowthAnalyticsValidation |