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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Funnel Analysis |  Growth Flywheel |  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. | Growth often breaks down where a reinforcing mechanism builds up too much friction. A Flywheel shows the cycle of value, repetition, and reinforcement meant to sustain growth. | 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 | 60-120 min | 1-4 Wochen |
Participantsdifferent | 1-8 | 1-5 | 3-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Funnel report, Drop-off analysis, Optimization hypotheses | Flywheel Map, Friction Points, Growth Levers, Experiment Backlog | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | AnalyticsConversionGrowth | GrowthRetentionConversion | ExperimentsGrowthAnalyticsValidation |