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| Criterion |  Growth Funnel Analysis |  Growth Flywheel |  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. | 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. | 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 | 60-120 min | 30-90 min Setup, danach laufend | 1-4 Wochen |
Participantsdifferent | 1-5 | 3-8 | 1-8 | 1-6 |
Formatdifferent | Async | Workshop | Workshop + async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Flywheel Map, Friction Points, Growth Levers, Experiment Backlog | Forecast Percentiles, Throughput Dataset, Risk Communication | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | GrowthRetentionConversion | ForecastingFlowDelivery | ExperimentsGrowthAnalyticsValidation |