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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Flywheel |  Product Discovery Smoke Test |  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. | 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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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 | Low | High |
Timedifferent | 30-90 min Setup, danach laufend | 60-120 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-8 | 3-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop | Async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Flywheel Map, Friction Points, Growth Levers, Experiment Backlog | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | GrowthRetentionConversion | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |