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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Hooked Model |  Growth A/B Testing |  Product Discovery Assumption Mapping |
|---|---|---|---|---|
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. | 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. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. |
Complexitydifferent | High | Medium | High | Medium |
Timedifferent | 30-90 min Setup, danach laufend | Multiple workshops over several weeks | 1-4 Wochen | 45-60 min |
Participantsdifferent | 1-8 | 2-8 | 1-6 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Hooked loop, Trigger map, Reward design, Ethics check | Experiment results, Decision log, Learning summary | Assumption map, Test backlog, Risk ranking |
Tagsno overlap | ForecastingFlowDelivery | GrowthBehaviorRetention | ExperimentsGrowthAnalyticsValidation | AssumptionsRiskExperimentsValidation |