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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Hooked Model |  Product Discovery Experiment Canvas |  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. | 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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | 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 | Multiple workshops over several weeks | 30-60 min | 1-4 Wochen |
Participantsdifferent | 1-8 | 2-8 | 1-5 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Hooked loop, Trigger map, Reward design, Ethics check | Completed Experiment Canvas, Success Metric | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | GrowthBehaviorRetention | ExperimentsValidationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |