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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Funnel Analysis |  Growth A/B Testing |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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. | 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 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. |
Complexitydifferent | High | Medium | High | Low |
Timedifferent | 30-90 min Setup, danach laufend | 1-3 h | 1-4 Wochen | 30-60 min |
Participantsdifferent | 1-8 | 1-5 | 1-6 | 1-5 |
Formatdifferent | Workshop + async | Async | Async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Funnel report, Drop-off analysis, Optimization hypotheses | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ForecastingFlowDelivery | AnalyticsConversionGrowth | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |