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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Growth Experiment |  Growth A/B Testing |  Product Discovery Smoke Test |
|---|---|---|---|---|
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 growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | 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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | High | Medium | High | Low |
Timedifferent | 30-90 min Setup, danach laufend | 1-2 Wochen | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-8 | 1-6 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Experiment card, Result summary, Next bet | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ForecastingFlowDelivery | MarketingGrowthExperimentsLearning | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |