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| Criterion |  Delivery Monte Carlo Forecasting |  Domain Modeling Context Map |  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 several contexts need to talk to each other, it makes their relationships and dependencies legible. It helps sort integration pressure and responsibilities across system boundaries. | 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 | 1-3 h | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-8 | 2-8 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Context Map, Integration Patterns, Boundary Notes | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ForecastingFlowDelivery | Domain-Driven DesignBoundariesStrategy | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |