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| Criterion |  Domain Modeling Context Map |  Product Discovery Smoke Test |  Systems Thinking Leverage Points |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | Leverage Points show exactly where interventions in a system produce a disproportionate effect. It draws relationships, patterns, and feedback loops. The result is captured as a leverage map and action strategy. | 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 | Medium | Low | High | High |
Timedifferent | 1-3 h | 1-5 Tage | Half day | 1-4 Wochen |
Participantsdifferent | 2-8 | Nutzertraffic | 3-12 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop | Async |
Outputdifferent | Context Map, Integration Patterns, Boundary Notes | Interest Metrics, Conversion Signal, Learning Note | Leverage Map, Action Strategy | Experiment results, Decision log, Learning summary |
Tagsno overlap | Domain-Driven DesignBoundariesStrategy | ValidationExperimentsDemandGrowth | Systems thinkingChangeStrategy | ExperimentsGrowthAnalyticsValidation |