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| Criterion |  Growth A/B Testing |  Domain Modeling Context Map |  Growth Growth Experiment |
|---|---|---|---|
Purposedifferent | 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 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 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. |
Complexitydifferent | High | Medium | Medium |
Timedifferent | 1-4 Wochen | 1-3 h | 1-2 Wochen |
Participantsdifferent | 1-6 | 2-8 | 1-6 |
Formatdifferent | Async | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Context Map, Integration Patterns, Boundary Notes | Experiment card, Result summary, Next bet |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | Domain-Driven DesignBoundariesStrategy | MarketingGrowthExperimentsLearning |
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