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| Criterion |  Operations After-Action Review |  Domain Modeling Context Map |  Growth A/B Testing |  Growth Growth Experiment |
|---|---|---|---|---|
Purposedifferent | After missions or project phases with unclear outcomes, the method makes visible what actually happened and what can be learned from it. It separates course, effect, and causes so experience turns into solid improvement. | 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 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 | Low | Medium | High | Medium |
Timedifferent | 20-45 min | 1-3 h | 1-4 Wochen | 1-2 Wochen |
Participantsdifferent | 3-12 | 2-8 | 1-6 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | Lessons learned, Action items, Event summary | Context Map, Integration Patterns, Boundary Notes | Experiment results, Decision log, Learning summary | Experiment card, Result summary, Next bet |
Tagsno overlap | LearningOperationsImprovement | Domain-Driven DesignBoundariesStrategy | ExperimentsGrowthAnalyticsValidation | MarketingGrowthExperimentsLearning |