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| Criterion |  Growth A/B Testing |  Domain Modeling Context Map |  Product Strategy DIBB |  Product Discovery Smoke Test |
|---|---|---|---|---|
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. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | 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 | Low | Low |
Timedifferent | 1-4 Wochen | 1-3 h | 1-2 h | 1-5 Tage |
Participantsdifferent | 1-6 | 2-8 | 2-8 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Context Map, Integration Patterns, Boundary Notes | DIBB document, Belief list, Bet list, Learning report | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | Domain-Driven DesignBoundariesStrategy | StrategyDecisionAssumptionsHypothesis | ValidationExperimentsDemandGrowth |