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| Criterion |  Product Discovery Context Canvas |  Knowledge Modeling Competency Questions |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When an environment of users, systems, and constraints is hard to grasp, it puts the context compactly on the table. It creates a clear reading of influence, boundary, and dependency. | Competency Questions translate a domain model into concrete questions that it must be able to answer. The method keeps the model's scope clean and prevents pretty but useless structures. | 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. | 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 | Low | Medium | Low | High |
Timedifferent | 45-90 min | 2-4 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 3-10 | 2-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop | Workshop + async | Async | Async |
Outputdifferent | Context canvas, Open questions, Shared context notes | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | ContextFramingRequirementsCanvas | OntologyKnowledge graphScopeRequirementsSemantic | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |