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| Criterion |  Growth A/B Testing |  Knowledge Modeling Ontology Design Patterns |  Knowledge Modeling Shape-First Modeling |  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. | Ontology Design Patterns provide reusable building blocks for recurring modeling problems. The method brings structure to the search for clean concepts, relationships, and roles. | Shape-First Modeling defines data quality through shapes before implementation or integration frays at the edges. The approach fits when validation and data contracts should be part of the design from the start. | 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 | Medium | Low |
Timedifferent | 1-4 Wochen | 1-3 h pro Pattern | Halber Tag pro Domain Slice | 1-5 Tage |
Participantsdifferent | 1-6 | 1-6 | 1-4 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Selected Patterns, Adapted Schema Fragments, Pattern Documentation | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | OntologyPatternsReuseSemanticModeling | Knowledge graphValidationSemantic | ValidationExperimentsDemandGrowth |