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| Criterion |  Growth A/B Testing |  Knowledge Modeling Shape-First Modeling |  Knowledge Modeling Ontology Design Patterns |  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. | 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. | Ontology Design Patterns provide reusable building blocks for recurring modeling problems. The method brings structure to the search for clean concepts, relationships, and roles. | 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 | Halber Tag pro Domain Slice | 1-3 h pro Pattern | 1-5 Tage |
Participantsdifferent | 1-6 | 1-4 | 1-6 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Selected Patterns, Adapted Schema Fragments, Pattern Documentation | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | Knowledge graphValidationSemantic | OntologyPatternsReuseSemanticModeling | ValidationExperimentsDemandGrowth |