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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Pretotyping |  Growth A/B Testing |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | 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. | Pretotyping clarifies whether a customer problem and solution idea create enough real demand. It separates problem, assumption, solution, and evidence, and captures the result as a pretotyping sketch, test setup, and conversion data. | 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 demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitydifferent | Medium | Low | High | Medium |
Timedifferent | Halber Tag pro Domain Slice | Stunden bis wenige Tage | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-4 | 1-4 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Pretotyping sketch, Test setup, Conversion data, Go or no-go decision | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tags1 shared | Knowledge graphValidationSemantic | ValidationDemandMVP | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |