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| Criterion |  Product Discovery Assumption Mapping |  Knowledge Modeling Shape-First Modeling |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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. | 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 | Medium | Medium | Low | High |
Timedifferent | 45-60 min | Halber Tag pro Domain Slice | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 2-8 | 1-4 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Assumption map, Test backlog, Risk ranking | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tags1 shared | AssumptionsRiskExperimentsValidation | Knowledge graphValidationSemantic | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |