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| Criterion |  Product Discovery Assumption Mapping |  Knowledge Modeling Shape-First Modeling |  Product Discovery Smoke Test |  Product Discovery Fake Door Test |
|---|---|---|---|---|
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 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 | Medium | Low | Medium |
Timedifferent | 45-60 min | Halber Tag pro Domain Slice | 1-5 Tage | 1-5 Tage |
Participantsdifferent | 2-8 | 1-4 | Nutzertraffic | Nutzertraffic |
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 | Click Data, Interest Signal, Learning Decision |
Tags1 shared | AssumptionsRiskExperimentsValidation | Knowledge graphValidationSemantic | ValidationExperimentsDemandGrowth | ValidationExperimentsDemandDiscovery |