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| Criterion |  Product Discovery Problem Interview |  Knowledge Modeling Event Modeling |  Product Discovery Experiment Canvas |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | When the picture of the problem still needs to become solid, it asks about real situations and consequences. It separates genuine suffering from mere interest in a solution. | Event Modeling connects business workflows with commands, events, and views into one coherent mental model. It helps design behavior, UI, and technical slices from the same underlying logic. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | 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 | 30-60 min je Interview | 2-6 h | 30-60 min | 1-5 Tage |
Participantsdifferent | 5-12 Interviews | 2-8 | 1-5 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Problem Evidence, Risk Notes, Customer Segments | Event Model, UI Flow, Commands, Read Models | Completed Experiment Canvas, Success Metric | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | DiscoveryInterviewsValidation | EventsBlueprintDomain-Driven DesignBehavior | ExperimentsValidationDiscoveryHypothesis | ValidationExperimentsDemandDiscovery |