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| Criterion |  Product Discovery Concierge MVP |  Innovation Lean Startup |  Knowledge Modeling Event Modeling |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | When an idea can first fail or grow through genuine hands-on support, it relies on manual work instead of automation. It shows whether user value holds up even under manual execution. | For uncertain business assumptions, the method forces the idea into contact with real market reactions early. It separates wishful picture, assumption, and observable behavior, so that learning becomes faster than planning. This translates uncertainty into measurable insight. | 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 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. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | 1-4 Wochen | Wochen bis Monate je Lernzyklus | 2-6 h | 1-5 Tage |
Participantsdifferent | 3-10 Kunden | 2-8 | 2-8 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Concierge Learnings, Service Blueprint, MVP Risks | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Event Model, UI Flow, Commands, Read Models | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | MVPValidationServiceDiscovery | LeanStartupValidationMVP | EventsBlueprintDomain-Driven DesignBehavior | ValidationExperimentsDemandDiscovery |