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| Criterion |  Product Discovery Fake Door Test |  Product Discovery Concierge MVP |  Product Discovery Opportunity Solution Tree |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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. | 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. | When discovery keeps swinging between goals, ideas, and learning loops, it creates clear steering logic. It connects the desired outcome, opportunities, and experiments into a legible structure. | 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. |
Complexitydifferent | Medium | Medium | Medium | Low |
Timedifferent | 1-5 Tage | 1-4 Wochen | 1-2 h Setup, laufend | 30-60 min |
Participantsdifferent | Nutzertraffic | 3-10 Kunden | 2-6 | 1-5 |
Formatdifferent | Async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Click Data, Interest Signal, Learning Decision | Concierge Learnings, Service Blueprint, MVP Risks | Opportunity Solution Tree, Experiment Backlog, Learning Log | Completed Experiment Canvas, Success Metric |
Tags1 shared | ValidationExperimentsDemandDiscovery | MVPValidationServiceDiscovery | DiscoveryOutcomesExperimentsOpportunity | ExperimentsValidationDiscoveryHypothesis |