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| Criterion |  Product Discovery Experiment Canvas |  Agile Affinity Estimation |  Product Discovery Concierge MVP |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | 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 many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes. | 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 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 | Low | Medium | Medium | Medium |
Timedifferent | 30-60 min | 30-90 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-5 | 3-12 | 3-10 Kunden | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Async |
Outputdifferent | Completed Experiment Canvas, Success Metric | Affinity Size Map, Grouped Estimates, Unclear Items | Concierge Learnings, Service Blueprint, MVP Risks | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ExperimentsValidationDiscoveryHypothesis | EstimationBacklogRelative sizing | MVPValidationServiceDiscovery | ValidationExperimentsDemandDiscovery |