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| Criterion |  Agile Affinity Estimation |  Product Discovery User Story Mapping |  Product Discovery Experiment Canvas |  Product Discovery Concierge MVP |
|---|---|---|---|---|
Purposedifferent | 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 user goals and delivery scope could drift apart, it orders the journey from goal to slice. It makes visible where an MVP can genuinely be cut. | 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 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. |
Complexitydifferent | Medium | Medium | Low | Medium |
Timedifferent | 30-90 min | 2-4 h | 30-60 min | 1-4 Wochen |
Participantsdifferent | 3-12 | 4-10 | 1-5 | 3-10 Kunden |
Formatdifferent | Workshop | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Story Map, Release Slices, Backlog Candidates | Completed Experiment Canvas, Success Metric | Concierge Learnings, Service Blueprint, MVP Risks |
Tagsno overlap | EstimationBacklogRelative sizing | BacklogJourneyPlanningMVP | ExperimentsValidationDiscoveryHypothesis | MVPValidationServiceDiscovery |