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| Criterion |  Product Discovery Concierge MVP |  Agile Bucket System |  Product Discovery User Story Mapping |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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. | When a large batch of work needs a fast, rough estimate, it sorts items into predefined size buckets. It sorts work by value, risk, and delivery ability. The result is captured as a Bucketed Backlog, Relative Estimates, and Split Candidates. | 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. |
Complexitydifferent | Medium | Medium | Medium | Low |
Timedifferent | 1-4 Wochen | 30-90 min | 2-4 h | 30-60 min |
Participantsdifferent | 3-10 Kunden | 3-12 | 4-10 | 1-5 |
Formatdifferent | Workshop + async | Workshop | Workshop | Workshop + async |
Outputdifferent | Concierge Learnings, Service Blueprint, MVP Risks | Bucketed Backlog, Relative Estimates, Split Candidates | Story Map, Release Slices, Backlog Candidates | Completed Experiment Canvas, Success Metric |
Tagsno overlap | MVPValidationServiceDiscovery | EstimationBacklogRelative sizing | BacklogJourneyPlanningMVP | ExperimentsValidationDiscoveryHypothesis |