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| Criterion |  Product Discovery Concierge MVP |  Product Strategy KPI Tree |  Product Discovery Experiment Canvas |  Engineering Goal Question Metric |
|---|---|---|---|---|
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 metrics across the organization drift apart, it arranges drivers and effects under one shared logic. It connects customer value, product logic, and decision priorities. The result is captured as a KPI tree and owner list. | 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. | Helps clarify technical problems, hypotheses, and next steps in concrete terms. It breaks a technical problem into testable parts. The result is captured as a GQM table and metric briefs. |
Complexitydifferent | Medium | Medium | Low | Medium |
Timedifferent | 1-4 Wochen | 90-180 min initial, dann laufend | 30-60 min | 90-180 min |
Participantsdifferent | 3-10 Kunden | 3-8 | 1-5 | 3-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Concierge Learnings, Service Blueprint, MVP Risks | KPI Tree, Owner List | Completed Experiment Canvas, Success Metric | GQM Table, Metric Profiles |
Tagsno overlap | MVPValidationServiceDiscovery | MetricsStrategyAlignmentMeasurement | ExperimentsValidationDiscoveryHypothesis | MetricsMeasurementEngineeringAlignment |