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| Criterion |  Decision Making Decision Tree |  Product Discovery Problem Interview |  Product Discovery Concierge MVP |  Decision Making Constraint Analysis |
|---|---|---|---|---|
Purposedifferent | For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch. | When the picture of the problem still needs to become solid, it asks about real situations and consequences. It separates genuine suffering from mere interest in a solution. | 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 an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. |
Complexitydifferent | Medium | Medium | Medium | Low |
Timedifferent | 30-90 min | 30-60 min je Interview | 1-4 Wochen | 30-90 min |
Participantsdifferent | 1-6 | 5-12 Interviews | 3-10 Kunden | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Problem Evidence, Risk Notes, Customer Segments | Concierge Learnings, Service Blueprint, MVP Risks | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | DecisionTreeOptions | DiscoveryInterviewsValidation | MVPValidationServiceDiscovery | ConstraintsDecisionPlanningOptions |