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| Criterion |  Decision Making Constraint Analysis |  Decision Making Decision Tree |  Product Discovery Concierge MVP |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | 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. | 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 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 | 30-90 min | 30-90 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 2-8 | 1-6 | 3-10 Kunden | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Decision Tree, Option Map, Assumption List | Concierge Learnings, Service Blueprint, MVP Risks | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ConstraintsDecisionPlanningOptions | DecisionTreeOptions | MVPValidationServiceDiscovery | ValidationExperimentsDemandDiscovery |