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| Criterion |  Innovation Design Sprint |  Decision Making Decision Tree |  Product Discovery Concierge MVP |  Decision Making Constraint Analysis |
|---|---|---|---|---|
Purposedifferent | When the product question is unclear and time pressure is high, the method takes a team from an open idea to a testable solution. It connects problem understanding, decision, and learning in one compact format, lowering the risk of spending a lot of energy on a mere guess. | 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 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 | High | Medium | Medium | Low |
Timedifferent | 4-5 Tage | 30-90 min | 1-4 Wochen | 30-90 min |
Participantsdifferent | 5-8 | 1-6 | 3-10 Kunden | 2-8 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Prototype, Test Findings, Decision Rationale | Decision Tree, Option Map, Assumption List | Concierge Learnings, Service Blueprint, MVP Risks | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | Design sprintPrototypeValidationInnovation | DecisionTreeOptions | MVPValidationServiceDiscovery | ConstraintsDecisionPlanningOptions |