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| Criterion |  Decision Making Decision Tree |  Product Discovery Outcome-Driven Innovation |  Decision Making Constraint Analysis |  Innovation Design Thinking |
|---|---|---|---|---|
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. | Outcome-Driven Innovation clarifies customer problems, solution opportunities, and evidence. It separates jobs, desired outcomes, importance, satisfaction, and opportunity, and captures results as desired outcome statements, an opportunity landscape, and segments by underserved needs. | 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 complex user problems with an uncertain cause, the method connects observation, interpretation, and experiment into a learning cycle. It keeps the focus on real needs and avoids premature solution language. This produces robust decisions for product and service. |
Complexitydifferent | Medium | High | Low | High |
Timedifferent | 30-90 min | Mehrere Wochen | 30-90 min | 1 Tag bis mehrere Wochen |
Participantsdifferent | 1-6 | 2-6 researchers plus sample | 2-8 | 4-10 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Decision Tree, Option Map, Assumption List | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Problem Statement, Prototype, Test Learnings |
Tagsno overlap | DecisionTreeOptions | OutcomesInnovationResearchQuantitative | ConstraintsDecisionPlanningOptions | InnovationPrototypeResearch |