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| Criterion |  Decision Making Decision Tree |  UX Research Surveys |  Product Discovery Outcome-Driven Innovation |  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. | When a topic needs to be validated broadly and many people can answer the same question, surveys gather structured feedback in a scalable form. Answers become comparable and segmentable instead of remaining merely anecdotal. | 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. | 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 | Medium | High | High |
Timedifferent | 30-90 min | 3-14 Tage | Mehrere Wochen | 1 Tag bis mehrere Wochen |
Participantsdifferent | 1-6 | 50+ | 2-6 researchers plus sample | 4-10 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop |
Outputdifferent | Decision Tree, Option Map, Assumption List | Survey Results, Charts, Segment Insights | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Problem Statement, Prototype, Test Learnings |
Tagsno overlap | DecisionTreeOptions | QuantitativeResearchValidation | OutcomesInnovationResearchQuantitative | InnovationPrototypeResearch |