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| Criterion |  Decision Making Decision Tree |  UX Research Surveys |  Product Discovery Outcome-Driven Innovation |  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 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. | 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 | High | Low |
Timedifferent | 30-90 min | 3-14 Tage | Mehrere Wochen | 30-90 min |
Participantsdifferent | 1-6 | 50+ | 2-6 researchers plus sample | 2-8 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop + async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Survey Results, Charts, Segment Insights | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | DecisionTreeOptions | QuantitativeResearchValidation | OutcomesInnovationResearchQuantitative | ConstraintsDecisionPlanningOptions |