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| Criterion |  Product Discovery Outcome-Driven Innovation |  Decision Making Trade-off Analysis |  Decision Making Decision Tree |  Innovation Design Thinking |
|---|---|---|---|---|
Purposedifferent | 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. | With limited resources, quality, speed, cost, and risk almost always compete with each other. Trade-off Analysis makes these tensions explicit and prevents decisions from producing hidden side effects. | 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. | 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 | High | Medium | Medium | High |
Timedifferent | Mehrere Wochen | 45-120 min | 30-90 min | 1 Tag bis mehrere Wochen |
Participantsdifferent | 2-6 researchers plus sample | 2-8 | 1-6 | 4-10 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Trade-off Matrix, Criteria List, Decision Rationale, Accepted Downsides | Decision Tree, Option Map, Assumption List | Problem Statement, Prototype, Test Learnings |
Tagsno overlap | OutcomesInnovationResearchQuantitative | TradeoffsDecisionCriteriaOptions | DecisionTreeOptions | InnovationPrototypeResearch |