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| Criterion |  Product Discovery Outcome-Driven Innovation |  UX Research Tree Testing |  Innovation Design Thinking |  Innovation Design Sprint |
|---|---|---|---|---|
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. | When a navigation exists but search paths still fail, tree testing checks findability without visual distraction. The method shows whether labels, levels, and paths really lead to the intended destination. | 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. | 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. |
Complexitydifferent | High | Medium | High | High |
Timedifferent | Mehrere Wochen | 1-2 Tage | 1 Tag bis mehrere Wochen | 4-5 Tage |
Participantsdifferent | 2-6 researchers plus sample | Based on research question | 4-10 | 5-8 |
Formatdifferent | Workshop + async | Async | Workshop | Workshop |
Outputdifferent | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Findability Metrics, Path Analysis, Revised IA | Problem Statement, Prototype, Test Learnings | Prototype, Test Findings, Decision Rationale |
Tagsno overlap | OutcomesInnovationResearchQuantitative | Information architectureNavigationFindability | InnovationPrototypeResearch | Design sprintPrototypeValidationInnovation |