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Selected:Outcome-Driven InnovationTree TestingDesign ThinkingCard Sorting
Criterion
Paper illustration of Outcome-Driven Innovation with its method-specific working model.
Product Discovery
Outcome-Driven Innovation
Paper illustration of a branching category tree with a highlighted search path and possible dead ends.
UX Research
Tree Testing
Design Thinking as a learning loop: an observation notebook, a focused problem statement, different idea sketches, a cardboard prototype and user-test notes. Feedback leads to new understanding.
Innovation
Design Thinking
Paper illustration showing three sorted card stacks and an open question about a possible group.
UX Research
Card Sorting
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 content only makes sense internally and users can't find the structure again, card sorting exposes their mental order. Terms, groups, and naming are then aligned with the target group's expectations.
Complexitydifferent
HighMediumHighLow
Timedifferent
Mehrere Wochen1-2 Tage1 Tag bis mehrere Wochen20-45 min
Participantsdifferent
2-6 researchers plus sampleBased on research question4-10Based on research question
Formatdifferent
Workshop + asyncAsyncWorkshopWorkshop + async
Outputdifferent
Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypothesesFindability Metrics, Path Analysis, Revised IAProblem Statement, Prototype, Test LearningsContent groups, Label set, IA hypotheses
Tagsno overlap
OutcomesInnovationResearchQuantitative
Information architectureNavigationFindability
InnovationPrototypeResearch
Information architectureNavigationStructure
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