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| Criterion |  Product Discovery Outcome-Driven Innovation |  UX Research Surveys |  Operations Causal Factor Analysis |
|---|---|---|---|
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 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. | For an event with a complicated course, the method breaks down the contributing factors along the timeline. It shows how conditions, decisions, and reactions together produce a course of events. |
Complexitydifferent | High | Medium | High |
Timedifferent | Mehrere Wochen | 3-14 Tage | 2-6 h |
Participantsdifferent | 2-6 researchers plus sample | 50+ | 3-10 |
Formatdifferent | Workshop + async | Async | Workshop + async |
Outputdifferent | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Survey Results, Charts, Segment Insights | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions |
Tagsno overlap | OutcomesInnovationResearchQuantitative | QuantitativeResearchValidation | CausalityIncidentRoot causeTimeline |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
