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| Criterion |  Operations Fault Tree Analysis |  Product Discovery Outcome-Driven Innovation |  Growth A/B Testing |  UX Research Surveys |
|---|---|---|---|---|
Purposedifferent | For a critical top event with several possible triggers, the method logically models failure paths. It makes visible which combinations of conditions can lead to damage. | 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 two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | 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. |
Complexitydifferent | High | High | High | Medium |
Timedifferent | 2-6 h | Mehrere Wochen | 1-4 Wochen | 3-14 Tage |
Participantsdifferent | 3-8 | 2-6 researchers plus sample | 1-6 | 50+ |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Fault Tree, Critical Paths, Cause Hypotheses, Control Actions | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Experiment results, Decision log, Learning summary | Survey Results, Charts, Segment Insights |
Tagsno overlap | RiskRoot causeSafety | OutcomesInnovationResearchQuantitative | ExperimentsGrowthAnalyticsValidation | QuantitativeResearchValidation |