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| Criterion |  UX Research Surveys |  Operations Causal Factor Analysis |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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. |
Complexitydifferent | Medium | High | Low | High |
Timedifferent | 3-14 Tage | 2-6 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 50+ | 3-10 | Nutzertraffic | 1-6 |
Formatdifferent | Async | Workshop + async | Async | Async |
Outputdifferent | Survey Results, Charts, Segment Insights | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | QuantitativeResearchValidation | CausalityIncidentRoot causeTimeline | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |