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| Criterion |  Growth Funnel Analysis |  Operations Causal Factor Analysis |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses. | 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 | 1-3 h | 2-6 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-5 | 3-10 | Nutzertraffic | 1-6 |
Formatdifferent | Async | Workshop + async | Async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | CausalityIncidentRoot causeTimeline | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |