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| Criterion |  Operations Causal Factor Analysis |  Product Discovery Smoke Test |  Product Strategy ICE Scoring |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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 ideas need sorting quickly, it condenses impact, confidence, and effort into one compact score. It connects customer value, product logic, and decision priorities. The result is captured as an ICE table and top-ideas list. | 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 | High | Low | Low | High |
Timedifferent | 2-6 h | 1-5 Tage | 30-60 min | 1-4 Wochen |
Participantsdifferent | 3-10 | Nutzertraffic | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Interest Metrics, Conversion Signal, Learning Note | ICE Table, Top Idea List | Experiment results, Decision log, Learning summary |
Tagsno overlap | CausalityIncidentRoot causeTimeline | ValidationExperimentsDemandGrowth | PrioritizationScoringGrowthDecision | ExperimentsGrowthAnalyticsValidation |