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| Criterion |  Growth A/B Testing |  Engineering Goal Question Metric |  Growth North Star Metric |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | 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. | Helps clarify technical problems, hypotheses, and next steps in concrete terms. It breaks a technical problem into testable parts. The result is captured as a GQM table and metric briefs. | When product teams track many metrics, the view of the actual customer outcome easily gets lost. A North Star Metric bundles growth, usage, and value contribution into one signal that makes the system's direction visible. | 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. |
Complexitydifferent | High | Medium | Medium | Low |
Timedifferent | 1-4 Wochen | 90-180 min | 1-2 h | 1-5 Tage |
Participantsdifferent | 1-6 | 3-6 | 3-8 | Nutzertraffic |
Formatdifferent | Async | Workshop | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | GQM Table, Metric Profiles | North Star metric, Input metric tree, Measurement cadence | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | MetricsMeasurementEngineeringAlignment | GrowthMetricsAlignmentRetention | ValidationExperimentsDemandGrowth |