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| Criterion |  Growth A/B Testing |  Engineering Goal Question Metric |  Growth Funnel Analysis |  Growth North Star Metric |
|---|---|---|---|---|
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 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. | 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. |
Complexitydifferent | High | Medium | Medium | Medium |
Timedifferent | 1-4 Wochen | 90-180 min | 1-3 h | 1-2 h |
Participantsdifferent | 1-6 | 3-6 | 1-5 | 3-8 |
Formatdifferent | Async | Workshop | Async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | GQM Table, Metric Profiles | Funnel report, Drop-off analysis, Optimization hypotheses | North Star metric, Input metric tree, Measurement cadence |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | MetricsMeasurementEngineeringAlignment | AnalyticsConversionGrowth | GrowthMetricsAlignmentRetention |