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| Criterion |  Growth North Star Metric |  Product Discovery Hypothesis Prioritization Canvas |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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 many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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 | Medium | Low | High |
Timedifferent | 1-2 h | 60-90 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 3-8 | 3-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop | Async | Async |
Outputdifferent | North Star metric, Input metric tree, Measurement cadence | Prioritization Canvas, Hypothesis Backlog | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | GrowthMetricsAlignmentRetention | ExperimentsPrioritizationDiscoveryHypothesis | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |