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| Criterion |  Growth North Star Metric |  Product Strategy DIBB |  Product Discovery Assumption Mapping |  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. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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 | Low | Medium | High |
Timedifferent | 1-2 h | 1-2 h | 45-60 min | 1-4 Wochen |
Participantsdifferent | 3-8 | 2-8 | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | North Star metric, Input metric tree, Measurement cadence | DIBB document, Belief list, Bet list, Learning report | Assumption map, Test backlog, Risk ranking | Experiment results, Decision log, Learning summary |
Tagsno overlap | GrowthMetricsAlignmentRetention | StrategyDecisionAssumptionsHypothesis | AssumptionsRiskExperimentsValidation | ExperimentsGrowthAnalyticsValidation |