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| Criterion |  Product Strategy DIBB |  Product Discovery Hypothesis Prioritization Canvas |  Product Strategy KPI Tree |  Engineering Goal Question Metric |
|---|---|---|---|---|
Purposedifferent | 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 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 metrics across the organization drift apart, it arranges drivers and effects under one shared logic. It connects customer value, product logic, and decision priorities. The result is captured as a KPI tree and owner list. | 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. |
Complexitydifferent | Low | Medium | Medium | Medium |
Timedifferent | 1-2 h | 60-90 min | 90-180 min initial, dann laufend | 90-180 min |
Participantsdifferent | 2-8 | 3-8 | 3-8 | 3-6 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Prioritization Canvas, Hypothesis Backlog | KPI Tree, Owner List | GQM Table, Metric Profiles |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | ExperimentsPrioritizationDiscoveryHypothesis | MetricsStrategyAlignmentMeasurement | MetricsMeasurementEngineeringAlignment |