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| Criterion |  Product Strategy DIBB |  Growth A/B Testing |  Product Strategy KPI Tree |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 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. | 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. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Low | High | Medium | Low |
Timedifferent | 1-2 h | 1-4 Wochen | 90-180 min initial, dann laufend | 30-60 min |
Participantsdifferent | 2-8 | 1-6 | 3-8 | 1-5 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop + async |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Experiment results, Decision log, Learning summary | KPI Tree, Owner List | Completed Experiment Canvas, Success Metric |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | ExperimentsGrowthAnalyticsValidation | MetricsStrategyAlignmentMeasurement | ExperimentsValidationDiscoveryHypothesis |