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| Criterion |  Growth Funnel Analysis |  Product Discovery Experiment Canvas |  Product Strategy DIBB |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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 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. | 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. |
Complexitydifferent | Medium | Low | Low | High |
Timedifferent | 1-3 h | 30-60 min | 1-2 h | 1-4 Wochen |
Participantsdifferent | 1-5 | 1-5 | 2-8 | 1-6 |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Completed Experiment Canvas, Success Metric | DIBB document, Belief list, Bet list, Learning report | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | ExperimentsValidationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis | ExperimentsGrowthAnalyticsValidation |