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| Criterion |  Product Strategy DIBB |  Growth Funnel Analysis |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
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 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 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 | Low | Medium | Low | High |
Timedifferent | 1-2 h | 1-3 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 2-8 | 1-5 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Async | Async | Async |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Funnel report, Drop-off analysis, Optimization hypotheses | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | AnalyticsConversionGrowth | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |