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Criterion
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
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 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.
Complexitydifferent
LowHighMediumLow
Timedifferent
1-2 h1-4 Wochen1-3 h1-5 Tage
Participantsdifferent
2-81-61-5Nutzertraffic
Formatdifferent
Workshop + asyncAsyncAsyncAsync
Outputdifferent
DIBB document, Belief list, Bet list, Learning reportExperiment results, Decision log, Learning summaryFunnel report, Drop-off analysis, Optimization hypothesesInterest Metrics, Conversion Signal, Learning Note
Tagsno overlap
StrategyDecisionAssumptionsHypothesis
ExperimentsGrowthAnalyticsValidation
AnalyticsConversionGrowth
ValidationExperimentsDemandGrowth
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