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Criterion
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
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
Purposedifferent
For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch.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.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
MediumMediumLowHigh
Timedifferent
30-90 min1-3 h1-2 h1-4 Wochen
Participantsdifferent
1-61-52-81-6
Formatdifferent
Workshop + asyncAsyncWorkshop + asyncAsync
Outputdifferent
Decision Tree, Option Map, Assumption ListFunnel report, Drop-off analysis, Optimization hypothesesDIBB document, Belief list, Bet list, Learning reportExperiment results, Decision log, Learning summary
Tagsno overlap
DecisionTreeOptions
AnalyticsConversionGrowth
StrategyDecisionAssumptionsHypothesis
ExperimentsGrowthAnalyticsValidation
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