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Criterion
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
Paper illustration of an assumption matrix with prioritized test cards
Decision Making
Assumption Surfacing
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
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.Plans often contain assumptions that were never openly stated and remain dangerous precisely because of that. Assumption Surfacing makes these silent premises visible and prioritizes which of them carry the initiative or could sink it.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
MediumLowLowHigh
Timedifferent
1-3 h45-90 min1-2 h1-4 Wochen
Participantsdifferent
1-52-82-81-6
Formatdifferent
AsyncWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
Funnel report, Drop-off analysis, Optimization hypothesesAssumption List, Critical Assumptions, Learning PlanDIBB document, Belief list, Bet list, Learning reportExperiment results, Decision log, Learning summary
Tagsno overlap
AnalyticsConversionGrowth
AssumptionsRiskDecisionDiscovery
StrategyDecisionAssumptionsHypothesis
ExperimentsGrowthAnalyticsValidation
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