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Criterion
A hypothetical failure makes concrete risks, signals, and fitting mitigations visible.
Decision Making
Pre-Mortem
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
In early initiatives with many uncertainties, planning quickly turns too optimistic. A pre-mortem makes the expected failure visible in advance and sharpens the view of causes, gaps, and countermeasures.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
LowMediumLowHigh
Timedifferent
20–45 min1-3 h1-2 h1-4 Wochen
Participantsdifferent
Small cross-functional group1-52-81-6
Formatdifferent
WorkshopAsyncWorkshop + asyncAsync
Outputdifferent
Risk list, Mitigation plan, Assumption logFunnel report, Drop-off analysis, Optimization hypothesesDIBB document, Belief list, Bet list, Learning reportExperiment results, Decision log, Learning summary
Tagsno overlap
RiskDecisionFailurePlanning
AnalyticsConversionGrowth
StrategyDecisionAssumptionsHypothesis
ExperimentsGrowthAnalyticsValidation
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