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Criterion
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
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
When knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together.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
HighMediumLowHigh
Timedifferent
1-4 Wochen1-3 h1-2 h1-4 Wochen
Participantsdifferent
6-30 Experten1-52-81-6
Formatdifferent
AsyncAsyncWorkshop + asyncAsync
Outputdifferent
Expert Forecast, Consensus Range, Assumption NotesFunnel report, Drop-off analysis, Optimization hypothesesDIBB document, Belief list, Bet list, Learning reportExperiment results, Decision log, Learning summary
Tagsno overlap
ForecastingExpertsDecisionStrategy
AnalyticsConversionGrowth
StrategyDecisionAssumptionsHypothesis
ExperimentsGrowthAnalyticsValidation
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