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Criterion
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
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.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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all.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
HighLowLowHigh
Timedifferent
1-4 Wochen1-2 h1-5 Tage1-4 Wochen
Participantsdifferent
6-30 Experten2-8Nutzertraffic1-6
Formatdifferent
AsyncWorkshop + asyncAsyncAsync
Outputdifferent
Expert Forecast, Consensus Range, Assumption NotesDIBB document, Belief list, Bet list, Learning reportInterest Metrics, Conversion Signal, Learning NoteExperiment results, Decision log, Learning summary
Tagsno overlap
ForecastingExpertsDecisionStrategy
StrategyDecisionAssumptionsHypothesis
ValidationExperimentsDemandGrowth
ExperimentsGrowthAnalyticsValidation
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