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Criterion
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
Paper illustration of Counter Metrics with its method-specific working model.
Product Strategy
Counter Metrics
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.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 a headline metric could mask side effects, it adds guardrail signals against unwanted outcomes. It connects customer value, product logic, and decision priorities. The result is captured as a counter-metric list and guardrail definitions.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 h1-2 h30-60 min1-4 Wochen
Participantsdifferent
1-52-82-61-6
Formatdifferent
AsyncWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
Funnel report, Drop-off analysis, Optimization hypothesesDIBB document, Belief list, Bet list, Learning reportCounter Metric List, Guardrail DefinitionsExperiment results, Decision log, Learning summary
Tagsno overlap
AnalyticsConversionGrowth
StrategyDecisionAssumptionsHypothesis
MetricsMeasurementStrategyExperiments
ExperimentsGrowthAnalyticsValidation
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