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Criterion
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration for Root Cause Analysis.
Operations
Root Cause Analysis
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
Paper illustration for Root Cause Tree Analysis
Operations
Root Cause Tree Analysis
Purposedifferent
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.When a problem keeps recurring and the cause stays unclear, the method exposes the underlying mechanism. It separates symptom, guess, and robust explanation from one another.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.For a problem with several suspected causes, the method builds a causal structure instead of a mere list. It makes visible how causes connect and where the strongest points of leverage lie.
Complexitydifferent
HighMediumMediumMedium
Timedifferent
1-4 Wochen1-4 h1-3 h1-3 h
Participantsdifferent
1-63-81-52-8
Formatdifferent
AsyncWorkshop + asyncAsyncWorkshop
Outputdifferent
Experiment results, Decision log, Learning summaryProblem Statement, Cause Hypotheses, Confirmed Causes, Action PlanFunnel report, Drop-off analysis, Optimization hypothesesCause Tree, Evidence Notes, Countermeasures
Tagsno overlap
ExperimentsGrowthAnalyticsValidation
Root causeProblem solvingQualityIncident
AnalyticsConversionGrowth
Root causeTreeIncidentQuality
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