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Criterion
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Paper illustration of ICE Scoring with its method-specific working model.
Product Strategy
ICE Scoring
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Purposedifferent
For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch.When ideas need sorting quickly, it condenses impact, confidence, and effort into one compact score. It connects customer value, product logic, and decision priorities. The result is captured as an ICE table and top-ideas list.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.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
MediumLowMediumHigh
Timedifferent
30-90 min30-60 min1-3 h1-4 Wochen
Participantsdifferent
1-62-81-51-6
Formatdifferent
Workshop + asyncWorkshop + asyncAsyncAsync
Outputdifferent
Decision Tree, Option Map, Assumption ListICE Table, Top Idea ListFunnel report, Drop-off analysis, Optimization hypothesesExperiment results, Decision log, Learning summary
Tagsno overlap
DecisionTreeOptions
PrioritizationScoringGrowthDecision
AnalyticsConversionGrowth
ExperimentsGrowthAnalyticsValidation
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