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Criterion
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
Paper illustration for Fake Door Test
Product Discovery
Fake Door Test
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.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 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 demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small.
Complexitydifferent
MediumMediumLowMedium
Timedifferent
1-3 h30-90 min1-5 Tage1-5 Tage
Participantsdifferent
1-51-6NutzertrafficNutzertraffic
Formatdifferent
AsyncWorkshop + asyncAsyncAsync
Outputdifferent
Funnel report, Drop-off analysis, Optimization hypothesesDecision Tree, Option Map, Assumption ListInterest Metrics, Conversion Signal, Learning NoteClick Data, Interest Signal, Learning Decision
Tagsno overlap
AnalyticsConversionGrowth
DecisionTreeOptions
ValidationExperimentsDemandGrowth
ValidationExperimentsDemandDiscovery
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