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Criterion
Paper illustration for Constraint Analysis.
Decision Making
Constraint Analysis
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
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Purposedifferent
When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped.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.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.
Complexitydifferent
LowMediumHighMedium
Timedifferent
30-90 min1-3 h1-4 Wochen30-90 min
Participantsdifferent
2-81-51-61-6
Formatdifferent
Workshop + asyncAsyncAsyncWorkshop + async
Outputdifferent
Constraint List, Hard/Soft Classification, Option Impact Notes, Decision BoundariesFunnel report, Drop-off analysis, Optimization hypothesesExperiment results, Decision log, Learning summaryDecision Tree, Option Map, Assumption List
Tagsno overlap
ConstraintsDecisionPlanningOptions
AnalyticsConversionGrowth
ExperimentsGrowthAnalyticsValidation
DecisionTreeOptions
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