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Criterion
Paper illustration of Hooked Model with its method-specific working model.
Growth
Hooked Model
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
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
The Hooked Model helps clarify engagement loops, user behavior, and experiments. It makes repeat behavior measurable and captures the result as a hooked loop, trigger map, and reward design.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 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
MediumMediumMediumHigh
Timedifferent
Multiple workshops over several weeks30-90 min1-3 h1-4 Wochen
Participantsdifferent
2-81-61-51-6
Formatdifferent
Workshop + asyncWorkshop + asyncAsyncAsync
Outputdifferent
Hooked loop, Trigger map, Reward design, Ethics checkDecision Tree, Option Map, Assumption ListFunnel report, Drop-off analysis, Optimization hypothesesExperiment results, Decision log, Learning summary
Tagsno overlap
GrowthBehaviorRetention
DecisionTreeOptions
AnalyticsConversionGrowth
ExperimentsGrowthAnalyticsValidation
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