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Criterion
Systems Mapping method illustration showing its working structure
Systems Thinking
Systems Mapping
Growth Experiment workspace showing the question, observations, and next decision.
Growth
Growth Experiment
Context Map workspace showing the question, observations, and next decision.
Domain Modeling
Context Map
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Purposedifferent
Systems Mapping arranges elements, relationships, and boundaries so a complex field becomes legible at a glance. The overview stabilizes the overall picture before detail work or steering begins.When growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment.When several contexts need to talk to each other, it makes their relationships and dependencies legible. It helps sort integration pressure and responsibilities across system boundaries.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
1-3 h1-2 Wochen1-3 h1-4 Wochen
Participantsdifferent
3-121-62-81-6
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
System Map, Dependencies, Leverage PointsExperiment card, Result summary, Next betContext Map, Integration Patterns, Boundary NotesExperiment results, Decision log, Learning summary
Tagsno overlap
Systems thinkingMappingBoundariesChange
MarketingGrowthExperimentsLearning
Domain-Driven DesignBoundariesStrategy
ExperimentsGrowthAnalyticsValidation
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