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Criterion
ChatOps workspace showing the question, observations, and next decision.
DevOps
ChatOps
Paper illustration of Pretotyping with its method-specific working model.
Product Discovery
Pretotyping
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration for Fake Door Test
Product Discovery
Fake Door Test
Purposedifferent
For operational work in a distributed team, the method links communication with direct system control. It records decisions right where the signal, the context, and the execution come together.Pretotyping clarifies whether a customer problem and solution idea create enough real demand. It separates problem, assumption, solution, and evidence, and captures the result as a pretotyping sketch, test setup, and conversion data.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.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
MediumLowHighMedium
Timedifferent
OngoingStunden bis wenige Tage1-4 Wochen1-5 Tage
Participantsdifferent
2-201-41-6Nutzertraffic
Formatdifferent
Workshop + asyncWorkshop + asyncAsyncAsync
Outputdifferent
Chat Transcript, Action Log, AutomationsPretotyping sketch, Test setup, Conversion data, Go or no-go decisionExperiment results, Decision log, Learning summaryClick Data, Interest Signal, Learning Decision
Tagsno overlap
CollaborationEngineeringIncident
ValidationDemandMVP
ExperimentsGrowthAnalyticsValidation
ValidationExperimentsDemandDiscovery
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