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Criterion
Paper illustration of Outcome-Driven Innovation with its method-specific working model.
Product Discovery
Outcome-Driven Innovation
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Design sheets and interface cards connect to jointly written pseudocode with highlighted unresolved questions.
Architecture
ARID (Active Reviews for Intermediate Designs)
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
Purposedifferent
Outcome-Driven Innovation clarifies customer problems, solution opportunities, and evidence. It separates jobs, desired outcomes, importance, satisfaction, and opportunity, and captures results as desired outcome statements, an opportunity landscape, and segments by underserved needs.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.ARID examines how well developers can work with a technical design. It focuses on a part of the system that can still be revised, between the architecture overview and the detailed specification.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.
Complexitydifferent
HighHighMediumLow
Timedifferent
Mehrere Wochen1-4 Wochen2–4 h review as a planning range; preparation and evaluation additional1-5 Tage
Participantsdifferent
2-6 researchers plus sample1-6Designers, developers as users, and facilitationNutzertraffic
Formatdifferent
Workshop + asyncAsyncWorkshop + asyncAsync
Outputdifferent
Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypothesesExperiment results, Decision log, Learning summaryPrioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisionsInterest Metrics, Conversion Signal, Learning Note
Tagsno overlap
OutcomesInnovationResearchQuantitative
ExperimentsGrowthAnalyticsValidation
ArchitectureEngineeringFeedbackValidationTasks
ValidationExperimentsDemandGrowth
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