# UX Metrics

Create measurable evidence for decisionsUX Metrics are the measurement system teams use to understand how people experience a product, workflow, service, interface, or AI-assisted system.Product and design teams are constantly having to make decisions::Which concept moves forwardWhere onboarding breaks downWhy users hesitateWhether AI recommendations actually helpWhich workflow creates less frictionWhat deserves more investmentMost teams already have data. The harder part is understanding what the experience actually feels like while work is still evolving.UX Metrics help teams:Evaluate workflowsCompare product directionsExpose frictionReduce uncertaintyStrengthen reviewsGuide decisions earlierInstead of waiting until after launch to understand what happened, teams can evaluate direction while concepts, workflows, and product decisions are still moving.AI increased the need for stronger measurement systems.AI made it easy for teams to generate more ideas, screens, prompts, workflows, prototypes, and experiments. What did not speed up at the same pace was evaluation. Teams now create more output than they can easily judge, compare, or align around.What did not speed up at the same pace was evaluation.Many organizations now produce more ideas than they can consistently review, compare, or validate clearly. Product reviews become fragmented. Stakeholders interpret success differently. Teams move quickly, but confidence weakens underneath the process.UX Metrics help teams slow down the right part of the work:EvaluationInterpretationComparisonValidationDecision-makingThe goal is a clearer direction under pressure. Scrap the reporting.The Four UX Metric TypesGlare organizes UX Metrics into four connected measurement areas. Each one helps teams understand a different part of the experience.Attitudinal MetricsAttitudinal metrics measure how users feel about the experience. These metrics reveal:AppealBrand ScoreDesirabilityExpectationsFeelingLoyaltySatisfactionSentimentUsefulnessThese metrics often reveal problems before larger business metrics begin changing.For example:users may complete onboarding successfully while confidence quietly dropsAI recommendations may increase engagement while trust weakens underneath the workflowsatisfaction may remain positive while frustration slowly grows over timeAttitudinal metrics help teams understand the emotional side of the experience.Behavioral MetricsBehavioral metrics measure what users actually do. These metrics help teams understand:CompletionComprehensionEffortEngagementFrequencyIntentSuccessUsabilityBehavioral signals reveal where users:struggleabandon workflowsslow downbecome confusedrepeatedly hesitateFor example:users may repeatedly scan navigation before taking actiononboarding may technically work while users struggle to understand what happens nextAI-assisted workflows may create hidden confusion even when completion appears highBehavioral metrics help teams understand what users experience while interacting with the workflow itself.Performance MetricsPerformance metrics measure efficiency, reliability, and operational friction. These metrics expose:Abandonment RateBounce RateClick-Through RateCompletion RateDrop-off RateError FrequencyError RateRecencySession DurationTime on TaskVisit FrequencyThese metrics help teams identify where systems create unnecessary effort or friction.For example:a checkout flow may have strong completion but extremely high effortusers may finish a task successfully while taking much longer than expectederror frequency may increase after introducing AI-generated recommendationsPerformance metrics help teams understand whether workflows function smoothly under real usage conditions.Intelligence MetricsIntelligence metrics measure whether AI-assisted workflows, recommendations, and guidance actually improve user outcomes. These metrics help teams evaluate:recommendation qualityprompt effectivenessAI guidance usefulnessadaptation qualityconfidence calibrationworkflow assistanceAs AI becomes part of product workflows, teams need stronger ways to evaluate whether intelligence improves understanding or simply increases output.For example:recommendations may appear useful while users quietly lose confidence in the systemAI guidance may speed up workflows while increasing comprehension problemsautomated suggestions may increase activity while creating more hesitation during decisionsIntelligence metrics help teams evaluate whether AI improves the experience in meaningful ways.Explore UX MetricsThe UX Metrics section is organized into several connected areas that help teams build stronger evaluation systems across product and design work.Why UX Metrics MatterLearn why stronger evaluation systems matter as AI increases output, experimentation, and decision complexity.Measurement SystemUnderstand how UX Metrics work together as a connected operational system across workflows, reviews, testing, and decision-making.What Is a UX Metric?See how UX metrics are structured, measured, benchmarked, and interpreted.Attitudinal MetricsMeasure how users feel about the experience.Behavioral MetricsMeasure what users do while interacting with workflows and systems.Performance MetricsMeasure efficiency, friction, reliability, and operational performance.UX Metric StacksCombine multiple metrics together to evaluate larger workflows, journeys, and product experiences.Mapping Design ImpactConnect UX metrics to product outcomes, business goal, and organizational decisions/Applying UX MetricsLearn how teams use UX metrics inside testing, reviews, benchmarking, and decision workflows.How UX Metrics Fit Into GlareUX Metrics are the measurement layer underneath the larger Glare system.They help support:Design SignalsAI SkillsDesign ReviewsDecision MapsOrganizational assessmentsAs metrics accumulate across workflows and decisions, patterns become easier to see. Teams begin understanding:Where users hesitateWhere trust weakensWhich concepts perform betterWhere workflows create frictionWhat deserves momentumThis helps teams create stronger signals before weak decisions spread further into the product.From Metrics to DecisionsOrganizations already have more dashboards, analytics, and feedback than they can consistently interpret. The challenge is not collecting more numbers, but turning those numbers into clearer decisions.That is where UX metrics create value inside Glare. They help teams:Compare onboarding flowsIdentify hesitation before launchExpose friction in AI-assisted workflowsStrengthen design reviewsImprove stakeholder alignmentReduce rework laterValidate concepts earlierBuild more confidence in product directionOver time, UX Metrics become part of how teams make product decisions together.They help teams compare ideas earlier, identify friction before launch, and build stronger alignment around what deserves momentum. As patterns build over time, teams gain clearer visibility into what helps users move forward and where the experience still breaks down.