# What is a UX Metric?

A UX metric is a way to measure how people experience a product, workflow, or system.Product teams make decisions every day:Changing onboardingUpdating navigationRedesigning dashboardsAdding AI recommendationsLaunching new workflowsBut many teams still struggle to clearly understand what users are actually experiencing underneath those decisions. A workflow may technically succeed while users quietly feel confused. Engagement may increase while trust weakens over time. Users may finish onboarding while still feeling unsure about what to do next.Without measurement, teams often rely on:OpinionsAssumptionsStakeholder reactions"Gut feelings”Surface-level analyticsThis is where UX metrics help. A UX metric gives teams a clearer way to understand how the experience is actually performing for users. Instead of saying:“This feels confusing”“Users probably like it”“The workflow seems better”teams can measure trust, usability, confidence, comprehension, effort and satisfaction. Over time, these measurements help teams move from vague reactions to clearer evidence about:Where users hesitateWhat creates frictionWhich workflows perform betterWhere confidence weakensWhat deserves more momentumThe metric becomes valuable because it helps explain what is happening underneath the experience, not just what happened on the surface.UX Metrics Turn Fuzzy Ideas Into Clearer SignalsMost product teams already collect lots of information. Teams look at:Analyticssupport ticketsInterviewsUsability testsAI feedbackSurveysFeature usageBut raw numbers alone rarely explain the full experience.A completion score may look healthy while users quietly struggle underneath the workflow. Engagement may rise while trust slowly weakens over time. Users may finish onboarding while still feeling confused about what happens next.This is where UX metrics become useful. A UX metric helps give structure to what users are actually experiencing. Over time, the metric becomes more than just a number. It becomes a signal teams can use to:Compare workflowsValidate ideasIdentify frictionImprove onboardingGuide product directionThe metric becomes valuable because it helps explain what is happening underneath the experience, not just what happened on the surface.Mehdi Zaidivalidates this clearly,Without clear usage data, it can be challenging to quantify the effect of UX changes. I often rely on qualitative feedback, usability testing, and stakeholder input.Four Parts of a UX metricIn Glare a UX metric is built from four connected parts. These layers help teams turn raw information into something clearer, measurable, and actionable.1. User AttributeThis is the part of the experience being measured.What are you measuring? This is going to align back to your audience and their needs. This connects the metric back to the user need or experience you want to understand. The attribute helps teams decide what they want to learn about the experience.For example, a team redesigning onboarding may care most about:ConfidenceComprehensionTrustwhile a checkout workflow may focus more on:EffortCompletionClarityDifferent workflows often need different types of measurement.2. Data TypeThis is the type of signal being collected. UX Metrics usually fall into three main groups.AttitudinalMetricsHow users feel.Examples:confidencetrustsatisfactiondesirabilityThese metrics help teams understand emotional response and perception.BehavioralMetricsWhat users do.Examples:clicksnavigation behaviorengagementhesitationcompletion patternsThese metrics help teams understand interaction behavior across the workflow.PerformanceMetricsHow well the workflow functions.Examples:completion ratetime on taskerror rateabandonmentworkflow efficiencyThese metrics help teams understand operational friction and efficiency.Each type reveals a different part of the experience. When used together, they help teams understand the larger pattern forming across the workflow.3. Collection MethodThis is where the information comes from. For example:Usability testingSurveysAnalyticsInterviewsSession recordingsAI-assisted evaluationsDifferent collection methods reveal different kinds of problems. Analytics may reveal:AbandonmentSlower onboardingRepeated navigation loopswhile interviews may reveal:HesitationConfusionLow confidenceTrust concernsThe collection method shapes the type of understanding teams gain from the metric.4. BenchmarkA number by itself usually does not mean very much. Teams need comparison points to understand whether something is improving or getting worse. Benchmarks may include:Previous versionsIndustry averagesCompetitor comparisonsInternal targetsEarlier test resultsThis gives the metric context.For example:A 70% completion score may sound strongBut not if the earlier workflow scored 88%Or if competitors consistently score higherBenchmarks help teams understand whether the experience is actually improving over time.Example: Measuring Trust During OnboardingA team may want to understand whether users feel confident during onboarding.The team could measure:Attribute:TrustData TypeAttitudinalCollection MethodPost-test surveyBenchmarkCompare against previous onboarding flow, competitor onboarding, target confidence scoreAt first, the onboarding flow may appear successful because completion remains high. But the UX metric may reveal:Lower confidenceWeaker trustRising hesitationConfusion during setupWithout the metric, the team may assume the onboarding experience is healthy simply because users finished the workflow. The metric helps explain how the experience actually felt while users moved through it.UX Metrics Become More Valuable TogetherA single metric only explains one small part of the experience. But when metrics begin working together, larger patterns start appearing across workflows and systems.For example:High completion + low confidence may reveal a trust problemStrong engagement + rising effort may reveal frictionLow errors + weak comprehension may reveal hidden confusionThe metrics begin explaining each other. Over time, teams begin seeing patterns that isolated dashboards often miss:Confusion trapsFriction wallsTrust gapsRepeated hesitationUX Metric StacksIn Glare, these connected groups of metrics are called UX Metric Stacks. UX Metric Stacks combine multiple signals together to help teams understand the shape of an experience more clearly. An onboarding stack, for example, may combine:CompletionConfidenceComprehensionTime on taskSatisfactionTogether, these metrics help teams understand whether users are moving smoothly through onboarding or quietly struggling underneath the workflow. As teams measure more workflows over time, these stacks help reveal recurring patterns across onboarding, navigation, dashboards, and AI-assisted experiences.Learn more in the UX Metric Stacks section.UX Metrics Help Teams Make Clearer DecisionsUX metrics help teams:Compare workflowsEvaluate onboardingValidate ideas earlierIdentify friction before launchImprove stakeholder alignmentBenchmark progress over timeThey also help product, design, research, and leadership teams work from the same signals instead of disconnected opinions or scattered dashboards.Over time, UX metrics become part of how organizations build confidence in product direction, reduce uncertainty, and improve experiences through repeated learning and clearer evidence.Take This Further with the UX Metrics AI SkillsStill fuzzy on which metrics apply to your work? TheUX MetricsAI Skillsis a package you load into your LLM so you can ask questions and get expert answers anytime.Find out which metrics fit your productLearn the difference between a metric and a KPISpot vanity metrics before they waste your timeBuild a starting point for your measurement planDrop it into your LLM and start asking questions right away.