Error Frequency tells you how often users encounter problems like failed form submissions, invalid inputs, or blocked interactions. It helps surface usability or technical issues in real time.Use this metric to monitor high-importance flows—like login, checkout, or data entry tasks—where issues can break trust. It’s especially valuable in live environments to detect patterns or spikes in failed interactions that may otherwise go unnoticed.Frequent errors can lead to frustration and drop-off if left unresolved. This metric helps you identify pain points fast so you can deliver a smoother, more reliable experience.Interpreting the ResultsUse this key to understand what your Error Frequency score means and how to interpret that for your product experience. The following ranges represent average scores for an e-commerce platform's checkout flow:How to Calculate Error FrequencyThe Error Frequency metric measures how often users encounter errors during a session, helping you assess the stability and usability of a feature or flow.Define what to trackTo measure Error Frequency, determine which error events should be logged—such as failed form submissions, invalid field entries, server errors, or dead-end clicks. Set up event tracking to capture each occurrence of these errors within a user session. These can be tracked using product analytics tools, error monitoring systems, or session replay platforms.Collect dataOnce error tracking is in place, collect the number of error events and the number of user sessions where those errors occurred. For example, you might track how many error messages were triggered while users tried to set up an ad campaign.Plug data into formulaError Frequency is calculated using this formula:You’ll need:Error events: the total count of errors triggeredSessions: the number of user sessions observed during the test or tracking period This formula reveals the average number of errors experienced per session.Calculate the Error FrequencyFor example, if there were 114 error events across 50 sessions:This results in an Error Frequency of 2.28, which may be considered High or Poor depending on your scoring scale. Lower frequency is better—it indicates a smoother, more stable experience with fewer obstacles for users.When to Use Error FrequencyTracking error frequency is valuable when evaluating the usability and efficiency of key user flows, particularly those that are complex or essential to the user journey.For instance, this case study explains how tracking errors helps identify areas that need improvement in software. Reducing errors makes the product more reliable and enjoyable, which keeps users satisfied.Form CompletionTracking error frequency on forms helps identify fields or instructions that users frequently get wrong, indicating a need for clearer guidance or simpler formats.Feature TestingMonitoring error frequency on new or updated features can reveal whether users find these features intuitive or if they encounter unexpected issues.Checkout ProcessTracking errors in the checkout process helps identify where users struggle, enabling improvements to reduce abandoned carts and increase conversions.How We Measured Error Frequency for Advent’s Audience Targeting PageTo assess the reliability of a key workflow in Advent’s ad campaign management platform, we evaluated Error Frequency on their Audience Targeting page. This performance metric helps teams uncover areas where users are consistently making mistakes that disrupt task flow.The SetupError Frequency is measured by tracking the total number of user errors made during a session and calculating an average number of errors per session. This highlights which interfaces or actions may be unclear, misleading, or overly complex.The ResultsAdvent’s audience targeting page had an Error Frequency of 2.28, rated Poor on the Glare scaleCommon mistakes included misclicks on expandable dropdowns, incorrect audience segment selections, and confusion over how to apply filtersParticipants often backtracked or needed multiple attempts to complete a single targeting adjustmentThe ImpactThese findings signaled critical usability concerns within one of the platform’s most important tools. The Advent team prioritized design updates to clarify control group segmentation, improve dropdown labeling, and streamline the filter interface. By addressing the most common pain points, they aimed to cut down on error loops and reduce cognitive strain during audience targeting.SourceCSVHow to Use AI to Measure Error FrequencyThis AI prompt can be used to calculate the error frequency of your platform. Once you've collected data on total number of error versus total users, you can feed that data into an AI software using a CSV file along with this prompt.Copy this AI prompt to calculate your own Error Frequency, and check out the type of output it would produce:Technicals for Measuring Error FrequencyOverviewOurUX Metric frameworkintegratesError Frequencytracking within the Helio platform, enabling teams to identify and reduce usability issues across critical user flows. Below, we outline the steps to implement Error Frequency tracking and link to resources for developers who wish to contribute to ourUX Metric framework.How to Use Error FrequencyTheGlare::UxMetric::ErrorFrequencymodule enables tracking of user errors, providing a key metric for assessing usability and task completion.Steps:Insert property ID: Retrieve the property ID for Google Analytics and confirm that the application has GA4 and Google Tag Manager set up.Input the property ID intoGoogleAnalytics::Credentialsto create a credential instance for accessing error data.Initialize client and calculate error frequency: Create an instance ofGoogleAnalytics::Clientusing the credentials.Use theerror_frequencymethod to calculate the average frequency of user errors, returning a rate as a percentage.This method provides an error frequency score, allowing teams to identify and prioritize areas for usability improvements.require "glare/ux_metrics"
credentials = Glare::Analytics::GoogleAnalytics::Credentials.new( property_id: "my-property-id", )
client = Glare::Analytics::GoogleAnalytics::Client.new( credentials: credentials )
client.error_frequency # returns error rate as a percentageResourcesThe Resources section provides a collection of articles, case studies, methods, and blog posts to support your work within the UX metrics framework. These materials offer insights into best practices, research methodologies, and practical applications for improving design comprehension and usability. Whether you're refining your design process or conducting user research, these resources will help guide you towards data-informed, user-centered decisions.ArticlesMeasuring Errors in the User Experienceby,Jeff Sauro, PhDLearn how to measure user errors in usability studies by identifying where users slip or make mistakes and calculating the error frequency.Helio MethodsVideo Testingby HelioInteraction Matrixby HelioHelio Case studiesHelloFresh Membership Offer Effectivenessby HelioValidated Banking Site Landing Page Concepts, by HelioHelio Blog PostsMastering Copy Testing: Your Ultimate Guide to Crafting Irresistible CopybyBryan ZmijewskiWho’s the Heavyweight in the Fight Between Long and Short Copy?byBryan ZmijewskiUnraveling Buyer IntentbyBryan ZmijewskiFrom Mobile-First to User-First: Rethinking Responsive Landing Pages, byBryan ZmijewskiThe Helio Data-informed Design Process, byBryan ZmijewskiTake This Further with the UX Metrics AI SkillsError Frequency tracks how often users run into errors across your product. TheUX Metrics AI Skillsis a package you load into your LLM so you can ask questions and get expert answers anytime.Find out which errors are happening most oftenKnow when error frequency is high enough to act onCompare error patterns across flows or releasesUse error data to find and fix recurring design problemsDrop it into your LLM and start asking questions right away.
