Error Rate

Error Rate measures the percentage of users who experience at least one error while attempting a task. It reveals how well your design supports successful, trouble-free use.Use this metric during testing or in production environments to evaluate task-critical experiences. It’s particularly helpful when validating form logic, input validation, or new interactions that require precision.A high error rate indicates usability issues, broken logic, or confusing instructions. Reducing it leads to more confident users and fewer support needs.Interpreting the ResultsUse this key to understand what your Error Rate means and how to interpret that for your product experience. The following ranges represent average scores for a b2b ad campaign platform:How to Calculate Error RateThe Error Rate measures how many user sessions include at least one error, helping you understand how frequently users encounter issues during a typical interaction with your product.Define what to trackTo calculate Error Rate, set up tracking for distinct error events—such as failed submissions, broken links, validation failures, or system errors. You’ll also need to track user sessions to know how many people were exposed to potential failure points. Most product analytics tools, logging systems, or bug monitoring platforms can capture this data.Collect dataOnce error and session tracking is active, gather the number of sessions where at least one error occurred and the total number of sessions during the analysis period. For example, in a checkout flow, you might count how many users experienced an error submitting their shipping information.Plug data into formulaError Rate is calculated using this formula:You’ll need:Sessions with errors: the number of unique sessions where an error occurredTotal sessions: all sessions observed for that feature or flow
 Divide to get the percentage of sessions affected by errors.Calculate the Error RateFor example, if 8 out of 50 sessions included at least one error:This gives you an Error Rate of 16%, which may be considered Average or Good depending on your benchmarks. A lower error rate indicates higher system reliability and fewer disruptions in the user experience.When to Use Error RateError rate is useful for identifying and troubleshooting areas in a product where users encounter difficulties. For example, a financial app might measure error rate in its account setup process to understand if users are experiencing problems with input fields or verification steps.Case Study Link.Lead Form SubmissionsLead capture forms can have high error rates if fields are confusing or required data is too complex.Onboarding New UsersTracking error rates during initial onboarding helps to identify where users struggle to complete setup or first-time tasks.Checkout ProcessError rates in checkout often indicate user friction, such as confusing payment options or fields that don't validate correctly.How We Measured Error Rate for Getup’s E-commerce Checkout FlowTo assess the reliability of Getup’s online shopping experience, we measured Error Rate during the checkout process. This metric reveals how often users encounter mistakes while attempting to complete a flow, helping teams identify areas where users are likely to get stuck or make incorrect selections.The SetupError Rate is calculated by dividing the number of users who made at least one error by the total number of participants. This helps quantify how many users experienced friction in the experience, regardless of how many total errors occurred.The ResultsGetup’s checkout flow produced an Error Rate of 8%, rated Very Good on the Glare scaleThe small number of issues were mostly tied to shipping method selection and confusion around the “Ship to Store” toggleThe majority of users navigated the flow without incident, suggesting clear structure and strong interface clarityThe ImpactWith a Very Good error rate, the Getup team validated that their checkout flow is reliably supporting user task success. Minor edge cases uncovered in testing, such as toggle confusion, pointed to opportunities for small refinements—but overall, the experience proved stable and low-risk for user error.SourceCSVHow to Use AI to Measure Error RateThis AI prompt can be used to calculate the error rate of your platform. Once you've collected data on number of errors vs total user interactions, 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 Rate, and check out the type of output it would produce:Technicals for Measuring Error RateOverviewWe’ve built out a UX Metric framework that we’re using in our Helio platform. Here, we’ve laid out what we’ve done, and how other developers can also start to use this. You can also become a Glare Code Contributor to help implement these in ourUX Metric framework.How to Use Error RateWith our framework, you can use theGlare::UxMetric::ErrorRatemodule grab your site or application’s Error Rate.Here are the steps:First we have to require the module in order for us to use it.We need tograb our property idforGoogle analyticsfor starters and make sure our application has both GA4 and Google Tag Manager hooked up.We’ll insert our property id into ourGoogleAnalytics::Credentialsmodule so that we can create a credential instance.Credential will now be used in ourGoogleAnalytics::Clientto generate a client instance.With this client instance, we can now use ourerror_ratemethod which will return a percentage point as a float.require \u0022glare/ux_metrics\u0022\n\ncredentials = Glare::Analytics::GoogleAnalytics::Credentials.new(\n\tproperty_id: \u0022my-property-id\u0022,\n)\n\nclient = Glare::Analytics::GoogleAnalytics::Client.new(\n\tcredentials: credentials\n)\n\nclient.error_rate # 10.0Take This Further with the UX Metrics AI SkillsError Rate tracks the percentage of actions in your product that result in an error. TheUX Metrics AI Skillsis a package you load into your LLM so you can ask questions and get expert answers anytime.Set an error rate baseline and track it over timeFind out which tasks or flows have the highest error rateCompare error rates before and after design changesUse error rate data to improve form design and user guidanceDrop it into your LLM and start asking questions right away.