Abandonment Rate

Abandonment Rate shows how often users start a process but leave before finishing. It helps you spot where interest or momentum drops before conversion happens.Use this metric for multi-step flows that require commitment, like sign-ups, checkouts, or application forms. It's especially useful for finding moments of hesitation, friction, or confusion that prevent users from completing what they started.A high abandonment rate is a signal to dig deeper. It helps you improve clarity, reduce distractions, and build confidence at key points in the journey.Interpreting the ResultsUse this key to understand what your Abandonment Rate score means and how to interpret that for your product experience:How to Calculate Abandonment RateThe Abandonment Rate measures how often users begin a task but leave before completing it. This metric helps pinpoint where friction is causing drop-off in key flows like checkout, signup, or onboarding.Set up questionsTo calculate Abandonment Rate, identify the specific user flow you want to monitor—such as a multi-step checkout, form completion, or booking process. Then set up event tracking to capture both the entry point (e.g., “Checkout Started”) and the completion point (e.g., “Order Placed”). These events should be logged consistently using your product analytics platform or backend event logs.Collect dataOnce tracking is in place, collect the number of sessions or users who started the flow and the number who successfully completed it. For example, if you're analyzing checkout abandonment, track how many users reached the first page of checkout vs. how many completed the final purchase.Plug data into formulaThe Abandonment Rate is calculated with the following formula:You’ll need two key values:Started: the number of users who entered the flowCompleted: the number of users who reached the final success pointSubtract completed from started to get the number of abandoners, then divide by the total who started.Calculate the Abandonment RateFor example, if 500 users started checkout and 218 completed it:This results in an Abandonment Rate of 56%, which might be considered Average depending on your scoring scale. A lower rate is better—it means more users are making it to the finish line.When To Use Abandonment RateAbandonment rate is particularly valuable when analyzing critical tasks or processes that are essential for user engagement and conversion. By tracking abandonment rates, teams can pinpoint where users may be encountering issues that deter them from completing a process.For instance, this case study shows how lowering the rate at which people abandon calls in a health system can lead to happier patients. It explains how reducing call drop-offs helps keep patients engaged and boosts loyalty to the health service.Case Study Link.Checkout ProcessTracking abandonment rates in the checkout process helps uncover specific obstacles that may be causing users to abandon their purchases, allowing for targeted optimizations that reduce missed conversions.Onboarding FlowsMonitoring abandonment during onboarding highlights any friction points that prevent users from completing initial setup, impacting early-stage retention. Identifying these points enables design adjustments that create a smoother user experience from the start.Form SubmissionsMeasuring form abandonment rate reveals where users encounter difficulties or confusion within form fields or steps. These insights guide refinements to improve form clarity and encourage successful submissions.How We Measured Abandonment Rate for Jackets in Getup’s E-Commerce CartTo assess where potential revenue was being lost in the purchase journey, we measured the Abandonment Rate for jackets added to the cart on Getup’s e-commerce site. This metric helped identify how many users were failing to follow through after adding items to their cart—highlighting opportunities to reduce friction before checkout.The SetupAbandonment Rate is a performance metric that tracks the percentage of users who begin a flow—such as adding a product to cart—but exit before completing it. It’s calculated by entering the amount of complete purchases and total add-to-cart events into the formula above. This measurement gives a clear signal of drop-off behavior during critical moments in the conversion funnel.The ResultsJackets added to cart on Getup’s website generated an Abandonment Rate of 56.5%, rated Average on the Glare scaleWhile over 40% of users did proceed to checkout, a significant portion exited before purchasing, with some citing unexpected tax or delivery information as reasonsAdditional qualitative feedback revealed confusion around return policies and limited sizing availabilityThe ImpactThese findings point to opportunities for Getup to optimize the cart and checkout experience by addressing buyer hesitation. Adding more transparent shipping, tax, and return policy information earlier in the flow could help increase confidence and reduce cart drop-offs. This data will also guide future A/B tests targeting conversion improvement.SourceCSVHow to Use AI to Measure Abandonment RateThis AI prompt can be used to calculate the abandonment rate of your platform. Once you've collected data on task starts and task completions on your site, 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 Abandonment Rate, and check out the type of output it would produce:Technicals for Measuring Abandonment 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 Abandonment RateWith our framework, you can use the Glare::Analytics::GoogleAnalytics module grab your site or application’s Abandonment 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 our GoogleAnalytics::Credentials module so that we can create a credential instance.credential will now be used in our GoogleAnalytics::Client to generate a client instance.With this client instance, we can now use our abandonment_rate method which will return a percentage point as a float.ResourcesThe 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.ArticlesAbandonment Rate: Key Metric for Sales & Marketingby CensusA great article that offers a clear definition and calculation formula, but breaks down different types of abandonment (cart, form, etc.) in a valuable way.Abandonment Rate: Definition, Examples, and Applicationsby Launch NotesThis article defines abandonment rate in the context of product management and operations and highlights its implications on user experience, retention, and revenue.Reduce Checkout Abandonment And Increase Conversionsby ChargebeeSpecifically speaking to B2C SaaS, after providing a specific calculation for checkout abandonment, this article begins outlining common reasons for it.How To Understand, Calculate, And Reduce Shopping Cart AbandonmentbyLaura Boscoat The GoodA detailed guide for B2C e-commerce, this article explains the calculation and provides insights into why customers abandon their carts, along with actionable solutions.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 Copyby Bryan ZmijewskiWho’s the Heavyweight in the Fight Between Long and Short Copy?by Bryan ZmijewskiUnraveling Buyer Intentby Bryan ZmijewskiFrom Mobile-First to User-First: Rethinking Responsive Landing Pages, by Bryan ZmijewskiThe Helio Data-informed Design Process, by Bryan ZmijewskiTake This Further with the UX Metrics AI SkillsAbandonment Rate tracks how often users start something in your product and leave before finishing. TheUX Metrics AI Skillsis a package you load into your LLM so you can ask questions and get expert answers anytime.Find out where and why users are abandoning flowsSet a baseline and know when your rate is a problemCompare abandonment across pages, steps, or segmentsUse abandonment data to prioritize design fixesDrop it into your LLM and start asking questions right away.