Drop-off Rate tracks where users exit within a flow, showing the specific steps where progress stalls or stops. It helps you pinpoint which moments in a journey lose momentum.Use this metric for multi-step processes like onboarding, checkout, or setup wizards. It’s especially useful when you need to troubleshoot task abandonment or compare the impact of different design changes across steps.Each drop-off is an opportunity to investigate usability issues or friction in the experience. Reducing drop-off means tightening your flow, eliminating confusion, and better guiding users forward.Interpreting the ResultsUse this key to understand what your Drop-off Rate score means and how to interpret that for your product experience:How to Calculate Drop-Off RateThe Drop-Off Rate measures how many users exit or abandon a specific step within a flow, helping you pinpoint where users are getting stuck or leaving before completion.Set up questionsTo measure Drop-Off Rate, define the specific step in a multi-step flow you want to analyze—like Step 2 of a 4-step signup. Set up tracking to capture when users arrive at that step and whether they progress to the next one. You can instrument these events using your analytics platform or product telemetry tools/Collect dataOnce tracking is active, collect the number of users who reached the target step and how many continued to the next step. For example, in a 3-step onboarding, you might track how many users reached Step 2 and how many made it to Step 3.Plug data into formulaDrop-Off Rate is calculated using this formula:You’ll need:Step Entrants: the number of users who landed on the stepStep Completers: the number who moved on to the next stepSubtract completers from entrants to get the number who dropped off, then divide by entrants.Calculate the Drop-Off RateFor example, if 200 users landed on Step 2 and 108 continued to Step 3:This results in a Drop-Off Rate of 54%, which may be considered Poor or Very Poor depending on the complexity of the step. A lower drop-off rate indicates better clarity, motivation, or usability at that point in the flowWhen to Use Drop-Off RateDrop-off rate is especially useful when analyzing multi-step processes where users need to complete a series of actions to reach a goal.For example, this case study focuses on how making search easier and reducing drop-offs can encourage first-time users to stay on the site. By improving the search experience, users are more likely to have a positive first impression and return.Case Study Link.Onboarding FlowsMonitoring drop-off rate during onboarding helps identify stages where new users may experience friction, allowing for a more seamless introduction to the product.Checkout ProcessTracking drop-off rate in checkout can highlight steps where users encounter obstacles, providing insights to reduce cart abandonment.Form SubmissionsDrop-off rate on forms can reveal fields or steps where users get confused or frustrated, improving data collection processes for sign-ups or lead generation.How We Measured Drop-off Rate for Getup’s Checkout FlowAs part of our evaluation of Getup’s online purchasing experience, we measured the Drop-off Rate for their multi-step checkout flow. Drop-off Rate provides insight into where users abandon a process before completing their intended task.The SetupDrop-off Rate is calculated by tracking how many users begin a process—such as checking out—and then fail to complete it. This metric helps identify points of friction or confusion that lead users to exit before finishing the task.The ResultsGetup’s checkout flow had a Drop-off Rate of 54%, rated Poor on the Glare scaleMost drop-offs occurred after the shipping step, with fewer users making it through the payment and review stagesQualitative feedback suggested hesitation around payment entry and order confirmation clarityThe ImpactThis elevated drop-off rate pointed to a breakdown in user trust and momentum partway through the checkout experience. As a result, Getup’s team began exploring improvements like progress indicators, simplified form inputs, and clearer reassurances about return policies and payment security to help reduce abandonment mid-flow.SourceCSVHow to Use AI to Measure Drop-Off RateThis AI prompt can be used to calculate the drop-off rate of your platform. Once you've collected data on the amount of users who enter a flow and where they exit, 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 Drop-Off RateOverviewOurUX Metric frameworkincludesDrop-Off Ratetracking within the Helio platform, enabling teams to analyze where users exit multi-step processes. Here, we provide steps for implementing Drop-Off Rate tracking and link to resources for developers who wish to contribute to ourUX Metric framework.How to Use Drop-Off RateThe Glare::UxMetric::DropOffRate module tracks user exits at specific stages, providing a key metric for identifying friction points in user flows.StepsInsert property ID: Retrieve the property ID for Google Analytics and ensure GA4 and Google Tag Manager are implemented in the application.Input this ID into GoogleAnalytics::Credentials to create a credential instance for accessing drop-off data.Initialize client and calculate drop-off rate: Create an instance of GoogleAnalytics::Client using the credentials.Use the drop_off_rate method to calculate the percentage of users who exit at each stage, returning a score in percentage format.This method provides a drop-off rate score, helping teams pinpoint where users abandon tasks and identify opportunities to optimize user flow.require "glare/ux_metrics"
credentials = Glare::Analytics::GoogleAnalytics::Credentials.new( property_id: "my-property-id", )
client = Glare::Analytics::GoogleAnalytics::Client.new( credentials: credentials )
client.drop_off_rate(stage: "checkout_step_2") # returns drop-off rate at the specified stageResourcesThe 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.ArticlesDrop-Off Analysis: How to Find Friction PointsbyUserpilot TeamThe article explains that drop‑off rate refers to the percentage of users who begin but don’t complete a specific action, and it’s calculated by dividing the number of users who finished the activity by the number who started.Funnel Drop-Off Rate - Benchmarks & Strategies to ImprovebyTope LongeThere’s no universal benchmark for a "good" drop‑off rate, improvement comes from benchmarking within your industry or your own funnels and leveraging tools like session replays and heatmaps to diagnose and reduce friction.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 SkillsDrop-off Rate tracks where users exit a multi-step flow before reaching the end. TheUX Metrics AI Skillsis a package you load into your LLM so you can ask questions and get expert answers anytime.Find the exact steps where users are leavingUnderstand why a specific step has a high drop-offCompare drop-off patterns across flows or user groupsUse drop-off data to fix the weakest points in a flowDrop it into your LLM and start asking questions right away.
