What Is Exit Rate? Difference from Bounce Rate and Tips to Improve It
When you dig into your site's issues through analytics, you'll come across a metric called exit rate. It is easily confused with bounce rate, and it's also a metric people tend to judge too quickly as "a high number is bad." This article explains what exit rate means and how it is calculated, how it differs from the closely related bounce rate, how GA4 handles it, and the concrete tactics you can use to improve the number.
Exit rate is a metric that shows the percentage of times a given page was the last page a user viewed before leaving the site. Users move through multiple pages within a site, but eventually they leave from some page. Exit rate aggregates, on a per-page basis, which page was the "last one viewed."
The formula is as follows.
Exit rate = number of times the page was an exit (the last view) ÷ number of pageviews (PV) for that page
For example, if a page is viewed 100 times and 30 of those were the last view before the user left the site, the exit rate is 30%. The important point is that exit rate does not ask how many pages the user saw before reaching that page. Regardless of the path taken just before, if that page was the last one, it is counted as an exit.
The metric most often confused with exit rate is bounce rate. Both relate to "leaving the site," but the scope of user behavior they cover is different.
Put another way, a bounce refers to a visit that was "both the entry and the exit (completed in a single page)," while an exit refers to a page being "the last one, regardless of whether it was mid-path or final." Every bounced session is also included in exits, but the reverse is not true.
For instance, if a user goes "top page → product list → product detail" and leaves from the product detail page, that product detail page is counted as an exit, but it is not a bounce because the user viewed multiple pages. On the other hand, if a user views only the top page and leaves, that is both a bounce and an exit.
In the older Universal Analytics (UA), exit rate was provided as a standard report metric. However, in the current Google Analytics 4 (GA4), the measurement philosophy has shifted from being pageview-centered to being event-centered and engagement-centered, so exit rate does not appear front and center as a standard metric.
In GA4, the focus is instead on metrics that emphasize whether users actually engaged, such as "engagement rate" and "engaged sessions." If you want to see information close to exits, you'll need to take a workaround such as combining per-page view counts and metrics related to session endings in the exploration reports. Because the metric names and the assumptions behind their calculation differ from the UA era, be careful not to compare them directly with past data.
The most important thing when looking at exit rate is not to mechanically conclude that "a high exit rate equals a problem." If users achieve their goal, they will naturally leave the site from some page. Therefore, depending on a page's role, a high exit rate divides into cases that are natural and cases that signal a problem.
In short, what you should examine is the gap between a page's role—"how do you want this page to move the user"—and the actual exit situation. Pages where exits are concentrated partway along the path to conversion are precisely the ones that should be prioritized for improvement.
Once you have identified the pages that need improvement, the next step is to work on concrete tactics. Here are some representative approaches.
Create a state where users can see at a glance "what they should do next." Encourage natural navigation within the site through links to related pages, clear CTA (call-to-action) buttons, and suggestions of recommended articles or related products. The basic idea is to prepare the next step so that users don't hit a dead end at the bottom of the page.
Slow-loading pages can cause users to leave before they even see the content. Speeding up load times through image compression, reducing unnecessary scripts, and reviewing your server and cache helps suppress exits. It's a good idea to improve while checking metrics such as Core Web Vitals.
Forms are a representative place where exits occur. EFO (entry form optimization)—narrowing input fields to the minimum necessary, making input examples and error messages clear, and making forms easy to use on smartphones—reduces mid-process exits. When exits are concentrated only on the page right before conversion, suspecting the form first is the standard move.
If the information users are looking for doesn't match the page content, they will leave early. Align the content of your title and headings with the body text, and structure the page so it answers what visitors want to know without excess or shortfall. Update outdated information and supplement hard-to-understand parts with diagrams and concrete examples to encourage longer stays and more navigation.
Numbers alone won't tell you "why" users left. Using heatmaps or session recording tools, you can visually grasp how far down the page was read, where scrolling stopped, and what is being clicked. Combining this kind of qualitative data with pages that have many exits makes improvement measures easier to see.
Exit rate is a metric that shows the percentage of times a given page was "the last page before leaving the site." Unlike bounce rate, which shows the percentage of visits that left after only the first page, exit rate also includes exits after viewing multiple pages. Because it does not appear as a standard metric in GA4, it is realistic to supplement and confirm it with engagement-related metrics and exploration reports.
A higher exit rate is not necessarily worse; the evaluation changes depending on the page's role. First, identify pages where exits are concentrated partway along the conversion path, then combine tactics such as improving navigation, optimizing loading speed, improving forms, reviewing content, and analyzing causes with heatmaps. Rather than chasing the number in isolation, viewing it from the perspective of "how do you want users to move" is the key to using exit rate correctly.
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