Bounce rate and exit rate metrics

Bounce rate and exit rate metrics let you measure how engaging your website’s pages are. In the context of experimentation, bounce rate and exit rate metrics let you see how your experiments affect engagement levels. Typically, bounce rate measures the effectiveness of landing pages, and exit rate measures the effectiveness of down-funnel pages.

The bounce rate metric is useful for measuring visitor engagement when visitors are first interacting with your website. For example, you may hypothesize that changing your landing pages to mirror the value proposition used in your ad campaigns will create a more compelling experience and drive visitors to engage further down your funnel. Measuring an experiment’s impact on the bounce rate of your landing pages can help you validate or disprove this hypothesis.

The bounce rate metric uses the following definitions:

Example with 5 sessions:

Using these definitions, Page A had 1 total bounce (in session 3) and 3 qualified sessions (sessions 1, 2, and 3). Therefore, Page A has a bounce rate of 33% (1 divided by 3).

The global bounce rate is the percentage of website visits where a visitor viewed only one page. To track the global bounce rate, configure a global page (a page that fires everywhere the Optimizely snippet is implemented). Then, configure a bounce rate metric on the global pageview event.

Knowing the global bounce rate is useful when your experiment makes changes to an element that displays on all pages to make your site more engaging and reduce the bounce rate. There is no equivalent global metric for exit rate, which only has meaning in the context of a specific page.

GA4 and AA both count a bounce on a page under the following conditions:

By contrast, Optimizely does not consider subsequent interaction events when defining bounces. This means that in most cases, Optimizely bounce rates are higher than bounce rates in GA4 and AA.

Consider the following example session:

Optimizely would count this as a bounce from the homepage, whereas GA4 and AA would not count this as a bounce.

The exit rate metric is useful for measuring visitor engagement on certain pages that are further down in your conversion funnel. For example, you may observe that your plans and pricing page has a high exit rate. Your hypothesis might be that adding a global navigation bar will give visitors more options to continue researching your product, and in turn you will see a decrease in the exit rate for your plans and pricing page.

The exit rate metric uses the following definitions:

Example with 5 sessions:

Using the above definitions, Page A has 2 total exits (sessions 3 and 5) and 4 qualified sessions (sessions 1, 2, 3, and 5). Therefore, Page A has an exit rate of 50% (2 divided by 4).

To count a visitor as bounced or exited, Optimizely needs to wait for the visitor's session to end (in other words, wait until the visitor has not triggered any events for more than 30 minutes). This means that the numbers displayed on the Results page for bounce and exit rate metrics are at least 30 minutes behind other real-time metrics, like binary conversions and revenue.

Optimizely relies on client_activation events to determine when visitors move to a new page on your website. This information is necessary to accurately calculate bounce rate and exit rate. client_activation events are generated when the Optimizely snippet activates, typically when a page loads.

If you are implementing Optimizely on a single-page application (SPA), you may have chosen to implement a call to Optimizely's Activate API on each navigation. Calling this API causes the snippet to generate a client_activation event (among other things). Bounce and exit rate metrics work as expected with SPAs that are set up this way.

Currently, SPAs that trigger pages through manual or conditional activation are incompatible with bounce and exit rate metrics.

Similarly, the support for dynamic websites feature provides additional activation triggers: "When the DOM Changes" and "When the URL Changes." Sites that use these are also incompatible with bounce and exit rate metrics.

If you compare Optimizely's bounce rate or exit rate to a similar metric in another analytics platform, you may notice unintuitive discrepancies. If you have confirmed that Optimizely and your analytics platform define bounce and exit rate metrics similarly, the discrepancy is likely due to inconsistent implementations.

Imagine a website where Optimizely is implemented on marketing pages but not post-login application pages. By contrast, an analytics platform is implemented both on marketing pages and application pages. Consider the following session, which consists of 4 pageviews:

If Optimizely is implemented on all pages except the account page, it calculates that an exit occurred on the login page (because the pageview on the account page is not tracked). Conversely, an analytics platform implemented on all pages calculates that an exit occurred on the account page.

To eliminate this implementation discrepancy, confirm that the Optimizely snippet is implemented on all pages where your analytics platform is tracking events.

See Add bounce or exit rate metric to an experiment for information.