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.

Bounce rate

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:

• Total bounces – Total number of sessions where visitors who were exposed to a variation viewed only this page.
• Qualified sessions – Total number of sessions where visitors who were exposed to a variation started the session on this page.
• Bounce rate – Total bounces divided by qualified sessions.

Example with 5 sessions:

• Session 1 – Page A > Page B > Page C > exit
• Session 2 – Page A > Page B > exit
• Session 3 – Page A > exit
• Session 4 – Page B > Page C > exit
• Session 5 – Page B > Page A > exit

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).

Global bounce rate

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 Web Experimentation 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 with the goal of making your site more engaging (thereby reducing global bounce rate). There is no equivalent global metric for exit rate, which only has meaning in the context of a specific page.

Compare bounce rate to Google Universal Analytics and Adobe Analytics

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

• The visitor only views one page during their session
• The visitor does not trigger any additional interaction events after viewing the page

By contrast, Optimizely Web Experimentation does not take into account subsequent interaction events when defining bounces. This means that in most cases, Optimizely Web Experimentation bounce rates are higher than bounce rates in UA and AA.

Consider the following example session:

1. Visitor begins a session on the homepage.
2. Visitor plays a video, triggering an interaction event in UA or AA and a click event in Optimizely Web Experimentation.
3. Visitor ends their session without navigating to another page.

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

Exit rate

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:

• Total exits – Total number of sessions where visitors who were exposed to a variation ended the session on this page.
• Qualified sessions – Total number of sessions where visitors who were exposed to a variation viewed this page.
• Exit rate – Total exits divided by qualified sessions.

Example with 5 sessions:

• Session 1 – Page A > Page B > Page C > exit
• Session 2 – Page A > Page B > exit
• Session 3 – Page A > exit
• Session 4 – Page B > Page C > exit
• Session 5 – Page B > Page A > exit

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).

Data freshness

To count a visitor as bounced or exited, Optimizely Web Experimentation 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.

Compatibility with single-page applications

Optimizely Web Experimentation 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 Web Experimentation snippet activates, which typically happens when a page loads.

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

Currently, SPAs that trigger pages through manual or conditional activation are not compatible 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 not compatible with bounce and exit rate metrics.

Implementation discrepancies

If you compare Optimizely Web Experimentation'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 Web Experimentation and your analytics platform define bounce and exit rate metrics in the same way, the discrepancy is likely due to inconsistent implementations.

Imagine a website where Optimizely Web Experimentation 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:

• Pageview 1 – landing page
• Pageview 2 – homepage
• Pageview 3 – login page
• Pageview 4 – account page

If Optimizely Web Experimentation 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 that is implemented on all pages calculates that an exit occurred on the account page.

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

For information on how to add bounce and exit rate metrics to your experiments, see Add bounce or exit rate metric to an experiment.