Figure 2-2. Different metrics reported by Google Analytics at a page level
Bounce rate is a popular metric used to describe the quality of a particular page on a site. There are competing definitions for bounce rate. We define bounce rate as the percentage of visitors who arrive at a landing page but leave it without ever navigating to a second page on the site. Google Analytics defines bounce rate as follows:
[T]he percentage of single-page visits or visits in which the person left your site from the entrance (landing) page. Use this metric to measure visit quality—a high bounce rate generally indicates that site entrance pages aren’t relevant to your visitors. The more compelling your landing pages, the more visitors will stay on your site and convert. You can minimize bounce rates by tailoring landing pages to each keyword and ad that you run. Landing pages should provide the information and services that were promised in the ad copy.[14]
Based on this definition, you can calculate bounce rate like this:
| Bounce rate = Number of single page visits / Total number of entrances to that page |
A less popular definition of bounce rate is the percentage of visitors who arrive at a landing page but leave in less than x seconds. The numerical value for x varies from one landing page to the next. It reflects a reasonable amount of time it would take for someone who clicked on a landing page to determine whether the page has what she is looking for. On most landing pages and websites, five to 10 seconds is the number we use. For our purposes, we will use our first definition throughout the discussion.
Most analytics packages report the bounce rate for the different pages on a website. These packages usually report a website average bounce rate as well. The website bounce rate is usually calculated by averaging the bounce rate for all pages on the site. Figure 2-2 shows some of the metrics reported by Google Analytics at a page level.
Bounce rate is a great way to get a quick read of how well a particular page is performing on your website. The traditional approach is to compare the bounce rate of a page to the site’s average bounce rate. For example, if a website has an average bounce rate of 52%, pages with a bounce rate higher than that should be optimized first.