Bounce rate refers to visits to a site that are only single page sessions, ie. where the user leaves without viewing a second page on the website. It is referenced as a percentage and is usually used as a top-level measurement of engagement or ‘stickiness’ of a website.
To calculate the bounce rate of simply take the total number of single page visits and divide this by the total number of visits to the website. For example, a website gets 100,000 visits per month, and 50,000 of those leave the site without viewing a second page then the bounce rate would be calculated to be 50%.
In short, no, however they are often confused, and both are strong measures of website engagement. The key differences are that bounce rate looks at the percentage of users that exit the site having only visited one page, whereas exit rate looks at the percentage of users leaving the site on a specific page ie. where it’s the last page in the users session, even if they have viewed multiple pages.
For example, if a site has a page with 500,000 pageviews and 100,000 of those pageviews were the last in the users’ session (ie. they left the site after viewing this page) then the exit rate of this page would be 20% (100,000/500,000).
This is very subjective and a page’s bounce rate can vary quite significantly based on several factors such as page type, traffic sources, and the objective of the page, but average bounce rates can also vary across different industry, and website, types.
It may be that the user reached the page found the information they were looking for and left the page, so a successful visit, but this would also contribute to a high bounce rate. The flip of this is that a page may have a low bounce rate because it maybe has poor UX that forces users into the site on a longer journey to find the content they’re looking for.
To better understand what a site’s bounce rate should look like it’s possible to utilise a web analytics platform’s benchmarking functionality, but it also important to review what the specific website and web page KPIs and objective are to be able to apply the rationale effectively.
Before going any further the first thing to do is to have reviewed your website analytics to understand the key problem areas. Webtrends Optimize integrates with all leading Analytics platforms and can help support this process. From here the next step should be to run a baseline on the page in question, helping to build up a deeper understanding of user behaviour on the page helping to identify positive and negative areas to work with.
This will help to develop a hypothesis on why visitors do not engage. For example are visitors felt to be less engaged because the proposition is uncompelling – or perhaps the next step isn’t clear or out of view. Test these hypotheses by presenting alternative experiences via AB, ABn or Multivariate testing and comparing changes in response.
Two key areas to experiment with in your testing should be focused around content (different strap lines, copy changes, moving elements that resonate with users above the fold etc.) and functionality (CTA copy/colour, search, site speed, break points etc.).
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