The bounce rate describes the proportion of users who visit a website and leave it without further interaction (e.g. click on a link, view of another page, long dwell time). This metric is expressed as a percentage and indicates the ratio between individual sessions and all sessions on the website.
If a page has 100 visitors, of which 60 leave the page directly without further interaction, the bounce rate is 60 percent.
The relevance of the Bounce Rate
From the perspective of the website operator, the bounce rate is very important. High bounce rates are often a sign that the user identifies the page as not relevant for him/her after the click-in. However, the bounce rate must be put into the right proportion and must be viewed in a differentiated manner. There is no universal number of when a bounce rate is considered too high – this varies from industry to industry and from topic to topic. The bounce rate is naturally higher on pages such as blog posts or detailed individual pages on which no further interaction is necessary and relevant content is already there. This must be taken into account in the evaluation.
Reasons for high bounce rates
The reasons for a high bounce rate of websites can vary widely and may show significant defects of a site. A lot of / dubious advertising, an unappealing layout, hard to find information or misleading titles often lead to users dropping out of the site. Even elementary aspects such as long load times or incorrect content cause users to leave the page again.
Bounce rate optimization
As soon as a user reaches a website, it should be clearly indicated that he has clicked on the right website and can find the information he is looking for. This can be communicated by a suitable page title, a clear structure and, if necessary, graphics.
A layout that appeals to the user can also be one aspect of convincing him or her of the website’s continued use and relevance. In addition, usability and short load times are decisive factors for the bounce rate.
Especially layout, usability and possibly also load times can be tested and optimized by using A/B or multivariate tests.