Understanding bounce rate is an important aspect of analyzing your overall statistics, especially when it comes to determining the effectiveness of an individual page. The bounce rate measures the number of visitors to a website that leave before a specified amount of time has elapsed (this time period varies among analytics tools, but typically it is 30 minutes). This means that if a user accesses your site and leaves it within 30 minutes or leaves their browser idle for that time, they will be registered as a bounce. The bounce rate for an individual page of a website is determined by the number of users that access a page and leave the site without clicking to another page within the specified time period.
Avinash Kaushik, Googles Analytics Evangelist, has blogged about measuring the effectiveness of your web pages and writes:
My own personal observation is that it is really hard to get a bounce rate under 20%, anything over 35% is cause for concern, 50% (above) is worrying. I stress that this is my personal analysis based on my experience, but hopefully it gives you a feel for what you are shooting for.
One thing to keep in mind is that your expectation for meeting Kaushiks standard on any given page of your site should also be measured against the entrance sources for that page. Depending upon how a user is referred to your site, his or her understanding of the relevance of your sites content to their query will vary quite a bit. For example, if a user searches for Chris Butler blog and clicks the link on the search results page that leads them to my blog, it is quite possible that they will immediately leave once they realize that my blog is about web technology and strategy (perhaps the Chris Butler they were looking for is a wedding photographer). The point is that the more specific the search query, the more likely that a user will come to your page pre-qualified for the content he or she is about to receive and will not leave the page immediately.
If you take a look at the image to the left, youll see the top seven entrance sources for this blog, and the corresponding bounce rates for users that entered from those sources. Notice that users that came to my blog directly register a very low bounce rate. This is to be expected since these users know the blog and therefore know generally what information they will be getting. On the other hand, notice that users entering the blog from Google register a higher bounce rate (overall, not too low, though). This is likely due to what I mentioned above about search query specificity. In fact, one of the top search terms that lead users to my blog is alexa above the fold, yet I only used this phrase once in my blog in a slightly peripheral comment. This means that users coming to my blog after having searched for that phrase should be expected to register a relatively high bounce rate (their bounce rate is 40%).
One of the best ways that I have found to increase the number of pre-qualified users has been to post my blog articles to Digg. Since Ive just gotten started with this, my Digg performance isnt that significant, but what has been important to me is that the users that do come to my blog via Digg register a very low bounce rate consistently. This is because Digg allows you to post a link to an article and then place it within a set of pre-defined categories. If a user clicks to your article from Digg, he or she will theoretically have at least narrowed down to a category of interest and will be pre-qualified for the information you provide.