Paul M. Banas on Consumer Insights, Marketing Research, and the Digital Media Landscape
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Category — Web Analytics

Bounce Rate Benchmarks to Measure Blog Engagement

Below are some rough benchmarks for determining if your blog is delivering relevant and engaging content by measuring its bounce rate.

Rubberband Ball

Bounce rate is a web metric that measures what percent of site visitors leave immediately after viewing your page. According to Google Analytics “a high bounce rate generally indicates that site entrance (landing) pages aren’t relevant to your visitors”.

Therefore, the more sticky and engaging your content is, the less bounce you’re likely to have. This works well at evaluating main pages; however I think it’s even more insightful when looking at individual blog posts.

Since pages tend to vary significantly, by things like length or if they include some form of multimedia, these benchmarks are rough, relative measures. You can use bounce rate in conjunction with other metrics to determine your most engaging posts. Additionally, bounce rate is specific to site traffic; other measures are needed to evaluate your feed traffic.

Bounce Rate Benchmarks:

0% to 40% – Your blog is sticky with engaging content that makes visitors stop and stay.

40% to 65% – You have something of interest in your blog for the reader, but not enough to have someone stay and explore your site extensively.

65% to 100% – Your blog is a rubber band. Visitors bounce in and bounce out. Your page is either not what they were looking for, or it holds minimal interest.

For more information on bounce rate as a web metric, make sure you visit Occam’s Razor by Avinash Kaushik. He delves extensively into bounce rate and other key web metrics through his excellent blog.

December 17, 2007   No Comments

Multiple Measures Means Better Web Analytics

An interesting post on the Metrics Insider by Josh Chasin of ComScore describes the potential conflict of two forms of online measurement tools, panel based and site based. I agree with Chasin that the question should not be which data source is “right”, since both have their strengths and limitations and neither method can lay claim to absolute accuracy. The question should be to look at both types of data and ask why they may bring one to a similar conclusion or to a divergent one on a particular analytic issue.

For those who have worked in the CPG industry with both traditional retailer store scanning data (IRI, Nielsen) and their panel data services, you know that you can have two data sources looking at similar events (product purchasing) and see differences and still get value out of both. You have to know their roles and limitations, and you need to triangulate them with other sources to get the best answers.

I realize from an advertiser or portal standpoint, they’d like to focus on just one form of measurement and preferably one they have some influence over, but I agree with Chasin that in general we should all really “take advantage of this embarrassment of riches”.

September 11, 2007   No Comments