A few months ago I started an experiment. I added some code to my site to better track how visitors interacted with my content. The new code tracks various user activities, like scrolling and reaching certain locations in the page. Not only did the code collect more data about user behavior it also changed many established metrics, like time on site & page and bounce rate.
The result is an entirely different view of a user visit and new benchmark metrics for blog performance.
At least that’s my opinion. I’d like to hear yours :)
You can read more about the code in these articles:
Advanced Content Tracking with Google Analytics: Part 1
Time & Bounce Rate
The main reason I added the code was to get a more accurate measurement of the time people spend reading the content on the blog and the bounce rate.
Before implementing the code the average visit length for my blog was 1:40 (one minute and forty seconds) and the average bounce rate for the blog was 78.34%. This was fairly consistent with what I had seen for other blogs.
But after adding the code the average time on site for the blog adjusted to 9:34 (nine minutes and 34 seconds) and the bounce rate for the blog fell to 23.11%.
Over the last 4 months these metrics have averaged 8:30 (eight minutes thirty seconds) for time on site and 22% for bounce rate.
The reason the metrics changed so dramatically was due to how Google Analytics calculates time and bounce rate. By adding more information about on-page behavior, Google Analytics had more data point to calculate time.
You can read more about how Google Analytics calculate time.
Let’s dig deeper.
Averages can be easily skewed, so I often use the Audience > Behavior > Engagement report to view a distribution of visit length. After the code change this showed some interesting results.
Using a distribution of time we see that the 0-10 second visits decreased and there was a ‘flattening’ in the time distribution. Again, more accurate measurement of people reading.
Moving beyond some of the standard metrics and reports, you can use the Event Flow to visualize how people move through the content, scrolling and reading articles. I wrote about this when the Event Flow reports launched.
I really like this view. What’s particularly interesting to me is that most people leave the site when they get to the bottom of the content. They don’t read the comments and only a few actually click through to another article.
All of these metrics are available in tabular form as well in the Event reports. I don’t use these reports too much, instead I’ve created a number of goals to track micro conversions on the site.
I track how many people get to the bottom of the content and how many get to the bottom of the page.
Tracking these actions as micro conversions makes it easier to segment the data by traffic source and other dimensions.
This is particularly useful when segmenting by mobile. Mobile visitors don’t reach the bottom of my articles as much as non-mobile readers. 48% of desktop users read to the bottom of an article while 32% of mobile readers reach the bottom.
As I get ready to redesign the site I’m going to dig deeper into mobile segments and review screen sizes and how reading behavior changes for those on different devices.
Did changing this code revolutionize how I track my site? No, not really. But it gave me more confidence in the data and painted a more accurate picture of what happens during a visit.
Am I glad I did this? Absolutely. I have more confidence in the data and a much better understanding of my users.
Should you do this? That depends. It could be a big challenge for your IT team. You need to decide how important accurate data is and prioritize this work.
BUT, the next time someone asks you the average time on site for a blog you can say 8:30 (eight minutes thirty seconds) the bounce rate should be 22%.