Tips for Interpreting Analytics Data
Having a wealth of data at your fingertips is never a bad thing, but it’s a good idea to be cautious when interpreting it. Sometimes the data can actually be misleading.
The Trouble with Bounce Rate
As Andy mentions in the video, bounce rate is a metric that has to be looked at in context. A target bounce rate on a blog or article page should not be the same as one for your homepage or landing page.
Think of it from the perspective of a site visitor. They find your content via a RSS feed or other aggregate site. Intrigued by your headline, they click and visit, consume your content, and then go about their day. In a perfect world, every visitor would read (as opposed to skim) your content, click on some links or a call-to-action button, and convert through a contact form submission or pop-up ad. We know that this is an unrealistic expectation for every visitor to do this, but what is an acceptable bounce rate for a blog or article page?
Well, it depends.
Bounce rate KPIs are such that you have to make a judgment call and weigh a lot of factors. Does your content pop up on a lot of feeds, or is most of your traffic coming from other sources? Are you aggressively marketing your blog through social media postings or ads, and if so, is it more of a mass marketing approach or are you marketing to a niche? Generally, the more niche and targeted your marketing, the lower you should set your target bounce rate.
Even before setting targets, you can see what type of effects small refinements and improvements have on bounce rate, such as these:
- Formatting – ditch the long paragraphs. Focus on readability
- Responsible font choice – Comic Sans is never a good idea
- Bold and italics – can be highly effective, when used conservatively
- Spelling and grammar – excessive mistakes makes you lose a lot of credibility
- Relevancy – are your titles and meta descriptions accurate?
These can all make incremental improvements on your bounce rate, but finding the target range is still something that you should prioritize.
Drawing the Wrong Conclusions
As I mentioned before, correlation does not imply causation. If you observe an effect that seems to be connected to a variable you are tracking, you will have to do a little research to find the actual cause. A lot of the time the data can be misleading, and the truth is rarely surface-level.
This is a common mistake we see when during our analytics audit process, and one of the biggest takeaways for our clients is getting to a better understanding of how all of the moving parts fit together. There is nothing worse than the feeling of chasing your tail, shooting yourself in the foot, and the powerlessness that comes from mismanaging analytics data.
Hopefully you will find the information we have presented here useful, and we welcome you to explore our other videos and articles related to the topic. This past month we covered the following topics:
To learn more, contact us today, and consider an analytics audit. We’ll build you a custom dashboard, help you identify relevant KPIs, and provide specific feedback and insight into your analytics numbers.
You can only fix what you know.