Descriptive Analytics vs Diagnostic Analytics

Recently, David Attard wrote about analytics and KPIs (key performance indicators), and how they can be used to understand our website users better --- and, in turn, to help us design better experiences for those users. He told us about the important metrics to analyze (time on site, bounce rate, conversions, exit rates, etc.), but also mentioned that, while these metrics help us to understand what users are doing (or not doing) on our website, the reasons why can still be a bit of a blur. This is because, while data is objective, the conclusions drawn from it are often subjective.

Even though KPIs describe our users' behavior, more context is needed to draw solid conclusions about the state of our UX. In order for this to happen, we have to use other techniques --- such as A/B testing and usability testing --- to diagnose the UX flaws we identify through descriptive analytics. In this article, I'm going to explain the difference between descriptive analytics and diagnostic analytics, so that you have a realistic expectation of what descriptive analytics can do, and what you'll need to gain from descriptive analytics before you begin A/B testing and usability testing.

Descriptive Analytics

Descriptive analytics in a nutshell: what has happened?

When you visit a nurse or doctor, it's because you have undesirable symptoms that indicate bad health. You have trouble doing the things you need to do because of this. You don't know what's going on exactly, only that you aren't functioning at an optimal level. This is likened to analytics, where business goals can't be met because of bad user experience. Certain KPIs might indicate this, such as high bounce rate or low Avg. Time on Site.

KPIs describe the symptoms, but they don't actually diagnose what the underlying issue is, and this is why we call them descriptive analytics. However, we can use the symptoms to help diagnose the UX flaws.

We can use tools like Kissmetrics to track and analyze KPIs, although many companies choose to use Google Analytics because it's rather sophisticated for a free tool. As well as the KPIs mentioned in David's article, analytics tools like Google Analytics can reliably tell us things about our users' demographic and interests (that is, who they are and what they like), and also other important tidbits of information such as what device they're using and where they're from. This kind of data, even though it can't be used to indicate website performance, can tell us a little more about the user intent.

Consider these descriptive analytics as background information that we can use to narrow down what's going wrong exactly (i.e. user research).

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by Daniel Schwarz via SitePoint