In this step, we get to know, about, ‘why?’ The objective is to get understanding of the root causes of observed user patterns. Diagnostic analytics generates and participates in questions concerning in out company’s goals and imperatives. We can see, for example, whether the report is trending upward or downward for a specific piece of content, page, or media at a specific location.
Here are a few examples:
Why are 90% of my visitors leaving my home page without taking any action, increasing the bounce rate?
There are various possibilities: Is site architecture becoming inefficient? Or does the page require a more effective call-to-action? Or is the content not up to the mark? Or is the user data less important?
Can we really say that the content or structure of a page needs to be changed if the reading time of that page is, say, 9 minutes and the report shows that 90% of the user’s dwell time here is less than 1 minute? Or can we say that users are only looking at the titles and that this action is sufficient to generate leads or sales?
However, these are very simple examples, and we need to use artificial intelligence technology to delve deeper into large data diagnostic analytics. After determining our areas of strength and weakness, we can make precise predictions, in the next step of actionable analytics.