Web Analytics: Why You Should Still Look at Your Raw Data

When you think about how much time you spend in your various analytics platforms, I bet you haven’t really divided up your work between pre-packaged “Cooked” reports and actual raw data. Pre-packaged would include custom queries, comparison tables, excel inspired dashboards with lots of fun colors and charts affirming just how smart you are, and so on. On the other hand, the land of raw data includes just that; raw, un-segmented data representing an unfiltered, ungrouped look at every aspect of a visit from the time of entry to the time of departure. All actions including individual page bounce rate, time on site, pages viewed, time on page, path flow, conversion, purchase, day since last visit, etc. are all included here.

Most of us practitioners produce very beautiful, color-rich reporting for our various department heads, C-level folks (no numbers here, just colors and arrows right), Customer service and Sales to name a few.

However, what some of us seem to be moving away from is the actual data itself. I am seeing more and more “canned” comparison reporting that is the culmination of several data variables showing nothing more than a macro view of a complete mash-up of data points. Unfortunately, it tells me nothing as to what is truly driving the trend/path/action/etc. Even worse, it can often lead to the wrong conclusion and resulting course of action.

Two great examples presented at the Online Marketing Summit conference by my colleague Adam Proehl illustrate the point.

The first is very simply, averages can be very misleading. In this case, while the average lake depth is 3 feet, there can still be some deep spots. I like to think of bounce rate as another area where it can give very misleading information and resulting conclusions. Bounce rate is a great metric, but its greatness comes with a price and that is time. You need time to look at bounce rates across all landing pages, not just site wide or home page. Doing so can give you a false sense of security or a false sense of fear.

The second illustration demonstrates the problems you can encounter when co-mingling two variables and drawing conclusions about them. In this case my partner Adam, while a very accomplished hoopster, is certainly no king of the rock.

While these examples are a bit tongue and cheek, I hope the point is clear. Regardless of what form your data and reporting takes at its final stages, try to always start with the raw data itself. You will gain tremendous insights about true performance across various segments and in turn you will be able to give your teams and recipients more insights and better direction going forward.