Your eCommerce platform looks awesome. It has a logical user interface, the color scheme is really nice and you have some great specials displayed right on the home page. Yet despite all of this, only a fraction of the thousands of users who visit your site each day actually buy something. You look at the multitudes of metrics you gather to help increase sales, but you still don’t know why so few are buying, or how you can change this.
The secret to learning from your data requires an analysis that turns information into a representation of user behavior. We call this behavioral analytics.
Regular business analytics is about the who, what, where and when. Behavioral analytics, on the other hand, focuses on the why and how. It takes a more holistic and human view of data. Behavioral analytics allows us to use seemingly unrelated data points in order to identify errors and predict future trends in the way users behave within eCommerce, gaming and web applications.
For example, your data tells you that a large percentage of users found your site by searching for “Thai food” on Google. After arriving on your homepage, most users spend a few seconds on your “Asian Food” page but then they log off without placing an order. Looking at each of these events as separate data points doesn’t show you what’s going on. However, viewing these data points as a representation of overall user behavior enables you to interpolate how and why users acted in this particular case.
We Look at all your page views as a timeline of connected events that did not lead to orders. Since most users left after viewing the “Asian Food” page, there could be a disconnect between what they are searching for on Google and what your “Asian Food” page displays. Knowing this, you realize that your “Asian Food” page does not display Thai food prominently and thus people do not think you actually offer it, even though you do.
Retail companies track user behavior in order to recommend other products that the user is likely to buy; music services look at listening patterns to suggest similar types of music that a user may enjoy; and advertising firms personalize marketing campaigns so that they are relevant to individual consumers. Behavioral analytics looks beyond the data and searches for patterns and groupings of individual actions that can lead to actionable change and improvement.