Behavioral analytics is a subset of business analytics that focuses on how and why users of eCommerce platforms, online games, & web applications behave. While business analytics has a more broad focus on the who, what, where and when of business intelligence, behavioral analytics narrows that scope, allowing one to take seemingly unrelated data points in order to extrapolate, predict and determine errors and future trends. It takes a more holistic and human view of data, connecting individual data points to tell us not only what is happening, but also how and why it is happening.
Behavioral analytics utilizes user data captured while the web application, game, or website is in use by analytic platforms like Google Analytics. Platform traffic data like navigation paths, clicks, social media interactions, purchasing decisions and marketing responsiveness is all recorded. Also, other more specific advertising metrics like click-to-conversion time, and comparisons between other metrics like the monetary value of an order and the amount of time spent on the site. These data points are then compiled and analyzed, whether by looking at the timeline progression from when a user first entered the platform until a sale was made, or what other products a user bought or looked at before this purchase. Behavioral analysis allows future actions and trends to be predicted based on all the data collected.
Examples and real world applications
Data shows that a large percentage of users using a certain eCommerce platform found it by searching for “Thai food” on Google. After landing on the homepage, most people spent some time on the “Asian Food” page and then logged off without placing an order. Looking at each of these events as separate data points does not represent what is really going on and why people did not make a purchase. However, viewing these data points as a representation of overall user behavior enables one to interpolate how and why users acted in this particular case.
Behavioral analytics looks at all site traffic and 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 the “Asian Food” page displays. Knowing this, a quick look at the “Asian Food” page reveals that it does not display Thai food prominently and thus people do not think it is actually offered, even though it is.
Behavioral analytics is becoming increasingly popular in commercial environments. Amazon.com is a leader in using behavioral analytics to recommend additional products that customers are likely to buy based on their previous purchasing patterns on the site. Behavioral analytics is also used by Target to suggest products to customers in their retail stores, while political campaigns use it to determine how potential voters should be approached. In addition to retail and political applications, behavioral analytics is also used by banks and manufacturing firms to prioritize leads generated by their websites. Behavioral analytics also allow developers to manage users in online-gaming and web applications.
Types of behavioral analytics
- Ecommerce and retail – Product recommendations and predicting future sales trends
- Online gaming – Predicting usage trends, load, and user preferences in future releases
- Application development – Determining how users use an application to predict future usage and preferences.
- Cohort analysis – Breaking users down into similar groups to gain a more focused understanding of their behavior.
- Security – Detecting compromised credentials and insider threats by locating anomalous behavior.
- Yamaguchi, Kohki. "Leveraging Advertising Data For Behavioral Insights". Analytics & Marketing Column. Marketing Land.
- "Oh behave! How behavioral analytics fuels more personalized marketing" (PDF). IBM Software. Retrieved 3 July 2013.
- Homa, Ken. "Behavioral analytics … bad when Target does it … OK for political campaigns?". The Homa Files.
- Nagaitis, Mark. "Behavioral Analytics: The Why and How of E-Shopping". eCommerce Times.