Behavioral analytics is a recent advancement in business analytics that reveals new insights into the behavior of consumers on eCommerce platforms, online games, web and mobile applications, and IoT. The rapid increase in the volume of raw event data generated by the digital world enables methods that go beyond typical analysis by demographics and other traditional metrics that tell us what kind of people took what actions in the past. Behavioral analysis focuses on understanding how consumers act and why, enabling accurate predictions about how they are likely to act in the future. It enables marketers to make the right offers to the right consumer segments at the right time.
Behavioral analytics utilizes the massive volumes of raw user event data captured during sessions in which consumers use application, game, or website, including traffic data like navigation path, clicks, social media interactions, purchasing decisions and marketing responsiveness. Also, the event-data can include advertising metrics like click-to-conversion time, as well as 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 session 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 the collection of such data.
Since the analysis requires collection and aggregation of large amounts of personal data, including highly sensitive one (such as sexual orientation or sexual preferences, health issues, location) which is then traded between hundreds of parties involved in targeted advertising, behavioral analytics is causing significant concerns about privacy violations.
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.
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.
IBM and Intel are creating ecosystems of connected solutions and advanced analytics. In retail, this is the Internet of Things (IoT) for tracking shopping behaviors.
- 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.
- Suggestions – People who liked this also liked...
- Presentation of relevant content (preferences, user groups, etc.) based on user behavior.
An ideal behavioral analytics solution would include:
- Real-time capture of vast volumes of raw event data across all relevant digital devices and applications used during sessions
- Automatic aggregation of raw event data into relevant data sets for rapid access, filtering and analysis
- Ability to query data in an unlimited number of ways, enabling users to ask any business question
- Extensive library of built-in analysis functions such as cohort, path and funnel analysis
- A visualization component
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- "Oh behave! How behavioral analytics fuels more personalized marketing" (PDF). Archived from the original (PDF) on 2014-07-14.
- Behrooz Omidvar-Tehrani; Sihem Amer-Yahia; Alexandre Termier. Interactive User Group Analysis. International Conference on Information and Knowledge Management (CIKM) 2015.
- Nagaitis, Mark. "Behavioral Analytics: The Why and How of E-Shopping". eCommerce Times.
- Jack, Smith. "The Best Technologies to Track Customer Behavior in Retail". V-Count Technology.