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The popularity of e-commerce platforms continues to grow. Being able tounderstand, and predict customer behavior is essential for customizing the userexperience through personalized result presentations, recommendations, andspecial offers. Previous work has considered a broad range of prediction modelsas well as features inferred from clickstream data to record sessioncharacteristics, and features inferred from user data to record customercharacteristics. So far, most previous work in the area of purchase predictionhas focused on known customers, largely ignoring anonymous sessions, i.e.,sessions initiated by a non-logged-in or unrecognized customer. However, in thede-identified data from a large European e-commerce platform available to us,more than 50% of the sessions start as anonymous sessions. In this paper, wefocus on purchase prediction for both anonymous and identified sessions on ane-commerce platform. We start with a descriptive analysis of purchase vs.non-purchase sessions. This analysis informs the definition of a feature-basedmodel for purchase prediction for anonymous sessions and identified sessions;our models consider a range of session-based features for anonymous sessions,such as the channel type, the number of visited pages, and the device type. Foridentified user sessions, our analysis points to customer history data as avaluable discriminator between purchase and non-purchase sessions. Based on ouranalysis, we build two types of predictors: (1) a predictor for anonymous thatbeats a production-ready predictor by over 17.54% F1; and (2) a predictor foridentified customers that uses session data as well as customer history andachieves an F1 of 96.20%. Finally, we discuss the broader practicalimplications of our findings.

Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers