OPAM: Online Purchasing-behavior Analysis using Machine learning
This work provides a system for online vendors to categorize customers and understand their purchasing behavior, which can inform targeted communication strategies.
This paper introduces OPAM, a system that analyzes online purchasing behavior using supervised, unsupervised, and semi-supervised machine learning. It achieves 91-98% accuracy and 73-99% recall for session-level predictions and identifies five distinct user clusters, including 'New Shoppers' and 'Impulsive Shoppers'.
Customer purchasing behavior analysis plays a key role in developing insightful communication strategies between online vendors and their customers. To support the recent increase in online shopping trends, in this work, we present a customer purchasing behavior analysis system using supervised, unsupervised and semi-supervised learning methods. The proposed system analyzes session and user-journey level purchasing behaviors to identify customer categories/clusters that can be useful for targeted consumer insights at scale. We observe higher sensitivity to the design of online shopping portals for session-level purchasing prediction with accuracy/recall in range 91-98%/73-99%, respectively. The user-journey level analysis demonstrates five unique user clusters, wherein 'New Shoppers' are most predictable and 'Impulsive Shoppers' are most unique with low viewing and high carting behaviors for purchases. Further, cluster transformation metrics and partial label learning demonstrates the robustness of each user cluster to new/unlabelled events. Thus, customer clusters can aid strategic targeted nudge models.