OPAM: Online Purchasing-behavior Analysis using Machine learning

TL;DR

This paper presents a machine learning-based system for analyzing online customer purchasing behaviors at session and user-journey levels, enabling targeted marketing strategies with high accuracy and robustness.

Contribution

It introduces a comprehensive system combining supervised, unsupervised, and semi-supervised learning for detailed customer segmentation in online shopping.

Findings

High accuracy in session-level purchase prediction (91-98%)

Identification of five distinct user clusters

Robustness of clusters to new and unlabelled data

Abstract

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.