2021/02/02 by Sohini Roychowdhury, Roychowdhury, Sohini, Ebrahim Alareqi +4 · 1 citation
Business, Management and Accounting · Computer Science · Psychology · Social Sciences · #Artificial intelligence #Business #Computer science #Consumer Market Behavior and Pricing #Customer churn and segmentation #Digital Marketing and Social Media #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine learning #Marketing #Psychology #Purchasing #Recall #Robustness (evolution) #Session (web analytics) #Supervised learning #World Wide Web #cs.LG
paper · pdf · doi:10.48550/arxiv.2102.01625
published in arXiv (Cornell University) (Cornell University) · 8 pages, 8 figures, 5 tables
arxiv created 2021/02/02 · openalex publication_date 2021/02/02 · arxiv updated 2021/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.