Big Data Predictive Analytics for Customer Behavior Insights in ECommerce

Abstract

E-commerce platforms generate massive volumes of structured andunstructured data, offering an unparalleled opportunity to apply predictiveanalytics for understanding customer behavior. This paper explores howintelligent data systems leverage transaction logs, browsing histories, product reviews, and demographic profiles to predict purchasing patterns, personalize recommendations, and optimize marketing strategies. The integration of machine learning algorithms such as collaborative filtering, neural networks, and reinforcement learning in e-commerce predictive models is examined in detail. A key focus is placed on customer lifetime value prediction, churn analysis, and the role of real-time data in improving customer experience. The study further discusses the ethical issues of data privacy, algorithmic transparency, and consumer trust in predictive systems. By presenting both opportunities and limitations, this paper contributes to the growing body of literature on data-driven e-commerce innovation. KEYWORDS: Predictive analytics, E-commerce, Customer behavior,Machine learning, Personalization