Application of Big Data in E-commerce User Behavior Analysis

Abstract

In the context of white-hot e-commerce competition and increasingly fragmented user demand, the traditional experience-driven operation model has been difficult to meet the precise and real-time business needs. The rise of big data technology provides a key path to crack this problem. By integrating multi-dimensional behavioral data such as user browsing, searching, purchasing, evaluation, etc., and combining machine learning and real-time analysis technology, it builds up a decision-making system covering intelligent recommendation, user life cycle management, supply chain optimization and other scenarios. The purpose of this paper is to systematically explore the core application of big data in e-commerce user behavior analysis: on the one hand, it reveals how to improve user conversion and retention through data-driven refined operation, on the other hand, it analyzes its practical value in the supply chain to reduce costs and increase efficiency, and commercial decision-making intelligence, etc., so as to provide theoretical support and practical references for e-commerce enterprises to build a competitive advantage in data.