Progress in Application of Big Data Analytics Technology in E-Commerce Precision Marketing

Abstract

As e-commerce has expanded globally and the volume of data generated by online users has grown substantially, big data analytics has become an important part of how companies approach precision marketing. This paper reviews the application of big data analytics in e-commerce precision marketing, focusing on four areas: customer segmentation, personalized recommendation systems, sentiment analysis and opinion mining, and dynamic pricing strategies. Drawing on studies published between 2015 and 2023, the review summarizes methodological developments, discusses the main technical and ethical challenges in the field, and points to directions for future research. The results show that machine learning methods (such as clustering algorithm, recommendation model based on deep learning, natural language processing (NLP) technology and price reinforcement learning) greatly improve the accuracy and return on investment (ROI) of finding suitable customers in online marketing. In other words, there are still problems in data protection, model transparency and cold start in recommendation system. This review aims to provide useful help to researchers and professionals, who want to know the progress and development direction in this field.