E-COMMERCE CUSTOMER BEHAVIOR USING MACHINE LEARNING

Abstract

The landscape of e-commerce has witnessed a transformative shift in consumer behavior, driven by the rise of digital technologies and online platforms. Understanding and predicting this dynamic behavior is crucial for businesses to thrive in the competitive online market. This review paper explores the application of machine learning (ML) techniques in analyzing and forecasting e-commerce customer behavior, with a specific focus on customer reviews. The advent of the internet has empowered consumers to express their opinions through online reviews, influencing purchasing decisions.ML models are increasingly employed to extract valuable insights from these reviews, offering businesses a nuanced understanding of customer preferences. The paper synthesizes existing literature on motivations behind online shopping, the role of trust and security, user experience, social influence, personalization, and post-purchase behavior. The literature review underscores the multifaceted nature of factors influencing e-commerce customer behavior and the pivotal role ML plays in decoding the complexities of consumer sentiments expressed in reviews. The conclusion highlights the need for continued research in ML approaches, especially in the context of big data, to enhance the accuracy of predictions and improve the overall understanding of e-commerce customer behavior.