Machine Learning in E-Commerce: Analyzing and Predicting Customer Behavior
الموضوعات :
Manal Loukili
1
,
Raouya El Youbi
2
,
Fayçal Messaoudi
3
1 - Sidi Mohamed Ben Abdellah University
2 - Sidi Mohamed Ben Abdellah University
3 - Sidi Mohamed Ben Abdellah University
الکلمات المفتاحية: Machine learning, e-commerce, predictive analytics, customer behavior, customer engagement.,
ملخص المقالة :
This paper provides a comprehensive review of the use of machine learning in predicting e-customer behavior in the e-commerce sector, focusing on the last five years. Addressing the gap in current literature, it systematically examines the integration of machine learning techniques in e-commerce, particularly in relation to specific business goals and their impact on profitability and customer engagement. Through an extensive analysis of 21 recent peer-reviewed papers, the study explores a range of applications including customer behavior prediction, churn analysis, fraud detection, and personalized recommendation systems. It delves into the methodologies employed, highlighting the use of advanced predictive analytics, the integration of machine learning with technologies like Natural Language Processing and Big Data analytics, and the growing emphasis on personalization. The findings reveal that machine learning significantly enhances the understanding and prediction of e-customer behavior, leading to more effective e-commerce strategies. This review not only synthesizes current trends and challenges but also identifies key areas for future research, particularly the integration of machine learning with emerging technologies and the ethical use of customer data. Aimed at e-commerce administrators, researchers, and practitioners, this paper offers a comprehensive overview of machine learning's role in e-commerce, guiding future innovations in this rapidly evolving field.
