Enhancing productiveness in E-commerce through machine learning: Challenges, Future Perspectives, and a MATLAB-Based Approach

Abstract

The rapid growth of e-commerce has created an urgent need for more productive and efficient systems to handle the increasing complexity of online transactions and consumer behaviour. Machine Learning (ML) offers a transformative approach to enhancing productiveness in e-commerce by optimizing processes such as personalized recommendations, dynamic pricing, customer segmentation, and supply chain management. This article explores the integration of ML-driven initiatives in e-commerce, addressing key challenges such as data privacy, algorithmic bias, and the computational demands of large-scale ML applications. A MATLAB-based approach is proposed for developing and implementing these ML models, leveraging MATLAB’s robust toolboxes for data analysis, model training, and system simulation. Through an extensive literature review, this study highlights the current state of ML in e-commerce, identifies existing challenges, and discusses future perspectives. The introduction of ML-driven strategies not only improves operational efficiency but also enhances customer satisfaction, ultimately driving productivity in the highly competitive e-commerce landscape. The findings of this article are expected to provide valuable insights for both academic researchers and industry professionals aiming to harness the full potential of ML in e-commerce.