Leveraging big data and machine learning for optimizing business decisions in e-commerce

Abstract

In today’s digital economy, e-commerce companies face increasing pressure to remain agile and competitive in a rapidly evolving marketplace. The explosion of data from online transactions, customer interactions, and external sources has opened new opportunities for data-driven decision-making. This paper explores how companies can leverage big data, advanced analytics, and machine learning - particularly deep learning techniques such as transformer-based architectures - to optimize pricing, discounting, and demand forecasting strategies. By analyzing historical sales data alongside customer behavior, competitor pricing, and seasonal trends, businesses can implement dynamic and personalized pricing strategies that enhance profitability, improve customer satisfaction, and streamline operations. The paper also highlights the technical foundations of modern demand forecasting models, best practices for model training and evaluation, and the real-world challenges businesses face in implementing these solutions. Ultimately, the integration of AI and big data technologies represents a powerful shift in how strategic business and production decisions are made in the e-commerce sector.