The application of Big Data Analytics in improving eCommerce processes. The Retail sector user experience

Abstract

This paper explores the application of Big Data analytics in enhancing user experience within the retail sector. The first chapter of this paper provides an overview of eCommerce's current landscape and the challenges retailers face in delivering a seamless user experience. It highlights the importance of user experience in driving customer satisfaction, loyalty, and, ultimately, business growth. The second chapter delves into the concept of Big Data analytics and its potential to address the challenges retailers face. It discusses the different data types generated in eCommerce processes, including customer, transaction, and social media data. Furthermore, it explores various techniques and technologies in Big Data analytics, such as data mining, machine learning, and predictive analytics. The third chapter focuses on the specific applications of Big Data analytics in improving user experience in the retail sector. It examines how retailers can leverage Big Data analytics to personalize product recommendations, optimize pricing strategies, streamline inventory management, and enhance customer service. The fourth chapter discusses the challenges and considerations of implementing Big Data analytics in retail. It addresses issues related to data privacy, data security, data quality, and the need for skilled data analysts. Moreover, it explores the ethical implications of using customer data for business purposes. Finally, the paper emphasises the potential benefits of integrating Big Data analytics into ecommerce processes to enhance user experience. It highlights the importance of retailers adopting a data-driven approach to gain valuable insights and improve their operations, ultimately leading to improved customer satisfaction and increased competitiveness in the retail sector.