An Electronic Commerce Big Data Analytics Architecture and Platform

Abstract

The COVID-19 pandemic significantly increased e-commerce growth, adding more than 218 billion US dollars to the United States e-commerce sales. With this significant growth, various operational challenges have appeared, including logistic difficulties and customer satisfaction. Businesses that strive to take advantage of increased e-commerce growth must understand data and rely on e-commerce analytics. The large scale of e-commerce data requires sophisticated information technology techniques and cyber-infrastructure to leverage and analyze. This study presents a big e-commerce data platform to address several challenges in e-commerce. The presented platform’s design is based on a distributed system architecture that supports e-commerce analytics applications using historical and real-time data and features a continuous feedback loop to observe the decision-making and evaluation processes to achieve the desired objectives. The platform was validated using two analytical applications. The first application was to identify the periods in which customers prefer to place orders, while the second was used to verify the big e-commerce data platform. The resulting insights and findings promote informed e-commerce decisions. Furthermore, viewing and acting on insight results and findings promote informed decisions that potentially benefit the e-commerce industry. The proposed platform can perform numerous e-commerce applications that potentially benefit the e-commerce industry.