Design and Implementation of Business Intelligence Framework for a Global Online Retail Business

Due to the intense competition in today's online retail environment, companies seek to enhance their strategies by adopting effective analytical techniques and infrastructure, allowing them to quickly analyze critical information that supports decision-making. A Business intelligence (BI) framework can promptly fulfill such needs by processing massive amounts of collected data from multiple sources and representing them in a way companies can utilize in their strategic decisions. This research paper presents a detailed design and implementation of a BI framework for the online retail business industry. It includes requirement analysis, data modeling, BI framework design, and the implementation of descriptive and predictive analytic tools to provide insights and decision support for retail businesses. Moreover, the paper details the implementation of various machine learning algorithms used in sales predictive analytics, such as Linear Regression, Lasso Regression, XGBoost, Random Forest, and LSTM. Interactive charts are provided to assist decision-makers in carrying informed decisions.

I. Introduction

Online retail is one of the most diverse sectors of the " vertical industry" [1], as it is one of the industries with the most significant number of companies and employees worldwide. The tremendous growth of online shopping has led to a highly competitive business environment. So, an immediate response to market changes has become so crucial that collecting, storing, and analyzing the data continuously have shown high importance in achieving a competitive advantage in this field [2].