Utilizing Logistic Regression for Analyzing Customer Behavior in an E-Retail Company

Abstract

The e-retail sector is growing day by day and the competitive environment is getting harder. Businesses have to compete with their competitors in order to survive. In parallel with the increasing internet penetration, the trade volume in E-Retail sites is also increasing therefore the data generated on these sites is enormous. Understanding these data with traditional analysis methods is difficult due to the size problem mentioned. Difficult to understand data causes loss of time, money and customers. In recent years, machine-learning algorithms have been frequently used to analyse these large-sized data and to use them in decision-making. This study aimed to perform predictive analysis for the product recommendation system established by using logistic regression, which is a supervised machine-learning algorithm. In addition, the binary classification algorithm preferred to predict whether customers make a purchase or not. As a result, the accuracy degree of the model was 79.73%. This study has the potential to affect the understanding of customers, ensuring customer satisfaction, increasing profit and market share, and contributes to a sustainable business purpose.