Leveraging Machine Learning for Customer Segmentation in Retail

Leveraging Machine Learning for Customer Segmentation in Retail

Authors

Dr. Jensmon George1

Associate Professor and Dean of Studies

Monti International Institute of Management Studies, Puthanangadi, Kerala

University of Calicut

Phone:  +91 89 43 99 98 99

Mail ID: jensgeorgeply@gmail.com

Sona R2

Assistant Professor

Department of Mathematics

St. Mary’s College, Puthanangadi, Kerala

University of Calicut

Abstract

In the dynamic landscape of retail, understanding customer behavior is paramount for effective marketing strategies and business growth. Customer segmentation, the process of dividing customers into groups based on similar characteristics or behaviors, serves as a fundamental tool in this endeavor. Traditional methods of segmentation often fall short in capturing the complexity and nuances of customer preferences. This article explores the application of machine learning techniques in customer segmentation within the retail sector. Leveraging advanced algorithms, such as clustering and classification, machine learning enables retailers to uncover hidden patterns in vast datasets, leading to more accurate and actionable segmentation strategies. Through real-world examples and case studies, this article highlights the benefits, challenges, and best practices of employing machine learning for customer segmentation in retail.

Keywords

Machine Learning, Customer Segmentation, Retail, Clustering, Classification, Marketing Strategy