Customer Segmentation Using K-Means Clustering in Unsupervised Machine Learning

Aparna S Nair, Sindhu Daniel

Abstract: Customer segmentation is essential for improving marketing strategies and customer satisfaction. This study uses K-Means clustering to group consumers based on average shopping expenditure and annual store visits, incorporating demographic, geographic, psychographic, and behavioural data. Unlike traditional methods that rely mainly on demographics or past purchases, this approach offers a more comprehensive and data-driven solution. A Python-based model was developed using real-world delivery company data. The segmentation results enable more targeted marketing and support better business decisions, showing improved customer engagement and performance.

Keywords: Customer Segmentation, K-Means Clustering, Unsupervised Machine Learning, Behavioural Patterns, Data-Driven Marketing, Python, Consumer Classification, Personalized Marketing, Real-World Data, Business Intelligence