Customer Segmentation Analysis Using K-Means Clustering (Machine Learning Algorithm in case of tackling sales issues in the field of economy)

Abstract

This study investigates customer segmentation using the K-means clustering algorithm to analyze annual
income and spending scores. The elbow method identified five optimal clusters, each characterized by unique spending
and income patterns. Key findings reveal distinct customer groups, such as “High Income, High Spending” and “Low
Income, Low Spending,” with additional analysis of age and gender distributions. The research provides actionable
insights for businesses to develop targeted marketing strategies and improve customer retention. By leveraging K-means
clustering, companies can optimize resource allocation and better understand their customer base, driving data-informed
decision-making in a competitive market place.