Customer Segmentation in Marketing using K-Means Clustering

In today’s data-driven marketing world, understanding and segmenting customers based on their behavior and demographics is a crucial task. This paper presents a machine learning approach to customer segmentation using the K-Means clustering algorithm. The project uses the popular Mall_Customers.csv dataset and leverages Python with libraries such as pandas, matplotlib, seaborn, and scikit-learn. The Elbow Method is employed to determine the optimal number of clusters, followed by 2D and 3D visualization of the customer groups. The study demonstrates how segmentation can lead to targeted marketing and better business outcomes. This method empowers organizations to shift from generalized to personalized marketing tactics, enabling better allocation of resources, increased customer satisfaction, and improved sales conversions. Additionally, this approach proves to be scalable, allowing it to be adopted by businesses of various sizes and sectors.