Customer Segmentation Using Enhanced K -Means Algorithm

In the highly competitive retail sector, gaining insights into customer behavior plays a vital role in enhancing business growth and customer satisfaction. Retail organizations continuously generate large volumes of customer-related data through billing systems, loyalty cards, and online shopping platforms. However, manual analysis of such large datasets is complex, time- consuming, and inefficient. Customer segmentation provides an effective solution by grouping customers into meaningful categories based on their purchasing patterns and behavioral characteristics. Clustering algorithms, particularly K-Means, are commonly used for customer segmentation because of their simplicity and computational efficiency. Despite its popularity, the traditional K-Means algorithm has several limitations, including random selection of initial centroids, sensitivity to outliers, and inconsistent clustering outcomes. These drawbacks negatively impact the accuracy and reliability of customer segmentation. To address these challenges, this project adopts an Enhanced K-Means approach using the K-Means++ algorithm, which improves the centroid initialization process. The proposed system segments customers using key attributes such as purchase frequency, total expenditure, and product preference patterns. The system is developed using Python, and experimental analysis indicates that K-Means++ produces more stable, accurate, and well-defined clusters compared to conventional K-Means. The results highlight the effectiveness of enhanced clustering techniques in supporting targeted marketing, customer retention strategies, and personalized retail services.