An Elegant Evaluation: Triangulating Clustering Methods for Customer Segmentation

In the Business world, the customer satisfaction is vital. In order to improve the sales and patterned the satisfaction of customer, the preferences of the customer, quality and quantity of the product is analyzed. Various parameters are used in business aiming to improve the sales and customer satisfaction. The Agglomerative clustering, K-Means Clustering and Density Based Spatial Clustering (DBSCAN) techniques are used in this study. In the Density based clustering techniques used to fragment the customers based on the customer preference and behavior. The Intra-cluster variance is minimized with K-Means clustering. By employing the three algorithms, the customers are categorized based on who share similar interests by extracting and analyzing patterns from the customer data at hand. The evaluation reveals that K-means Clustering and Agglomerative Clustering achieves the highest silhouette score when conducting customer segmentation, surpassing other methods. The comparative analysis conducted in this study on feature-based customer segmentation provides organizations with valuable insights for implementing effective strategies in customer segmentation.