Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa

This study explores clustering techniques for customer segmentation, focusing on the K-Means algorithm in particular, and uses a dataset that was obtained from the customer data of an international supermarket company. Studying clustering methodologies, creating a K-Means clustering model, and assessing its quality for efficient client segmentation are the main goals. The Elbow method is employed to determine the ideal number of clusters (K value), resulting in the segmentation of customers according to their buying patterns. Customer profiling is the result of the segmentation process. The Silhouette Score is used to assess the quality of the clustering model, and it achieves a good value of 0.54. By demonstrating a good balance between cluster cohesiveness and cluster separation, this score shows that the K-Means method is successful in identifying unique customer categories. The supermarket company can improve overall business performance and customer satisfaction by using tailored customer engagement and targeted marketing strategies made possible by the insightful customer profiles that are produced.