Harnessing Machine Learning for Effective Customer Segmentation

The paper’s objective is to utilize machine learning techniques for customer segmentation. We aim to identify significant patterns and segments within a customer base to enhance marketing strategies, improve customer satisfaction, and optimize business operations. By mastering Python programming alongside essential libraries like Pandas, NumPy, and data visualization tools, we focus on data collection, training, and testing to construct a robust customer segmentation model. Such a model will enable businesses to enhance their marketing strategies, customer satisfaction, and streamline operations. This paper delves into customer segmentation using k-means clustering, analysing the Mall Customers dataset to demonstrate how machine learning can effectively segment customers created on their purchasing behaviour and demographic information. The study includes an extensive review of literature, detailed research methodology, implementation of the clustering algorithm, evaluation of models, and discussion of the results. The model’s accuracy is verified using two metrics: the Silhouette score, which is 0.69777, and the Davies-Bouldin Index (DBI), which is ${0. 4 2 1 1 0}$.