Enhancing Customer Segmentation: A Novel Approach using Machine Learning Algorithms

Customer segmentation is needed for every business to survive in the field. Grouping up of customers can be done by finding a similar pattern among customers by analyzing the sales dataset. This research introduces a novel methodology designed to enhance customer segmentation significantly, achieved through the integration of front-line machine learning algorithms, explicitly focusing on clustering and classification techniques, to overcome the inherent limitations and static nature of traditional segmentation methods. The proposed approach uses the well-established RFM model for feature engineering, enabling a comprehensive representation of customer behaviour and value, subsequently employing advanced clustering algorithms to classify different customer segments based on these RFM features. To further refine the segmentation, a weighted lifetime value calculation is introduced, serving as a critical classification criterion within each identified cluster, thereby facilitating a more nuanced understanding of customer potential by integrating AI techniques with RFM analysis, the recognition of sustainable consumer practices is enhanced, offering more detailed understanding of consumer behaviour and its impact on sustainability.