Machine learning approaches for customer segmentation

Abstract

Customer segmentation is one of the most significant responsibilities in the retail business and marketing. A thorough customer segmentation may assist managers in improving product quality and providing better services to the targeted segments. Various machine learning algorithm-based customer segmentation strategies are used to gain an understanding of the customer's behaviour and future consumers who may be targeted to maximise profit. Based on past research, this study provides enhanced machine learning models for e-commerce user segmentation. The agglomerative clustering techniques have been used to categorise clients using the new customer behaviour metric. In addition, we developed a systematic strategy to improving customer experience and retention by integrating an agglomerative clustering algorithm with a filtering-based recommender system.