Predicting Customer Segment Changes to Enhance Customer Retention: A Case Study for Online Retail using Machine Learning

In today’s highly competitive marketplace, advertisers strive to tailor their messages specific individuals or groups, often overlooking most significant clients. The Pareto principle, asserting that 80% of sales come from 20% customers, offers valuable insights, imagine if companies could accurately forecast this vital and recognize its historical significance. Predicting customer lifetime value (CLV) at juncture becomes crucial in aiding firms effectively prioritize efforts. To achieve this, organizations can leverage predictive models analytical tools target customers with tailored campaigns, enabling well-informed decisions about advertising investments. By being aware these segment transitions, efficiently deploy resources increase return on investment. implementing the strategies outlined study, businesses gain a edge by identifying retaining potential for growth client retention is immense when anticipating changes segments adjusting accordingly. This paper provides comprehensive methodology, tools, insights assist marketers optimizing campaigns actively predicting segmentation.