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

Abstract

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

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