Companies across industries face significant financial losses due to customer churn, impacting revenue stability and long-term growth. Recognizing the need for proactive retention strategies, we developed a comprehensive churn prediction model to enhance customer engagement, optimize retention efforts, and drive economic gains. This solution aligned with core business needs, leveraging historical transaction, behavioral, and demographic data for feature engineering. Advanced classification algorithms, including gradient boosting and random forests, were applied to achieve high predictive accuracy, and a churn risk segmentation framework enabled targeted interventions for high-risk customers.
By implementing this data-driven model, companies reduced customer attrition rates, focused resources efficiently, and deployed personalized retention strategies, leading to significant cost savings and increased customer lifetime value. Improved revenue predictability, reduced marketing inefficiencies, and strengthened profitability provided a substantial economic impact, positioning the organization for sustainable long-term growth.
