AI and ML Approaches for Boosting Comparison With Customer Churn

Abstract

In an era defined by data-driven decision-making, businesses grapple with the challenge of retaining their customer base. The work investigates the application of boosting algorithms to predict customer churn, a critical aspect of customer relationship management. In adhering to ethical considerations, the study prioritizes transparency and fairness in analyzing customer data, emphasizing responsible AI practices. The social relevance of this research is underscored by its potential to empower businesses to reduce customer churn, thereby fostering stronger customer relationships and sustainable growth. Moreover, by contributing to developing effective customer retention strategies, the study aligns with ethical business practices prioritizing long-term customer satisfaction over short-term gains. By enhancing predictive models, businesses can implement targeted retention strategies, reducing unnecessary communications and resource consumption.