AI-Based Nose for Detecting Churn in Captive Customers

Abstract

Customer churn is a significant challenge for businesses, particularly those with captive customer bases such as subscription services, utilities, and enterprise software providers. Traditional churn detection methods often rely on historical data and conventional analytics, which may not provide real-time insights. This paper explores an innovative approach using an AI-based nose, which mimics human sensory perception to detect early signs of dissatisfaction and disengagement in captive customers. The study integrates machine learning, natural language processing (NLP), and behavioral analytics to create a predictive model for identifying churn risks. We conducted extensive experiments with real-world datasets, demonstrating the model's effectiveness in improving retention strategies. The results indicate that AI-driven churn detection significantly enhances customer experience management and reduces attrition.