AI-Based Hotel Customer Churn Prediction Model

Abstract

This study delves into the application of artificial intelligence technology in predicting hotel customer churn, aiming to reduce churn rates and enhance customer satisfaction through predictive analytics. By employing a comprehensive array of statistical and machine learning models, including logistic regression, random forests, neural networks, and support vector machines, we analyzed key data features such as customer behavior, transaction history, and service interactions. The findings indicate that artificial intelligence technology can effectively predict customer churn, providing a basis for the hotel industry to implement personalized marketing and customer loyalty enhancement programs. Furthermore, this study assessed the effectiveness of intervention strategies and optimized them through A/B testing and customer feedback loops. Ultimately, we propose long-term customer relationship management strategies to continuously enhance customer value. The research emphasizes the importance of utilizing data effectively while protecting customer privacy and points to future research directions, including algorithm innovation, improving model interpretability, and addressing data privacy and security challenges.