Data Science Approaches for Churn Prediction

Customer churn poses a significant challenge for subscription-based businesses, leading to revenue losses and decreased profitability. Predicting and managing churn is crucial for sustaining business growth and ensuring customer satisfaction. This paper reviews state-of-the-art machine learning techniques employed in churn prediction, ranging from traditional algorithms like logistic regression and decision trees to advanced approaches such as ensemble methods and neural networks. By exploring methodologies and practical applications, this paper provides insights for researchers and practitioners aiming to optimize churn prediction strategies. Additionally, the paper discusses the background of churn prediction, highlighting the limitations of traditional statistical models and the emergence of data science techniques. Through feature engineering, exploratory data analysis, and the utilization of various machine learning algorithms, businesses can develop effective churn prediction systems to enhance customer retention and satisfaction.