Survival analysis as a tool for predicting and reducing customer churn

Introduction. Customer churn is one of the key challenges in the telecommunications sector, significantly impacting companies' financial performance. Losing customers leads to substantial economic losses, as acquiring new users is typically more expensive than retaining existing ones. This creates a need for the development of effective analytical approaches to predict customer churn and identify the factors influencing it. Objective. This study aims to assess the effectiveness of survival analysis methods in predicting customer churn in the telecommunications sector. The primary objective is to identify key factors affecting churn rates and evaluate the impact of targeted retention strategies on customer behaviour. Methodology. The study employs survival analysis methods to evaluate customer churn dynamics. The Kaplan-Meier estimator is used to analyse the temporal patterns of customer retention, identifying high-risk periods. The Cox proportional hazards model is applied to determine the influence of various factors on churn probability. The dataset consists of real-world telecommunications customer data, including demographic characteristics, contract details, and financial indicators. Preprocessing steps include data cleaning, feature selection, and transformation of categorical variables into numerical format. Model performance is evaluated using metrics such as the Concordance Index (C-index) and Brier Score. Finally, A/B testing is conducted to assess the effectiveness of retention strategies in reducing customer churn. Results. The findings indicate that the highest risk of churn occurs within the first 20 months after subscription activation. It was determined that long-term contracts and high-quality service reduce the likelihood of churn, whereas high monthly costs and certain payment methods increase it. The developed predictive models achieved an accuracy level of 85–87%. Implemented retention strategies within A/B testing resulted in an increase in the retention rate from 74% to 91%. The proposed methods can be applied by telecommunications companies, financial institutions, and other businesses operating in the subscription-based service industry to enhance loyalty programs and reduce customer churn.