Churn Rate Modeling for Telecommunication Operators Using Data Science Methods

Abstract

The telecommunication company functioned in the market with extremely high competitiveness. Attracting new customers needs 5-10 times more expenses than maintaining an existing one. As a result, effective customer churn management and analysis of the reasons for customer churn are vital tasks for telecommunication operators. As a result, predicting subscriber churn by switching on the competitors becomes very important. Data Science and machine learning create enormous opportunities for solving this task to evaluate customer satisfaction with company services, determine factors that cause disappointment, and forecast which clients are at a greater risk of abandoning and changing services suppliers. A company that implements data analysis and modelling to develop customer churn prediction models has an opportunity to improve customer churn management and increase business results. The purposes of the research are the application of machine learning models for a telecommunications company, in particular, the construction of models for predicting the user churn rate and proving that Data Science models and machine learning are high-quality and effective tools for solving the tasks of forecasting the key marketing metrics of a telecommunications company. Based on the example of Telco, the article contains the results of the implementation of various models for classification, such as logistic regression, Random Forest, SVM, and XGBoost, using Python programming language. All models are characterised by high quality (the general accuracy is over 80%). So, the paper demonstrates the feasibility and possibility of implementing the model to classify customers in the future to anticipate subscriber churn (clients who may abandon the company’s services) and minimise consumer outflow based on this. The main factors influencing customer churn are established, which is basic information for further forecasting client outflow. Customer outflow prediction models implementation will help to reduce customer churn and maintain their loyalty. The research results can be useful for optimising marketing activity of managing the outflow of consumers of companies on the telecommunication market by developing effective decisions based on data and improving the mathematical methodology of forecasting the outflow of consumers. Therefore, the study’s main theoretical and practical achievements are to develop an efficient forecasting tool for enterprises to control outflow risks and to enrich the research on data analysis and Data Science methodology to identify essential factors that determine the propensity of customers to churn.