Predicting Customer Churn in Telecommunication Services Using Machine Learning

Abstract

Customer churn occurs when users stop using a service, and is a serious headache for telecommunication companies. To tackle this, we dove into machine learning techniques, specifically Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models, to predict churn patterns. Our study is based on an online survey, gathered via Google Forms, that captures various aspects, including demographics, service usage, and satisfaction levels. We applied machine learning techniques like ANN and LSTM, to evaluate the churn trends.
Our study shows that LSTM outshines ANN when it comes to accuracy. These insights can be helpful to telecommunication providers to define actionable strategies to improve customer retention and build stronger relationships with their user base.