Predicting Consumer Churn in Telecommunications

The telecommunications industry is highly competitive, with consumer churn being a significant concern due to market saturation. Churn, or customer attrition, refers to customers leaving a service provider due to dissatisfaction or better offers from competitors. This phenomenon is particularly impactful in the telecom sector, where retaining customers is more cost-effective than acquiring new ones. To address this issue, telecom companies increasingly rely on machine learning techniques to predict customer churn and implement strategies to retain at-risk customers. The study highlights the importance of understanding the factors contributing to customer churn, including service quality, pricing, customer service interactions, and competition. It emphasizes that technological advancements and data analytics can be crucial in identifying customers likely to churn, allowing telecom companies to take proactive measures to prevent it. The research involved applying machine learning algorithms—such as k-Nearest Neighbors (kNN), Random Forest, Multilayer Perceptron (MLP), Gradient Boosting, and XGBoost—on a dataset from a telecommunication company. The objective was to develop a predictive model that accurately identifies customers at high risk of churning. The study also aimed to analyze customer behavior, demographics, usage patterns, and other relevant data to determine the primary drivers of churn. The project aims to help telecom companies reduce churn rates, enhance customer satisfaction, and improve financial stability and growth by developing a robust predictive model. This research provides valuable insights into customer retention strategies, emphasizing the need for telecom companies to focus on retaining existing customers in an increasingly competitive market.