Customer Churn Prediction for a Telecommunication Company

Abstract

This research project delves into the critical issue of customer churn prediction within the context of a telecommunication company. Customer churn, the phenomenon where subscribers terminate their services, presents a significant challenge in an industry marked by intense competition and evolving customer preferences. The study employs advanced machine learning techniques to analyze vast volumes of historical customer data, comprising demographic information, service usage patterns, billing records, and past interactions with the company’s services. Through meticulous data preprocessing, feature engineering, and model selection, a predictive model is developed to forecast the likelihood of churn for individual customers. By accurately identifying customers at risk of churn, the telecommunication company can proactively implement targeted retention strategies. These strategies may include personalized incentives, tailored service offerings, proactive customer support, or loyalty programs. The effectiveness of the churn prediction model is evaluated using various metrics, ensuring its reliability and accuracy in real-world scenarios. Ultimately, the goal of this project is to equip the telecommunication company with actionable insights to reduce churn rates, optimize resource allocation, and foster long-term customer loyalty. This research contributes to enhancing the company’s competitive edge by enabling data-driven decision-making and proactive customer relationship management.