Real-Time Customer Churn Prediction in Telecommunication

In the competitive telecommunication market, predicting churn has become the key to retaining customers and achieving maximum profitability. Traditional churn prediction models are, however, based on static data and batch processing, which are not suitable to analyze real-time customer opinions and resolve dynamic problems. Designing a system to process real-time complaint data was one of the biggest challenges of this project, which included addressing inconsistent inputs, noisy text data, and continuous, precise prediction requirements. Also, aggregating multiple features like complaint frequency, complaint type, and resolution time into one prediction model increased complexity. Overcoming these, the project suggests a hybrid churn prediction model by exploiting the power of deep learning and XGBoost. The deep learning model recognizes sophisticated patterns from text complaints, whereas XGBoost processes structured features well and improves interpretability. A real-time complaint form, churn prediction report, and provision to the preprocessed data are incorporated through a user-friendly dashboard. Telecom operators can thereby monitor real-time customer dissatisfaction and make timely data-driven decisions to minimize churn, enhance customer satisfaction, and enhance overall profitability.