Deep Learning-Based Customer Churn Prediction in Telecommunications

1G.MANOJ KUMAR,2 Muppidi NANI

1 Associate Professor & Training & Placement Officer, 2 MCA Final Semester,

Master of Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam,

Andhra Pradesh, India

ABSTRACT
Customer churn is one of the major challenges faced by telecommunication companies, leading to significant revenue loss and reduced customer retention.This project presents a Deep Learning-Based Customer Churn Prediction System that utilizes artificial intelligence and deep neural networks to identify customers who are likely to discontinue services. The proposed system analyzes customer demographics, service usage patterns,billing information, and customer support interactions to predict churn behavior accurately.

The system employs deep learning algorithms for feature extraction and classification, enabling organizations to take proactive measures to retain valuable customers. By leveraging largescale customer datasets and advanced predictive analytics, the model improves prediction accuracy and supports data-driven decision-making.The proposed solution enhances customer satisfaction, reduces churn rates, and increases profitability for telecommunication service providers.

Losing customers, or customer churn, is a major problem for companies that has an impact on growth and revenue. With the help of deep learning techniques, this research seeks to precisely identify which customers are most likely to depart, allowing businesses to take proactive measures to retain them. This study uses sophisticated neural networks, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to identify trends in consumer interactions and behavior. To guarantee high predicted accuracy, the process includes feature engineering, data preparation, and model optimization. Effectiveness will be assessed using performance indicators like F1 score, recall, and precision. Additionally, interpretability methods like SHAP (Shapley Additive explanations) will pinpoint the primary causes of attrition, offering useful information to direct client retention tactics.

Keywords: Deep Learning, Customer Churn Prediction, Telecommunications, Artificial Intelligence, Neural Networks, Customer Retention, Predictive Analytics, Machine Learning.