CUSTOMER CHURN PREDICTION IN THE TELECOMMUNICATION INDUSTRIES USING RNN

CUSTOMER CHURN PREDICTION IN THE TELECOMMUNICATION INDUSTRIES USING RNN

Himakar Chigurupati, Akshay Reddy chada, Krishna Reddy Chilkuri, Laxmiprashanth Reddy Chandrapu , Dr.S.Satyanarayana

Malla Reddy University Hyderabad

ABSTRACT:

Customer churn is major concern in the telecommunication industry, as losing customers can drastically impact revenue and market share. Predicting the reason for customer churn is a proactive measure to retain customers and improve overall business performance. In this research, we introduced a deep learning approach using Recurrent Neural Networks (RNN) for customer churn prediction in the telecommunication industry. RNNs are very familiar with sequential data analysis and can effectively capture temporal dependencies on customer behavior. With historical customer data, including usage patterns, billing information, and service interactions, train the RNN model. The model learns to recognize patterns and relationships between various customer attributes and churn behavior. To evaluate the performance of the proposed approach, we conducted experiments using a real-world telecommunication dataset. The RNN model showed greater predictive accuracy than traditional machine learning algorithms, such as logistic regression and decision trees. Additionally, we employed various techniques, such as hyperparameter tuning and cross-validation, to optimize the model's performance. The results indicate that the RNN-based approach can effectively predict customer churn in the telecommunication industry. Overall, this study contributes to the understanding and knowledge of customer churn prediction in the telecommunication industry. The proposed RNN-based approach offers a powerful prediction method for telecommunication companies to make informed decisions regarding customer retention, leading to improved business outcomes and enhanced customer loyalty.