Predicting Customer Churn in Telecom Industry using Deep Learning Techniques

Abstract: Customer churn is a major concern for telecommunication companies, as it can have a significant impact on their revenue and profitability. In this study, we investigate the effectiveness of deep learning techniques in predicting customer churn in the telecom industry. We use a dataset containing information about customer demographics, usage patterns, and account information to train and test several deep learning models, including convolutional neural networks (CNN) and recurrent neural networks (RNN).

Our results show that the deep learning models outperform traditional machine learning models in terms of accuracy, with the CNN model achieving the highest accuracy among the models tested. We also perform feature importance analysis to identify the most important factors affecting customer churn, and find that factors such as customer tenure, call duration, and data usage have the greatest impact on churn.

Our findings suggest that deep learning techniques can be a valuable tool for predicting customer churn in the telecom industry, and can help companies develop targeted retention strategies to reduce churn and improve customer satisfaction.

Keywords: deep learning, customer churn, telecom industry, convolutional neural networks, recurrent neural networks, feature importance analysis.

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