Churn Prediction and Factor Identification in Telecommunication Industry Using Deep Learning

In the telecom sector, where customer retention has a direct impact on profitability, customer churn prediction is essential. This study focuses on proposing a method using Deep Learning Techniques to Predict and Identify Churn Factors for supporting proactive client retention initiatives. By leveraging data from customer usage patterns, service preferences, and demographics, the model identifies potential churners and the key factors influencing their decisions. This approach surpasses traditional machine learning models, offering improved prediction accuracy and actionable insights. In order to improve prediction capabilities in intricate consumer datasets, the suggested system makes use of neural networks, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to capture both static and sequential behavioral data. In the highly competitive telecom industry, customer churn poses a significant challenge, directly impacting profitability and market share. The idea behind this project is the development of sustainable deep learning framework for predicting customer churn and determining the essential parameters that control customer retention. This study employs a variety of deep learning techniques, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), to develop predictive models that accurately classify customers at risk of churning by utilizing historical customer data, such as usage patterns, service interactions, and demographic information.