Enhancing Customer Retention through Deep Learning and Imbalanced Data Techniques

Abstract

Accurately predicting customer churn is considered crucial by businesses in order to take proactive measures to retain their customers and avoid financial losses. In this paper, a customer churn prediction model is proposed that incorporates deep neural networks and imbalanced data techniques. The approach involves applying oversampling and undersampling methods to address class imbalances in the dataset. The model's performance is evaluated using various evaluation metrics and compared to other methods. The results demonstrate that superior performance in predicting customer churn is achieved by the proposed model compared to traditional statistical methods and deep neural networks without imbalanced data techniques. Important implications for businesses seeking to reduce customer churn and improve customer retention are provided by our findings. By using the suggested model, customers can be retained, and financial losses can be avoided proactively.