Customer Churn Prediction Using Deep Learning: Implications for Retention Marketing

Abstract

This study reviews how deep learning is transforming customer churn prediction and its implications for retention marketing in service industries. Using a systematic literature review, the article examines empirical work on churn models that exploit behavioural and transactional data in sectors such as telecommunications, banking, and e commerce. The findings show that deep learning architectures, including convolutional and representation learning models, consistently outperform traditional statistical and machine learning methods by capturing nonlinear and temporal patterns in customer behaviour. However, the review reveals that many studies still optimise generic accuracy metrics rather than aligning model design with profit oriented objectives and customer value. Research that incorporates profit based loss functions and recognises churn heterogeneity shows that integrating predictive scores with segmentation, campaign cost, and expected response can enhance the impact of retention programmes. The study concludes that future research should embed deep learning churn models within holistic retention systems that link data, models, and decision rules into prioritised interventions over time.