In the contemporary business world where competition is stiff, it becomes very important to retain customers. Real time customer churn prediction prepares organizations to intervene and since centralized machine learning involves data gathering from sources located in the actual customer environment such a method would be highly invasive of the customer’s privacy. This paper aims at proposing a new approach to integrate artificial intelligence technology into real-time customer churn prediction through federated learning where nobody has access to another person’s data and the training is done collectively across the devices or organizations. Thus, to provide a solution where public and sensitive data is analyzed while adhering to legal norms like GDPR, FL is used in conjunction with deep learning models. Not only the proposed system can accurately predict the churn rate but also the features or data insights are provided to an AI-based marketing fighting chance repository to engage the customer properly at an opportune time. In the experiment part, by testing on artificially generated customer databases, we prove that our method can obtain satisfactory predictive accuracy and at the same time, avoid leaking individuals’ information. Also, there is the evaluation of the effectiveness of the system on marketing with major benefits such as enhanced targeting of campaigns and low customer churn rates. It belongs to the area of privacy-preserving machine learning and intelligent marketing that enables the development of cost-effective and efficient real-time customer churn management resources for data-sensitive industries.
Keywords: AI in marketing, Data Privacy, customer churn prediction, federated learning, privacy-preserving machine learning
