Federated Learning-Based Privacy-Preserving Customer Analytics System for Real-Time Marketing Optimisation

Abstract

Customer analysis in real time has developed as an important part of digital marketing as businesses constantly analyse the actions, behaviours, and buying patterns of customers to enhance the efficacy of marketing campaigns. However, centralised systems used in traditional customer analytics pose risks related to data leakage, access issues, and slow analysis, which can have negative impacts on the marketing efforts of businesses. To overcome these challenges, privacy-aware customer analytics can be considered as the solution. The research work describes a Federated Learning-Based Privacy-Preserving Customer Analytics System for optimal marketing processes. This study uses a federated learning-based system framework incorporating decentralised analysis, behaviour analysis, and model combination for supporting customer analytics while ensuring protection against data leaks by avoiding the transfer of customer data directly to the centralised server. From the analysis above, there are enhancements in customer engagement, recommendation accuracy, privacy protection, and processing speed in comparison to the existing customer analytics approaches. The proposed framework could also reduce privacy issues while improving marketing decisions and customer retention strategies. Overall, the proposed framework provides a robust, scalable, and data-driven strategy for integrating customer analytics, privacy protection, and real-time marketing improvement in today’s digital business environments.