Lifecycle-Aware Marketing Automation Using Federated Learning for Secure Cross-Organizational Data Management
Joanne Osuashi Sanni, Uzoamaka Azuka Iwuanyanwu, Mmedo Anietie Essien, Adumaza Attah
Abstract
Marketing automation has evolved beyond rule-based personalization toward intelligent, data-driven lifecycle management that respects privacy and regulatory constraints. This review explores the convergence of federated learning (FL) and lifecycle-aware marketing automation as a framework for secure cross-organizational data collaboration. Traditional centralized models for customer profiling and segmentation face increasing challenges related to data silos, compliance with privacy laws such as GDPR and CCPA, and the risk of data leakage during model training. Federated learning mitigates these risks by enabling organizations to collaboratively train predictive models without sharing raw data, ensuring data sovereignty and confidentiality. The paper analyzes key applications of FL in lead scoring, churn prediction, and adaptive campaign optimization across distributed business networks. Furthermore, it examines how lifecycle-aware models integrate real-time behavioral data and contextual signals to personalize interactions throughout the customer journey. Technical considerations such as differential privacy, model aggregation techniques, and communication efficiency are also discussed. By synthesizing findings from marketing science, machine learning, and information security, the review highlights best practices and open research directions for building federated, privacy-preserving marketing systems. Ultimately, this study underscores how lifecycle-aware automation using FL can enhance personalization accuracy, compliance, and trust across organizational boundaries.