Optimizing SaaS User Retention Using Federated Learning and Edge-Centric Analytics in Cloud-Native Architectures-Low Cost

Abstract

User retention has become a vital measure of success, and in the highly competitive Software as a Service (SaaS) environment, it can also spell out the viability of a platform in the long term. The conventional retention solutions, which rely on analytics at a central place, are extremely expensive, hard to scale, slow, and lack user privacy. This paper delves into the ways in which using Federated Learning and Edge-Centric Analytics in Cloud-Native Architectures can provide a breakthrough and low-cost solution to how SaaS utilization can be optimized to ensure user retention. This method can maintain privacy and minimise reliance on clouds to process data and train models, as well as personalize in real-time by decentralizing data processing and model training. The paper will describe the technical and strategic synergy of these technologies as well as provide practical situations to explain their effectiveness. Finally, this integration enables SaaS vendors to provide smart, scalable, and affordable retention plans, which are in line with the current requirements of privacy, flexibility, and user experience.