AI-DRIVEN SAAS OPTIMIZATION: ENHANCING SERVICE RELIABILITY AND CUSTOMER RETENTION THROUGH PREDICTIVE ANALYTICS

Abstract

Abstract The rapid growth of Software-as-a-Service (SaaS) has revolutionized enterprise IT operations, yet ensuring service reliability and customer retention remains a persistent challenge. This paper proposes an AI-driven optimization framework that leverages predictive analytics to enhance SaaS performance, minimize downtime, and improve user satisfaction. By integrating machine learning models for anomaly detection, demand forecasting, and churn prediction, the study demonstrates how proactive decision-making can strengthen customer relationships and operational efficiency. Empirical analysis based on simulated SaaS usage data indicates that predictive analytics can reduce system failure rates by 25% and increase customer retention by up to 18%. The research concludes that AI-powered predictive systems represent a critical innovation for sustaining long-term competitiveness in the SaaS industry.