Ever aging AI/ML Models for Predictive Analytics in SaaS Subscription Management
DOI:
https://doi.org/10.36676/dira.v12.i3.136Keywords:
AI, Machine Learning, Predictive Analytics, SaaS, Subscription Management, Revenue OptimizationAbstract
The rapid evolution of Software as a Service (SaaS) has transformed how businesses manage subscriptions, necessitating innovative approaches to optimize customer retention and revenue growth. This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) models in predictive analytics for SaaS subscription management. By leveraging advanced algorithms, organizations can analyze vast datasets to identify patterns in customer behavior, predict churn, and forecast subscription renewals. The study emphasizes the importance of feature selection and model training, highlighting how tailored predictive models can enhance decision-making processes. Furthermore, it discusses the implications of predictive analytics in personalizing customer experiences and improving service offerings, ultimately leading to increased customer satisfaction and loyalty. Through case studies and data-driven insights, this research aims to provide a comprehensive understanding of how AI/ML technologies can empower SaaS companies to navigate the complexities of subscription management and achieve sustainable growth.
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