International Journal of Social Analytics

Cloud-Based Big Data Systems for AI-Driven Customer Behavior Analysis in Retail: Enhancing Marketing Optimization, Customer Churn Prediction, and Personalized Customer Experiences

Kaushik Sathupadi

Staff Engineer, Google LLC, Sunnyvale, CA

https://orcid.org/0009-0007-1189-2293

Abstract

Cloud computing has become the backbone of modern retail analytics, providing the scalability and computational power necessary to apply artificial intelligence (AI) for customer behavior analysis. Using cloud-based big data systems, retailers can analyze massive datasets in real time, uncovering patterns in customer interactions, purchase histories, and feedback. This paper explores how machine learning (ML), deep learning (DL), and natural language processing (NLP) are applied in the cloud to derive actionable insights that optimize marketing strategies, predict customer churn, and improve personalized customer experiences. The use of cloud infrastructure allows retailers to process high-velocity data streams, integrate multiple data sources, and run advanced AI models with minimal latency. Additionally, cloud-native tools like serverless computing, distributed data storage, and real-time data processing frameworks are highlighted as critical enablers of AI-driven analytics. This work outlines how cloud architectures support seamless data handling, rapid AI model training, and deployment to improve decision-making. Data security, privacy concerns, and cloud cost management are also discussed.