Customer Profiling with Big Data in Omni-Channel Retail

Abstract

The exponential growth of digital commerce, mobile applications, and in-store analytics has transformed the retail landscape into a fully integrated omni-channel ecosystem. In this environment, customer profiling powered by big data analytics has become a strategic necessity rather than a competitive advantage. This study investigates the role of big data-enabled customer profiling in enhancing personalization, predictive targeting, and customer experience in omni-channel retailing. Drawing on data-driven marketing theory, relationship marketing, and consumer behavior analytics, the paper develops a conceptual framework linking big data dimensions (volume, velocity, variety, and veracity) with real-time customer profiling outcomes. A quantitative survey of 524 omni-channel consumers across India, the United States, and Southeast Asia was analyzed using Structural Equation Modeling (SEM). The findings reveal that real-time analytics, AI-driven segmentation, and cross-channel data integration significantly influence customer satisfaction, purchase frequency, and long-term loyalty. This research contributes theoretically by extending omni-channel retail analytics literature and practically by offering a scalable blueprint for data-driven retail personalization.