The Application Path of Consumer Behavior Data in Precision Marketing in Retail Management

Abstract

In the digital age, competition in the retail industry is shifting from “traffic wars” to “precision targeting,” with consumer behavior data collection and analysis becoming crucial for businesses to enhance market competitiveness. This article explores the application pathways of consumer behavior data in precision marketing within retail management, discussing how data-driven strategies can achieve more efficient consumer insights and marketing effectiveness. Research reveals that establishing an integrated online-offline data collection system forms the foundation of precision marketing, enabling comprehensive tracking of consumers’ digital and physical behavioral patterns. With this data in hand, businesses can build dynamic user profiles using tools like the RFM model and behavioral tagging systems—which in turn help them achieve consumer segmentation and precise stratification. Then, by rolling out algorithmic recommendations, smart shopping carts, and personalized promotions, companies can close the loop on a full-cycle marketing process: “consumer profiling – content adaptation – scenario-based engagement.” On top of that, linking online and offline scenarios seamlessly and connecting data across all channels doesn’t just boost users’ shopping experience—it also ramps up conversion efficiency. The study also notes that right now, when businesses use consumer behavior data, they run into several challenges: things like inconsistent data quality, not having enough technical capacity, a shortage of interdisciplinary talent, and risks around privacy compliance. To address these, the study offers solutions such as setting up a master data management system, using open-source tools, stepping up industry-academia-research collaboration to train talent, and refining privacy protection frameworks.