Spatiotemporal analysis of consumer scanning behavior using integrated QR code and POI data

In the era of digital analytics, the integration of QR code scan data and Point of Interest (POI) information offers new opportunities to understand consumer behavior with unprecedented granularity. This study analyzes over 134,000 scan records from 25 product brands in 2023, combining temporal usage data with spatial POI classifications to uncover patterns in consumer interaction. We conduct principal component analysis (PCA) and K-Means clustering based on the division of time periods. Results show substantial heterogeneity in consumption behavior across time periods and venue types (e.g., residential zones, dining areas, nightlife districts). Through visual heat maps and brand clustering, we highlight the dynamic interplay between product segmentation and user environments. These findings not only provide data-driven insights for optimizing marketing strategies and distribution planning but also offer reference value for policymakers. This study demonstrates the value of integrating behavioral data with geographic analysis to support more refined, scenario-specific decision-making in both commercial and public sectors.