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

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Analyzing QR code scans and location data reveals diverse consumer behaviors across different times and venues. This integration offers valuable insights for marketing and policy decisions.

Keywords:
Consumer behavior analysisK-Means clustering analysisPOIPrincipal component analysisQR codeSpatiotemporal patterns

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Area of Science:

  • Digital Analytics
  • Consumer Behavior Analysis
  • Geographic Information Systems

Background:

  • Digital analytics and Point of Interest (POI) data integration offer granular consumer behavior insights.
  • Understanding consumer interaction patterns is crucial in the digital age.

Purpose of the Study:

  • To analyze QR code scan data combined with POI information to understand consumer behavior patterns.
  • To uncover heterogeneity in consumption behavior across different time periods and venue types.

Main Methods:

  • Analysis of over 134,000 QR code scan records from 25 product brands in 2023.
  • Integration of temporal usage data with spatial POI classifications.
  • Application of Principal Component Analysis (PCA) and K-Means clustering based on time periods.

Main Results:

  • Substantial heterogeneity observed in consumption behavior across various time periods and venue types (residential, dining, nightlife).
  • Visual heat maps and brand clustering highlight the dynamic interplay between product segmentation and user environments.
  • Demonstrated value of integrating behavioral data with geographic analysis for refined decision-making.

Conclusions:

  • The integration of QR code scan data and POI information provides deep insights into consumer behavior.
  • Findings support data-driven optimization of marketing strategies, distribution planning, and policy development.
  • This approach enables more refined, scenario-specific decision-making for commercial and public sectors.