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Related Experiment Video

Updated: May 28, 2025

Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior
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Customer Context Analysis in Shopping Malls: A Method Combining Semantic Behavior and Indoor Positioning Using a

Ye Tian1, Yanlei Gu2, Qianwen Lu1

  • 1Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan.

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Summary
This summary is machine-generated.

This study introduces a novel system for offline customer context analysis (CCA) in shopping malls. It integrates customer behavior and location data to enhance marketing decisions for retailers.

Keywords:
customer context analysishuman behavior recognitionindoor positioningpedestrian dead reckoning

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Area of Science:

  • Marketing Analytics
  • Behavioral Science
  • Computer Science

Background:

  • Offline customer context analysis (CCA) is crucial for retail marketing decisions.
  • Understanding customer interest and purchase behavior in physical stores is challenging.

Purpose of the Study:

  • To propose an integrated system for offline customer context analysis.
  • To enhance marketing decision-making for brick-and-mortar retailers.

Main Methods:

  • Customer behavior modeling using time-frequency domain analysis, distinguishing movement-related behaviors (MB) and semantic-related behaviors (SB).
  • Deep-learning-based pedestrian dead reckoning (DPDR) for store-level autonomous positioning, aided by behavior recognition and a node map.

Main Results:

  • The system effectively integrates customer behavior and location data for comprehensive CCA.
  • Movement-related behaviors assist localization, while positioning refines semantic behavior recognition, enabling accurate CCA generation.

Conclusions:

  • The proposed system offers a robust solution for offline customer context analysis.
  • This approach provides valuable insights for retailers to optimize marketing strategies and customer engagement.