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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.
Sensors (Basel, Switzerland)
|February 13, 2025
Summary
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.
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.
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