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A framework for mining actionable navigation patterns from in-store RFID datasets via indoor mapping.

Bin Shen1, Qiuhua Zheng2, Xingsen Li3

  • 1Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China. tsingbin@nit.zju.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a framework for analyzing in-store customer paths using Radio Frequency Identification (RFID) data. It effectively filters redundant shopping path data to reveal actionable customer behavior patterns.

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Area of Science:

  • Computer Science
  • Data Science
  • Retail Analytics

Background:

  • Radio Frequency Identification (RFID) technology is increasingly adopted in retail for customer tracking.
  • Analyzing in-store shopping paths and purchase data can reveal valuable customer behavior insights.

Purpose of the Study:

  • To propose a unified framework for RFID-based path analytics in retail environments.
  • To mine actionable navigation patterns from customer shopping paths and purchasing data.
  • To address the challenge of processing redundant data in shopping path sequences.

Main Methods:

  • Developed a unified framework with four modules: physical-to-cyber space mapping, data preprocessing, pattern mining, and knowledge utilization.
  • Proposed algorithms to identify and remove loop repeat and palindrome-contained patterns from RFID data.
  • Utilized multi-source RFID data for comprehensive customer behavior analysis.

Main Results:

  • The proposed data preprocessing module effectively filters redundant patterns from shopping path sequences.
  • Experimental results demonstrate the effectiveness and scalability of the redundant pattern filtering functions.
  • The framework successfully bridges indoor positioning with advanced data mining for retail analytics.

Conclusions:

  • The developed framework provides a feasible method for studying customer shopping behaviors using multi-source RFID data.
  • This approach enhances the potential of RFID technology in intelligent retail services.
  • Actionable navigation patterns can be mined by analyzing customer paths and purchasing data.