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Store layout optimization using indoor positioning system

Author

Listed:
  • Hyunwoo Hwangbo
  • Jonghyuk Kim
  • Zoonky Lee
  • Soyean Kim

Abstract

Indoor positioning systems have attracted considerable attention from practitioners and firms seeking to optimize the consumer shopping experience with the goal of attaining increased revenue and profitability. Acknowledging the importance of indoor positioning systems in store layout optimization, we conducted a field experiment for 11 months in order to develop algorithms for connecting indoor positioning data with customer transaction data. Using fingerprinting as a primary data collection technique, we compared positioning and transaction data before and after critical store layout optimization decisions in order to identify which customer movement patterns generated the highest sales. In contrast to previous works on indoor positioning systems, which focused solely on developing algorithms or techniques to increase accuracy rates, our algorithms in principle integrate computing and marketing perspectives. Our findings can be applied to store layout optimization and personalized marketing.

Suggested Citation

  • Hyunwoo Hwangbo & Jonghyuk Kim & Zoonky Lee & Soyean Kim, 2017. "Store layout optimization using indoor positioning system," International Journal of Distributed Sensor Networks, , vol. 13(2), pages 15501477176, February.
    • Handle: RePEc:sae:intdis:v:13:y:2017:i:2:p:1550147717692585
    DOI: 10.1177/1550147717692585
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    References listed on IDEAS

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    1. Nan Li & Jiabin Chen & Yan Yuan & Xiaochun Tian & Yongqiang Han & Mingzhe Xia, 2016. "A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks," International Journal of Distributed Sensor Networks, , vol. 12(3), pages 4583147-458, March.
    2. Kornkanok Khaoampai & Kulit Na Nakorn & Kultida Rojviboonchai, 2015. "FloorLoc-SL: Floor Localization System with Fingerprint Self-Learning Mechanism," International Journal of Distributed Sensor Networks, , vol. 11(11), pages 523403-5234, November.
    3. Sergio Ilarri & Ramón Hermoso & Raquel Trillo-Lado & María del Carmen Rodríguez-Hernández, 2015. "A Review of the Role of Sensors in Mobile Context-Aware Recommendation Systems," International Journal of Distributed Sensor Networks, , vol. 11(11), pages 489264-4892, November.
    4. De Marco, Alberto & Cagliano, Anna C. & Nervo, Mauro L. & Rafele, Carlo, 2012. "Using System Dynamics to assess the impact of RFID technology on retail operations," International Journal of Production Economics, Elsevier, vol. 135(1), pages 333-344.
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