DOI QR코드

DOI QR Code

Shoppers' Shopping Path Pattern Analysis using RFID Data

RFID 데이터를 이용한 고객 쇼핑 동선 패턴 분석

  • Received : 2012.02.15
  • Accepted : 2012.11.20
  • Published : 2012.11.30

Abstract

As the retail industry has been challenged by stiff competition, the retailer becomes more interested in better understanding consumers' in-store behavior to gain and sustain competitive advantage. Consumers' shopping paths provide valuable clues to understanding customers' in-store behavior, which has been a long standing research issue in business. This study is to explore the shopping path patterns in a grocery using RFID technology and clustering method. To this end, we designed the RFID systems, affixing active RFID tags to the bottom of grocery carts. The tag emit signal that is received by receptors installed at various location throughout the store. The RFID systems provide the time and location of the cart while consumers shop around the store. The point of sale data are matched with the cart movement records to provide a complete picture of each shopping path. To find the distinctive patterns of consumers' shopping paths, we proposed the distance-index matrix using dijkstra method and normalization method to conduct the clustering in order to handle the problem in measuring the similarity among shopping paths, which is raised by the spatial nature of consumer movement in a grocery. After analyzing the RFID data obtained in one of the groceries in a major Korean retailer, we could successfully identify several distinctive patterns of shopping paths, which prove to provide the valuable implications for store management.

Keywords

References

  1. 서성보, 이용미, 이준욱, 남광우, 류근호, 박진수, "RFID 데이터 스트림에서 이동궤적 패턴의 탐사", 한국공간정보시스템학회논문지, 제11권, 제1호(2009), pp.127-136.
  2. 심재헌, 이성호, "대형할인점의 입지선정을 위한 의사결정에 관한 연구", 대한토목학회논문집, 제28권, 제5D호(2008), pp.705-712.
  3. 유 군, "대형 할인점의 점포특성이 고객 구매 행동에 미치는 영향에 관한 연구", 한양대학교 경영학과 학위논문, 2008.
  4. Farley, J. U. and L. A. Ring, "Stochastic Model of Supermarket Traffic Flow", Operations Research, Vol.14, No.4(1966), pp.555- 567. https://doi.org/10.1287/opre.14.4.555
  5. Gil, J., E. Tobari, M. Lemlij, A. Rose, and A. Penn, "The Differentiating Behavior of Shoppers:Clustering of Individual Movement Trace in a Supermarket", Proceeding of the 7th International Space Syntax Symposium, 2009.
  6. Larson, J. S., E. T. Bradlow, and P. S. Fader, "An exploratory look at supermarket shopping paths", Intern. J. of Research in Marketing, Vol.22(2005), pp.359-414.
  7. Liao, I. and W. C. Lin, "Shopping path analysis and transaction mining based on RFID technology", RFID Eurasia 1st Annual, (2007), pp.1-5.
  8. Lim, H. S., H. S. Kim, H. S. Yong, S. H. Lee, and S. S. Park, "Density Based Spatial Clustering Method Considering Obstruction", Korea Multimedia Society, Vol.6, No.3(2003), pp.375-383.
  9. Newman, A. J., D. K. C. Yu, and D. P. Oulton, "New insights into retail space and format planning from customer-tracking data", Journal of Retailing and Consumer Services, Vol.9, No.5(2002), pp.253-258. https://doi.org/10.1016/S0969-6989(02)00010-3
  10. Sugar, C., "Techniques for clustering and classification with applications to medical problems", Unpublished Phd dissertation, Stanford University, 1998.
  11. Tan, P. N., M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison Wesley, Pearson International Edition, 2006.
  12. Tung, A. K. H., J. Hou, and J. Han, Spatial Clustering in the Presence of Obstacles, Data Engineering, Proceedings 17th International Conference, 2001.
  13. Underhill, P., Why We Buy, N. Y., 1999.