Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Grocery Customer Behavior Analysis using RFID-based Shopping Paths Data

Authors: In-Chul Jung, Young S. Kwon

Abstract:

Knowing about the customer behavior in a grocery has been a long-standing issue in the retailing industry. The advent of RFID has made it easier to collect moving data for an individual shopper's behavior. Most of the previous studies used the traditional statistical clustering technique to find the major characteristics of customer behavior, especially shopping path. However, in using the clustering technique, due to various spatial constraints in the store, standard clustering methods are not feasible because moving data such as the shopping path should be adjusted in advance of the analysis, which is time-consuming and causes data distortion. To alleviate this problem, we propose a new approach to spatial pattern clustering based on the longest common subsequence. Experimental results using real data obtained from a grocery confirm the good performance of the proposed method in finding the hot spot, dead spot and major path patterns of customer movements.

Keywords: customer path, shopping behavior, exploratoryanalysis, LCS, RFID

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081169

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2760

References:


[1] Cox, K. (1964), The Responsiveness of Food Sales to Shelf Space Changes in Supermarkets, Journal of Marketing Research, 1(2), 63-67.
[2] Dickson, P. R. and Sawyer, A. G. (1986), Point-of-Purchase Behavior and Price Perceptions of Supermarket Shoppers, Working Paper No. 86-102, Marketing Science Institute, 1000 Massachusetts Ave., Cambridge, MA 02138.
[3] Farley, J. U. and Ring, L. W. (1996), A Stochastic Model of Supermarket Traffic Flow, OPERATIONS RESEARCH, 14(4), 555-567.
[4] Gil J., Tobari E., Lemlij M., Rose A., Penn A. (2009), The Differentiating Behaviour of Shoppers: Clustering of Individual Movement Traces in a Supermarket, Proceedings of the 7th International Space Syntax Symposium.
[5] Harris, D. H. (1958), The effect of display width in merchandising soap, Journal of Applied Psychology, 42(4), 283-284.
[6] Hirschberg, D. S. (1977), Algorithms for the longest common subsequence problem, Journal of ACM, 24(4), 664-675.
[7] Hou, J-L. and Chen, T-G. (2011), An RFID-based Shopping Service System for retailers, Advanced Engineering Informatics, 25(1), 103-115.
[8] Hoyer, W. D. (1984), An Examination of Consumer Decision Making for a Common Repeat Purchase Product, Journal of Consumer Research, 11(3), 822-829.
[9] Hui, S. K., Bradlow, E. T. and Fader, P. S. (2009), Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping path and purchase Behavior, Journal of consumer research, 36, 478-493.
[10] Hui, S. K., Fader, P. S. and Bradlow, E. T. (2009), Path Data in Marketing: An Integrative Framework and Prospectus for Model Building, Marketing Science, 28(2), 320-335.
[11] Larson J. S., Bradlow E. T. and Fader P. S. (2005), An exploratory look at supermarket shopping paths, International Journal of Research in Marketing, 22(4), 395- 414.
[12] McClure, P. J. and West, E. J. (1969), Sales Effects of a New Counter Display, Journal of Advertising Research, 9, 29-34.
[13] Newman, A. J., Yu, D. K. C. and Oulton , D. P. (2002), New insights into retail space and format planning from customer-tracking data, Journal of Retailing and Consumer Services, 9(5), 253-258.
[14] Uotila, V. and Skogster, P. (2007), Space management in a DIY store analyzing consumer shopping paths with data-tracking devices, Facilities, 25(9), 363-374.