JIANG Tao, SHI Lei, ZHAO Ye, ZHANG Xiatian, LU Yao, HUANG Congcong. Visual Comparison of Customer Stickiness in Retail Stores[J]. Chinese Journal of Electronics, 2018, 27(5): 951-958. doi: 10.1049/cje.2018.05.009
Citation: JIANG Tao, SHI Lei, ZHAO Ye, ZHANG Xiatian, LU Yao, HUANG Congcong. Visual Comparison of Customer Stickiness in Retail Stores[J]. Chinese Journal of Electronics, 2018, 27(5): 951-958. doi: 10.1049/cje.2018.05.009

Visual Comparison of Customer Stickiness in Retail Stores

doi: 10.1049/cje.2018.05.009
Funds:  This work is supported by the National Basic Research Program of China (No.2014CB340301), the National Natural Science Foundation of China (No.61379088, No.61772504), the Key Research Program of Frontier Sciences, CAS (No.QYZDY-SSW-JSC041), and U.S. National Science Foundation (No.1535031, No.1637242).
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  • Corresponding author: SHI Lei (corresponding author) is a professor in the State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences. He holds B.S., M.S. and Ph.D. degrees from the Department of Computer Science and Technology, Tsinghua University. His research interests span information visualization, visual analytics and data mining. He has published 70 papers in refereed conferences and journals. He serves program committee member on several international conferences, a note paper chair for PacificVis'18, and was the recipient of VAST Challenge Award in 2010 and 2012. Corresponding author. (Email:shijim@gmail.com)
  • Received Date: 2017-03-13
  • Rev Recd Date: 2018-01-16
  • Publish Date: 2018-09-10
  • Understanding market trends and forming competitive promotion strategies has always been a major task of retail store managers. One big challenge is the lack of effective tools for in-depth customer behavior analysis. In this paper, we apply visual analytics techniques to address the challenge, which is built up on the emerging and mobile location big data. We present a system that focuses on the analysis of customer stickiness which represents customers' affinity to retail stores. The system integrates mobile data pre-processing, customer stickiness analysis, multi-view visualization, and a set of interactions. The visual analytics techniques are mainly designed for two types of user tasks:1) understanding the spatio-temporal distribution of customer traces related to retail stores; 2) evaluating the performance and trend of multiple retail stores through visual comparison. We have demonstrated the effectiveness of the system through two case studies including advertisement placement and business branch reconfiguration.
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  • M. Gleicher, D. Albers, R. Walker, et al., “Visual comparison for information visualization”, Information Visualization, Vol.10, No.4, pp.289-309, 2011.
    Q. Li, Y. Zheng, X. Xie, et al. “Mining user similarity based on location history”, Proc. of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp.34, 2008.
    Y. Qu and J. Zhang, “Trade area analysis using user generated mobile location data”, Proc. of the 22nd International Conference on World Wide Web, pp.1053-1064, 2013.
    J.K. Laurila, D. Gatica-Perez, I. Aad, et al., “The mobile data challenge: big data for mobile computing research”, Proc. of Pervasive Computing, 2012.
    Y. Zheng, L. Zhang, X. Xie, et al., “Mining interesting locations and travel sequences from gps trajectories”, Proc. of the 18th international conference on World wide web, pp.791-800, 2009
    J. Wu, C. Jiang, D. Houston, et al., “Automated time activity classification based on global positioning system (GPS) tracking data”, Environmental Health, Vol.10, No.1, pp.1-13, 2011.
    W. Wu, J. Xu, H. Zeng, et al., “Telcovis: Visual exploration of co-occurrence in urban human mobility based on telco data”, IEEE Transactions on Visualization and Computer Graphics, Vol.22, No.1, pp.935-944, 2016.
    S. Ko, R. Maciejewski, Y. Jang, et al., “Marketanalyzer: An interactive visual analytics system for analyzing competitive advantage using point of sale data”, Computer Graphics Forum, Vol.31, No.3pt3, pp.1245-1254, 2012.
    M. Nanni, B. Kuijpers, C. Korner, et al., “Spatio-temporal data mining”, Mobility Data Mining & Privacy, pp.267-296, 2008.
    H.J. Miller and J. Han, Geographic Data Mining and Knowledge Discovery, Taylor & Francis, pp.352-366, 2001.
    V.W. Zheng, Y. Zheng, X. Xie, et al., “Collaborative location and activity recommendations with gps history data”, In Proceedings of the 19th International Conference on World Wide Web, pp.1029-1038, 2010.
    Y. Zheng, Q. Li, Y. Chen, et al., “Understanding mobility based on gps data”, Proc. of the 10th International Conference on Ubiquitous Computing, pp.312-321, 2008.
    N. Andrienko, G. Andrienko and P. Gatalsky, “Exploratory spatio-temporal visualization: An analytical review”,, Journal of Visual Languages & Computing, Vol.14, No.6, pp.503-541, 2003.
    T. von Landesberger, S. Bremm, N. Andrienko, et al., “Visual analytics methods for categoric spatio-temporal data”, Proc. of IEEE Conference on Visual Analytics Science and Technology (VAST), pp.183-192, 2012.
    D. Guo, S. Liu and H. Jin, “A graph-based approach to vehicle trajectory analysis”, Journal of Location Based Services, Vol.4, No.3-4, pp.183-199, 2010.
    J. Kehrer and H. Hauser, “Visualization and visual analysis of multifaceted scientific data: A survey”, IEEE Transactions on Visualization and Computer Graphics, Vol.19, No.3, pp.495-513, 2013.
    M. Shimrat, “Algorithm 112: position of point relative to polygon”, Communications of the ACM, Vol.5, No.8, pp.434, 1962.
    G. Van Brummelen, “Heavenly mathematics: The forgotten art of spherical trigonometry”, European Legacy, Vol.20, No.5, pp.1-2, 2013.
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