Competitors’ Influence Analysis of a Retailer by Using Customer Value and Huff’s Gravity Model

Customer relationship analysis is vital for retail stores, especially for supermarkets. The point of sale (POS) systems make it possible to record the daily purchasing behaviors of customers as an identification point of sale (ID-POS) database, which can be used to analyze customer behaviors of a supermarket. The customer value is an indicator based on ID-POS database for detecting the customer loyalty of a store. In general, there are many supermarkets in a city, and other nearby competitor supermarkets significantly affect the customer value of customers of a supermarket. However, it is impossible to get detailed ID-POS databases of competitor supermarkets. This study firstly focused on the customer value and distance between a customer's home and supermarkets in a city, and then constructed the models based on logistic regression analysis to analyze correlations between distance and purchasing behaviors only from a POS database of a supermarket chain. During the modeling process, there are three primary problems existed, including the incomparable problem of customer values, the multicollinearity problem among customer value and distance data, and the number of valid partial regression coefficients. The improved customer value, Huff’s gravity model, and inverse attractiveness frequency are considered to solve these problems. This paper presents three types of models based on these three methods for loyal customer classification and competitors’ influence analysis. In numerical experiments, all types of models are useful for loyal customer classification. The type of model, including all three methods, is the most superior one for evaluating the influence of the other nearby supermarkets on customers' purchasing of a supermarket chain from the viewpoint of valid partial regression coefficients and accuracy.

Authors:

• Yepeng Cheng
• Yasuhiko Morimoto

Keywords:

• Customer value
• Huff's Gravity Model
• POS
• retailer.

References:

[1] M. Khajvand, K. Zolfaghar, S. Ashoori, S. Alizadeh, "Estimating customer lifetime value based on RFM analysis of customer purchase behavior: case study," Procedia Computer Science 3, pp. 57-63, 2011.
[2] A. M. Hughes, Strategic database marketing, Chicago: Probus Publishing Company, 1994.
[3] H. C. Chang, H. P. Tsai, "Group RFM analysis as a novel framework to discover better customer consumption behavior," Expert Systems with Applications 38, pp. 14499-14513, 2011.
[4] J. Wu, Z. Lin, "Research on customer segmentation model by clustering," In Proceedings of the 7th ACM ICEC international conference on electronic commerce, 2005.
[5] T. Tanaka, T. Hamaguchi, T. Saigo, K. Tsuda, "Classifying and Understanding Prospective Customers via Heterogeneity of Supermarket Stores," International Conference on Knowledge Based and Intelligent Information and Engineering Systems, pp. 956-964, 2017.
[6] D. W. Hosmer, S. Lemeshow, Applied Logistic Regression, 2nd ed. John Wiley \& Sons, Inc, 2000.
[7] T. Tjur, "Coefficients of determination in logistic regression models," American Statistician: pp. 366-372, 2009.
[8] D. A. Freedman, Statistical Models: Theory and Practice, Cambridge University Press, pp. 128, 2009.
[9] F. E. Harrell, Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer, 2010.
[10] J. A. Morris, M. J. Gardner, "Calculating confidence intervals for relative risks (odds ratios) and standardised ratios and rates," British Medical Journal, 1988.
[11] D. E. Farrar, R. R. Glauber, "Multicollinearity in Regression Analysis: The Problem Revisited," Review of Economics and Statistics, 49, issue 1, pp. 92-107, 1967.
[12] J. Correia, RFM-analysis, GitHub repository, 2016. (Online). Available: https://github.com/joaolcorreia/RFM-analysis
[13] D. L. Huff, "Defining and Estimating a Trade Area," Journal of Marketing, vol. 28, pp. 34-38, 1964.
[14] W. J. Reilly, The law of retail gravitation, New York: Knickerbocker Press, 1931.
[15] M. Nakanishi, L. G. Cooper, "Parameter estimation for a multiplicative competitive interaction model-least squares approach," Journal of Marketing Research, 11, pp. 303-311, 1974.
[16] D. B. Segal, "Retail Trade Area Analysis: Concepts and New Approaches," The Journal of Database Marketing, vol. 6, no. 3, pp. 267-277, 1999.
[17] K. Chen, Y. H. Hu, Y. C. Hsieh, "Predicting customer churn from valuable B2B customers in the logistics industry: a case study," Information Systems and e-Business Management, vol. 13, no. 3, pp. 475-494, 2015.
[18] H. Abdi, L. J. Williams, "Principal component analysis," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 4, pp. 433-459, 2010.
[19] D. Freedman, R. Pisani, R. Purves, Statistics: Fourth International Student Edition. W.W. Norton & Company, 2007.
[20] J. Fan, I. Gijbels, Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability, Chapman & Hall/CRC, 1996.
[21] S. Zenker, T. Gollan, N. V. Quaquebeke, "Using Polynomial Regression Analysis and Response Surface Methodology to Make a Stronger Case for Value Congruence in Place Marketing," Psychology and Marketing, vol. 31, issue 3, pp. 184-202, 2014.
[22] Ruth M. W. Yeung, Wallace M. S. Yee, "Logistic Regression: An advancement of predicting consumer purchase propensity," The Marketing Review, vol. 11, no. 1, 2011.
[23] C. Constantin, "Using the Logistic Regression model in supporting decisions of establishing marketing strategies," Bulletin of the Transilvania University of Braşov Series V: Economic Sciences, vol. 8, issue 57, no. 2, 2015.
[24] H. Anton, Elementary Linear Algebra, 7th ed. John Wiley & Sons, pp. 170-171, 1994.
[25] C. Cui, J. Wang, Y. Pu, J. Ma, G. Chen, "GIS-based method of delimitating trade area for retail chains," International Journal of Geographical Information Science, vol. 26, no. 10, pp. 1863-1879, 2012.
[26] A. I. Marqués, V. García, J. S. Sánchez, "On the suitability of resampling techniques for the class imbalance problem in credit scoring," Journal of the Operational Research Society, vol. 64, no. 7, pp. 1060-1070, 2013.