Customer Segmentation Model In E-Commerce Using Clustering Techniques And Lrfm Model: The Case Of Online Stores In Morocco

Given the increase in the number of e-commerce sites, the number of competitors has become very important. This means that companies have to take appropriate decisions in order to meet the expectations of their customers and satisfy their needs. In this paper, we present a case study of applying LRFM (length, recency, frequency and monetary) model and clustering techniques in the sector of electronic commerce with a view to evaluating customers' values of the Moroccan e-commerce websites and then developing effective marketing strategies. To achieve these objectives, we adopt LRFM model by applying a two-stage clustering method. In the first stage, the self-organizing maps method is used to determine the best number of clusters and the initial centroid. In the second stage, kmeans method is applied to segment 730 customers into nine clusters according to their L, R, F and M values. The results show that the cluster 6 is the most important cluster because the average values of L, R, F and M are higher than the overall average value. In addition, this study has considered another variable that describes the mode of payment used by customers to improve and strengthen clusters' analysis. The clusters' analysis demonstrates that the payment method is one of the key indicators of a new index which allows to assess the level of customers' confidence in the company's Website. {"references": ["Interbank Electronic banking Centre, Morocco, \"Activit\u00e9 mon\u00e9tique 1er\ntrimestre 2015 au Maroc\", cmi.co.ma", "G. C O'Connor, B O'Keefe, Viewing the web as a marketplace: the case\nof small companies, Decision Support Systems, vol. 21(3), 1997, pp.\n171\u2013183.", "H. H. Wu, S. Y. Lin, C. W. Liu, Analyzing Patients' Values by Applying\nCluster Analysis and LRFM Model in a Pediatric Dental Clinic in\nTaiwan, Hindawi Publishing Corporation The Scientific World Journal,\nVol 2014, Article ID 685495, 2014, 7 pages.", "C. H. Cheng, Y. S. Chen, Classifying the segmentation of customer\nvalue via RFM model and RS theory, Expert Systems with Applications,\nVol.36, N.3, 2009, pp.4176\u20134184.", "S. Irvin, Using lifetime value analysis for selecting new customers,\nCredit World, Vol.82, No.3, 1994, pp. 37-40.", "J. T. Wei, S. Y. Lin, C. C. Weng, H. H. Wu, A case study of applying\nLRFM model in market segmentation of a children's dental clinic.\nExpert Systems with Applications Vol.39, No.5, 2012, pp. 5529\u20135533.", "H. H. Chang, S. F. Tsay, Integrating of SOM and K-man in data mining\nclustering: An empirical study of CRM and profitability evaluation,\nJournal of Information Management, Vol.11, No.4, 2004, pp. 161\u2013203.", "W. J. Reinartz, V. Kumar, On the profitability of long-life customers in a\nnoncontractual setting: An empirical investigation and implications for\nmarketing. Journal of Marketing, Vol.64, No.4, 2000, pp. 17\u201335.", "A. M. Hughes, Boosting response with RFM. Marketing Tools, Vol.3,\nNo.3, 1996, pp. 4-7.\n[10] G. M. Marakas, Decision Support Systems in the 21st Century, Second\nEdition. Prentice Hall, Upper Saddle River, NJ, 2003.\n[11] A. X. Yang, How to develop new approaches to RFM segmentation,\nJournal of Targeting, Measurement and Analysis for Marketing, Vol.13,\nNo.1, 2004, pp. 50-60.\n[12] S. Golsefid, M.Ghazanfari, S. Alizadeh,'Customer Segmentation in\nForeign Trade based on Clustering Algorithms Case Study: Trade\nPromotion Organization of Iran'. World Academy of Science,\nEngineering and Technology, International Science Index 4,\nInternational Journal of Mechanical, Aerospace, Industrial, Mechatronic\nand Manufacturing Engineering, Vol.1, No.4, 2007, pp. 230 - 236.\n[13] C. H. Wang, Apply robust segmentation to the service industry using\nkernel induced fuzzy clustering techniques, Expert Systems with\nApplications, Vol.37, No.12, 2010, pp. 8395-8400.\n[14] J. T. Wei, S. Y. Lin, and H. H. Wu, A review of the application RFM\nmodel, African Journal of Business Management, Vol.4, No.19, 2010,\npp. 4199\u20134206.\n[15] A. M. Hughes, Strategic database marketing. Chicago, Probus\nPublishing Company, 1994.\n[16] R. Kahan, Using database marketing techniques to enhance your one-toone\nmarketing initiatives, Journal of Consumer Marketing, Vol.15, No.5,\n1998, pp. 491-493.\n[17] E. C. Chang, H. C. Huang, H. H Wu, Using K-means method and\nspectral clustering technique in an outfitter's value analysis, Quality &\nQuantity, Vol.44, No.4, 2010, pp. 807\u2013815.\n[18] S. M. S. Hosseini, A. Maleki, M. R. Gholamian, 2010, Cluster analysis\nusing data mining approach to develop CRM methodology to assess the\ncustomer loyalty, Journal of Expert Systems with Applications, Vol.37,\nNo.7, 2010, pp. 5259\u20135264.\n[19] I. C. Yeh, K. J. Yang, T. M. Ting, Knowledge discovery on RFM model\nusing Bernoulli sequence, Expert Systems with Applications, Vol.36,\nNo.3, 2009, pp. 5866\u20135871.\n[20] H. C. Chang, H. P. Tsai, Group RFM analysis as a novel framework to\ndiscover better customer consumption behavior, Expert Systems with\nApplications, Vol.38, No.12, 2011, pp.14499\u201314513.\n[21] L. H. Li, F. M. Lee, W. J. Liu, The timely product recommendation\nbased on RFM method, Proceedings of the International Conference on\nBusiness and Information, 2006, Singapore.\n[22] S. Chow, R. Holden, Toward an understanding of loyalty: The\nmoderating role of trust, Journal of Management issues, Vol.9, No.3,\n1997, pp. 275\u2013298.\n[23] S. Huang, E. C. Chang, H. H. Wu, A case study of applying data mining\ntechniques in an outfitter's customer value analysis, Expert Systems with\nApplications, Vol.36, No.3, 2009, pp 5909\u20135915.\n[24] J. T. Wei, S. Y. Lin, C. C. Weng, H. H. Wu, Customer relationship\nmanagement in the hairdressing industry: An application of data mining\ntechniques, Expert Systems with Applications, Vol.40, No.18, 2013, pp.\n7513\u20137518.\n[25] S. Wang, Cluster analysis using a validated self-organizing method:\nCases of problem identification, Intelligent Systems in Accounting,\nFinance and Management, Vol.10, No.2, 2001, pp. 127\u2013138.\n[26] K. E. Fish, P. Ruby, An artificial intelligence foreign market screening\nmethod for small businesses, International Journal of Entrepreneurship,\nVol.13, 2009, pp. 65\u201381.\n[27] D. Ordonez, C. Dafonte, M. Manteiga, B. Arcay, Hierarchical Clustering\nAnalysis with SOM Networks, World Academy of Science, Engineering\nand Technology, International Science Index 45, Vol.4, No.9, 2010, pp.\n213 - 219.\n[28] L. Churilov, A. Bagirov, D. Schwartz, K. Smith, D. Michael, Data\nmining with combined use of optimization techniques and selforganizing\nmaps for improving risk grouping rules: Application to\nprostate cancer patients, Journal of Management Information Systems,\nVol.21, No.4, 2005, pp. 85\u2013100.\n[29] A. Sindhuja, V. Sadasivam, Automatic Detection of Breast Tumors in\nSonoelastographic Images Using DWT, World Academy of Science,\nEngineering and Technology, International Science Index 81,\nInternational Journal of Biological, Biomolecular, Agricultural, Food\nand Biotechnological Engineering, Vol.7, No.9, 2013, pp. 590-596.\n[30] D. Birant, Data Mining Using RFM Analysis, Knowledge-Oriented\nApplications in Data Mining, ISBN: 978-953-307-154-1, 2011, InTech,\nDOI: 10.5772/13683, Available from: intechopen.com] G. Punj, D. W. Steward, (1983), Cluster analysis in marketing research:\nreview and suggestions for applications, Journal of Marketing Research,\nVol.20, No.2, 1983, pp. 134-148.\n[32] R. J. Kuo, L. M. Ho, C. M. Hu, Integration of self-organizing feature\nmap and K-means algorithm for market segmentation, Computers &\nOperations Research, Vol.29, No.11, 2002, pp. 1475-1493.\n[33] J. Han, M. Kamber, Data Mining: Concepts and Techniques, CA:\nMorgan Kaufmann, San Francisco, 2006.\n[34] P. N. Tan, M. Steinbach, V. Kumar, Introduction to data mining,\nPearson education, 2005.\n[35] O. Krivobokova, 'Evaluating Customer Satisfaction as an Aspect of\nQuality Management'. World Academy of Science, Engineering and\nTechnology, International Science Index 29, Vol.3, No5, 2009, pp. 482 -\n485.\n[36] R. M. Schindler, T. M. Kibarian, Increased Consumer Sales Response\nthrough Use of 99 Ending Prices, Journal of Retailing, Vol.72, No.2,\n1996, pp. 187-199.\n[37] N. GUEGUEN, 100 petites exp\u00e9riences en psychologie du\nconsommateur pour mieux comprendre comment on vous influence,\nParis: Dunod, 2005.\n[38] H. Isaac, P. Volle, E-Commerce De la strat\u00e9gie \u00e0 la mise en oeuvre\nop\u00e9rationnelle, Pearson Education, 2008.\n[39] S. Bellman, G. Lohse, E. Johnson, Predictors of online buying behavior,\nCommunications of the ACM, Vol.42, Vo.12, 2000, pp. 32-38."]}