Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31741
Customer Segmentation Model in E-commerce Using Clustering Techniques and LRFM Model: The Case of Online Stores in Morocco

Authors: Rachid Ait daoud, Abdellah Amine, Belaid Bouikhalene, Rachid Lbibb

Abstract:

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.

Keywords: Customer value, LRFM model, Cluster analysis, Self-Organizing Maps method (SOM), K-means algorithm, loyalty.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110165

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5748

References:


[1] Interbank Electronic banking Centre, Morocco, “Activité monétique 1er trimestre 2015 au Maroc”, https://www.cmi.co.ma/PDF/Mon%C3% A9tique%20Marocaine%20au%2031%20Mars%202015.pdf
[2] G. C O’Connor, B O’Keefe, Viewing the web as a marketplace: the case of small companies, Decision Support Systems, vol. 21(3), 1997, pp. 171–183.
[3] H. H. Wu, S. Y. Lin, C. W. Liu, Analyzing Patients’ Values by Applying Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in Taiwan, Hindawi Publishing Corporation The Scientific World Journal, Vol 2014, Article ID 685495, 2014, 7 pages.
[4] C. H. Cheng, Y. S. Chen, Classifying the segmentation of customer value via RFM model and RS theory, Expert Systems with Applications, Vol.36, N.3, 2009, pp.4176–4184.
[5] S. Irvin, Using lifetime value analysis for selecting new customers, Credit World, Vol.82, No.3, 1994, pp. 37-40.
[6] J. T. Wei, S. Y. Lin, C. C. Weng, H. H. Wu, A case study of applying LRFM model in market segmentation of a children’s dental clinic. Expert Systems with Applications Vol.39, No.5, 2012, pp. 5529–5533.
[7] H. H. Chang, S. F. Tsay, Integrating of SOM and K-man in data mining clustering: An empirical study of CRM and profitability evaluation, Journal of Information Management, Vol.11, No.4, 2004, pp. 161–203.
[8] W. J. Reinartz, V. Kumar, On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing, Vol.64, No.4, 2000, pp. 17–35.
[9] A. M. Hughes, Boosting response with RFM. Marketing Tools, Vol.3, No.3, 1996, pp. 4-7.
[10] G. M. Marakas, Decision Support Systems in the 21st Century, Second Edition. Prentice Hall, Upper Saddle River, NJ, 2003.
[11] A. X. Yang, How to develop new approaches to RFM segmentation, Journal of Targeting, Measurement and Analysis for Marketing, Vol.13, No.1, 2004, pp. 50-60.
[12] S. Golsefid, M.Ghazanfari, S. Alizadeh,'Customer Segmentation in Foreign Trade based on Clustering Algorithms Case Study: Trade Promotion Organization of Iran'. World Academy of Science, Engineering and Technology, International Science Index 4, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, Vol.1, No.4, 2007, pp. 230 - 236.
[13] C. H. Wang, Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques, Expert Systems with Applications, Vol.37, No.12, 2010, pp. 8395-8400.
[14] J. T. Wei, S. Y. Lin, and H. H. Wu, A review of the application RFM model, African Journal of Business Management, Vol.4, No.19, 2010, pp. 4199–4206.
[15] A. M. Hughes, Strategic database marketing. Chicago, Probus Publishing Company, 1994.
[16] R. Kahan, Using database marketing techniques to enhance your one-toone marketing initiatives, Journal of Consumer Marketing, Vol.15, No.5, 1998, pp. 491-493.
[17] E. C. Chang, H. C. Huang, H. H Wu, Using K-means method and spectral clustering technique in an outfitter’s value analysis, Quality & Quantity, Vol.44, No.4, 2010, pp. 807–815.
[18] S. M. S. Hosseini, A. Maleki, M. R. Gholamian, 2010, Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty, Journal of Expert Systems with Applications, Vol.37, No.7, 2010, pp. 5259–5264.
[19] I. C. Yeh, K. J. Yang, T. M. Ting, Knowledge discovery on RFM model using Bernoulli sequence, Expert Systems with Applications, Vol.36, No.3, 2009, pp. 5866–5871.
[20] H. C. Chang, H. P. Tsai, Group RFM analysis as a novel framework to discover better customer consumption behavior, Expert Systems with Applications, Vol.38, No.12, 2011, pp.14499–14513.
[21] L. H. Li, F. M. Lee, W. J. Liu, The timely product recommendation based on RFM method, Proceedings of the International Conference on Business and Information, 2006, Singapore.
[22] S. Chow, R. Holden, Toward an understanding of loyalty: The moderating role of trust, Journal of Management issues, Vol.9, No.3, 1997, pp. 275–298.
[23] S. Huang, E. C. Chang, H. H. Wu, A case study of applying data mining techniques in an outfitter’s customer value analysis, Expert Systems with Applications, Vol.36, No.3, 2009, pp 5909–5915.
[24] J. T. Wei, S. Y. Lin, C. C. Weng, H. H. Wu, Customer relationship management in the hairdressing industry: An application of data mining techniques, Expert Systems with Applications, Vol.40, No.18, 2013, pp. 7513–7518.
[25] S. Wang, Cluster analysis using a validated self-organizing method: Cases of problem identification, Intelligent Systems in Accounting, Finance and Management, Vol.10, No.2, 2001, pp. 127–138.
[26] K. E. Fish, P. Ruby, An artificial intelligence foreign market screening method for small businesses, International Journal of Entrepreneurship, Vol.13, 2009, pp. 65–81.
[27] D. Ordonez, C. Dafonte, M. Manteiga, B. Arcay, Hierarchical Clustering Analysis with SOM Networks, World Academy of Science, Engineering and Technology, International Science Index 45, Vol.4, No.9, 2010, pp. 213 - 219.
[28] L. Churilov, A. Bagirov, D. Schwartz, K. Smith, D. Michael, Data mining with combined use of optimization techniques and selforganizing maps for improving risk grouping rules: Application to prostate cancer patients, Journal of Management Information Systems, Vol.21, No.4, 2005, pp. 85–100.
[29] A. Sindhuja, V. Sadasivam, Automatic Detection of Breast Tumors in Sonoelastographic Images Using DWT, World Academy of Science, Engineering and Technology, International Science Index 81, International Journal of Biological, Biomolecular, Agricultural, Food and Biotechnological Engineering, Vol.7, No.9, 2013, pp. 590-596.
[30] D. Birant, Data Mining Using RFM Analysis, Knowledge-Oriented Applications in Data Mining, ISBN: 978-953-307-154-1, 2011, InTech, DOI: 10.5772/13683, Available from: http://www.intechopen.com/ books/knowledge-oriented-applications-in-data-mining/data-miningusing- rfm-analysis
[31] G. Punj, D. W. Steward, (1983), Cluster analysis in marketing research: review and suggestions for applications, Journal of Marketing Research, Vol.20, No.2, 1983, pp. 134-148.
[32] R. J. Kuo, L. M. Ho, C. M. Hu, Integration of self-organizing feature map and K-means algorithm for market segmentation, Computers & Operations Research, Vol.29, No.11, 2002, pp. 1475-1493.
[33] J. Han, M. Kamber, Data Mining: Concepts and Techniques, CA: Morgan Kaufmann, San Francisco, 2006.
[34] P. N. Tan, M. Steinbach, V. Kumar, Introduction to data mining, Pearson education, 2005.
[35] O. Krivobokova, 'Evaluating Customer Satisfaction as an Aspect of Quality Management'. World Academy of Science, Engineering and Technology, International Science Index 29, Vol.3, No5, 2009, pp. 482 - 485.
[36] R. M. Schindler, T. M. Kibarian, Increased Consumer Sales Response through Use of 99 Ending Prices, Journal of Retailing, Vol.72, No.2, 1996, pp. 187-199.
[37] N. GUEGUEN, 100 petites expériences en psychologie du consommateur pour mieux comprendre comment on vous influence, Paris: Dunod, 2005.
[38] H. Isaac, P. Volle, E-Commerce De la stratégie à la mise en oeuvre opérationnelle, Pearson Education, 2008.
[39] S. Bellman, G. Lohse, E. Johnson, Predictors of online buying behavior, Communications of the ACM, Vol.42, Vo.12, 2000, pp. 32-38.