{"title":"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","volume":104,"journal":"International Journal of Computer and Information Engineering","pagesStart":1993,"pagesEnd":2004,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10002969","abstract":"Given the increase in the number of e-commerce sites,\r\nthe number of competitors has become very important. This means\r\nthat companies have to take appropriate decisions in order to meet the\r\nexpectations of their customers and satisfy their needs. In this paper,\r\nwe present a case study of applying LRFM (length, recency,\r\nfrequency and monetary) model and clustering techniques in the\r\nsector of electronic commerce with a view to evaluating customers\u2019\r\nvalues of the Moroccan e-commerce websites and then developing\r\neffective marketing strategies. To achieve these objectives, we adopt\r\nLRFM model by applying a two-stage clustering method. In the first\r\nstage, the self-organizing maps method is used to determine the best\r\nnumber of clusters and the initial centroid. In the second stage, kmeans\r\nmethod is applied to segment 730 customers into nine clusters\r\naccording to their L, R, F and M values. The results show that the\r\ncluster 6 is the most important cluster because the average values of\r\nL, R, F and M are higher than the overall average value. In addition,\r\nthis study has considered another variable that describes the mode of\r\npayment used by customers to improve and strengthen clusters\u2019\r\nanalysis. 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