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Given the increase in the number of e-commerce sites,the number of competitors has become very important. This meansthat companies have to take appropriate decisions in order to meet theexpectations 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 thesector of electronic commerce with a view to evaluating customers'values of the Moroccan e-commerce websites and then developingeffective marketing strategies. To achieve these objectives, we adoptLRFM model by applying a two-stage clustering method. In the firststage, the self-organizing maps method is used to determine the bestnumber of clusters and the initial centroid. In the second stage, kmeansmethod is applied to segment 730 customers into nine clustersaccording to their L, R, F and M values. The results show that thecluster 6 is the most important cluster because the average values ofL, R, F and M are higher than the overall average value. In addition,this study has considered another variable that describes the mode ofpayment used by customers to improve and strengthen clusters'analysis. The clusters' analysis demonstrates that the payment method isone of the key indicators of a new index which allows to assess thelevel of customers' confidence in the company's Website.

Customer Segmentation Model in E-commerce Using Clustering Techniques and LRFM Model: The Case of Online Stores in Morocco