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A case study of applying LRFM (length, recency, frequency, and monetary) model and clustering techniques in evaluating an outfi tter's customer values is presented. Self-organizing maps is fi rst used to determine the best number of clusters and then K-means method is applied to classify 551 customers into twelve clusters when L, R, F, and M are the segmenting variables. The results show that Cluster 5 might be the most important cluster because the average values of L, R, F, and M are well above the averages. In contrast, the customers in Clusters 7 and 10 have low contributions since L, R, F, and M values are below the average values. As a result, with the applications of LRFM model and clustering techniques, the outfi tter can allocate and utilize resources eff ectively and eff iciently to fi rst identify high-value and profi t potential customers and then design diff erent marketing strategies to maximize its profi ts for diff erent types of clusters.

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