In this decade, SM Es have experienced substantial growth. According to the results of research conducted by the Retail Research Center, this sector experienced a growth rate of 18.6% in Europe in 2015 and 16.7% in 2016. The increasing co mpetition in the SM Es demanded this effort to improve techniques and strategies to maintain customer satisfaction levels to continue to increase .[1]. The SM Es sector has an important role in the country' s economy, especially Indonesia. They have proven their existence in the past few years. SM Es have a proportion of 99.99% of the total business actors in Indonesia or as many as 56.54 million units. Based on data achieved by SMEs, in 2013 the Abstract: The CLV model is a measure of customer profit fo r a co mpany that can be used to evaluate the future value of a customer. The CLV model is a measure of customer profit fo r a co mpany that can be used to evaluate the future value of a customer. This study aims to obtain Customer Lifetime Value (CLV) in each customer segment. Grouping uses the K-Means Clustering method based on the LRFM model (Length, Recency, Frequency, Monetary). The cluster formation process uses the Elbow Method and SSE with the best number of clusters = 2 clusters. CLV values are generated fro m the mu ltip lication of the results of normalizat ion of LRFM and the LFRM weight values are then summed, and carried out on each cluster that has been formed. The highest ranking among the 2 clusters is at the second cluster with the CLV value being far the h ighest from the other cluster average of 0.362. Based on LRFM matrix, this cluster has a high loyalty value with the symbol LRFM L ↑ R ↑ F ↑ M ↑ wh ich is a loyal customer (the best segment that has high customer loyalty value). Based on the LRFM symbol, the company can make a strategy to retain customers and acquire customers to become loyal customers with high profitability.

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Business capital and revenue are not only the decisive of the health of SMEs but also they must be balanced. In general, customers find their benefit from the flexible payment methods while on the other hand the SMEs should get their benefit too. So that, it needs to be studied whether it is necessary for SMEs to get their profit in accordance to this situation. One of the methods that suitable to be applied is by applying customer groupings based on revenue and payment namely the K-means clustering method since it can raise several groups that have not been known before. This information is useful for SMEs to be utilized based on their needs. Data in this study were gathered from customer attributes, number of transactions, and payment methods. The number of centroids was 3. The grouping results were stopped at the 5th iteration. The finding showed that the ratio value of the 4th iteration and the 5th iteration having the same ratio value, 0.07393. From the results of the iterations can be found; first, based on the customers’ number, the groups can be classified into three C1(18%), C2 (45%), C3 (36%). Second, based on the average number of transactions, post-paid payments was in the first rank (12.7 / week). From the results, it can be analyzed that this situation is burdensome for SMEs because the more the number of transactions, the more investment must be prepared for accounts receivable.