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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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In this study, we develop a comprehensive design process of granular fuzzy rule-based systems. These constructs arise as a result of a structural compression of fuzzy rule-based systems in which a subset of originally existing rules is retained. Because of the reduced subset of the originally existing rules, the remaining rules are made more abstract (general) by expressing their conditions in the form of granular fuzzy sets (such as interval-valued fuzzy sets, rough fuzzy sets, probabilistic fuzzy sets, etc.), hence the name of granular fuzzy rule-based systems emerging during the compression of the rule bases. The design of these systems dwells upon an important mechanism of allocation of information granularity using which the granular fuzzy rules are formed. The underlying optimization consists of two phases: structural (being of combinatorial character in which a subset of rules is selected) and parametric (when the conditions of the selected rules are made granular through an optimal allocation of information granularity). We implement the cooperative particle swarm optimization to solve optimization problem. A number of experimental studies are reported; those include fuzzy rule-based systems.

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