Clustering of Customer Lifetime Value With Length Recency Frequency and Monetary Model Using Fuzzy C-Means Algorithm

In the practice of implementing customer relationship management or Customer Relationship Management (CRM), many companies that have not properly used CRM as a company's business strategy. As is the case with CV. New Taste has not differentiated which customers provide profit and which have not provided profit to the company. During the process of processing customer transaction data, CV. Cita Rasa Baru has used the system, but in practice the system is only used for profit calculations and recapitulation to agents registered with the company. Therefore, it is necessary to determine customer segmentation based on customer lifetime value so that companies can find out the characteristics of their customers. Processing of customer transaction data is carried out using the clustering method with the Fuzzy c-Means algorithm and LRFM. The validity used in the cluster was the Partition Coefficient Index (PCI) and the CL V calculation uses AHP weighting to produce cluster rankings. Thus, the results of customer clustering consisting of 3 segments were obtained, namely cluster 2 which has the highest CLV of 0.20756 with 107 customers in the high value loyal customer segmentation, the second rank was cluster 3 with a value of 0.18056 with a total of 97 customers who were in the uncertain new segmentation. customer and ranked third in cluster 1 with a value of 0.17409 with a total of 78 customers who are in the uncertain lost customer segmentation.