K-Mean Clustering Method For Analysis Customer Lifetime Value With LRFM Relationship Model In Banking Services

ABSTRACT: In today’s businesses, achieving customers satisfaction have critical role in organizationʹs goals. On the other hand, all of customers hasn’t equal share in profitability of organization. Therefore, identification key customers will be more sensitive. Calculate the lifetime value assist organizations to rank customers based on their contribution to profitability. The purposeofthispaperisintroduced a model to calculate customer lifetime value (CLV) based on LRFM customer relationship model which consists of four dimensions: relation length (L), recent transaction time (R), buying frequency (F), and monetary (M) in banking services. We proceed with this clustering analysis to classify customers in order to set marketing strategies.Inthisresearch, K-Mean clustering methodas one of the main problems in unsupervised learning emphasizes.Achieving this, we used crisp method and implemented them on real data from an Iranian state bank. Validity of clustering process analyzed with R-Squared index. The results show nine cluster patterns between customers. Finally, in terms of this clustering, we proposed customer strategies. Thus, this study considers useful for customer relationship management.