Behavioral segmentation of bank's Point-of-Sales using RF*M* approach

POSs (Point-of-Sale) are one of the major electronic payment systems in banking industry with more cost. The bank needs to know its customers behavior to find interesting segments to attract more transactions which results in the growth of its income and assets. The RFM (recency, frequency and monetary) analysis is a famous approach for extracting behavior of customers and is a basis for marketing and CRM (customer relationship management) processes, but is not aligned enough for banking context. So RF*M* is proposed in this article. It is used for both preprocess and segmentation phases. Segmentation based on the RF*M* helps us to achieve a better understanding of groups of customers. For segmentation, ULVQ (unsupervised learning vector quantization) algorithm along with thirteen clustering validation indices is applied to find more exact and understandable results.