Analyzing Customers of South Khorasan Telecommunication Company with Expansion of RFM to LRFM Model

Telecommunication companies use data mining techniques to maintain good relationships with their existing customers, attract new customers, and identify profitable/unprofitable customers. Clustering leads to a better understanding of customers and its results can be used for definition and decision-making for promotional schemes. In this research work, we use the 999-customer purchase records in the South Khorasan Telecommunication Company collected during a year. The purpose of this work is to classify customers into several clusters. Since the clusters and the number of their members are determined, the high-consumption users will be logged out of the system and the high-value customers who have been missed will be identified. We divide the customers into five categories: loyal, potential, new, missed, and high-consumption using the Clementine software, developing the RFM model to the LRFM model and the Two_Step and K_Means algorithms. Thus this category will be a good benchmark for a company's future decisions, and we can make better decisions for each group of customers in the future