@article { author = {Babaiyan, V. and Sarfarazi, Seyyede A.}, title = {Analyzing Customers of South Khorasan Telecommunication Company with Expansion of RFM to LRFM Model}, journal = {Journal of AI and Data Mining}, volume = {7}, number = {2}, pages = {331-340}, year = {2019}, publisher = {Shahrood University of Technology}, issn = {2322-5211}, eissn = {2322-4444}, doi = {10.22044/jadm.2018.6035.1715}, abstract = {Telecommunication Companies use data mining techniques to maintain good relationships with their existing customers and attract new customers and identifying profitable/unprofitable customers. Clustering leads to better understanding of customer and its results can be used to definition and decision-making for promotional schemes. In this study, we used the 999-customer purchase records in South Khorasan Telecommunication Company which has been collected during a year. The purpose of this study is to classify customers into several clusters. Since the clusters and the number of their members are determined, high-consumption users will be logged out of the system and high-value customers who are missed will be identified. In this research we divided our customers into five categories: loyal, potential, new, missed and high-consumption by using the Clementine software, developing the RFM model to the LRFM model and TwoStep and k_Means algorithms. Thus, this category will be a good benchmark for company's future decisions and we can make better decisions for each group of customers in the future.}, keywords = {LRFM Model,TwoStep algorithm,k_Means algorithm}, url = {https://jad.shahroodut.ac.ir/article_1187.html}, eprint = {https://jad.shahroodut.ac.ir/article_1187_2c51c149ee9fdd0b4dc816a5ba235c0f.pdf} }