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

Vahide Babaiyan · Seyyede A. Sarfarazi

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

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.

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1972Principles of Marketing 2002The mismanagement of customer loyalty.2011Data Mining Using RFM Analysis 2013Customer Churn Prediction in Telecommunication A Decade Review and Classification 2019RFM-Based customer segmentation as an elaborative analytical tool for enriching the creation of sales and trade marketing strategies

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2021Identifying customer priority for new products in target marketing: Using RFM model and TextRank 2022A Novel Hybrid Segmentation Approach for Decision Support: A Case Study in Banking 2022The Implementation of K-Means dan K-Medoids Algorithm for Customer Segmentation on E-commerce Data Transactions 2020A Literature Review of Data Mining Techniques for Enhancing Digital Customer Engagement 2021E-Commerce Customer Segmentation Using K-Means Algorithm and Length, Recency, Frequency, Monetary Model

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