Improve Customer Relationship Management With The Developed RFM Approach In The Communications Industry

Subject Areas : Business and Marketing

Mohammad Naderi Dehkordi ^{1 *} , Maryam Sadeghi ² , Behrang Barekatain ³ , naser khani ⁴

1 - Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran
3 - Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
4 - Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Received: 2023-10-29 Accepted : 2025-01-08 Published : 2025-05-12

Keywords: RFM Analysis, K Means clustering Algorithm, Customer Relationship Management, Data Mining, Communication Industry,

Abstract :

With the competitiveness of the communications industry and the growth of customer expectations, new customer relationship strategies are felt in this industry. Customer Relationship Management is a valuable process in marketing to better understand customers in order to improve profitability and long-term customer relationships. Effective knowledge of customer information can provide services tailored to the functional and behavioral characteristics of each category to improve customer relationship management. Data mining is a powerful tool for companies to extract knowledge from their customers' transaction data. One of the useful applications of data mining is segmentation. In this article, customer transaction data of one of the fixed internet service providers in Isfahan province based on the analysis of the RFM model and the proposed LRFM-Ccr model (length of the customer relationship, recent purchase, number of purchases, paid expenses, and customer consumption records) are selected and are categorized and then clustered using the K Means algorithm. The value of the Davis Boldin index for each number of clusters in the proposed LRFM-Ccr model and its corresponding value in the RFM model have been compared and evaluated. The models are compared with Recall, cluster compactness, accuracy, Number of correct predictions, and correct weight average.