List of Articles RFM model

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  1 - A method for clustering customers using RFM model and grey numbers in terms of uncertainty
  azime mozafari
  The purpose of this study is presentation a method for clustering bank customers based on RFM model in terms of uncertainty. According to the proposed framework in this study after determination the parameter values of the RFM model, including recently exchange (R), fre More

  The purpose of this study is presentation a method for clustering bank customers based on RFM model in terms of uncertainty. According to the proposed framework in this study after determination the parameter values of the RFM model, including recently exchange (R), frequency exchange (F), and monetary value of the exchange (M), grey theory is used to eliminate the uncertainty and customers are segmented using a different approach. Thus, bank customers are clustered to three main segments called good, ordinary and bad customers. After cluster validation using Dunn index and Davis Bouldin index, properties of customers are detected in any of the segments. Finally, recommendations are offered to improve customer relationship management system. Manuscript profile
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  2 - Provide a method for customer segmentation using the RFM model in conditions of uncertainty
  mohammadreza gholamian azime mozafari
  The purpose of this study is to provide a method for customer segmentation of a private bank in Shiraz based on the RFM model in the face of uncertainty about customer data. In the proposed framework of this study, first, the values ​​of RFM model indicators including e More

  The purpose of this study is to provide a method for customer segmentation of a private bank in Shiraz based on the RFM model in the face of uncertainty about customer data. In the proposed framework of this study, first, the values ​​of RFM model indicators including exchange novelty (R), number of exchanges (F) and monetary value of exchange (M) were extracted from the customer database and preprocessed. Given the breadth of the data, it is not possible to determine the exact number to determine whether the customer is good or bad; Therefore, to eliminate this uncertainty, the gray number theory was used, which considers the customer's situation as a range. In this way, using a different method, the bank's customers were segmented, which according to the results, customers were divided into three main sections or clusters as good, normal and bad customers. After validating the clusters using Don and Davis Boldin indicators, customer characteristics in each sector were identified and at the end, suggestions were made to improve the customer relationship management system. Manuscript profile
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  3 - A RFMV Model and Customer Segmentation Based on Variety of Products
  Saman  Qadaki Moghaddam Neda Abdolvand Saeedeh Rajaee Harandi
  10.7508/jist.2017.19.002
  Today, increased competition between organizations has led them to seek a better understanding of customer behavior through innovative ways of storing and analyzing their information. Moreover, the emergence of new computing technologies has brought about major change More

  Today, increased competition between organizations has led them to seek a better understanding of customer behavior through innovative ways of storing and analyzing their information. Moreover, the emergence of new computing technologies has brought about major changes in the ability of organizations to collect, store and analyze macro-data. Therefore, over thousands of data can be stored for each customer. Hence, customer satisfaction is one of the most important organizational goals. Since all customers do not represent the same profitability to an organization, understanding and identifying the valuable customers has become the most important organizational challenge. Thus, understanding customers’ behavioral variables and categorizing customers based on these characteristics could provide better insight that will help business owners and industries to adopt appropriate marketing strategies such as up-selling and cross-selling. The use of these strategies is based on a fundamental variable, variety of products. Diversity in individual consumption may lead to increased demand for variety of products; therefore, variety of products can be used, along with other behavioral variables, to better understand and categorize customers’ behavior. Given the importance of the variety of products as one of the main parameters of assessing customer behavior, studying this factor in the field of business-to-business (B2B) communication represents a vital new approach. Hence, this study aims to cluster customers based on a developed RFM model, namely RFMV, by adding a variable of variety of products (V). Therefore, CRISP-DM and K-means algorithm was used for clustering. The results of the study indicated that the variable V, variety of products, is effective in calculating customers’ value. Moreover, the results indicated the better customers clustering and valuation by using the RFMV model. As a whole, the results of modeling indicate that the variety of products along with other behavioral variables provide more accurate clustering than RFM model. Manuscript profile