Article


Article Code : 13950929132204235

Article Title : A RFMV Model and Customer Segmentation Based on Variety of Products

Keywords :

Journal Number : 19 Summer 2017

Visited : 71

Files : 335 KB


List of Authors

  Full Name Email Grade Degree Corresponding Author
1 Saman Qadaki Moghaddam sam13671367@yahoo.com Graduate M.Sc
2 Neda Abdolvand Abdolvand@gmail.com Assistant Professor PhD
3 Saeedeh Rajaee Harandi saeedeh.rh@gmail.com Graduate M.A

Abstract

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. Since the variety of products is 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-mean 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.