PRFM Model Developed for the Separation of Enterprise Customers Based on the Distribution Companies of Various Goods and Services

Journal of System Management

دوره 6، شماره 3، بهمن 2020، صفحه 77-99   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.30495/jsm.2021.678895

نویسندگان

Mohammad Mahdi Hajmohamad^{* 1}؛ Narges Rahimi²؛ Behzad Sasanizadeh³

¹Department of Industrial Engineering, Faculty of Industrial Engineering & Management, Shahrood University of Technology, Shahrood, Iran

²Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

³Department of Statistics, Faculty of Science, Razi University, Kermanshah, Iran

چکیده

In this study, a new model of combining variables affecting the classification of customers is introduced which is based on a distribution system of goods and services. Given the problems that the RFM model has in various distribution systems, a new model for resolving these problems is presented. The core of this model is the older RFM. The new model that has been proposed as PRFM, consists of four dimensions: Profit margins (P), time period from customer's last purchase (R), Frequency of transactions (F) and the Monetary Value (M). Adding variable (P) makes a huge change in customer clustering and classification systems and makes it more optimized for future planning. For review and approval, the model was implemented in one of the largest and most diversified distribution companies in Iran. Using Ward's clustering, the optimal number of clusters was prepared and entered by hierarchical clustering and based on Euclidian distance customers are clustered and separated. One of the most important results of this study is introducing a new model and resolving the problems of the old RFM model in determining customer's value.      

کلیدواژه‌ها

RFM Model؛ PRFM Model؛ Clustering؛ WARD'S Method؛ Customer's Value

مراجع

Abbasimehr, H., & Shabani, M. (2020). A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING.

Bin, D., Peiji, S., & Dan, Z. (2008). Data mining for needy students identify based on improved RFM model: A case study of university. Paper presented at the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering.

Blattberg, R. (2001). Customer Equity: Building and Managing Relationships as Valuable Assets.

Buttle, F. (2004). Client Relationship Management: concepts and tools. In: Oxford: Elsevier Butterworth-Heinemann.

Cheng, C.-H., & Chen, Y.-S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert systems with applications, 36(3), 4176-4184.

Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of cybernetics, 4(1), 95-104.

Duran, B. S., & Odell, P. L. (2013). Cluster analysis: a survey (Vol. 100): Springer Science & Business Media.

Hartigan, J. A. (1975). Clustering algorithms: John Wiley & Sons, Inc.

Heldt, R., Silveira, C. S., & Luce, F. B. (2019). Predicting customer value per product: From RFM to RFM/P. Journal of Business Research.

Hosseini, S. M. S., Maleki, A., & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37(7), 5259-5264.

Huang, Y., Zhang, M., & He, Y. (2020). Research on improved RFM customer segmentation model based on K-Means algorithm. Paper presented at the 2020 5th International Conference on Computational Intelligence and Applications (ICCIA).

Hughes, A. M. (2000). Strategic database marketing: the masterplan for starting and managing a profitable, customer-based marketing program (Vol. 12): McGraw-Hill New York, NY.

Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert systems with applications, 26(2), 181-188.

Jing, W. H. Z. (2008). Study of Customer Segmentation for Auto Services Companies Based on RFM Model.

Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis (Vol. 5): Prentice hall Upper Saddle River, NJ.

Kafashpour, A., Tavakoli, A., & Alizadeh, S. (2012). Customers segmentation base on lifetime value, use RFM data mining. Iran J Public Manag, 5(15), 63-84.

Kim, K.-j., & Ahn, H. (2008). A recommender system using GA K-means clustering in an online shopping market. Expert systems with applications, 34(2), 1200-1209.

Kotler, P. (1994). Marketing management, analysis, planning, implementation, and control, Philip Kotler: London: Prentice-Hall International.

Kumar, V., & Reinartz, W. J. (2006). Customer relationship management: A databased approach: Wiley Hoboken, NJ.

Lee, R. C. T. (1981). Clustering Analysis and Its Applications. In J. T. Tou (Ed.), Advances in Information Systems Science: Volume 8 (pp. 169-292). Boston, MA: Springer US.

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.

Mishra, A., & Mishra, D. (2009). Customer Relationship Management: implementation process perspective. Acta Polytechnica Hungarica, 6(4), 83-99.

Ngai, E. W. (2005). Customer relationship management research (1992‐2002). Marketing intelligence & planning.

Ngai, E. W., Xiu, L., & Chau, D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert systems with applications, 36(2), 2592-2602.

Razmi, J., & Ghanbari, A. (2009). Introducing a novel model to determine CLV. Journal of Information technology management, 1(2).

Sohrabi, B., & Khanlari, A. (2007). Customer lifetime value (CLV) measurement based on RFM model.

Stone, B. (1995). Successful direct marketing methods NTC Business Books. Lincolnwood, IL.

Turban, E., Aronson, J., Liang, T., & Sharda, R. (2007). Decision support and business intelligence systems, 8^ th Edition Prentice Hall. Upper Saddle River NJ.

Wind, Y., & Saaty, T. L. (1980). Marketing applications of the analytic hierarchy process. Management science, 26(7), 641-658.

Wu, H.-H., Chang, E.-C., & Lo, C.-F. (2009). Applying RFM model and K-means method in customer value analysis of an outfitter. In Global Perspective for Competitive Enterprise, Economy and Ecology (pp. 665-672): Springer.