Proposing a New Method for Customer Segmentation Based on Their Level of Loyalty and Defining Appropriate Strategies for Each Segment

Document Type : Research Paper

Authors

1 MSc. of Information Technology Engineering; Department of Electronic and Computer Engineering; Graduate University of Advanced Technology; Kerman; Iran

2 Assistant Prof.; Faculty of Computer Engineering Department; Shahid Bahonar University of Kerman; Kerman; Iran

Abstract

The evaluation of customer loyalty have a significant impact on improving business processes. Ordinary methods of customer loyalty evaluation have been designed based on three components including; recency of transactions (R), the frequency of transactions (F) and the monetary value of transactions (M). In this study, it has been attempted to examine some affective factors, including the total number of purchased goods, returned goods, discounts and the average delay of distribution and their impact on increasing quality of assessment be measured. The main objective of the current study is to propose a new model for customer segmentation based on their level of loyalty and to define appropriate strategies for each segment. The data set for this study is obtained for the customers of a food wholesale. The obtained data have been analyzed using Clementine 14.2 software application using MLP and RBF neural networks as well as the K-means algorithm. The results of the study show that the proposed method provides the highest level of accuracy for predicting the customers’ loyalty. Based on this proposed method, the customers are divided into five clusters (Loyal, potential, new, lost and churn customers) from the point of view of loyalty, with the characteristics of each cluster expressed based on the status of seven factors. Based on these characteristics, appropriate approaches for managing the customers in each segment are proposed.

Keywords

Main Subjects


Akhundzade Noghabi, A., Al-Badawi, A. & Aghdasieh, M. (2014). Explore the dynamics of the customer in the design of segmentation using data mining techniques. Information Technology Management, 6 (1): 1-30. (in Persian)
Al-Shayea, Q. K., Member, I. & Al-Shayea, T. K. (2014). Customer Behavior on RFMT Model Using Neural Networks. WCE, 1: 49-52.
Ansari, M., Mir-Kazemi Moode, M., Rahmani Yushanluyi, H. & Ghasemi, A. (2015). A model for measuring customer knowledge absorption capacity (Survey on food), Information Technology Management, 6 (4): 529-550.
(in Persian)
Azizi, S. H., Hossein Abadi, V. & Balaghi Enanel, M. (2014). Segmentation of internet banking users based on expectations. Information Technology Management, 6 (3): 419-434. (in Persian)
Cao, S., Zhu, Q. & Hou, Z. (2009). Customer Segmentation based on a Novel Hierarchical Clustering Algorithm, IEEE Chinese Conference on Pattern Recognition, 4-6 Nov. PP. 1-5.
Chang, H. H. & Tsay, S. F. (2004). Integrating of SOM and K-meanin Data Mining Clustering: An Empirical Study of CRM and Profitability Evaluation. Journal of Information Management, 11(4): 161-203.
Chen, Y. & Li, X. (2009). The Effect of Customer Segmentation on an Inventory System in the Presence of Supply Distributions. WSC, 2343-2352.
Dick, A. & Basu, k. (2003). Customer Loyalty: Toward an Integrated Conceptual Framework. Journal of the Academy of Marketing Science, 22: 99-113.
(in Persian)
Fathian, A. & Hosseini, M. (2014). Investigate the effect of virtual communities to strengthen the customers' purchasing behavior. Information Technology Management, 6 (3): 435-454. (in Persian)
Griffin, J. & Lowenstein, W. M. (2001). Customer winback: How to recapture lost customers and keep them loyal, San Francisco: Jossey-Bass.
Hughes, A. M. (1996). Boosting reponse with RFM. Marketing Tools, 5: 4-10.
Jiang, T. & Tuzhilin, A. (2006). Improving Personalization Solutions through Optimal Segmentation of Customer Bases. Knowledge and Data Engineering, 21(3): 305-320.
Kafashpour, A. & Alizadeh Zavarem A. (2012). Implementing Fuzzy DELPHI Analytical Hierarchy Process (FSAHP) and Hierarchical Clustering Analysis (HCA) in RFM Model for Determining the Value of Customer’s Life Cycle. Scientific and Research Periodical of Modern Marketing Research, 2(3): 51-68. (in Persian)
Karimzadeh, A. (1998). Developing Scientific Methods of Food Marketing Need to Grow Food Industry and the Development of Non-Oil Exports, Agricultural Economics and Development, Information Technology Management, 22: 67-80. (in Persian)
Lai, X. (2009). Segmentation Study on Enterprise Customers Based on Data Mining Technology. IEEE Chinese First International Workshop on Database Technology and Applications, 247-250.
Lewis, P. & Thornhill, A. (2000). Research Methods for Business Students. Prentice Hall, SAS Institute, Best practice in churn prediction. A SAS Institute White Paper.
Madani, S. (2009). Mining Changes in Customer Purchasing Behavior, a Data Mining Approach. Master Thesis. Dissertation, Sweden Lula University of Technologhy.
Menhaj, M. B. (2000). Fundamentals of Neural Networks. Tehran, Amir Kabir Industrial University. (in Persian)
Moslehi, S. N., Kafashpour, A. & Naji Azimi, Z. (2014). Using LRFM Model for Segmentation Customers Based on the Value of Their Life Cycle. public management research, 7 (25): 119-140. (in Persian)
Murakani, K. & Natori, S.H. (2013). New Customer Management Technique: CRM by "RFM + I" Analysis. NRI Papers, 186.
Noyan, F. & Simsek, G. G. (2013). The Antecedents of Customer Loyalty. WC-BEM, 109: 1220-1224.
Qiasi, R., Baqeri-Dehnavi, M., Minaei-Bidgoli, B. & Amooee, G. (2012). Developing a Model for Measuring Customer’s Loyalty and Value with RFM Technique and Clustering Algorithms. The Journal of Mathematics and Computer Science, 4 (2): 172-181.
Radfar, R., Nezafati, N. & Yousefi Asli., S. (2014). Internet Customer Classification Using Data Mining Algorithms. Information Technology Management, 6 (1): 71-90. (in Persian)
Sen, B., Ucar, E., & Delen, D. (2012). Predicting and analyzing secondary education placement-test scores: A Data mining approach. Expert Systems with Applications,  39(10): 9468-9476.
Sohrabi, B. & Khanlari, A. (2007). Customer Lifetime Value (CLV) Measurement Based on RFM Model. Iranian Accounting & Auditing Review, 14(47): 7-20.
Soudagar, R. (2012). Customer Segmentation and Strategy Definition in Segments. Dissertation, Sweden Lula University of Technologhy.
Tamaddoni Jahromi, A. (2009). Predicting Customer Churn in Telecommunication Service Providers. Dissertation, Sweden Lula University of Technologhy.
Tarokh, M.J. & Sharifian K. (2010). Application of Data Mining to Improve Customer Relationship Management. Scientific and Research Periodical of Industrial Management Studies, 6(17): 153-181. (in Persian)
Tsai, C.Y. & Chiu, C.C. (2004). A Purchase-Based Market Segmentation Methodology. Expert Systems with Applications, 27: 265-276.
Yeh, I. C., Yang, K. J. & Ting, T. M. (2008). Knowledge Discovery on RFM model using Bernoulli sequence. Expert Systems with Applications, 36: 5866-5871.
Zakaria, B., Rahman, A. B., Othman, A., Yunus, N., Szulkipli, M. R. & Osman, M. A. (2014). The Relationship between Loyalty Program, Customer Satisfaction and Customer Loyalty in Retail Industry: A Case Study. ICIMTR, 129: 22-30.
Zalaghi, Z., & Abbasnejad Varzi, Y. (2014). Measuring Customer Loyalty Using an Extended RFM and Clustering Technique. Management Science Letters, 4: 905-912.
Zare Ravasan, A., & Mansouri, T. (2015). A Fuzzy ANP Based Weighted RFM Model for Customer Segmentation Auto Insurance Sector. International Journal of Information Systems in the Service Sector, 7 (2): 71-86.