Authors
Abstract
Interaction of companies with customers in the form of customer relationship management has changed significantly. Identifying characteristics of different customers and allocating resources to them according to their value to the firm has become one of the main concerns in customer relationship management. The purpose of this paper is to provide an appropriate model for customer segmentation based on some of the most important financial and demographics characteristics influencing factors of customer lifetime value (CLV). The process proposed in this study was performed in Saman insurance company. After determining RFM model indices, which include date, frequency and monetary of purchase, AHP method used for weighting them among 180000 customers. The optimal number of clusters based on the silhouette and impact of RFM indicators was done by using Two-step algorithm and then customers classified through K-Means clustering algorithm. Results provided a platform to analyze the characteristics of customers in three main sections. Also, by prioritizing clusters based on the RFM indices, valuable customers were identified. Finally, some suggestions were presented to the company to improve its customer relationship management system.
Keywords
[1] Mahmoudi K., Rostami H., Sayebani M., Moradi A. (2013) "An overview of data mining and its applications in the offshore industry”, The 5th National Conference on Offshore Industries, Tehran, Sharif University of Technology, May and June 23.
[2] Vafaei B., Chegini V., Saqaei A., Ezam M. (2013) "Categorizing water masses in Chabahar Bay using clustering method", Oceanography, 4(13):1-19.
[3] Jahanian S., Ebrahimi B. (2010) Customer relationship management a competitive advantage in the chain of today's business value, Industry Engineering Information Center, < http://www.iie.ir/index.php? option= com_content&tas.
[4] Gurau C., Ranchhod A., Hackney R. (2003) “Customer-centric strategic planning: Integrating CRM in online business systems”, Information Technology and Management, The Netherlands: Springer Netherlands, (4):199-214.
[5] Ryals L., Knox S. (2001) "Cross-FunctionalIssues in the Implementation of Relationship Marketing Trogh Customer Relationship Management", European Management Journal, 19(4): 534-542.
[6] Kotler Philip (1994) "Marketing management, analysis, planning, implementation, and control", Prentice Hall. New Jersey, US.
[7] Blattberg R.C, Gary G., Jacquelyn S.T. (2001) Customer equity: Building and managing relationships as valued assets, Boston, Massachusetts: Harvard Business School Press.
[8] Lai X. (2009) "Segmentation study on enterprise customers based on data mining technology", Paper presented at the Database Technology and Applications, pp. 247-250.
[9] Kumar L., Reinartz W.J. (2006) Customer relationship management: A data based approach, New York: John Wiley.
[10] Mishar Alok, Mishar Deepti (2009) "Customer relationship management: Implementation process perspective", Acta Polytechnic Hungarica, 6(4): 83-99.
[11] Ngai E.W.T, Xiu Li, Chau D.C.k. (2009) "Application of data mining techniques in customer relationship management: A literature review and classification", Expert Systems with Applications, 36(1):2592-2602.
[12] Alpaydin E. (2004) "Introduction to machine learning", The MIT Press.
[13] T.L Oshini Goonetilleke and H.A Caldera. (2013)" Mining Life Insurance Data for Customer Attrition Analysis ", Journal of Industrial and Intelligent Information, 1(1).
[14] Hughes Arthur M. (1994) Strategic database marketing, Chicago: Probus Publishing Company.
[15] Akhundzade Noghabi E., Al-Badawi A., Aghdasi M. (2014) "Exploring the dynamics of the customer in the design of segmentation using data mining techniques", Journal of Information Technology Management, 6(1): 1-30.
[16] Ghazanfari M., Alizadeh S., Teimourpour B. (2014) Data mining and knowledge discovery, Publications of the University of Science and Technology of Iran.
[17] Bashiri mousavi S.A, Afsar A., Mahjubifard A. (2015) "Customer value analysis in bank with data mining technique and fuzzy analytic hierarchy process", Management Researches in Iran, 19(1):23-43, (in Persian).
[18] Mozaffari A., Bonyadi A., Alizadeh F. (2013) "Clustering and discovering the damage patterns of the third party insurance customers by using data mining techniques", The 10th International Conference on Industrial Engineering.
[19] Izadparast S.M. (2011) "Classifying insurance customers by data mining", The 1st Conference on Intelligent Computer Systems and Their Application.
[20] Mousavi F., Khademi M., Ahmadifar H. (2013) "Identification and classification of insurance customers using data mining", The 1st Regional Conference on Information Technology.
[21] K. A., Smith R.J Willis , Brooks M. (2000) "An analysis of customer retention and insurance claim patterns using data mining: A case study", The Journal of the Operational Research Society, 51(5):532-541.
[22] Mehregan S., Samizadeh R. (2012) "Customer Retention Based on the Number of Purchase: A Data Mining Approach", Inte. J. Manag. Bus. Res, 2(1):41- 50.
[23] Zaïane O.R. (1999) "Data Clustering", In Principles of Knowledge Discovery in Databases (Internet)..
<http://webdocs.cs.ualberta.ca/~zaiane/courses/cmput690/slides/Chapter8/index.html>.
<http://webdocs.cs.ualberta.ca/~zaiane/courses/cmput690/slides/Chapter8/index.html>.
[24] Neelamadhab Padhy, Rasmita Panigrahi. (2012) "Survey of data mining application and Feature scope", International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2(3).
[25] Mariscal G., Marbán Ó., Fernández C. (2010) "A survey of data mining and knowledge discovery process models and methodologies", The Knowledge Engineering Review, 25(02):137-166.
[26] Hasanzadeh A., Hesam M., Ghanbari (2012) "Classification of mobile banking users by data mining approach: Comparison between artificial neural networks and naïve bayes techniques", Management Researches in Iran, 16(2):57-71, (in Persian).
[27] Hand D.J., Mannila H., Smyth P. (2001) “Principles of data mining”, and MIT Press.
[28] Forcht K. A., Cochran K. (1999) "Using data mining and data warehousing techniques", Industrial Management & Data Systems, pp. 189-196.
[29] Sefidari E., Kadkhodaei A., Sharifi M. (2013) "Comparison of self-building neural network and cluster analysis for the assessment of organic carbon in formations containing hydrocarbons using intelligent systems", Journal of Oil Research, 23(75): 117-130.
[30] D.C Li, Wen-Li Dai, Wan-Ting Tseng (2011) "A two-stage clustering method to analyze customer characteristics to build discriminative customer management": A case of textile manufacturing business”, expert systems with applications, 14(1):1-6.
[31] Hu Wang and Jing Zhang (2008) "Study of segmentation for auto services companies based on RFM model [online]".
)