New Approach for Customer Clustering by Integrating the LRFM Model and Fuzzy Inference System

Document Type: Research Paper

Authors

1 Department of Management, Faculty of Economics & Administrative Sciences, Ferdowsi University of Mashhad; Researcher at ACECR: Academic Center for Education, Culture and Research-Khorasan Razavi, Mashhad, Iran

2 Department of Management, Faculty of Economics & Administrative Sciences, Ferdowsi University of Mashhad; Researcher at Boshra Research Institute, Mashhad, Iran

Abstract

This study aimed at providing a systematic method to analyze the characteristics of customers’ purchasing behavior in order to improve the performance of customer relationship management system. For this purpose, the improved model of LRFM (including Length, Recency, Frequency, and Monetary indices) was utilized which is now a more common model than the basic RFM model apt for analyzing the customer lifetime value. Since the RFM model does not take the customers’ loyalty into consideration, the LRFM model has instead been applied for making amendments. Contrary to most of the past studies in which the statistical clustering techniques were used besides the RFM or LRFM model, the current study has provided the possibility of clustering analysis by importing the LRFM indices into the framework of a fuzzy inference system. The results obtained for a wholesale firm based on the proposed approach indicated that there was a significant difference between clusters in terms of the four indices of LRFM. Therefore, this approach can be well utilized for clustering the customers and for studying their characteristics. The strong point of this approach compared to the older ones is its high flexibility, because in which it is not needed to re-cluster the customers and to reformulate the strategies when the number of customers is increased or decreased. Finally, after analyzing the attributes of each cluster, some suggestions on marketing strategies were made to be compatible with clusters, and totally, to improve the performance of customer relationship management system.

Keywords

Main Subjects


Article Title [Persian]

رویکردی جدید برای خوشه‌بندی مشتریان با یکپارچه‌سازی مدل LRFM و سیستم استنتاج فازی

Authors [Persian]

  • علی علیزاده زوارم 1
  • احمدرضا کریمی مزیدی 2
1 گروه مدیریت، دانشکدة علوم اداری و اقتصادی، دانشگاه فردوسی مشهد؛ پژوهشگر جهاد دانشگاهی خراسان رضوی، مشهد، ایران
2 گروه مدیریت، دانشکدة علوم اداری و اقتصادی، دانشگاه فردوسی مشهد؛ پژوهشگر مؤسسة مطالعات راهبردی بشرا پژوه، مشهد، ایران
Abstract [Persian]

این مطالعه بر ارائة یک روش نظام‌مند برای تجزیه‌وتحلیل ویژگی‌های رفتار خرید مشتریان درراستای بهبود عملکرد سیستم مدیریت ارتباط با مشتری، هدف‌گذاری شده است. بدین‌منظور و برای تحلیل ارزش طول‌عمر مشتری، مدل بهبودیافتة LRFM (دربرگیرندة شاخص‌های طول‌مدت رابطه، تازگی رابطه، تعداد دفعات خرید، و ارزش پولی خرید) به‌کار گرفته شد، که در حال حاضر نسبت به مدل پایه‌ای RFM رایج‌تر است. از آنجا که مدل RFM وفاداری مشتریان را لحاظ نمی‌کند، به‌جای آن مدل اصلاح شدة LRFM مورد استفاده قرار گرفته است. برخلاف غالب مطالعات پیشین که در آنها تکنیک‌های خوشه‌بندی آماری در کنار مدل RFM یا LRFM مورد استفاده بوده، مطالعة حاضر امکان تحلیل خوشه‌بندی با وارد کردن شاخص‌های LRFM به چارچوب یک سیستم استنتاج فازی را فراهم آورده است. نتایج به‌دست آمده برای یک شرکت عمده‌فروشی براساس رویکرد پیشنهادی، نشان داد که در رابطه با چهار شاخص LRFM تفاوت معناداری بین خوشه‌ها وجود دارد. بنابراین، این رویکرد را می‌توان به‌خوبی برای خوشه‌بندی مشتریان و مطالعة ویژگی‌های آنها مورد استفاده قرار داد. نقطه‌قوت این رویکرد نسبت به موارد پیشین انعطاف‌پذیری آن است؛ چراکه در آن با افزایش یا کاهش تعداد مشتریان، نیازی به خوشه‌بندی مجدد آنها و تدوین دوبارة استراتژی‌ها نیست. درنهایت پس از تحلیل خصایص هر خوشه، برای استراتژی‌های بازاریابی هم‌ساز با خوشه‌ها و به‌طور کلی برای بهبود عملکرد سیستم مدیریت ارتباط با مشتری، پیشنهاداتی ارائه شد.

Keywords [Persian]

  • مدیریت ارتباط با مشتری
  • ارزش طول عمر مشتری
  • مدل LRFM
  • تحلیل خوشه‌بندی مشتری
  • سیستم استنتاج فازی
Alvandi, M., Fazli, S., & Abdoli, F. S. (2012). K-Mean clustering method for analysis customer lifetime value with LRFM relationship model in banking services. International Research Journal of Applied and Basic Sciences, 3(11), 2294-2302.

Bin, D., Peiji, S., & Dan, Z. (2008, December). Data mining for needy students identify based on improved RFM model: A case study of university. Proceedings from the International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII), 1, 244-247.

Blattberg, R. C., & Deighton, J. (1996). Managing marketing by the customer equity Test. Harvard Business Review, 75(4), 136-144.

Blattberg, R. C., Gary, G., & Jacquelyn, S. T. (2001). Customer equity: Building and managing relationships as valued assets. Boston, Massachusetts: Harvard Business School Press.

Castéran, H., Meyer-Waarden, L., & Reinartz, W. (2017). Modeling customer lifetime value, retention, and churn. In Handbook of Market Research (pp. 1-33). Springer International Publishing.

Chang, E. C., Huang, H. C., & Wu, H. H. (2010). Using K-means method and spectral clustering technique in an outfitter’s value analysis. Quality & Quantity, 44(4), 807-815.

Chang, H. H., & Tsay, S. F. (2004). Integrating of SOM and K-means in data mining clustering: An empirical study of CRM and profitability evaluation. Journal of Information Management, 11(4), 161-203.

Chen, X. (2006). Customer lifetime value: An integrated data mining approach (Master thesis). Lingnan University.

Chen, T. (2012). The RFM–FCM approach for customer clustering. International Journal of Technology Intelligence and Planning, 8(4), 358-373.

Chow, S., & Holden, R. (1997). Toward an understanding of loyalty: The moderating role of trust. Journal of Management Issues, 9(3), 275-298.

Daoud, R. A., Amine, A., Bouikhalene, B., & Lbibb, R. (2015). Customer segmentation model in e-commerce using clustering techniques and LRFM model: The case of online stores in Morocco. International Journal of Computer and Information Engineering, 9(8), 2000-2010.

 Donkers, B., Verhoef, P. C., & Jong, M. G. D. (2007). Modeling CLV: A test of competing models in the insurance industry. Quantitative Marketing and Economics, 5(2), 163-190.

Foong, K. C., Chee, C. T., & Wei, L. S. (2009, April). Adaptive network fuzzy inference system (ANFIS) handoff algorithm. Proceedings from ICFCC 2009 International Conference on Future Computer and Communication, 195-198.

Gupta, S., Hanssens, D., Hardie, B, Kahn, W., Kumar, V., Lin, N., & Sriram, N. R. S. (2006). Modeling customer lifetime value. Journal of Service Research, 9(2), 139-155.

Gupta, S., & Zeithaml, V. (2006). Customer metrics and their impact on financial performance. Marketing Science, 25(6), 718-739.

He, Z., Xu, X., Huang, J. Z., & Deng, S. (2004). Mining class outliers: Concept, algorithms and applications in CRM. Expert Systems with Applications, 27(4), 681-697.

Hu, W., & Jing, Z. (2008, December 10-11). Study of segmentation for auto services companies based on RFM model. Proceedings from the 5th International Conference on Innovation and Management. Maastricht, The Netherlands.

Hughes, A. M. (1994). Strategic database marketing. Chicago: Probus Publishing Co.

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.

Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2000). Switching barriers and repurchase intentions in services. Journal of Retailing, 76(2), 259-374.

Kafashpoor, A., & Alizadeh Z. A. (2012). Application of Fuzzy Delphi Analytical Hierarchy Process (FDAHP) and Hierarchical Cluster Analysis (HCA) in RFM model for measuring the customer life time value. New Marketing Research Journal, 2(3), 51-68.

Kahreh, M. S., Tive, M., Babania, A., & Hesan, M. (2014). Analyzing the applications of Customer Lifetime Value (CLV) based on benefit segmentation for the banking sector. Procedia-Social and Behavioral Sciences, 109, 590-594.

Kao, Y. T., Wu, H. H., Chen, H. K., & Chang, E. C. (2011). A case study of applying LRFM model and clustering techniques to evaluate customer values. Journal of Statistics and Management Systems, 14(2), 267-276.

Keiningham, T. L., Aksoy, L., & Bejou, D. (2006). Approaches to measurement and management of customer value. Journal of Relationship Marketing, 5(2), 37-54.

Kim, J., Suh, E.-H., & Hwang, H. (2003). A model for evaluating the effectiveness of CRM using the balanced scorecard. Journal of Interactive Marketing, 17(2), 5-19.

Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4). New Jersey: Prentice Hall.

Kotler, P. (2003). Marketing management (11th ed.). Upper Saddle River, NJ: Prentice-Hall.

Krstevski, D., & Mancheski, G. (2016). Managerial accounting: Modeling customer lifetime value- An application in the telecommunication industry. European Journal of Business and Social Sciences, 5(1), 64-77.

Kumar, V. (2010). Customer relationship management. John Wiley & Sons, Ltd.

Kumar, V., & Pansari, A. (2016). National culture, economy, and customer lifetime value: Assessing the relative impact of the drivers of customer lifetime value for a global retailer. Journal of International Marketing, 24(1), 1-21.

Kumar, V., & Reinartz, W. (2006). Customer relationship management: A data based approach. New York: John Wiley.

Kumar, V. & Shah, D. (2004). Building and sustaining profitable customer loyalty for the 21st century. Journal of Retailing, 80(4), 317-329.

Li, D.-C., Dai, W.-L., & Tseng, W.-T. (2011). A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business. Expert Systems with Application, 38(6), 7186-7191.

Ling, R., & Yen, D .C. (2001). Customer relationship management: An analysis framework and implementation strategies. Journal of Computer Information Systems, 41(3), 82-97.

Mamdani, E. H. & Assilian, S. (1975). An experimental in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13.

Miglautsch, J. R. (2000). Thoughts on RFM scoring. Journal of Database Marketing, 8(1), 67–72.

Mishra, A., & Mishra, D. (2009). Customer relationship management: Implementation process perspective. Acta Polytechnica Hungarica, 6(4), 83-99.

 Ngai, E. W. T. (2005). Customer relationship management research (1992-2002): An academic literature review and classification. Marketing Intelligence Planning, 23(6), 582-605.

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

Opresnik, D., Fiasché, M., Taisch, M., & Hirsch, M. (2017). An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy. Information Technology and Management, 18(2), 131-147.

Parvatiyar, A., & Sheth, J. N. (2001). Customer relationship management: Emerging practice, process and discipline. Journal of Economic and Social Research, 3(2), 1-34.

Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing, 64(October), 17–35.

Romano, N. C. (2001). Customer relationship management research: An assessment of sub field development and maturity.Proceedings from the 34th Hawaii International Conference on System Sciences.

Rust, R. T., Lemon, K., & Zeithaml, V. (2004). Return on marketing: using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109-26.

Shih, Y.-Y.., & Liu, C-Y. (2003). A method for customer lifetime value ranking: Combining the analytic hierarchy process and clustering analysis. Database Marketing & Customer Strategy Management, 11(2), 159–172.

Snoeck, S. M. J. (2012). Customer value models in the energy market understanding the role of acquisition and retention effects. Groningen, the Netherlands: University of Groningen.

Teo, T. S. H., Devadoss, P., & Pan, S. L. (2006).Towards a holistic perspective of customer relationship management implementation: A case study of the housing and development board. Decision Support Systems, 42(3), 1616-1627.

Venkatesan, R., & Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing, 68(4), 106-125.

Verhoef, P., Franses, P., & Hoekstra, J. (2001). The impact of satisfaction and payment equity on cross-buying: A dynamic model for a multi-service provider. Journal of Retailing, 77(3), 359-78.

Vigneau, E., Endrizzi, I., & Qannari, E. M. (2011). Finding and explaining clusters of consumers using the CLV approach. Food Quality and Preference, 22(8), 705-713.‏

Wei, J.-T., Lin, S.-Y., Weng, C.-C., & Wu, H.-H. (2012). A case study of applying LRFM model in market segmentation of a children’s dental clinic. Expert Systems with Application, 39(5), 5529-5533.

Wu, H.-H., Chang, E.-C., & Lo, C.-F. (2009). Applying RFM model and K-means method in customer value analysis of an outfitter. Proceedings from International Conference on Concurrent Engineering, New York.

Xu, P., Tang, X., & Yao, S. (2008). Application of circular laser vision sensor (CLVS) on welded seam tracking. Journal of Materials Processing Technology, 205(1), 404-410.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.

Zalaghi, Z., & Varzi, Y. (2014). Measuring customer loyalty using an extended RFM and clustering technique. Management Science Letters, 4(5), 905-912.