بخش‌بندی مشتریان صنایع تولید و پخش کالاهای پرگردش بر اساس مدل بهبود‌یافته RFM (مطالعه موردی: شرکت گلستان)

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استادیار گروه مهندسی صنایع، دانشکدة فنی و مهندسی، دانشگاه آزاد اسلامی واحد تهران‌شمال، تهران، ایران

2 کارشناس ارشد مهندسی صنایع، دانشکدة فنی و مهندسی، دانشگاه شمال، آمل، ایران

چکیده

بخش‌بندی مشتریان و تحلیل رفتار آنها در صنایع تولید و پخش کالاهای پرگردش، با تعداد کثیری از مشتریان متفاوت در نقاط پراکنده، سبب هدفمندشدن فعالیت­های بازاریابی و ارتباط مؤثر آنها با مشتریان می‌شود. بخش‌بندی مشتریان از رویکردهای داده‌کاوی که به کشف گروه‌های مشابه از مشتریان منجر می‌شود، عمدتاً براساس متغیرهای تازگی، تکرار و حجم خرید در مدل RFM انجام می‌شود. کیفیت بخش‌بندی، به انتخاب مناسب متغیرهای عملکردی مشتریان بستگی دارد. ارزیابی کیفیت بخش‌بندی مشتریان بزرگ­ترین شرکت تولید و پخش کالاهای پرگردش، مؤید فرضیة تأثیرگذاری اندک متغیر تازگی خرید بر بخش‌بندی مشتریان این صنایع است. در این مقاله، متغیر توالی خرید (C) به­عنوان متغیر عملکردی مشتریان در این صنایع معرفی شده و با جایگزینی آن با متغیر تازگی خرید در مدل RFM، کیفیت بخش‌بندی مشتریان در این صنایع بهبود داده شده است. کاهش 11 درصدی شاخص دیویس- بولدین در خوشه‌بندی مشتریان شرکت گلستان و افزایش 1 درصدی دقت پیش‌بینی خوشة مشتریان در مدل شبکه‌های عصبی براساس مدل پیشنهادی این تحقیق (CFM) در مقایسه با مدل RFM، بیانگر دقت بالاتر مدل CFM است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Customer segmentation in Fast Moving Consumer Goods (FMCG) Industries by using developed RFM model

نویسندگان [English]

  • Vahid Baradaran 1
  • Mohammad Biglari 2
1 Assistant Prof., Industrial Engineering Department, North Tehran Branch, Islamic Azad University, Tehran, Iran
2 MSc. in Industrail Engineering, Faculty of Engineering, University of Shomal, Amol, Iran
چکیده [English]

Customers segmentation and analyzing their behavior at fast moving costumer goods (FMGS) industries which deal with a large number of customers with a variety of characteristics causes the marketing activities to be targeted and leads to effective communication with the customers. Segmentation, a data mining approach, which leads to the discovery of similar groups of customers, is usually done by recency, frequency and purchased volume variables in RFM model. Using proper segmentation variables affects the quality of segmentation. Analyzing the quality of Golsetan customer segments, the biggest FMCG industry in Iran, confirms the hypothesis which the recency variable is not effective in customer segmentation in FMCG industries. In this paper, purchase sequence (continuity) variable is defined as a new customer performance variable in FMCG industries. By replacing the continuity variable (C) with recency in RFM model, the quality of segmentation has been improved. Customers of Golestan Company were segmented by two RFM and proposed (CFM) models. The Davis-Bouldin criterion reduced more than 11 percent and the forecast accuracy for customers cluster in artificial neural networks increased about 1 percent.

کلیدواژه‌ها [English]

  • customer segmentation
  • Data Mining
  • Fast-Moving Consumer Goods Industry
  • RFM Model
Ahmadi, P., Azar, A. & Samsami, F. (2011). Drug market segmentation with neural networks approach (Case Study: Drug market in Iran). Quarterly Journal Business Management, 2(6): 1-10. (In Persian)

 

Allahyari Soeini, R. & Fathalizadeh, E. (2012). Customer segmentation based on modified RFM model in insurance industry. International Journal of Modeling and Optimization, 25: 101-104.

 

Amin-Nasseri, M. R. & Baradaran, V. (2008). Development of travel time forecasting models in public transportation systems (Case Study: Tehran bus transportation system). Transportation Reaaerch Journal, 6(3): 219-232. (In Persian)

 

Berson, A., Smith, S. & Thearling, K. (2000). Building data mining application for CRM, McGraw-Hill.

 

Buckinx, W. & Van Den Poel, D. (2005). Customer base analysis: Partial defection of behaviorally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164: 252-268.

 

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

 

Chang, H-C. & Tsai, H-P. (2011). Group RFM analysis as a novel framework to discover better customer consumption behavior. Expert Systems with Applications, 38(12): 14499–14513.

 

Chapman, P., Clinton, J. & Kerber, R. (2000). CRISP-DM 1.0 Step-by-step data mining guide, 1, SPSS. USA.

 

Chen, Y-L., Kuo, M-H., Wu, S-Y. & Tang, K. (2009). Discovering recency, frequency and monetary (RFM) sequential patterns from customers’ purchasing data. Electronic Commerce Research and Applications, 8:
241-251.

 

Christodoulakis, D. & Aggelis, V. (2009). Customer clustering using RFM analysis. Expert System With Applications, 36: 2678-2685.

 

Coussement, K. & Poel, D.V. (2008). Improving customer attrition prediction by integrating emotions from client / compnay interaction emails and evaluating mutiple clasifiers. Expert System with Applications, 36: 6127-6134.

 

Dash, P. & Mishra, S. (2010). Developing RFM model for customer segmentation in retail industry. International Journal of Marketing & Human Resource Management, 1:58-69.

 

Dehdashti Shahrokh, Z. & Pourhosseini, A.R. (2013). A model to investigate the impact of sales and marketing strategy on sales performance. Quarterly Journal Business Management, 5(1): 61-84. (In Prsian)

 

Fader, P. S., Hardie, B. G. S. & Lee, K. L. (2005). RFM and CLV: Using ISO-value curves to customer base analysis. Marketing Research, 42: 415-430.

 

Ghazanfari, M., Alizadeh S., Malekmohammadi, S. & Fathollah, M. (2009). Segmentation of export’s customer edible fruits. Iranian Journal of Trade Studies (IJTS), 55:151-181. (In Persian)

 

Hassangholipour, T., Miri, S. M. & Morovati Sharifabadi, A. (2007). Market segmentation by using artificial neural networks, Case Study: Meat products (Sausages). Quarterly Journal of Human Sciences MODARES (Management Special Issue), 55(11):57-80. (In Persian)

 

Hsieh, N. C. (2004). An integrated data mining and behavioral acoring model for analyzing bank customers. Expert System with Applications, 27: 623-633.

 

Hu, Y-H. & Yeh, T-W. (2014). Discovering valuable frequent patterns based on RFM analysis without customer identification information. Knowledge-Based Systems, 61: 76-88.

 

Hung, C. & Tsai, C. (2008). Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert System with Applications, 34(1): 780-787.

 

King, S. F. (2007). Citizens as customers: Exploring the future of CRM in UK local government. Government Information Quarterly, 24: 47-63.

 

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 Applications, 38(6): 7186–7191.

 

Li, S. T., Shue, L. Y. & Lee, S. F. (2008). Business intelligence approach to supportng strategy-making of ISP service management. Expert System with Applications, 35: 739-754.

 

Li, YM., Lin, CH. & Lai, C. Y. (2010). Identifying influential reviewers for word-of-mouth marketing. Electronic Commerce Research and Applications, 9 (4): 294-304.

 

Marcus, C. (1998). A practical yet meaningful approach to customer segmentation. Journal of Consumer Marketing, 15: 494-504.

 

Mollahosseini, A. & Alimirzaee, Gh. (2011). Segmenting customers of Iran Khodro and Saipa groups in Kerman. Quarterly Journal of Business Management, 2(6): 135–146. (In Persian)

 

Momeni, M. (2011). Data Segmentation (Clustering Analysis), Foroozesh press. Tehran. (In Persian)

 

Mortazavi, S., Asseman-Darreh, Y., Najafi Siahroodi, M. & Alavi, M. (2011). Mobile handset market segmentation based on the expected customer benefits. Quarterly Journal of Business Management, 3(8): 115–132. (In Persian)

 

Namvar, M., Gholamian M. R. & Khakabi, S. (2010). A two phase clustering method for intelligent customer segmentation. Intelligent System, Modelling and Simulation, IEEE. (In Persian)

 

Rygielski, C., Wang, J-C. & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in Society, 24: 483–502.

 

Samizadeh, R. (2007). Data Mining and Customer Relashenship Management, Roshd Andisheh Press. Tehran. (In Persian)

 

Seyyed Hashemi, M. R. & Mamdouhi, A. R. (2009). Cluster analysis of the barriers to the implementation of marketing strategies in the automobile industry (Case Study: Iran Khodro Company), Quarterly Journal of Business Management, 2(6): 156–186. (In Persian)

 

Seyed Hosseini, M., Maleki, A. & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to asses the customer loyalty. Expert System With Applications, 37: 5259-5264. (In Persian)

 

Sohrabi, B. & Khanlari, A. (2007). Customer lifetime value (CLV) measurement based on RFM Model. Iranian Accounting & Auditing Review, 14: 7-20. (In Persian)

 

Tsiptsis, K. & Chorianopoulos A. (2010). Data Mining Techniques in CRM: Inside Customer Segmentation. WILEY. United Kingdom.

 

Wang, Y., Ma, X., Lao, Y. & Wang, Y. (2014). A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization. Expert Systems with Applications, 41(2): 521–534.

 

Wei, J., Lee, M. & Chen, H. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques. Expert System with Application, 40: 7513-7518.

 

Wei, J., Lin, Sh., & Weng, Ch. (2012). A case study of applying LRFM model in market segmentation of a children’s dental clinic. Expert Systems with Applications, 39: 5529-5533.

 

Wei, J.-T., Lin, S.-Y. & Wu, H. H. (2010). A review of the application of RFM model. African Journal of Business Management, 4: 4199-4206.

 

Yeh, I. C. Yang, K. J. & Ting, T. M. (2008). Knowledge discovery on RFM model using Bernoulli sequence. Expert System With Applications,36: 5866-5871.