Customer Segmentation Using Fuzzy C-Means Method and Fuzzy Rfm

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The study was conducted to explore the application of data mining in customer segmentation for laundry business. Intense competition in similar business encourage the company to manage its customer optimally. With a large number customers, the problem that has to be faced is how to determine potential customers. The process conducted is to divide customers into several segments with the aims to build customer profiles based on patterns of transactions that have been carried out.  Customer profile that is created is a profile that shows the potential level of the customer. There are five categories of potential customers form highest to lowest. Implementations is done using two methods of data mining, namely clustering, and segmentation. Clustering method using Fuzzy C-Means algorithm while segmentation using Fuzzy RFM (Recency, Frequency, and Monetary) models. Studies conducted succeeded in  grouping customers based on transcations conducted (Recency, Frequency, and Monetary). Therefore the mining results can be used to assist companies in the process of identifying the customer and also as an alternative marketing strategy.

     

     


  • Keywords


    data mining, customer segmentation, Fuzzy C-Means, Fuzzy RFM Model

  • References


      [1] A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” ACM Comput. Surv., vol. 31, no. 3, pp. 264–323, 1999.

      [2] M. Khajvand, K. Zolfaghar, S. Ashoori, and S. Alizadeh, “Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study,” Procedia Comput. Sci., vol. 3, pp. 57–63, 2011.

      [3] D. Zhao, “Integrating RFM model and cluster for students loan subsidy valuation,” 2008 Int. Semin. Bus. Inf. Manag. ISBIM 2008, vol. 2, pp. 461–464, 2009.

      [4] Y. et al. Sung Cho, “Incremental Weighted Mining based on RFM Analysis for Recommending Prediction in u-commerce,” Int. J. Smart Home Vol. 7 No. 6 (2013)., 2013.

      [5] T. Chen and Y. Wang, “Fuzzified FCM for Mining Sales Data and Establishing Flexible Customer Clusters,” Int. J. Hybrid Inf. Technol., vol. 5, no. 4, pp. 79–84, 2012.

      [6] N. I. Putu, P. Yuliari, I. K. Gede, D. Putra, N. I. Kadek, and D. W. I. Rusjayanti, “Customer Segmentation Through Fuzzy C-Means and Fuzzy Rfm Method,” vol. 78, no. 3, pp. 380–385, 2015.

      [7] I. Chen and K. Popovich, “Understanding customer relationship management (CRM),” Bus. Process Manag. J., vol. 9, no. 5, pp. 672–688, 2003.

      [8] A. Tsiptis and A. Chorianopoulus, Data Mining Techniques in CRM. United Kingdom: John Wiley and Sons, 2009.

      [9] D. Zumstein, “Customer Performance Measurement: Analysis of the Benefit of a Fuzzy Classification Approach in Customer Relationship Management,” Challenges, no. March, 2007.

      [10] “CRISP-DM 1.0. Step by Step Data Mining Guide.” 2000.

      [11] T. C. H. Wu, “A Fuzzy Set Approach for Analyzing Customer RFM Data,” pp. 2–5.


 

View

Download

Article ID: 26678
 
DOI: 10.14419/ijet.v8i1.9.26678




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.