CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

New Insight into Customer Value Analysis using Data Mining Techniques

by Nesma Taher, Shaimaa Salama, Doaa ElZanfaly
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 3
Year of Publication: 2017
Authors: Nesma Taher, Shaimaa Salama, Doaa ElZanfaly

Nesma Taher, Shaimaa Salama, Doaa ElZanfaly . New Insight into Customer Value Analysis using Data Mining Techniques. International Journal of Computer Applications. 176, 3 ( Oct 2017), 27-38. DOI=10.5120/ijca2017915560

@article{ 10.5120/ijca2017915560,
author = { Nesma Taher, Shaimaa Salama, Doaa ElZanfaly },
title = { New Insight into Customer Value Analysis using Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2017 },
volume = { 176 },
number = { 3 },
month = { Oct },
year = { 2017 },
issn = { 0975-8887 },
pages = { 27-38 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017915560 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:41:10.042568+05:30
%A Nesma Taher
%A Shaimaa Salama
%A Doaa ElZanfaly
%T New Insight into Customer Value Analysis using Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 3
%P 27-38
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Recency, Frequency, Monetary model (RFM) has been widely used to analyze the customers’ value in traditional market using three purchasing behavior attributes. This is considered one dimensional view of customers’ value that is based on profit and purchasing criteria and ignores other useful attributes. Online customers have additional attributes that when captured and analyzed can give more details about customers’ value other than provided by traditional RFM model. This gives companies better vision of their customers, and therefore serve them effectively, resulting in strong and long relationship with them. New Behavioral RFM Model (BRFM) is proposed in this paper to provide online retailers with a new customers' insight that reflects their web behavior beside their profitability. Three web behavioral attributes, represented in Recency of Session (Rs), Frequency of Session (Fs), and Number of clicks (NoC) are added to the traditional RFM attributes for customer value segmentation in online market using K-means clustering algorithm. The effectiveness of BRFM model is compared against the traditional RFM using Dunn index and Davies- Bouldin measures. Results show that the BRFM model enhances the clustering accuracy and reveals new customers’ clusters disregarded by the traditional RFM model.

  1. Tsiptsis, K., & Chorianopoulos, A., (2009). Data Mining Techniques in CRM: Inside Customer segmentation, first ed. Wiley Publishing Inc., United Kingdom.
  2. Kim, M., Eun Park, J., Dubinsky, A. J., & Chaiy, S., (2012). Frequency of CRM implementation activities: a customer-centric view. Journal of Services Marketing Journal of Services Marketing. 26 (2), 83 - 93.
  3. Berry, M., & Linoff, G., (2004). Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, second ed. Wiley Publishing Inc., United Kingdom.
  4. Ngai, E., Xiu, L., & Chau, D., 2009. Application of data mining techniques in customer relationship management. Expert Systems with Applications. 36 (2), 2592-2602.
  5. Kumar, V., & Reinartz, W., (2012). Strategic Customer Relationship Management Today, In: Kumar, V., Reinartz, W. (Eds.), Customer Relationship Management. Springer, Berlin, pp. 3-20.
  6. Hughes, A., 1994. Strategic database marketing, first ed. Probus Publishing Company Inc., Chicago.
  7. Facca, F., M., & Lanzi, P., L., (2005). Mining interesting knowledge from weblogs: a survey. Data & Knowledge Engineering. 53 (3), 225-241.
  8. Cheng, C., & Chen, Y., (2008). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications. 36 (3), 4176-4184.
  9. 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. Journal of Mathematics and Computer Science. 4 (2), 172-181.
  10. Bunnak, P., Thammaboosadee, S., & Kiattisin, S., (2015). Applying Data Mining Techniques and Extended RFM Model in Customer Loyalty Measurement. Journal of Advances in Information Technology. 6 (4), 238-248.
  11. Stone, B., & Jacobs, R., (1995). Successful direct marketing methods, fifth ed. NTC Business Books, Chicago.
  12. Liu, D., & Shih, Y., (2005). Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. Journal of Systems and Software. 77 (2), 181-191.
  13. Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S., (2011). Estimating customer lifetime value based on RFM analysis of customer purchase behavior. Procedia Computer Science. 3, 57-63.
  14. Li, D., Dai, W., & Tseng, W., (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.
  15. Parvaneh, A., Abbasimehr, H., &Tarokhc, M., (2012). Integrating AHP and Data Mining for Effective Retailer Segmentation Based on Retailer Lifetime Value. Journal of Institutional Economics. 5 (11) 25-31.
  16. Mesforoush, A., & Tarokh, M.J., (2013). Customer Profitability Segmentation for SMEs Case Study: Network Equipment Company. International Journal of Research in Engineering and Technology. 2 (1), 30-44.
  17. Chen, D., Sain, S., & Guo, K., (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing & Customer Strategy Management. 19 (3), 197-208.
  18. Shim, B., Keunho Choi, K., & Yongmoo Suh, Y., (2012). CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns. Expert Systems with Applications. 39 (9), 7736- 7742.
  19. Birant, D., (2011). Knowledge-Oriented Applications in Data Mining, In: Funatsu, K. (Eds.), Data Mining Using RFM Analysis. INTECH publisher, Croatia, PP. 91-108.
  20. Tabaei, Z., & Fathian, M. (24-26 Oct. 2011). Developing W-RFM Model for Customer Value: An Electronic Retailing Case Study. In The 3rd International Conference on Data Mining and Intelligent Information Technology Applications. The Westin Resort Coloane, Macao.
  21. Ansari, A., & Ghalamkari, S., (2014). Segmenting Online Customers Based on their Lifetime Value and RFM Model by Data Mining Techniques. International Journal of Information Science and Management, 70-82.
  22. Taher, N., Elzanfaly, D., & Salama, S. (2016) ‘b’. Investigation in customer value segmentation quality under different preprocessing types of RFM attributes. International Journal of Recent Contributions from Engineering, Science & IT. 4 (4), 5-10.
  23. Prasad G.S., Reddy N.V.S., & Acharya U.D., (2010). Knowledge Discovery from Web Usage Data: A Survey of Web Usage Pre-processing Techniques, In: Das V.V. et al. (Eds.), Information Processing and Management. Communications in Computer and Information Science. Springer Heidelberg, Berlin, pp. 505-507.
  24. Srivastava, J., Cooley, R., Deshpande, M., & Tan, P.N., (2000). Web usage mining: discovery and applications of usage patterns from Web data. SIGKDD Explorations Newsletter.1 (2), 12-23.
  25. Han, J., Kamber, M., & Pei, J., (2011). Data Mining: Concepts and Techniques, third ed. Morgan Kaufmann Publishers, Burlington, Massachusetts.
  26. [Dataset] Martin, M., (2012). Sample - Superstore Sales. Tableau, v1.
  27. Ansari, Z., Azeem, M., F., Ahmed, W., & Babu A., V., (2015). Quantitative Evaluation of Performance and Validity Indices for Clustering the Web Navigational Sessions. World of Computer Science and Information Technology Journal. 1(5), 217-226.
  28. Ray, S., Turi, & R., H., (1999). Determination of Number of Clusters in K-Means Clustering and Application in Color Image Segmentation. The 4th International Conference on Advances in Pattern Recognition and Digital Techniques, Portugal.
  29. Saitta, S., Raphael, B., & Smith, I., (2007). A Bounded Index for Cluster Validity, In: Perner P. (Eds.), Machine Learning and Data Mining in Pattern Recognition. Springer, Berlin, pp.174 -187.
  30. Kovács, F., Legány, C., & Babos, A. (15-17 Feb. 2006). Cluster Validity Measurement Techniques. In 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, Cambridge, United Kingdom.
Index Terms

Computer Science
Information Sciences


Customer value analysis Recency Frequency Monetary Model K-means clustering algorithm Dunn Index (DI) Davies Bouldin (DB)