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Reseach Article

CRM Customer Value based on Constrained Sequential Pattern Mining

by Bhawna Mallick, Deepak Garg, Preetam Singh Grover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 9
Year of Publication: 2013
Authors: Bhawna Mallick, Deepak Garg, Preetam Singh Grover
10.5120/10663-5434

Bhawna Mallick, Deepak Garg, Preetam Singh Grover . CRM Customer Value based on Constrained Sequential Pattern Mining. International Journal of Computer Applications. 64, 9 ( February 2013), 21-29. DOI=10.5120/10663-5434

@article{ 10.5120/10663-5434,
author = { Bhawna Mallick, Deepak Garg, Preetam Singh Grover },
title = { CRM Customer Value based on Constrained Sequential Pattern Mining },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 9 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 21-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number9/10663-5434/ },
doi = { 10.5120/10663-5434 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:15:57.326268+05:30
%A Bhawna Mallick
%A Deepak Garg
%A Preetam Singh Grover
%T CRM Customer Value based on Constrained Sequential Pattern Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 9
%P 21-29
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The role of data mining has become increasingly important for an organization that has large databases of information on customers. Customer Relationship Management (CRM) systems are implemented to identify the most profitable customers and manage the relationship of company with them. Intelligent data mining tools and techniques are used as backbone to the whole CRM initiative taken by the companies. Data mining tools search the data warehouse maintained by the companies and predict the hidden patterns and present them in form of a model. Strategic decisions about the customers can be taken based on the outcomes of these models. The data mining researchers have presented various mining algorithms to extract patterns in data for successful CRM approach. These approaches are however facing several problems like they are not business-focused and often results in enormous size of data after applying mining approaches. They have no relevant mechanism to provide guidance for focusing on specific category of customers for business profitability. In this article, the requirements of sequential pattern mining process for CRM environment is described, and then a novel constraint guided model for knowledge discovery process is proposed. We have suggested even how the selection of appropriate constraints can be made from the perspective of customer value analysis.

References
  1. Kracklauer, A. H. , Mills, D. Q. , and Seifert, D. 2004. Customer management as the origin of collaborative customer relationship management. Collaborative Customer Relationship Management, 3–6.
  2. Langley, P. , and Simon, H. A. 1995. Applications of machine learning and rule induction. Communications of the ACM, 38, 54–64.
  3. Lau, H. , Wong, C. , Hui, I. , and Pun, K. 2003. Design and implementation of an integrated knowledge system. Knowledge-Based Systems, 16, 69–76.
  4. Liao, S. H. , and Chen, Y. J. 2004. Mining customer knowledge for electronic catalog marketing. Expert Systems with Applications, 27, 521–532.
  5. Ling Xu, Song Li, and Jie Li. 2010. CRM Customer Value Based On Data Mining. Third International Conference on Knowledge Discovery and Data Mining, DOI 10. 1109/WKDD. 2010. 28. In proceedings of IEEE Computer Society, 32-37.
  6. Parvatiyar, A. , and Sheth, J. N. 2001. Customer relationship management: Emerging practice, process, and discipline. Journal of Economic and Social Research, 3, 1–34.
  7. Tsai, C. F. , and Lu, Y. H. 2009. Customer churns prediction by hybrid neural networks. Expert Systems with Applications, 36, 12547–12553.
  8. Lariviere, B. , and Van den Poel, D. 2005. Investigating the post-complaint period by means of survival analysis. Expert Systems with Applications, 29, 667–677.
  9. Chiang, D. A. , Wang, Y. F. , Lee, S. L. , and Lin, C. J. 2003. Goal-oriented sequential pattern for network banking churn analysis. Expert Systems with Applications, 25, 293–302.
  10. Aggarwal, C. C. , Procopiuc, C. , and Yu, P. S. 2002. Finding localized associations in market basket data. IEEE Transactions on Knowledge and Data Engineering, 14, 51–62.
  11. Changchien, S. , Lee, C. F. , and Hsu, Y. J. 2004. On-line personalized sales promotion in electronic commerce. Expert Systems with Applications, 27, 35–52.
  12. Tsai, C. F. , and Chen, M. Y. 2010. Variable selection by association rules for customer churn prediction of multimedia on demand. Expert Systems with Applications, 37, 2006–2015.
  13. Chen Y. L. and Hu Y. H. 2006. Constraint-based Sequential Pattern Mining: The Consideration of Recency and Compactness. Decision Support Systems, 42(2), 1023-1215.
  14. Chen Y. Liang, Kuo Mi-Hao, Wub Shin-Yi and Tang Kwei. 2009. Discovering recency, frequency, and monetary (RFM) sequential patterns from customers' purchasing data. Electronic Commerce Research and Applications 8 (2009) 241–251.
  15. Dennis, C. , Marsland, D. , and Cockett, T. 2001. Data mining for shopping centres-customer knowledge-management framework. Journal of Knowledge Management, 5, 368–374.
  16. Kim, Y. S. , and Street, W. N. 2004. An intelligent system for customer targeting: A data mining approach. Decision Support Systems, 37, 215–228.
  17. Baesens, B. , Verstraeten, G. , Van den Poel, D. , Egmont-Petersen, M. , Van Kenhove, P. , and Vanthienen, J. 2004. Bayesian network classifiers for identifying the slope on the customer lifecycle of long-life customers. European Journal of Operational Research, 156, 508–523.
  18. Hwang, H. , Jung, T. , and 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, 181–188.
  19. Yu, J. X. , Ou, Y. , Zhang, C. , and Zhang, S. 2005. Identifying interesting visitors through Web log classification. IEEE Intelligent Systems, 20, 55–59.
  20. Kim, Y. S. 2006. Toward a successful CRM: Variable selection, sampling, and ensemble. Decision Support Systems, 41, 542–553.
  21. Kim, S. Y. , Jung, T. S. , Suh, E. H. , and Hwang, H. S. 2006. Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications, 31, 101–107.
  22. Sinha, A. P. , and Zhao, H. 2008. Incorporating domain knowledge into data mining classifiers: An application in indirect lending. Decision Support Systems, 46,287–299.
  23. Hua Jiang, and Zhenxing Cui. 2009. Optimization of Data Mining in CRM Based on Rough Set Theory. International Forum on Information Technology and Applications, DOI 10. 1109/IFITA. 2009. 111. In proceedings of IEEE, 252- 257.
  24. Eirinaki M. and Vazirgiannis M. 2003. Web Mining for Web Personalization. ACM Transactions on Internet Technology (TOIT), 3(1), 1-27.
  25. Liu D. R. and Shih Y. Y. 2005. Integrating AHP and Data Mining for Product Recommendation Based on Customer Lifetime Value. Information and Management, 42(3), 387-400.
  26. Liu D. R. and Shih Y. Y. 2005. Hybrid Approaches to Product Recommendation Based on Customer Lifetime Value and Purchase Preferences. The Journal of Systems & Software, 77(2), 181–191.
  27. Liu D. R. , Lai C. H. and Lee W. J. 2009. A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation. Information Sciences, 179(20), 3505-3519.
  28. Miglautsch John R. 2002. Application of RFM Principles: What to Do with 1-1-1 Customers? Journal of Database Marketing, 9(4), 319-324.
  29. Cheng, C. H. , and Chen, Y. S. 2009. Classifying the segmentation of customer value via RFM model and Rough Set theory. Expert Systems with Applications, 36(3), 4176–4184.
  30. Jiang, T. , and Tuzhilin, A. 2006. Segmenting customers from population to individuals: Does 1-to-1 keep your customers forever. IEEE Transactions on Knowledge and Data Engineering, 1297–1311.
  31. Mallick Bhawna, Garg D. and Grover P. S. . 2012. CFM- PrefixSpan: A pattern growth algorithm incorporating compactness and monetary. International Journal of Innovative Computing, Information and Control. ISSN 1349-4198, Vol. 8, No. 7(A). 4509-4520.
  32. Huang, S. C. , Chang, E. C. , and Wu, H. H. 2009. A case study of applying data mining techniques in an outfitter's customer value analysis. Expert Systems with Applications, 36, 5909–5915.
  33. Luming Yang, and Benxin Lao. 2007. Customer relationship management. Chongqing University Press.
  34. He, Z. , Xu, X. , Huang, J. Z. , and Deng, S. 2004. Mining class outliers: concepts, algorithms and applications in CRM. Expert Systems with Applications, 27, 681–697.
  35. Hosseini, S. M. S. , Maleki, A. , and Gholamian, M. R. 2010. Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37, 5259–5264.
Index Terms

Computer Science
Information Sciences

Keywords

Customer Relationship Management CRM Sequential Pattern mining Constraint Customer Value Analysis