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Reseach Article

A Proposed Paradigm for Enhancing Customer Retention using Web Usage Mining

by Merna Ashraf, Shimaa Ouf, Yehia Helmy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 29
Year of Publication: 2020
Authors: Merna Ashraf, Shimaa Ouf, Yehia Helmy
10.5120/ijca2020919772

Merna Ashraf, Shimaa Ouf, Yehia Helmy . A Proposed Paradigm for Enhancing Customer Retention using Web Usage Mining. International Journal of Computer Applications. 177, 29 ( Jan 2020), 32-35. DOI=10.5120/ijca2020919772

@article{ 10.5120/ijca2020919772,
author = { Merna Ashraf, Shimaa Ouf, Yehia Helmy },
title = { A Proposed Paradigm for Enhancing Customer Retention using Web Usage Mining },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2020 },
volume = { 177 },
number = { 29 },
month = { Jan },
year = { 2020 },
issn = { 0975-8887 },
pages = { 32-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number29/31086-2020919772/ },
doi = { 10.5120/ijca2020919772 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:47:15.809198+05:30
%A Merna Ashraf
%A Shimaa Ouf
%A Yehia Helmy
%T A Proposed Paradigm for Enhancing Customer Retention using Web Usage Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 29
%P 32-35
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to the rapid growth of the internet and the emergence of the World Wide Web, there has been a huge amount of data stored in databases which increase the opportunities and relationships between companies and their customers. Although Companies find difficulty in satisfying customers with different backgrounds, so they find that it is important to adopt a strategy that helps them to understand and manage the needs of the organization’s current and potential customers which are called customer relationship management(CRM). The purpose of CRM is to create value for customers and help organizations to gain “competitive advantage” over competitors. Because many businesses face problems in how to benefit from this huge amount of data that come from the internet, this leads to the emergence of web mining, web mining uses data mining techniques to improve business. Because customer retention is the core of CRM. The purpose of this research is to improve customer retention by merging five data mining techniques in the pattern discovery phase of web usage mining in order to enhance the recommender system that will increase customer retention.

References
  1. Agrawal, N. and A. Jawdekar (2016). User-based approach for finding various results in web usage mining. 2016 Symposium on Colossal Data Analysis and Networking (CDAN), IEEE.
  2. Bahari, T. F. and M. S. J. P. c. s. Elayidom (2015). "An efficient CRM-data mining framework for the prediction of customer behaviour." 46: 725-731.
  3. Bose “A Practical Guide to Data Mining for E-Commerce Business”, 2016.
  4. Chavda, S., et al. (2017). "Recent Trends and Novel Approaches in Web Usage Mining."
  5. Gupta, A. and S. J. I. F. Kohli (2019). "FORA: An OWO based framework for finding outliers in web usage mining." 48: 27-38.
  6. Isinkaye, F., et al. (2015). "Recommendation systems: Principles, methods and evaluation." 16(3): 261-273.
  7. Ismail, M., et al. (2015). "Data Mining in electronic commerce: benefits and challenges." 8(12): 501.
  8. Kaur, N. and H. J. I. J. o. C. A. Aggarwal (2015). "Web log analysis for identifying the number of visitors and their behavior to enhance the accessibility and usability of website." 110(4): 25-30.
  9. Lopes, P. and B. J. P. C. S. Roy (2015). "Dynamic recommendation system using web usage mining for e-commerce users." 45: 60-69.
  10. Lu, J., et al. (2015). "Recommender system application developments: a survey." 74: 12-32.
  11. Ngai, E. W., et al. (2009). "Application of data mining techniques in customer relationship management: A literature review and classification." 36(2): 2592-2602.
  12. Nigam, B., et al. (2015). "Evaluation of models for predicting user’s next request in web usage mining." 4: 1-13.
  13. Sellamy, K., et al. (2018). Web mining techniques and applications: Literature review and a proposal approach to improve performance of employment for young graduate in Morocco. 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), IEEE.
  14. Soltani, Z. and N. J. J. C. i. H. B. Navimipour (2016). "Customer relationship management mechanisms: A systematic review of the state of the art literature and recommendations for future research." 61: 667-688.
  15. Suchacka, G., et al. (2017). "Using association rules to assess purchase probability in online stores." 15(3): 751-780.
  16. Vaish, A., et al. (2016). "Customer relationship management (CRM) towards service orientation in hospitals: A review." 13(4): 224-228.
  17. Vannieuwenborgh, S. (2018). Data Mining in Customer Relationship Management, Open Universiteit Nederland.
Index Terms

Computer Science
Information Sciences

Keywords

Web usage mining data pre-processing web server log Pattern discovery.