A Review of Customer Churn Prediction Related Issues Using Data Mining Methods

Authors

  • Venkatesh S Department of Computer and Information Science, Annamalai University, Tamilnadu, India
  • Jeyakarthic M epartment of Computer and Information Science, Annamalai University, Tamilnadu, India

Keywords:

Customer Churn, Customer Retention, Customer Relationship Management, Logistic regression, Linear regression, Knowledge discovery, Data mining

Abstract

Customer churn prediction is a challenging target but a very necessary and essential in emerging serviceoriented businesses. It is also one of the important issues in customer relationship management. To predict a customer there is a number of data mining techniques applied for churn prediction, this paper reviews some recent developments and compares them in terms of data pre-processing and prediction techniques

References

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[4] Nadeem Ahmad Naz, Umar Shoaib and M. Shahzad Sarfraz, A Review on Customer Churn Prediction Data Mining Modeling Techniques, Indian Journal of Science and Technology, Vol 11(27), 2018.

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[6] Praveen Asthana, A comparison of machine learning techniques forcustomer churn prediction, International Journal of Pure and Applied MathematicsVolume 119 No. 10, 1149-1169, 2018

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Published

2025-11-24

How to Cite

[1]
S. Venkatesh and M. Jeyakarthic, “A Review of Customer Churn Prediction Related Issues Using Data Mining Methods”, Int. J. Comp. Sci. Eng., vol. 7, no. 4, pp. 281–284, Nov. 2025.

Issue

Vol. 7 No. 4 (2019): IJCSE Special Issue Feb Edition

Section

Review Article

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