Open Access   Article Go Back

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

S. Venkatesh1 , M. Jeyakarthic2

Section:Review Paper, Product Type: Journal Paper
Volume-07 , Issue-04 , Page no. 281-284, Feb-2019

Online published on Feb 28, 2019

Copyright © S. Venkatesh, M. Jeyakarthic . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: S. Venkatesh, M. Jeyakarthic, “A Review of Customer Churn Prediction Related Issues Using Data Mining Methods,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.281-284, 2019.

MLA Style Citation: S. Venkatesh, M. Jeyakarthic "A Review of Customer Churn Prediction Related Issues Using Data Mining Methods." International Journal of Computer Sciences and Engineering 07.04 (2019): 281-284.

APA Style Citation: S. Venkatesh, M. Jeyakarthic, (2019). A Review of Customer Churn Prediction Related Issues Using Data Mining Methods. International Journal of Computer Sciences and Engineering, 07(04), 281-284.

BibTex Style Citation:
@article{Venkatesh_2019,
author = {S. Venkatesh, M. Jeyakarthic},
title = {A Review of Customer Churn Prediction Related Issues Using Data Mining Methods},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {07},
Issue = {04},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {281-284},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=772},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=772
TI - A Review of Customer Churn Prediction Related Issues Using Data Mining Methods
T2 - International Journal of Computer Sciences and Engineering
AU - S. Venkatesh, M. Jeyakarthic
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 281-284
IS - 04
VL - 07
SN - 2347-2693
ER -

           

Abstract

Customer churn prediction is a challenging target but a very necessary and essential in emerging service-oriented 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.

Key-Words / Index Term

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

References

[1]. Xiaohua Hu, A Data Mining Approach for Retailing Bank Customer Attrition Analysis, Applied Intelligence 22, 47–60, 2005.
[2]. Vandana Ahuja, Yajulu Medury, Corporate blogs as tools for consumer segmentation-using cluster analysis for consumer profiling, Journal of Targeting, Measurement and Analysis for Marketing 19, 173 – 182, 2011.
[3]. David L. García, Àngela Nebot, Alfredo Vellido, Intelligent data analysis approaches to churn as a business problem: a survey, Knowl Inf Syst 51:719–774, 2017.
[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.
[5]. Aleksandar J. Petkovski, Biljana L. Risteska Stojkoska, Kire V. Trivodaliev, and Slobodan A. Kalajdziski, Analysis of Churn Prediction: A Case Study on Telecommunication Services in Macedonia, 24th Telecommunications forum TELFOR 2016 Serbia, Belgrade, November 22-23, 2016.
[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