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Applications of Data Mining Techniques in Telecom Churn Prediction

by V. Umayaparvathi, K. Iyakutti
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 20
Year of Publication: 2012
Authors: V. Umayaparvathi, K. Iyakutti
10.5120/5814-8122

V. Umayaparvathi, K. Iyakutti . Applications of Data Mining Techniques in Telecom Churn Prediction. International Journal of Computer Applications. 42, 20 ( March 2012), 5-9. DOI=10.5120/5814-8122

@article{ 10.5120/5814-8122,
author = { V. Umayaparvathi, K. Iyakutti },
title = { Applications of Data Mining Techniques in Telecom Churn Prediction },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 20 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number20/5814-8122/ },
doi = { 10.5120/5814-8122 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:48.225519+05:30
%A V. Umayaparvathi
%A K. Iyakutti
%T Applications of Data Mining Techniques in Telecom Churn Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 20
%P 5-9
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this competitive world, business becomes highly saturated. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. This paper explores the application of data mining techniques in predicting likely churners and the impact of attribute selection on identifying the churn. It also compares the efficiency of Decision tree and Neural Network classifiers and lists their performances.

References
  1. Yu-Teng Chang, "Applying Data Mining To Telecom Churn Management", IJRIC , 2009 67 – 77.
  2. Rongbin Zhang, Ronghua, Yanying Zhrng, Michael Berry. "Behavior - Based Telecommunication Churn Prediction with Neural Network Approach", page 307- 310, ISCCS. 2011.
  3. M. Berry and G. Linoff. Mastering Data Mining. John Wiley and Sons, New York, USA, 2000.
  4. Gary Cokins, Ken King, "Managing Customer Profitability and Economic Value in the Telecommunication Indutry", SAS Institute White paper.
  5. Hangxia Ma, Min Qin, Jianxia Wang. (2009), "Analysis of the Business Customer Churn Based on Decision Tree Method", The Ninth International Conference on Control and Automation, Guangzhou, China.
  6. MO Zan, ZHOA Shan, LI Li, LIU Ai-Jun, 2007, "A predictive Model of Churn in Telecommunications Base on Data Mining". , IEEE International Conference on Control and Automation", Guangzhou, China.
  7. Yossi Ritcher, Elad Yom-Tov, Noam Slonim, "Predicting Customer Churn in Mobile Networks through Analysis of Social Groups". SIAM.
  8. PAKDD 2006 Data Mining Competition, http://www3. ntu. edu. sg/SCE/pakdd2006/competition/overview. htm
Index Terms

Computer Science
Information Sciences

Keywords

Churn Prediction Data Mining Decision Tree Neural Network

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