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Article:Applying Data Mining to Customer Churn Prediction in an Internet Service Provider

by Afaq Alam Khan, Sanjay Jamwal, M.M.Sepehri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 7
Year of Publication: 2010
Authors: Afaq Alam Khan, Sanjay Jamwal, M.M.Sepehri
10.5120/1400-1889

Afaq Alam Khan, Sanjay Jamwal, M.M.Sepehri . Article:Applying Data Mining to Customer Churn Prediction in an Internet Service Provider. International Journal of Computer Applications. 9, 7 ( November 2010), 8-14. DOI=10.5120/1400-1889

@article{ 10.5120/1400-1889,
author = { Afaq Alam Khan, Sanjay Jamwal, M.M.Sepehri },
title = { Article:Applying Data Mining to Customer Churn Prediction in an Internet Service Provider },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 9 },
number = { 7 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume9/number7/1400-1889/ },
doi = { 10.5120/1400-1889 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:57:59.402549+05:30
%A Afaq Alam Khan
%A Sanjay Jamwal
%A M.M.Sepehri
%T Article:Applying Data Mining to Customer Churn Prediction in an Internet Service Provider
%J International Journal of Computer Applications
%@ 0975-8887
%V 9
%N 7
%P 8-14
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A business incurs much higher charges when attempting to win new customers than to retain existing ones. As a result, much research has been invested into new ways of identifying those customers who have a high risk of churning. However, customer retention efforts have also been costing organizations large amounts of resources. Same is the situation in ISP industry in I.R.Iran. Exploiting the use of demographic, billing and usage data, this study tends to identify the best churn predictors on the one hand and evaluates the accuracy of different data mining techniques on the other. Clustering users as per their usage features and incorporating that cluster membership information in classification models is another aspect which has been addressed in this study

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Index Terms

Computer Science
Information Sciences

Keywords

ISP Churn Data mining Decision tree Regression Neural network

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