AUTHORS: Melike Günay, Tolga Ensari
Download as PDF
ABSTRACT: In this article, we propose a new approach for the churn analysis. Our target sector is Telecom industry, because most of the companies in the sector want to know which of the customers want to cancel the contract in the near future. Thus, they can propose new offers to the customers to convince them to continue using services from same company. For this purpose, churn analysis is getting more important. We analyze well-known machine learning methods that are logistic regression, Naïve Bayes, support vector machines, artificial neural networks and propose new prediction method. Our analysis consist of two parts which are success of predictions and speed measurements. Affect of the dimension reduction is also measured for the analysis. In addition, we test our new method with a second dataset. Artificial neural networks is the most successful as we expected but our new approach is better than artificial neural networks when we try it with data set 2. For both data sets, new method gives the better result than logistic regression and Naïve Bayes.
KEYWORDS: - artificial neural networks, churn analysis, logistic regression, Naïve Bayes, support vector machines
REFERENCES:
[1] O. Kaynar, M. Tuna, Y.Görmez, M.
Deveci,“Makine öğrenmesi yöntemleriyle
müşteri kaybı analizi”, C.Ü. İktisadi ve İdari
Bilimler Dergisi, Cilt 18, Sayı 1, 2017.
[2] K.Coussement, S. Lessmann, G. Verstraeten, “A
Comparative Analysis of Data Preparation
Algorithms for Customer Churn Prediction: A
Case Study in the Telecommunication
Industry”, Decision Support Systems, 95 (2017)
27–36 .
[3] P. Spanoudes, T. Nguyen, “Deep Learning in
Customer Churn Prediction: Unsupervised
Feature Learning on Abstract Company
Independent Feature Vectors”,Cornell
University, March, 2017.
[4] T. Lang, M. Rettenmeier, “Understanding
Consumer Behavior with Recurrent Neural
Networks”, Zalando SE Techblog, 2017.
[5]
[5] T.Zhang, X.Cheng, M.Yuan, L.Xu,
C.Cheng, K.Chao, “Mining Target Users for
Mobile Advertising Based on Telecom Big
Data”, 6th International Symposium on
Communications and Information Technologies
(ISCIT), 26-28 Sept. 2016.
[6] C.Cheng, X.Cheng, “Anovel Cluster Algorithm
for Telecom Customer Segmentation”,
International Symposium on Communications
and Information Technologies (ISCIT), 26-28
Sept. 2016.
[7] BIGML,https://bigml.com/user/bigml/gallery/da
taset/4f89bff4155268645c000030, 10.01.2018.
[8] IBM,
https://www.ibm.com/communities/analytics/wa
tson-analytics-blog/predictive-insights-in-thetelco-customer-churn-data-set/, 12.03.2018.
[9] A. Chaudhary, S.Kolhe, R.Kamal, “A Hybrid
Ensemble for Classification in mutliclass
datasets: An application to Oilseed Diasease
Dataset”, Computers and Electronics in
Agriculture, April, 2016.
