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The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis

Yıl 2022, Cilt: 26 Sayı: 3, 530 - 544, 30.06.2022
https://doi.org/10.16984/saufenbilder.1077229

Öz

Today's rising cutting-edge technology requirements and competitive environment in telecommunication industry has gained a remarkable importance due to the COVID-19 pandemics in terms of high need of information sharing and remote communication necessity. Telecommunication companies conduct significant analyses by highlighting that the customer data is the most valuable information. Besides, they obtain results emphasizing that acquiring new customers is costlier than retaining the existing ones. Therefore, the companies are willing to determine the important customer features in order to understand why they shift to the other telecommunication service providers. Hence, this study aims to conduct a churn analysis by feature selection approach with large volumes of telecommunication customer data in order to present what kind of customer behaviors and qualifications exist. Since there is a huge amount of data in this field, data mining is a vital requirement. The performance outputs were observed, and the features carrying these outputs to the highest value were identified. The data collection and analysis were carried out in mid-2019, and the same data collection and analysis were carried out again at the beginning of 2021, and these before and after results were compared. In addition, a comparison was made with the results obtained by the other churn analysis studies. This paper contributes to the practitioners by presenting the most important customer features in telecom customer churn, and a new approach in performance evaluation have been proposed specific to the telecommunication market with the industry experts’ guidance as a theoretical contribution.

Kaynakça

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Yıl 2022, Cilt: 26 Sayı: 3, 530 - 544, 30.06.2022
https://doi.org/10.16984/saufenbilder.1077229

Öz

Kaynakça

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  • [46] D.W. Hosmer & S. Lemeshow, “Applied logistic regressions”, RX Sturdivant, John Wiley & Sons, 1996.
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  • [48] B. Huang, M. T. Kechadi, & B. Buckley, “Customer churn prediction in telecommunications”. Expert Systems with Applications, vol.39 no.1, pp.1414–1425, 2011.
  • [49] R. Gandhi, “Support Vector Machine — Introduction to Machine Learning Algorithms”.https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47 2018.
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  • [52] Y. Miche, P. Bas, A. Lendasse, C. Jutten, O. Simula, “Advantages of Using Feature Selection Techniques on Steganalysis Schemes”. F. Sandoval et al. (Eds.) Springer-Verlag Berlin Heidelberg, pp. 606–613, 2007.
  • [53] J. Brownlee, “Feature Selection to Improve Accuracy and Decrease Training Time”. https://machinelearningmastery.com/feature-selection-to-improve-accuracy-and-decrease-training-time/ 2021.
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  • [55] M. Santini, “Decision Trees: Entropy, Information Gain, Gain Ratio”. https://www.slideshare.net/marinasantini1/lecture-4-decision-trees-2-entropy-information-gain-gain-ratio-55241087?from_action=save 2015.
  • [56] A. G. Karegowda, A. S. Manjunath, M.A. Jayaram, “Comparative study of attribute selection using gain ratio”. International Journal of Information Technology and Knowledge and Knowledge Management, vol.2 no.2, pp.271–277, 2010.
  • [57] Toppr “Calculation of Gaining Ratio”. https://www.toppr.com/guides/principles-and-practices-of-accounting/retirement-of-a-partner/calculation-of-gaining-ratio/ 2021.
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Toplam 77 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı, Endüstri Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Handan Donat 0000-0002-8006-0606

Saliha Karadayı Usta 0000-0002-8348-4033

Yayımlanma Tarihi 30 Haziran 2022
Gönderilme Tarihi 22 Şubat 2022
Kabul Tarihi 27 Nisan 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 26 Sayı: 3

Kaynak Göster

APA Donat, H., & Karadayı Usta, S. (2022). The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis. Sakarya University Journal of Science, 26(3), 530-544. https://doi.org/10.16984/saufenbilder.1077229
AMA Donat H, Karadayı Usta S. The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis. SAUJS. Haziran 2022;26(3):530-544. doi:10.16984/saufenbilder.1077229
Chicago Donat, Handan, ve Saliha Karadayı Usta. “The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis”. Sakarya University Journal of Science 26, sy. 3 (Haziran 2022): 530-44. https://doi.org/10.16984/saufenbilder.1077229.
EndNote Donat H, Karadayı Usta S (01 Haziran 2022) The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis. Sakarya University Journal of Science 26 3 530–544.
IEEE H. Donat ve S. Karadayı Usta, “The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis”, SAUJS, c. 26, sy. 3, ss. 530–544, 2022, doi: 10.16984/saufenbilder.1077229.
ISNAD Donat, Handan - Karadayı Usta, Saliha. “The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis”. Sakarya University Journal of Science 26/3 (Haziran 2022), 530-544. https://doi.org/10.16984/saufenbilder.1077229.
JAMA Donat H, Karadayı Usta S. The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis. SAUJS. 2022;26:530–544.
MLA Donat, Handan ve Saliha Karadayı Usta. “The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis”. Sakarya University Journal of Science, c. 26, sy. 3, 2022, ss. 530-44, doi:10.16984/saufenbilder.1077229.
Vancouver Donat H, Karadayı Usta S. The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis. SAUJS. 2022;26(3):530-44.