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

Year 2022, Volume: 26 Issue: 3, 530 - 544, 30.06.2022

Handan Donat , Saliha Karadayı Usta

https://doi.org/10.16984/saufenbilder.1077229

Cited By: 1

Abstract

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.

Keywords

telecommunication, customer churn, churn analysis, data mining, machine learning

References

• [1] P.J. Nesse, S.W. Svaet, D. Strasunskas, and A.A. Gaivoronski, “Assessment and optimisation of business opportunities for telecom operators in the cloud value network”, Transactions on emerging telecommunications technologies, vol.24, no.5, pp. 503-516, 2013.
• [2] T.-H. Chou, J.-L. Seng, “Telecommunication e-services orchestration enabling business process management”, Transactions on emerging telecommunications technologies, vol.23, no.7, pp. 646-659, 2012.
• [3] Y. Atlı, N. Yücel, “Hibrit iletişim teknolojileri”, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol.21, no.3, pp. 785-797, 2016.
• [4] Deloitte. “Covid-19 sonrası tedarik zincirlerinde kazananlar ve kaybedenler.” https://www2.deloitte.com/tr/tr/pages/consumer-business/articles/Covid-19-sonrasi-tedarik-zinciri.html 2021.
• [5] PwC. “COVID-19 salgınının telekom sektörü üzerinde olası etkileri”. https://www.pwc.com.tr/covid-19-telekom-sektoru 2020.
• [6] S. Tabassum, M.A. Azad, and J. Gama, “Profiling high leverage points for detecting anomalous users in telecom data networks”, Annals of Telecommunications, vol.75, no.9-10, pp. 573-581, 2020.
• [7] U. T. Şimşek Gürsoy, “Customer churn analysis in telecommunication sector”. Istanbul University Journal of the School of Business Administration, vol.39, no.1, pp.35–49, 2010.
• [8] García, D. L., Nebot, À., & Vellido, A. Intelligent data analysis approaches to churn as a business problem: a survey. Knowledge and Information Systems, 2017, 51(3), 719–774.
• [9] A. K. Ahmad, A. Jafar, & K. Aljoumaa, “Customer churn prediction in telecom using machine learning in big data platform”. Journal of Big Data, pp. 6-28, 2019.
• [10] M. Al-Mashraie, S.H. Chung, H.W. Jeon, “Customer switching behavior analysis in the telecommunication industry via push-pull-mooring framework: A machine learning approach”, Computers and Industrial Engineering, vol.144, 106476, 2020.
• [11] N. Alboukaey, A. Joukhadar, N. Ghneim, “Dynamic behavior based churn prediction in mobile telecom”, Expert Systems with Applications, 162, 2020.
• [12] M. Ahmed, H. Afzal, I. Siddiqi, M.F. Amjad, K. Khurshid, “Exploring nested ensemble learners using overproduction and choose approach for churn prediction in telecom industry”, Neural Computing and Applications, vol. 32, no.8, pp. 3237-3251, 2020.
• [13] M. Hemalatha, S. Mahalakshmi, “Customer churns prediction in telecom using adaptive logitboost learning approach”, International Journal of Scientific and Technology Research, vol.9 no. 2, pp. 5703-5713, 2020.
• [14] D. Kim, “Investor churn analysis in a P2P lending market”, Applied Economics, vol. 52 no. 52, pp. 5745-5755, 2020.
• [15] J. Kaur, V. Arora, S. Bali, “Influence of technological advances and change in marketing strategies using analytics in retail industry”, International Journal of Systems Assurance Engineering and Management, vol.11 no. 5, pp. 953-961, 2020.
• [16] M.A. De la Llave Montiel, F. López, “Spatial models for online retail churn: Evidence from an online grocery delivery service in Madrid”, Papers in Regional Science, vol. 99 no.6, pp. 1643-1665, 2020.
• [17] W. Jiang, Y. Luo, Y. Cao, G. Sun, C. Gong, “On the build and application of bank customer churn warning model”, International Journal of Computational Science and Engineering, vol.22 no.4, pp. 404-419, 2020.
• [18] P. Verma, “Churn prediction for savings bank customers: A machine learning approach”, Journal of Statistics Applications and Probability, vol.9 no.3, pp. 535-547, 2020.
• [19] S. Höppner, E. Stripling, B. Baesens, S.V. Broucke, T. Verdonck, “Profit driven decision trees for churn prediction”, European Journal of Operational Research, vol.284 no.3, pp. 920-933, 2020.
• [20] H. Li, D. Wu, G. X. Li, Y. H. Ke, W. J. Liu, Y. H. Zheng, & X. Lin, “Enhancing telco service quality with big data enabled churn analysis: infrastructure, model, and deployment”. Journal of Computer Science and Technology, vol.30 no.6, pp.1201–1214, 2015.
• [21] W. Hengliang, & W. Zhang, “A customer churn analysis model in e-business environment”. International Journal of Digital Content Technology and Its Applications, vol. 6 no.9, pp.296–302, 2012.
• [22] K. Dahiya, “Customer Churn Analysis in Telecom Industry”. 4th International Conference on Reliability, Infocom Technologies and Optimization, pp.1–6, 2015.
• [23] M. Günay, “Makine öğrenmesi yöntemleri ile kayıp müşteri analizi”, 26th Signal Processing and Communications Applications Conference, pp.1–4, 2018.
• [24] P. Kisioglu, & Y. I. Topcu, “Applying Bayesian belief network approach to customer churn analysis : A case study on the telecom industry of Turkey”. Expert Systems with Applications, vol.38, pp.7151–7157, 2010.
• [25] A. Chouiekh, E.H.I. El Haj, “Deep convolutional neural networks for customer churn prediction analysis”, International Journal of Cognitive Informatics and Natural Intelligence, vol.14 no.1, pp. 1-16, 2020.
• [26] T. Mandhula, S. Pabboju, N. Gugulotu, “Predicting the customer’s opinion on amazon products using selective memory architecture-based convolutional neural network”, Journal of Supercomputing, vol.76 no.8, pp. 5923-5947, 2020.
• [27] A. De Caigny, K. Coussement, K.W. De Bock, S. Lessmann, “Incorporating textual information in customer churn prediction models based on a convolutional neural network”, International Journal of Forecasting, vol.36 no.4, pp. 1563-1578, 2020.
• [28] F. Napitu, “Twitter opinion mining predicts broadband internet‘s customer churn rate”. IEEE International Conference on Cybernetics and Computational Intelligence, pp.141–146, 2010.
• [29] I. Amali, R. Arunkumar, “Particle swarm optimization with kernel support vector machine for churn prediction in telecommunication industry”, International Journal of Scientific and Technology Research, vol.9 no.4, pp. 253-257, 2020.
• [30] R. Dong, F. Su, S. Yang, & X. Cheng, “Customer Churn Analysis for Telecom Operators Based on SVM”. In: Sun S., Chen N., Tian T. (eds) Signal and Information Processing, Networking and Computer, vol.473, pp.327-333. Springer, Singapore. 2018.
• [31] N.N.A. Sjarif, M.R.M. Yusof, D.H.-T. Wong, S. Yakob, R. Ibrahim, M.Z. Osman, “A customer Churn prediction using Pearson correlation function and K nearest neighbor algorithm for telecommunication industry”, International Journal of Advances in Soft Computing and its Applications, vol.11 no. 2, pp. 46-59, 2019.
• [32] X. Long, Y. Wenjing, A. Le, N. Haiying, L. Huang, Q. Luo, & Y. Chen. “Churn analysis of online social network users using data mining techniques”, Lecture Notes in Engineering and Computer Science, vol.2195, pp.551-556, 2012.
• [33] F. Fessant, J. François, F. Clérot, “Characterizing ADSL customer behaviours by network traffic data-mining”, Annals of Telecommunications, vol.62 no.3-4, pp. 350-368, 2007.
• [34] V. Mahajan & Misra. “Review of data mining techniques for churn prediction in telecom”, Journal of Information and Organizational Sciences, vol.39 no.2, pp.183–197, 2015.
• [35] A. Rodan, A. Fayyoumi, H. Faris, J. Alsakran, & O. Al-kadi, “Negative correlation learning for customer churn prediction : a comparison study”. The Scientific World Journal, 1-7, 2015.
• [36] Y. Gao, G. Zhang, J. Lu, & J. Ma, “A bi-level decision model for customer churn analysis”, Computational Intelligence, vol.30 no.3, pp. 583-599, 2014.
• [37] Ç.K. Kontacı, O. Çetintürk, G. G. Polat, K. C. Özkısacık & A. A. Salah, “A simulator for generating realistic simulations of telecom customer behaviors”, 24th Signal Processing and Communication Application Conference (SIU), pp. 537-540, 2016.
• [38] P. Wanchai, “Customer churn analysis : a case study on the telecommunication ındustry of Thailand”. 12th International Conference for Internet Technology and Secured Transactions, pp.325–331, 2017.
• [39] V. Gülpınar, & D. Altaş, “Customer churn analysis through artificial neural networks in Turkish telecommunications market”, International Journal of Economic Perspectives, vol.7 no.4, pp.63–80, 2013.
• [40] N. Forhad, S. Hussain, R. M. Rahman, “Churn Analysis : Predicting Churners”. Ninth International Conference on Digital Information Management, pp.237–241, 2014.
• [41] Y. Zhao, B. Li, X. Li, W. Liu, S. Ren, “Customer Churn Prediction using improved one-class Support Vector Machine”, Lecture Notes in Computer Science, Editör: Li X, Wang S, Yang-Dong Z. 3584, Springer, Berlin, 300–306, 2005.
• [42] S. Jamil, & M. S. Cs, “Churn comprehension analysis for telecommunication ındustry using ALBA”. International Conference on Emerging Technologies, 1–5, 2016.
• [43] K. M. Leung, “Naive Bayesian Classifier”.https://web.archive.org/web/20160311202321/http://cis.poly.edu/~mleung/FRE7851/f07/naiveBayesianClassifier.pdf. 2007.
• [44] C. Budak, M. Türk, A. Toprak, “Removal of impulse noise in digital images with Naive Bayes classifier method”. Turkish Journal of Electrical Engineering and Computer Science, vol.24 no.4, pp.2717-2729, 2016.
• [45] T. Vafeiadis, K. I. Diamantaras, G. Sarigiannidis & K. C. Chatzisavvas, “A comparison of machine learning techniques for customer churn prediction”. Simulation Modelling Practice and Theory, vol.55, 1–9, 2015.
• [46] D.W. Hosmer & S. Lemeshow, “Applied logistic regressions”, RX Sturdivant, John Wiley & Sons, 1996.
• [47] StatisticsSolutions, “What is Logistic Regression?”https://www.statisticssolutions.com/what-is-logistic-regression/ 2021.
• [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.
• [50] F. Filiz, “Artificial neural networks”. https://medium.com/@fahrettinf/4-1-1-artificial-neural-networks-6257a7a54bb3 2017.
• [51] S. Haykin, “Neural Networks and Learning Machines”. Pearson Education, New Jersey, 1999.
• [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.
• [54] O. Kaynar, H. Arslan, Y. Görmez, & Y. E. Işık, “Makine öğrenmesi ve öznitelik seçim yöntemleriyle saldırı tespiti”. Bilişim Teknolojileri Dergisi, pp.175–185, 2018.
• [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.
• [58] L. Breiman, “Classification and regression trees”, Chapman & Hall/CRC, 1984.
• [59] S. Tahsildar, “Gini Index for Decision Trees”.https://blog.quantinsti.com/gini-index/ 2019.
• [60] F. Kayaalp, M. S. Başarslan, & K. Polat, “TSCBAS: A novel correlation based attribute selection method and application on telecommunications churn analysis”. International Conference on Artificial Intelligence and Data Processing, 2019.
• [61] S. Glen, “How to Calculate Pearson's Correlation Coefficient”. https://www.statisticshowto.com/probability-and-statistics/correlation-coefficient-formula/#Pearson 2020.
• [62] M. Yıldız & S. Albayrak, “Customer churn prediction in telecommunication”, 23nd Signal Processing and Communications Applications Conference, pp.256-259, 2015.
• [63] F. Salfner, M. Schieschke, & M. Malek, “Predicting failures of computer systems: A case study for a telecommunication system”. 20th International Parallel and Distributed Processing Symposium, 2006.
• [64] A. K. Yadav, H. Malik, & S. S. Chandel, “Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India”. Renewable and Sustainable Energy Reviews, vol.52, pp.1093–1106, 2015.
• [65] S. Dwivedi, P. Kasliwal, & S. Soni, “Comprehensive study of data analytics tools (RapidMiner, Weka, R tool, Knime)”. Symposium on Colossal Data Analysis and Networking, 1-8, 2016.
• [66] A. Tharwat, “Classification assessment methods”. Applied Computing and Informatics.doi:10.1016/j.aci.2018.08.003 2018.
• [67] M. Erkan, “Koronavirüs sürecinde telekom operatörleri nasıl çalıştı?” https://www.hurriyet.com.tr/teknoloji/koronavirus-surecinde-telekom-operatorleri-nasil-calisti-41539689 2020.
• [68] C. Deegan, “Koronavirüs sürecinde telekom operatörleri nasıl çalıştı?”. https://www.hurriyet.com.tr/teknoloji/koronavirus-surecinde-telekom-operatorleri-nasil-calisti-41539689 2020.
• [69] KocDigital. “Covid-19 Telekom sektörü etkileri”,https://www.kocdigital.com/blog/covid-19-telekom-sektoru-etkileri 2020.
• [70] O. Özdemir, M. Batar, A.H. Işık, “Churn Analysis with Machine Learning Classification Algorithms in Python”, Lecture Notes on Data Engineering and Communications Technologies, vol.43, pp. 844-852, 2020.
• [71] J. Pamina, J. Beschi Raja, S. Sam Peter, S. Soundarya, S. Sathya Bama, M.S. Sruthi, “Inferring machine learning based parameter estimation for telecom churn prediction”, Advances in Intelligent Systems and Computing, 1108 AISC, pp. 257-267, 2020.
• [72] P. Hooda, P. Mittal, “An exposition of data mining techniques for customer churn in telecom sector”, International Journal of Emerging Trends in Engineering Research, vol.7 no.11, pp. 506-511, 2019.
• [73] S. Sharm, S. S. Sushasukhanya “Prediction of customer churn in telecom industries”, International Journal of Recent Technology and Engineering, vol.8 no.1, pp. 369-372, 2019.
• [74] K. Eria, B.P. Marikannan, “Significance-based feature extraction for customer churn prediction data in the telecom sector”, Journal of Computational and Theoretical Nanoscience, vol.16 no.8, pp. 3428-3431, 2019.
• [75] A. Gaur, R. Dubey, “Predicting Customer Churn Prediction in Telecom Sector Using Various Machine Learning Techniques”, International Conference on Advanced Computation and Telecommunication, 8933783, 2018.
• [76] B. Al-Shboul, H. Faris, N. Ghatasheh, “Initializing Genetic Programming using Fuzzy Clustering and its Application in Churn Prediction in the Telecom Industry”, Malaysian Journal of Computer Science, vol.28 no.3, 213-22, 2015.
• [77] I. Brandusoiu, G. Toderean, “Churn Prediction in the Telecommunications Sector Using Support Vector Machines”, Annals of the Oradea University, Fascicle of Management and Technological Engineering, vol.1, pp. 19-22, 2013.