MENU

E-journal for electrical and electronic engineers
AUTOMATYKA, ELEKTRYKA, ZAKLOCENIA

(AUTOMATICS, ELECTROTECHNICS, DISTURBANCES)

Vol. 8, nr 3 (29) 2017

Publ. 30.09.2017

Artificial Neural Network in Forecasting the Churn Phenomena Among Costumers of IT and Power Supply Services

Przemyslaw WOJDA, Krzysztof NOWICKI

s. 34-41 DOI: 10.17274/AEZ.2017.29.03

Abstract

This paper presents an attempt to use an artificial neural network to investigate the churn phenomenon among the customers of a telecommunications operator. An attempt was made to create a data model based on the customer lifetime value (CLV) rather than on activity alone. A multilayered artificial neural network was used for the experiments. The results yielded a 99% successful identification rate for customers in no danger of leaving, while only 57% of those identified as in danger of leaving actually did so and stopped using the company's services.

Keywords

churn, ANN, CLV

Fig.

Bilbiography

[1] J. Ljungehed (2017), “Predicting Customer Churn Using Recurrent Neural Networks”, Master in Computer Science, School of Computer Science and Communication, Stockholm, Sverige, 2017
[2] 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, 55, pp. 1-9., 2015
[3] A. Ahmed, D.M. Linen, “A review and analysis of churn prediction methods for customer retention in telecom industries”, 4th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE Conference Publications, 2017
[4] Y. Zhang, J. Qi, H. Shu, Y. Li, “Predicting Churn Probability of Fixed-line Subscriber with Limited Information: A Data Mining Paradigm for Enterprise Computing”. In: A.M. Tjoa, L. Xu, S.S. Chaudhry (eds) “Research and Practical Issues of Enterprise Information Systems”. IFIP International Federation for Information Processing, vol 205. Springer, 2006
[5] M.B. Hosseni, M.J. Tarokh, “Customer Segmentation Using CLV Elements”, Journal of Service Science and Management, Vol.4, pp. 284-290, 2011
[6] K. Halicka, „Wykorzystanie sztucznych sieci neuronowych do prognozowania cen na giełdzie energii”, Rynek Energii, nr 1, 2010.
[7] R. Tadeusiewicz, „Sieci neuronowe”, Akademicka Oficyna Wydawnicza, Warszawa, 1993
[8] H. Abdi, “Coefficient of Variation”, Encyclopedia of Research Design, Thousand Oaks, CA, Sage, 2010
[9] J. Siderska, „Analiza możliwości zastosowania sieci neuronowych do modelowania wartości kapitału społecznego w firmach IT”, Economics and Management, no. 1, 2013.
[10] Y. Hifny, „Deep Learning Based on Manhattan Update Rule”, http://www.helwan.edu.eg/university/staff/~yhifny/publications/dnn_crf_MH.pdf, dostęp 7.12.2017
[11] Q. Liao, L.Z. Leibo, T. Poggio, „How Important Is Weight Symmetry in Backpropagation?”, https://arxiv.org/pdf/1510.05067.pdf,arXiv:1510.05067v4 [cs.LG] 4 Feb 2016,
[12] K. Gajowniczek, T. Ząbkowski, „Problemy modelowania rezygnacji klientów w telefonii komórkowej”, Metody Ilościowe w Badaniach Ekonomicznych, t. XIII/3, s. 65–78), 2012