Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article

Prediction of Customer Plan using Churn Analysis for Telecom Industry

Author(s): Ajitha P.*, Sivasangari A., Gomathi R.M. and Indira K.

Volume 13, Issue 5, 2020

Page: [926 - 929] Pages: 4

DOI: 10.2174/2213275912666190410114104

Price: $65

Abstract

Background: In creating nations like India, there are in excess of 10 administrators giving versatile administration in each circle. With the presentation of number convenience portable client are progressively changing starting with one administrator then onto the next. This conduct is called beat. The explanation behind beat might be many like valuing isn't alluring, visit call drops, message drops, more client care calls and so forth. Presently the administrator in INDIA is aware of the need of client. At that point, it is past the point of no return as the client has officially settled on choice and hard to persuade and retain. So a robotized instrument is needed at administrator end to predict which client may beat with high exactness.

Objective: With fast utilization of outfit classifiers to enhance exactness, we additionally propose a gathering cross breed classifier that predicts with more precision.

Methods: Hybrid model contains regression, perceptron and confrontation both regression and perceptron run parallel after completion execution both the results will be compared in a confrontation level.

Conclusion: The report of customer who are predicted to churn and the reason for churning if reported. Also it will store aggregate reporting HBASE database.

Keywords: Churn analysis, regression, hybrid prediction, call record data set, perceptron, HBASE database.

Graphical Abstract

[1]
I. Brandusoiu, B. Ionut, T. Gavril, and B. Horia, "Methods for churn prediction in the pre-paid mobile telecommunications industry", International Conference on Communications (COMM), 2016
[2]
M. Owczarczuk, "Churn models for prepaid customers in the cellular telecommunication industry using large data marts", Expert Syst. Appl., vol. 37, pp. 4710-4712, 2010.
[3]
K.H. Liao, and H.E. Chueh, "Applying fuzzy data mining to telecom churn management", International Conference on Intelligent Computing and Information Science Springer, Berlin, Heidelberg, pp. 259-264, 2011.
[4]
Y.S. Kim, H. Lee, and J. Johnson, "Churn management optimization with controllable marketing variables and associated management costs", Expert Syst. Appl., vol. 40, no. 6, pp. 2198-2207, 2012.
[5]
R. Obiedat, M. Alkasassbeh, H. Faris, and O. Harfoushi, "Customer churn prediction using a hybrid genetic programming approach", Sci. Res. Essays, vol. 8, no. 27, pp. 1289-1295, 2013.
[6]
M.R. Ismail, M.K. Awang, M.N.A. Rahman, and M. Mokhairi, "A multi-layer perceptron approach for customer churn prediction", Int. J. Multimed. Ubiquit. Eng., vol. 10, no. 7, pp. 213-222, 2015.
[7]
R.N. Bolton, P.K. Kannan, and M.D. Barletta, "Implications of loyalty program membership and service experiences for customer retention and value", J. Acad. Mark. Sci., vol. 28, no. 1, pp. 95-108, 2000.
[8]
C. Cortes, D. Perion, and C. Volinsky, "Communities of interest IDA ’01: Proceedings of the 4th International Conference on Advance in Intelligent Data Analysis, Springer- Verilog, London, UK,",
[9]
J. Dean, and S. Ghemawat, "Map Reduce: Simplified data processing on large clusters", Commun. ACM, vol. 51, no. 1, pp. 137-150, 2004.
[10]
P.A. Estevez, C.M. Held, and C.A. Perez, "Subscription fraud prevention in telecommunications using fuzzy rules and neural networks", Expert Syst. Appl., vol. 31, no. 2, pp. 337-344, 2006.
[11]
"Hila’s C., Sahalos, J.” User profiling for fraud detection in telecommunication networks", 5th International conference on technology and automation, pp. 382-387. 2005
[12]
M. Faloutsos, P. Faloutsos, and C. Faloutsos, "On power-law relationships of the Internet topology", In Proceedings of ACM SIGCOMM New York, NY, pp. 251-262, 1999.
[13]
D. Xing, and M. Girolami, "Employing latent dirichlet allocation for fraud detection in telecommunication pattern recognition", Letters, vol. 28, no. 13, pp. 1727-1734, 2007.
[14]
H. Zing, and J.C. Bolot, "Mining call and mobility data to improve paging efficiency in cellular networks", In Proceedings of the 13th annual ACM international conference on Mobile computing and networking ACM New York, 2007pp. 123-134

Rights & Permissions Print Cite
Article Metrics
3
© 2024 Bentham Science Publishers | Privacy Policy