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Towards real-time customer satisfaction prediction model for mobile internet networks


Yusuf-Asaju, Ayisat W. and Dahalin, Zulkhairi and Ta'a, Azman (2018) Towards real-time customer satisfaction prediction model for mobile internet networks. In: Recent Trends in Data Science and Soft Computing. Springer, Cham, Switzerland, pp. 95-104. ISBN 978-3-319-99006-4

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Abstract

Satisfying the customers’ service requirements and expectation, especially customer satisfaction had been one of the major challenges faced by the mobile network operators in most telecommunication organizations. This article implemented an analytical customer satisfaction prediction model by employing the mobile internet traffic datasets collected in real-time through the drive test measurement. To this end, the implementation phase has employed machine learning algorithms in the Microsoft Machine Learning R client Server. The results show that previous user’s traffic datasets can be used to predict customer satisfaction and identify the root cause of poor customer experience before the complete deterioration of the service performance, which could lead to larger percentage of customer dissatisfaction. The mobile network operators can also use the proposed model to overcome the drawbacks of the conventional subjective method of analysing customer satisfaction.

Item Type: Book Section
Uncontrolled Keywords: Telecommunication Quality of experience Mobile network operators Traffic datasets Machine learning algorithms Mean opinion score
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Mrs. Norazmilah Yaakub
Date Deposited: 22 Apr 2019 00:48
Last Modified: 03 Nov 2020 07:33
URI: http://repo.uum.edu.my/id/eprint/25964

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