Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/71396

TitlePredicting recurring telecommunications customer support problems using deep learning
Author(s)Castro, Vitor
Pereira, Carlos
Alves, Victor
KeywordsCustomer support
Deep neural networks
Machine learning
Quality of Experience
Telecommunications
Issue date2020
PublisherSpringer, Cham
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
CitationCastro V., Pereira C., Alves V. (2020) Predicting Recurring Telecommunications Customer Support Problems Using Deep Learning. In: Analide C., Novais P., Camacho D., Yin H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science, vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_18
Abstract(s)In search of a better quality of experience and more revenue, telecommunication companies are searching for proactive ways of dealing with unsatisfactory user experiences and predicting customer’s behavior. Customer Support (CS) is one of the key areas of customer satisfaction. A good CS enables customers to have a smooth interaction with the company and the services provided when there are doubts or malfunction. Frequently, the problems reported by customers are not resolved in the first interaction, which leads to greater dissatisfaction with the service provider and possibly to future churn. If the company knows in advance of a possible recurrence, it can respond and try to fix the problem without customers noticing or being affected. In this article, a data set of customer data, CS data, and historical service are used to create a deep learning-based model for predicting customer recurrence. Deep neural networks are well-known for their capability to model complex problems when compared to classical machine learning algorithms. The obtained model, with a decision threshold most appropriated for the business needs, presented an F1-score of 60% and AUC-ROC of 61%, with a Recall and Precision of the recurrent class of 29% and 21%, respectively.
TypeConference paper
URIhttp://hdl.handle.net/1822/71396
ISBN978-3-030-62364-7
e-ISBN978-3-030-62365-4
DOI10.1007/978-3-030-62365-4_18
ISSN0302-9743
Publisher versionhttps://link.springer.com/chapter/10.1007%2F978-3-030-62365-4_18
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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