On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks

Lorenzo Ricciardi Celsi, Andrea Caliciotti, Matteo D'Onorio, Eugenio Scocchi, Nour Alhuda Sulieman and Massimo Villari
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Lorenzo Ricciardi Celsi: ELIS Innovation Hub, Via Sandro Sandri 81, 00159 Roma, Italy
Andrea Caliciotti: Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza Università di Roma, Via Ariosto 25, 00185 Roma, Italy
Matteo D'Onorio: DIAEE, Sapienza Università di Roma, Corso Vittorio Emanuele II 244, 00186 Roma, Italy
Eugenio Scocchi: ERG S.p.A., Via Bissolati 76, 00187 Roma, Italy
Nour Alhuda Sulieman: Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università di Messina, Piazza Pugliatti 1, 98122 Messina, Italy
Massimo Villari: Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università di Messina, Piazza Pugliatti 1, 98122 Messina, Italy

Future Internet, 2021, vol. 13, issue 10, 1-16

Abstract: The paper proposes a data-driven strategy for predicting technical ticket reopening in the context of customer service for telecommunications companies providing 5G fiber optic networks. Namely, the main aim is to ensure that, between end user and service provider, the Service Level Agreement in terms of perceived Quality of Service is satisfied. The activity has been carried out within the framework of an extensive joint research initiative focused on Next Generation Networks between ELIS Innovation Hub and a major network service provider in Italy over the years 2018–2021. The authors make a detailed comparison among the performance of different approaches to classification—ranging from decision trees to Artificial Neural Networks and Support Vector Machines—and claim that a Bayesian network classifier is the most accurate at predicting whether a monitored ticket will be reopened or not. Moreover, the authors propose an approach to dimensionality reduction that proves to be successful at increasing the computational efficiency, namely by reducing the size of the relevant training dataset by two orders of magnitude with respect to the original dataset. Numerical simulations end the paper, proving that the proposed approach can be a very useful tool for service providers in order to identify the customers that are most at risk of reopening a ticket due to an unsolved technical issue.

Keywords: 5G fiber optic networks; data-driven service assurance; next generation networks; predictive analytics (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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