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.

On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks / Ricciardi Celsi, Lorenzo; Caliciotti, Andrea; D'Onorio, Matteo; Scocchi, Eugenio; Sulieman, Nour Alhuda; Villari, Massimo. - In: FUTURE INTERNET. - ISSN 1999-5903. - 13:10(2021), pp. 1-16. [10.3390/fi13100259]

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

Ricciardi Celsi, Lorenzo;D'Onorio, Matteo;
2021

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.
5G fiber optic networks; data-driven service assurance; next generation networks; predictive analytics
01 Pubblicazione su rivista::01a Articolo in rivista
On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks / Ricciardi Celsi, Lorenzo; Caliciotti, Andrea; D'Onorio, Matteo; Scocchi, Eugenio; Sulieman, Nour Alhuda; Villari, Massimo. - In: FUTURE INTERNET. - ISSN 1999-5903. - 13:10(2021), pp. 1-16. [10.3390/fi13100259]
File allegati a questo prodotto
File Dimensione Formato  
RicciardiCelsi_On Predicting_2021.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 471.12 kB
Formato Adobe PDF
471.12 kB Adobe PDF Visualizza/Apri PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1610993
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 2
social impact