# On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks
## 5 Conclusions  
The paper proposes a data-driven approach based on machine learning for predicting technical ticket reopening in customer service platforms of telecommunications companies providing 5G fiber optic networks, namely with respect to ensuring that, between end user and service provider, the Service Level Agreement in terms of perceived Quality of Service is satisfied.  
The activity was carried out within the framework of an extensive joint research initiative on Next Generation Networks between ELIS Innovation Hub and a major network service provider in Italy over the years 2018-2021.  
The authors compare the performance of different approaches to classification--ranging from decision trees to Artificial Neural Networks and Support Vector Machines--and establish that a Bayesian network classifier is the most accurate at predicting whether a monitored ticket will be reopened or not.  
In addition, the authors propose a suitable dimensionality reduction strategy that proves to be successful at increasing the computational efficiency by reducing the size of the relevant training dataset by two orders of magnitude with respect to the original dataset.  
Numerical simulations show the effectiveness of the proposed approach, proving it 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.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Event** & **Number of Occurrences in \(X^{\prime}_{red}\)** & **Percentage of Events Causing a Repeated Ticket** \\ \hline Event (1) & 7 & 42.86\% \\ Event (2) & 29 & 34.48\% \\ Event (3) & 17 & 29.41\% \\ Event (4) & 107 & 20.56\% \\ Event (5) & 102 & 17.65\% \\ Event (6) & 102 & 12.75\% \\ Event (7) & 372 & 12.37\% \\ Event (8) & 447 & 10.96\% \\ Event (9) & 504 & 10.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Combinations of features in \(X^{\prime}_{red}\) that are most probably the reason for ticket reopening according to the predictions of the Bayesian Network classifier.  
As future work, the authors look forward to testing the proposed method on Quality of Service datasets coming from additional sources and/or related to other 5G networks, as well as to testing the same method on even larger datasets in orders to further assess its scalability properties.  
Investigation, M.V.; Methodology, L.R.C., A.C., M.D., E.S. and M.V.; Software, A.C., M.D. and E.S.; Writing--original draft, L.R.C. and N.A.S.; Writing--review & editing, L.R.C. and N.A.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by ELIS Innovation Hub within a collaboration with Vodafone, grant number Joint Research Project within the framework of the Mindset Revolution Semester.  
Not Applicable, the study does not report any data.  
The work presented in this paper was carried out while [PERSON] and [PERSON] were with ELIS Innovation Hub and does not reflect the results of any activity carried out at Enel Green Power S.p.A. and ERG S.p.A., to which these two authors are currently affiliated, respectively.