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