# On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks
## 1 Introduction
Alternatively, Multinomial Naive Bayes (MNB) and Softmax Regression Neural Network (SNN) are used in [19] for text classification purposes aimed at categorizing user tickets. Finally, several methods to detect duplicate tickets/bugs are proposed in [20; 21; 22]. In particular, this paper, with respect to the emerging need for preventing customers from issuing a request for technical ticket reopening on customer service platforms of telecommunication companies, provides the following contributions:  
* the identification of relevant correlations between the reopening of a technical customer service ticket, on the one hand, and Quality of Service (QoS) parameters of the 5G fiber optic networks, on the other hand, based on the actual use the customer is currently making of the fixed network itself;
* based on such correlations, the design of a data-driven model capable of predicting whether a customer will call the assistance service once again even though his/her technical ticket has already been closed. Incidentally, reopened tickets are to be considered as those tickets that were formerly solved and have been reopened [23]. The proposed approach may prove particularly useful in the domain of 5G enabling technologies. Indeed, even with the advent of 5G, optical fiber is the most suitable meansfor wireless backhaul networks. Indeed, even in networks where this is not the case, the wireless backhaul actually has to be connected into a fiber backhaul. For this reason, fiber technology is increasingly being preferred for the so-called fronthaul, especially when it comes to connecting the dense mesh of 5G small cells. There are several benefits, such as increased speeds matched with lower attenuation, significant immunity with respect to electromagnetic interference, relatively small size, and practically unlimited potential in terms of bandwidth. Hence, customer service in order to address any technical issue relative to the Quality of Service (QoS) perceived in 5G fiber optic networks has a critical role, especially with the advent of the emerging Fixed Wireless Access (FWA) paradigm [24]. The paper contribution lies in the fact that the effectiveness of the proposed approach is evaluated on the customer complaints that arise in conditions of intensive usage of the fixed network of a major Italian network operator. Indeed, according to IBM analyses in [25] and to [26], by automating up to 85% of the customer service process thanks to the usage of predictive tools such as the one presented in this work, an increase in efficiency up to 90% can be obtained, together with a reduction in the operating costs between 25% and 30%. Similar machine learning methods have already been used in order to solve resource allocation problems in order to improve the perceived QoS in [27; 28; 29]. In more detail, [PERSON] et al. have already focused on distributed load balancing in Software Defined Networking relying on Lyapunov-based decision-making algorithms in [27], on optimal buffer allocation for guaranteeing QoS in multimedia Internet broadcasting for mobile networks in [28], and on predictor-based control design for improving Quality of Experience in delay-sensitive Future Internet frameworks in [29]. In contrast with the cited works, this paper specifically focuses on the prediction of the ticket reopening phenomena that characterize fixed networks and therefore also 5G fiber optic networks. To this aim, we exploited several machine learning approaches and compared the obtained performance results. The paper is organized as follows: Section 2 introduces the so-called Analytical Base Table (ABT), namely the dataset to be given as an input to the predictive model for training and test purposes, as well as the data collection and preparation activity that has been carried out to assemble said ABT. Section 3 presents the performance achieved by different machine learning based classifiers, comparing the results obtained on the original dataset against those obtained on the reduced dataset.