# On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks
## 1 Introduction  
Effective customer care and data-driven service assurance have become a vital need for telecommunication companies, especially as regards the progression towards automating the management of technical tickets: in addition, this enables a thorough and objective evaluation of the performance of the service assurance functions, based on generated reports and prescribed Key Performance Indicators (KPIs), which implies increased productivity, improved quality of service and, in some cases, even personalized satisfaction of the end user. More in detail, designing a successful data-driven service assurance system requires: in-house design and development in order to offer a solution that is at the same time cheap and adaptable to all service needs;  
tight integration with the customer portal in order to allow the access to all relevant online services with the same login credentials;support to all activities that are characteristic of an incident management process, such as opening, classifying, assigning, solving, closing and archiving an IT incident;
* enabling the exchange of files and comments regarding an IT incident, as this makes the served customers satisfied by allowing them to interact with IT agents in order to clarify the problem or understand the solution;
* provision of email notifications that allow for keeping all involved parties informed on any ticket update;
* automatic classification and ticket rerouting to the relevant IT agents, which is expected to significantly minimize resolution time of each ticket and consequently enhance user satisfaction;
* generation of reports to measure the KPIs that are chosen to evaluate the success of the customer care service. For detailed references, the advantages and challenges of using data-driven service assurance in an organization have already been explored in the Information Technology Infrastructure Library (ITIL) framework of best practices for delivering IT services (see [1; 2; 3; 4; 5; 6; 7; 8]). Moreover, the positive influence of ticketing services on the implementation of the incident management process is stressed in [1; 2; 3; 4; 5; 6; 7; 8]. Indeed, being able to automate several key activities such as identification, prioritization, assignment, diagnosis and closure of technical tickets plays a relevant role in enhancing the said process. In addition, data-driven service assurance (see [4; 5; 6]) facilitates the measurement and improvement of several important KPIs for the IT business processes, such as the percentage of incidents detected and solved in the first attempt, the mean number of incidents that occurred per day and the average lifetime of an incident. In particular, in [8], it is clearly stated that automatic incident classification proves to be extremely effective at minimizing ticket resolution time. Providing an automated solution to the challenge of ticket classification is a relevant emerging task also according to [10]. Some related additional features that are in part beyond the scope of this work but testify the interest of the scientific and industrial community are the multimedia chat service architecture introduced in [11], as well as rule-based reasoning for fault diagnosis and visual dashboards for helping desk tickets monitoring, which is illustrated in [12; 13]. In addition, a probabilistic framework for IT ticket annotation and search based on natural language processing is introduced in [14; 15; 16; 17]. In [15], a predictive model based on Support Vector Machines (SVMs) and K-Nearest Neighbours (KNN) is discussed with the aim to automate incident categorization with the specific help of ticket description and other relevant ticket attributes. In a similar way, in [18], the dispatch of a ticket to the correct resolution group is successfully automated by means of a tool that combines SVMs and discriminative term-based classification techniques.