Reducing user input requests to improve IT support ticket resolution process

Abstract

Management and maintenance of IT infrastructure resources such as hardware, software and network is an integral part of software development and maintenance projects. Service management ensures that the tickets submitted by users, i.e. software developers, are serviced within the agreed resolution times. Failure to meet those times induces penalty on the service provider. To prevent a spurious penalty on the service provider, non-working hours such as waiting for user inputs are not included in the measured resolution time, that is, a service level clock pauses its timing. Nevertheless, the user interactions slow down the resolution process, that is, add to user experienced resolution time and degrade user experience. Therefore, this work is motivated by the need to analyze and reduce user input requests in tickets' life cycle.To address this problem, we analyze user input requests and investigate their impact on user experienced resolution time. We distinguish between input requests of two types: real, seeking information from the user to process the ticket and tactical, when no information is asked but the user input request is raised merely to pause the service level clock. Next, we propose a system that preempts a user at the time of ticket submission to provide additional information that the analyst, a person responsible for servicing the ticket, is likely to ask, thus reducing real user input requests. Further, we propose a detection system to identify tactical user input requests.To evaluate the approach, we conducted a case study in a large global IT company. We observed that around 57% of the tickets have user input requests in the life cycle, causing user experienced resolution time to be almost twice as long as the measured service resolution time. The proposed preemptive system preempts the information needs with an average accuracy of 94-99% across five cross validations while traditional approaches such as logistic regression and naive Bayes have accuracy in the range of 50-60%. The detection system identifies around 15% of the total user input requests as tactical. Therefore, the proposed solution can efficiently bring down the number of user input requests and, hence, improve the user-experienced resolution time.

Improving Support Ticket Systems Using Machine Learning: A Literature Review

“…Providing technical support for own IT products is an integral part of software developing or software providing companies [1,2]. For this purpose, most companies provide their customers support ticket systems (STSs), in which users can create incident tickets describing their problem or request [1].…”

“…Providing technical support for own IT products is an integral part of software developing or software providing companies [1,2]. For this purpose, most companies provide their customers support ticket systems (STSs), in which users can create incident tickets describing their problem or request [1]. In most state-of-the-art STSs, at least some key decisions in distributing these support tickets to the responsible support assistant or support team are still made by support staff members [3].…”

“…This process of manually distributing emerging support tickets by often lessskilled human workers is on the one hand ineffective and expensive [3]. On the other hand, it mostly increases the ticket resolution time and therefore lowers the satisfaction of the customer initially creating the ticket [1,5,6].…”

“…At the same time, the volume of support tickets in IT-companies created by customers has significantly grown due to the digitalization efforts currently made across all industries [7]. This means that IT-companies face an increasing pressure in automating their STSs to cope with the rising volume of tickets [7], to increase customer satisfaction [1,8], to accelerate support management processes [8,9], and to reduce costs [4,10].…”

Mining software support tickets for assistive routing

“…In addition, some works attempt to infer root cause from the problem text [1,2,14,18,19]. Through analyzing user input, Gupta et al [20] proposed a system to help the user to provide useful information in terms of reducing overall processing time.…”