Escalation Prediction using Feature Engineering: Addressing Support Ticket Escalations within IBM's Ecosystem

Large software organizations handle many customer support issues every day in the form of bug reports, feature requests, and general misunderstandings as submitted by customers. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. Whenever insufficient attention is given to support issues, there is a chance customers will escalate their issues, and escalation to management is time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. This thesis provides a step towards simplifying the job for support analysts and managers, particularly in predicting the risk of escalating support tickets. In a field study at our large industrial partner, IBM, a design science methodology was employed to characterize the support process and data available to IBM analysts in managing escalations. Through iterative cycles of design and evaluation, support analysts' expert knowledge about their customers was translated into features of a support ticket model to be implemented into a Machine Learning model to predict support ticket escalations. The Machine Learning model was trained and evaluated on over 2.5 million support tickets and 10,000 escalations, obtaining a recall of 79.9% and an 80.8% reduction in the workload for support analysts looking to identify support tickets at risk of escalation. The features developed in the Support Ticket Model are designed to serve as a starting place for organizations interested in implementing the model to predict support ticket escalations, and for future researchers to build on to advance research in Escalation Prediction.

Customer Support Ticket Escalation Prediction using Feature Engineering

Understanding and keeping the customer happy is a central tenet of requirements engineering. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. If insufficient attention is given to support issues, however, their escalation to management becomes time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. Our work provides a step towards simplifying the job of support analysts and managers, particularly in predicting the risk of escalating support tickets. In a field study at our large industrial partner, IBM, we used a design science research methodology to characterize the support process and data available to IBM analysts in managing escalations. We then implemented these features into a machine learning model to predict support ticket escalations. We trained and evaluated our machine learning model on over 2.5 million support tickets and 10,000 escalations, obtaining a recall of 87.36% and an 88.23% reduction in the workload for support analysts looking to identify support tickets at risk of escalation. Finally, in addition to these research evaluation activities, we compared the performance of our support ticket model with that of a model developed with no feature engineering; the support ticket model features outperformed the non-engineered model. The artifacts created in this research are designed to serve as a starting place for organizations interested in predicting support ticket escalations, and for future researchers to build on to advance research in escalation prediction.