  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.
