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Large software organizations handle many customer support issues every day inthe form of bug reports, feature requests, and general misunderstandings assubmitted by customers. Strategies to gather, analyze, and negotiaterequirements are complemented by efforts to manage customer input afterproducts have been deployed. For the latter, support tickets are key inallowing customers to submit their issues, bug reports, and feature requests.Whenever insufficient attention is given to support issues, there is a chancecustomers will escalate their issues, and escalation to management istime-consuming and expensive, especially for large organizations managinghundreds of customers and thousands of support tickets. This thesis provides astep towards simplifying the job for support analysts and managers,particularly in predicting the risk of escalating support tickets. In a fieldstudy at our large industrial partner, IBM, a design science methodology wasemployed to characterize the support process and data available to IBM analystsin managing escalations. Through iterative cycles of design and evaluation,support analysts' expert knowledge about their customers was translated intofeatures of a support ticket model to be implemented into a Machine Learningmodel to predict support ticket escalations. The Machine Learning model wastrained and evaluated on over 2.5 million support tickets and 10,000escalations, obtaining a recall of 79.9% and an 80.8% reduction in the workloadfor support analysts looking to identify support tickets at risk of escalation.The features developed in the Support Ticket Model are designed to serve as astarting place for organizations interested in implementing the model topredict support ticket escalations, and for future researchers to build on toadvance research in Escalation Prediction.

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