  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.
