Improving Support Ticket Systems Using Machine Learning: A Literature Review

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dc.contributor.authorFuchs, Simon
dc.contributor.authorDrieschner, Clemens
dc.contributor.authorWittges, Holger
dc.date.accessioned2021-12-24T17:34:35Z
dc.date.available2021-12-24T17:34:35Z
dc.date.issued2022-01-04
dc.description.abstractProcessing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.238
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79570
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectService Analytics
dc.subjectautomating
dc.subjectliterature review
dc.subjectmachine learning
dc.subjectservice desk
dc.subjectsupport ticket systems
dc.titleImproving Support Ticket Systems Using Machine Learning: A Literature Review
dc.type.dcmitext

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