An intelligent framework for issue ticketing system based on machine learning

Qamili. Ruanda (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Shabani, Shaban (Department of Mathematics and Computer Science, University of Basel, Switzerland) ; Schneider, Johannes (Institute of Information Systems, University of Liechtenstein)

Due to the rapid shift of companies towards superb customer experience and satisfaction, ticketing systems have come into a prominence and represent a strategic element in business competitiveness. Different software companies have developed very effective software tools for issue tracking, nevertheless, some sub-processes and tasks within the ticketing systems are still performed manually. These manually performed tasks represent bottlenecks, especially at large organizations they result in declined productivity and increased response time. Advancements in machine learning can be used in a novel way in which they are combined with the traditional issue tracking and ticketing systems on the market, to enable optimal operational efficiency in the Customer Service and Support(CSS) Department of large-scale businesses that deal with customer reports. This paper proposes an integrated approach to customer support by treating three seemingly different bottlenecks in the ticketing system: spam detection, ticket assignment and sentiment analysis. We use primary data to implement and apply the proposed machine learning approach. The evaluation shows promising results in terms of accuracy and efficiency of our approach.


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Stockholm, Sweden, 16-19 October 2018
Date:
2018-10
Stockholm, Sweden
16-19 October 2018
Pagination:
8 p.
Published in:
Proceedings of the IEEE The Interprise Computing Conference EDOC 2018
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2019-10-17, last modified 2019-11-28

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