Résumé
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.