Abstract:
Nowadays, customer support systems are one of the key factors in maintaining any big
company's reputation and success. These systems are capable of handling a large number of
tickets systemically and provides a mechanism to track/logs the communication between
customer and support agents. Companies invest huge amounts of money in training support
agents and deploying customer care services for their products and services. Support agents
are responsible for handling different customer queries and implementing required actions
to solve a particular issue or problem raised by the service/product user. In a bigger picture,
customer support systems could receive a large amount of ticket/issue raised depending upon
the number of users and services being offered. Customer care service gets directly affected
due to the high volume of tickets and a limited number of support agents. Therefore,
providing support agents with the recommendations about the possible resolution actions for
a new ticket would be helpful and can save a lot of time. This research is focused on the
development of an end to end framework for suggesting resolution actions rather than
recommending free form resolution text against a newly raised ticket. To develop such a
system, the pipeline is broadly divided into four components that are data preprocessing,
actions extractor, resolution predictor, and evaluation.
