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Automated dispatch of helpdesk email tickets: Pushing the limits with AI

EasyChair Preprint no. 640

13 pagesDate: November 16, 2018


Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution.
The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of classifier that is accurate is one of the main challenges, we also need to address the need for designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources.
Our system has been deployed in production of three major service providers and currently assigning over 90,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of more than 50000 man-hours of effort per annum.

Keyphrases: Cognitive email assignment, deep learning, Ensemble classifiers, Helpdesk automation, Smart dispatch, Ticket resolver group

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Atri Mandal and Nikhil Malhotra and Shivali Agarwal and Anupama Ray and Giriprasad Sridhara},
  title = {Automated dispatch of helpdesk email tickets: Pushing the limits with AI},
  howpublished = {EasyChair Preprint no. 640},
  doi = {10.29007/ck5t},
  year = {EasyChair, 2018}}
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