Cognitive system to achieve human-level accuracy in automated assignment of helpdesk email tickets

TL;DR

This paper presents an automated helpdesk email ticket assignment system that achieves near human-level accuracy, significantly reducing manual effort and improving efficiency through an ensemble classifier and rule engine.

Contribution

It introduces an end-to-end, scalable, and adaptive ticket assignment system with high accuracy, deployed in production for major service providers.

Findings

Achieves approximately 90% accuracy in ticket classification

Handles over 40,000 emails per month in production

Saves about 23,000 man-hours annually

Abstract

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 of 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 40,000 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 about 23000 man-hours of effort per annum.