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

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

Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios

odern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F1- score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (ML-BERT) outperforms the best baseline by up to 5.7% in F1-score and 5.4% in accuracy

Automated Ticket Routing Helpdesk Portal

This report was commissioned to deliver the understanding of the chosen Final Year Project title "Automated Ticket Routing Helpdesk Portal". The report will be segregated into four chapters which are the introduction including problem statement and objectives; literature review; methodology and conclusion. Helpdesk is a very powerful tool to assist IT users. A good helpdesk system is very crucial to assist users and also improve service by the helpdesk team. At Universiti Teknologi Petronas, we already have an existing helpdesk system. However, I have identified a problem in the system; the current system still uses manual ticket routing and assignment. Automated ticket routing and assignment stop manually assigning tickets to the support personnel that you think is available and has the skill set to address the ticket. Automated ticket routing and assignment uses intelligent business logic to determine which support personnel is assigned to a new ticket using a combination of skill-set, work schedule and work load balancing criteria. In the introduction, problem statements that lead to the idea of developing the project title will be cleared up and the objectives are highlighted. Towards the development of this project, information gathering from the experts will be conducted. A comprehensive research also will determine the relevancy of this project. The literature reviews will explain in depth the understanding of the proposed project

Expert Group Formation for Task Performing: Competence-Based Method and Implementation

The problem of searching a group of experts to solve cross-domain problems remains an important problem in many applications. An automated expert search can make human resource management more efficient and reduce the number of problems. The paper presents a method of expert group formation for joint task performing. This method checks each available expert who can participate in task performing and sifts out the least effective of them. During this checking it forms several groups of experts and sorts them by their optimality based on their proficiency level, cost and influence of experts on each other. The method is implemented and approbated in a competence management system developed earlier

Organization and Management of the Sead Help Desk

ARNe (Regis University Academic Research Network Enterprise), is the graduate student run and managed intranet which is organized as an IT company. The operating structure is based upon a Service Oriented Architecture where each student is involved in an operating portion of the network. New students participating in the SEAD practicum are required to work the ARNe help desk as a requirement of their project. They are expected to login to Track-It! on a daily basis and check for new tickets in the queue. The tickets are submitted by students as well as faculty. Without having a defined walk through on what the duties and responsibilities are required of working the help desk, transitioning students may not know what is expected of them. By creating a tiered escalation structure with set demarcations, students will be able to utilize a process flow and work the trouble tickets accordingly. My thesis is that by establishing a clear and concise help desk schedule and having processes for working, escalating and resolving Track-It tickets, the mean time to repair (MTTR) will decrease significantly which will increase the amount of work that the practicum participants can complete. Also, by running reports that look for trends on customer reported problems, processes and procedures can be developed which will help identify and resolve these issues in a timely manner. It is also hoped that by implementing a root cause analysis (RCA) tool, the likelihood of future occurrences can be minimized

Using Text Analytics to Derive Customer Service Management Benefits from Unstructured Data

Deriving value from structured data is now commonplace. The value of unstructured textual data, however, remains mostly untapped and often unrecognized. This article describes the text analytics journeys of three organizations in the customer service management area. Based on their experiences, we provide four lessons that can guide other organizations as they embark on their text analytics journeys.Click here for podcast summary (mp3)Click here for free 2-page executive summary (pdf)Click here for free presentation slides (pptx

Automated Ticket Routing Helpdesk Portal

This report was commissioned to deliver the understanding of the chosen Final Year Project title "Automated Ticket Routing Helpdesk Portal". The report will be segregated into four chapters which are the introduction including problem statement and objectives; literature review; methodology and conclusion. Helpdesk is a very powerful tool to assist IT users. A good helpdesk system is very crucial to assist users and also improve service by the helpdesk team. At Universiti Teknologi Petronas, we already have an existing helpdesk system. However, I have identified a problem in the system; the current system still uses manual ticket routing and assignment. Automated ticket routing and assignment stop manually assigning tickets to the support personnel that you think is available and has the skill set to address the ticket. Automated ticket routing and assignment uses intelligent business logic to determine which support personnel is assigned to a new ticket using a combination of skill-set, work schedule and work load balancing criteria. In the introduction, problem statements that lead to the idea of developing the project title will be cleared up and the objectives are highlighted. Towards the development of this project, information gathering from the experts will be conducted. A comprehensive research also will determine the relevancy of this project. The literature reviews will explain in depth the understanding of the proposed project

Design an expert system for students graduation projects in Iraq universities: Basrah University

A graduation project is a form or work that the study authority requests from the student to measure what he made during the study. Designed an expert system for students’ graduation projects at the University of Basrah for students who are obligated to submit a project that qualifies them to graduate from the university. The system works according to a set of requirements, the most important is first: The student's possession of a high rate that qualifies him for the project. Second: he must possess half of the skills required for the project provided that it includes at least one programming language example (c ++, java, PHP, c #, etc ...). The system has many features that help the Supervisors and Students Committee to manage students' projects efficiently. System is built as a web-based system, with access limited only to the university's local network