TaDaa: real time Ticket Assignment Deep learning Auto Advisor for customer support, help desk, and issue ticketing systems

TL;DR

This paper introduces TaDaa, a deep learning system using Transformers to automatically assign support tickets to the correct groups and resolvers, significantly improving efficiency in customer support and help desk systems.

Contribution

The paper presents a novel deep learning approach with Transformers for real-time ticket assignment, achieving high accuracy on a large dataset and enhancing support system efficiency.

Findings

95.2% top 3 accuracy on group suggestions

79.0% top 5 accuracy on resolver suggestions

Improved issue resolution time in support systems

Abstract

This paper proposes TaDaa: Ticket Assignment Deep learning Auto Advisor, which leverages the latest Transformers models and machine learning techniques quickly assign issues within an organization, like customer support, help desk and alike issue ticketing systems. The project provides functionality to 1) assign an issue to the correct group, 2) assign an issue to the best resolver, and 3) provide the most relevant previously solved tickets to resolvers. We leverage one ticketing system sample dataset, with over 3k+ groups and over 10k+ resolvers to obtain a 95.2% top 3 accuracy on group suggestions and a 79.0% top 5 accuracy on resolver suggestions. We hope this research will greatly improve average issue resolution time on customer support, help desk, and issue ticketing systems.