TaDaa: real time Ticket Assignment Deep learning Auto Advisor for customer support, help desk, and issue ticketing systems
This work addresses efficiency in customer support and help desk systems by automating ticket routing, though it appears incremental as it applies existing Transformer models to a specific domain.
The paper tackles the problem of automating ticket assignment in customer support systems by proposing TaDaa, a deep learning model that achieves 95.2% top-3 accuracy for group suggestions and 79.0% top-5 accuracy for resolver suggestions on a dataset with over 3k groups and 10k resolvers.
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.