Improving Customer Service with Automatic Topic Detection in User Emails
This work addresses customer service efficiency for a specific company, with incremental improvements using existing methods on new data.
This study tackled the problem of improving customer service efficiency by developing an automated email topic detection and labeling system for a telecommunications company, achieving a weighted average F1 score of 0.96 and processing time of 0.041 seconds per email.
This study introduces a novel natural language processing pipeline that enhances customer service efficiency at Telekom Srbija, a leading Serbian telecommunications company, through automated email topic detection and labeling. Central to the pipeline is BERTopic, a modular framework that allows unsupervised topic modeling. After a series of preprocessing and postprocessing steps, we assign one of 12 topics and several additional labels to incoming emails, allowing customer service to filter and access them through a custom-made application. While applied to Serbian, the methodology is conceptually language-agnostic and can be readily adapted to other languages, particularly those that are low-resourced and morphologically rich. The system performance was evaluated by assessing the speed and correctness of the automatically assigned topics, with a weighted average processing time of 0.041 seconds per email and a weighted average F1 score of 0.96. The system now operates in the company's production environment, streamlining customer service operations through automated email classification.