Classifying Customer Complaints of a Large Fixed Broadband Service Provider using Machine Learning.

With the advancement in technology, many organizations use Trouble Ticket Systems (TTS) to record and manage problems, facilitating the process of assigning it to the right technical team. However, in large organizations, which receive a huge number of complaints, the task of allocating or classifying a problem becomes a challenge. In this paper, we propose a solution for automatically classifying customer complaints related to fixed broadband service for TIM, large Brazilian telecommunications company. We consider the fixed broadband customer complaints reported for a period of 31 days starting from December 28, 2020. We propose a custom textual preprocessing technique and use several machine learning classifiers to accurately classify a complaint in one of the six problem classes. Our results demonstrate that the proposed technique generated an increase in accuracy of 8.4% when compared to techniques commonly used in textual preprocessing. Our results also demonstrate that the Extra Tree Classifier achieves the best performance among all models with an accuracy of around 89%. Our work can assist TIM to improve their complaint resolution process.