Classification of customer complaints using machine learning algorithms

Poor handling of customer complaints leads to bad customer experience and impact brand reputation. With an ever-increasing volume of complaints facing customer services team(s), handling customer complaints by service desk agents becomes tedious, especially when pressed with time. For these reasons, many companies have adopted ML technologies to improve their customer services. Technologies like ML text classification have shown great potential in improving customer support. This research proposes an ML text classification approach to categorise customer complaint (s) into one of the thirteen relevant product complaint topics. This technique aims to reduce customer agent desks' customer complaints reading and classifying time. This research uses five ML algorithms namely: LR, SVM, LightGB, KNN, and CART DT to assess how text classification technology can be used to improve the classification of customer complaints in the financial services industry by assessing how accurately would the algorithms categorize customer complaints data. These algorithms are trained on three different word vectorisation techniques namely: CV, TFIDF, and Word2Vec word-embedding. The algorithms are meant to classify each customer complaint into one of the thirteen possible Products. Due to imbalanced distributions of the target (Product complaint topics), a balanced accuracy metric was used to evaluate the model's performance. The results show that LR with TFIDF word vectorisation produced the best model with 87.29 % balanced-accuracy on the OOT dataset. This shows that ML algorithms can be used to improve the customer complaints classification process. Furthermore, the solution can be extended to solve customer complaints emails. This has the potential to improve the company's customer response time and complaint classification from the customer service desk's team.

Grantor dc:publisher.institution

Department of Statistical Sciences

Year dc:date.issued

2024

Author dc:creator

• Kgomo, Teballo

Advisor dc:contributor.advisor

• Ngwenya, Mzabalazo

• Statistical Sciences

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/11427/41132

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/41132