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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,...
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| Format: | Thesis |
| Language: | English Eng |
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Department of Statistical Sciences
2025
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| _version_ | 1867613288594931712 |
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| access_status_str | Open Access |
| author | Kgomo, Teballo |
| author2 | Ngwenya, Mzabalazo |
| author_browse | Kgomo, Teballo Ngwenya, Mzabalazo |
| author_facet | Ngwenya, Mzabalazo Kgomo, Teballo |
| author_sort | Kgomo, Teballo |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/41132 |
| institution | University of Cape Town (South Africa) |
| language | English Eng |
| last_indexed | 2026-06-10T12:33:45.686Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/41132 Classification of customer complaints using machine learning algorithms Kgomo, Teballo Ngwenya, Mzabalazo Statistical Sciences 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. 2025-03-06T14:22:11Z 2025-03-06T14:22:11Z 2024 2025-03-06T08:32:39Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/41132 en Eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | Statistical Sciences Kgomo, Teballo Classification of customer complaints using machine learning algorithms |
| thesis_degree_str | Master's |
| title | Classification of customer complaints using machine learning algorithms |
| title_full | Classification of customer complaints using machine learning algorithms |
| title_fullStr | Classification of customer complaints using machine learning algorithms |
| title_full_unstemmed | Classification of customer complaints using machine learning algorithms |
| title_short | Classification of customer complaints using machine learning algorithms |
| title_sort | classification of customer complaints using machine learning algorithms |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/41132 |
| work_keys_str_mv | AT kgomoteballo classificationofcustomercomplaintsusingmachinelearningalgorithms |