Modernising customer service in retail: A Worten case study on automated complaint classification

• Autor(a) principal: Casimiro, Inês Rodrigues
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/30263
• topic: Worten; Electronics retail; Complaints; Machine learning; Processamento de linguagem natural - -- NLP Natural language processing; Text classification; Retalho de eletrónica; Reclamações; Classificação de texto

The emergence of retailers able to deliver products on the same day and at very competitive prices, such as Amazon, has caused customers to raise their expectations. When the quality of service falls short of the expected, customers resort to complaints to show their dissatisfaction, and it is in the retailers' interest to resolve the problem as quickly as possible to avoid losing customers. Since the process of analysing complaints is very time-consuming, this study aims to propose a method for classifying the complaints addressed to Worten, automatically. Thus, sixteen experiments were performed with eight different Machine Learning (ML) algorithms, following the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. The experiments included reducing the number of classes, Transfer Learning models, and different types of class balancing, among others. The Support Vector Machine (SVM) model obtained the best classification, with an Accuracy of 71.41%, in the experiment in which the three most diffuse of the six original classes (Time, Technical Problem, Client, Money, Service and Other) were eliminated.