Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making

Carla Vairetti*, Ignacio Aránguiz, Sebastián Maldonado, Juan Pablo Karmy, Alonso Leal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Complaint analysis is an essential business analytics application because complaints have a strong influence on customer satisfaction (CSAT). However, the process of categorising and prioritising complaints manually can be extremely time consuming for large companies. In this paper, we propose a framework for automatic complaint labelling and prioritisation using text analytics and operational research techniques. The labelling step of the training set is performed using a simple weighting approach from the multiple-criteria decision-making (MCDM) literature, while transformer-based deep learning (DL) techniques are used for text classification. We define two priority classes, namely, urgent complaints and other claims, and develop a system for automatic complaint categorisation. Our experimental results show that excellent predictive performance can be achieved with state-of-the-art text classification models. In particular, BETO, a bidirectional encoder representations from transformers (BERT) model trained on a large Spanish corpus, reaches an accuracy (ACCU) and area under the curve (AUC) of 92.1% and 0.9785, respectively. This positive result translates into a successful complaint prioritisation scheme, which improves CSAT and reduces the churn rate.

Original languageEnglish
Pages (from-to)1108-1118
Number of pages11
JournalEuropean Journal of Operational Research
Issue number3
StatePublished - 1 Feb 2024

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.


  • Analytics
  • BERT
  • Complaint management
  • Deep learning
  • Text analytics


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