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

1 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
JournalEuropean Journal of Operational Research
StateAccepted/In press - 2023

Bibliographical note

Funding Information:
The authors gratefully acknowledge financial support from ANID PIA/PUENTE AFB220003 and FONDECYT-Chile, grants 1200221 and 11200007. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions for improving the quality of the paper. Finally, the authors would like to thank Ronald Cohn from ACHS and Carolina Alcafuz and Víctor Herrera from Crossnet for their support during the project.

Publisher Copyright:
© 2023 Elsevier B.V.


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


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