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

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

European Journal of Operational Research, 2024, vol. 312, issue 3, 1108-1118

Abstract: 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.

Keywords: Analytics; Complaint management; Text analytics; Deep learning; BERT (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221723006562
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:312:y:2024:i:3:p:1108-1118

DOI: 10.1016/j.ejor.2023.08.027

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().