TY - JOUR
T1 - Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making
AU - Vairetti, Carla
AU - Aránguiz, Ignacio
AU - Maldonado, Sebastián
AU - Karmy, Juan Pablo
AU - Leal, Alonso
N1 - 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.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Analytics
KW - BERT
KW - Complaint management
KW - Deep learning
KW - Text analytics
UR - http://www.scopus.com/inward/record.url?scp=85171162315&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2023.08.027
DO - 10.1016/j.ejor.2023.08.027
M3 - Article
AN - SCOPUS:85171162315
SN - 0377-2217
JO - European Journal of Operational Research
JF - European Journal of Operational Research
ER -