Automated Customer Complaint Processing for Water Utilities Based on Natural Language Processing—Case Study of a Dutch Water Utility

Automated Customer Complaint Processing for Water Utilities Based on Natural Language Processing—Case Study of a Dutch Water Utility

Tian, Xin (KWR Water Research Institute)
Vertommen, Ina (KWR Water Research Institute)
Tsiami, Lydia (KWR Water Research Institute; National Technical University of Athens)
van Thienen, Peter (KWR Water Research Institute)
Paraskevopoulos, S. (TU Delft Sanitary Engineering; KWR Water Research Institute)

2022

Most water utilities have to handle a substantial number of customer complaints every year. Traditionally, complaints are handled by skilled staff who know how to identify primary issues, classify complaints, find solutions, and communicate with customers. The effort associated with complaint processing is often great, depending on the number of customers served by a water utility. However, the rise of natural language processing (NLP), enabled by deep learning, and especially the use of deep recurrent and convolutional neural networks, has created new opportunities for comprehending and interpreting text complaints. As such, we aim to investigate the value of the use of NLP for processing customer complaints. Through a case study about the Water Utility Groningen in the Netherlands, we demonstrate that NLP can parse language structures and extract intents and sentiments from customer complaints. As a result, this study represents a critical and fundamental step toward fully automating consumer complaint processing for water utilities.

Artificial intelligence
Customer complaint processing
Natural language processing
Water sector

http://resolver.tudelft.nl/uuid:2f219c22-e0c9-4559-acb0-0663b7af23ac

https://doi.org/10.3390/w14040674

2073-4441

Water, 14 (4)

Institutional Repository

journal article

© 2022 Xin Tian, Ina Vertommen, Lydia Tsiami, Peter van Thienen, S. Paraskevopoulos