TY - JOUR
T1 - Automated Customer Complaint Processing for Water Utilities Based on Natural Language Processing—Case Study of a Dutch Water Utility
AU - Tian, Xin
AU - Vertommen, Ina
AU - Tsiami, Lydia
AU - van Thienen, Peter
AU - Paraskevopoulos, Sotirios
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Customer complaint processing
KW - Natural language processing
KW - Water sector
UR - http://www.scopus.com/inward/record.url?scp=85125342454&partnerID=8YFLogxK
U2 - 10.3390/w14040674
DO - 10.3390/w14040674
M3 - Article
AN - SCOPUS:85125342454
SN - 2073-4441
VL - 14
JO - Water (Switzerland)
JF - Water (Switzerland)
IS - 4
M1 - 674
ER -