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

Abstract

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.

Clasificación de quejas de los clientes empleando procesamiento de lenguaje natural: revisión sistemática de la literatura

“…con la calidad de los productos y servicios ofrecidos por empresas nacionales y extranjeras con una participación significativa en los mercados de EE. UU Si bien es cierto, los usuarios cuentan con múltiples herramientas que permiten registrar sus quejas, el procesamiento se realiza de forma manual [13]. Además [6] refiere que las investigaciones acerca de análisis de textos de queja son escasos en la actualidad.…”