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Improving customer routing in contact centers: An automated triage design based on text analytics
Author(s) -
Ilk Noyan,
Shang Guangzhi,
Goes Paulo
Publication year - 2020
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1002/joom.1084
Subject(s) - triage , computer science , analytics , generalizability theory , knowledge management , machine learning , data science , world wide web , medicine , emergency medicine , statistics , mathematics
We propose an automated triage design for intelligent customer routing in live‐chat contact centers and demonstrate its implementation using a real‐world data set from an S&P 500 firm. The proposed design emerges as a synthesis of text analytics and predictive machine learning methods. Using numerical experiments based on the simulation of the firm's contact center, we demonstrate the service level, time, and labor cost benefits of the automated design over two other triage designs (i.e., customer choice triage and human expert triage) that are commonly employed in the real world. Through additional analyses, we explore the generalizability of the automated design for creating solutions for different types of communication channels. Our work has implications for managing customer relations under emerging communication technologies (e.g., live‐chat, e‐mail, and social media) and more broadly for demonstrating the use of text analytics and machine learning to improve Operations Management practice.