Improving customer routing in contact centers: An automated triage design based on text analytics

Noyan Ilk, Guangzhi Shang, Paulo Goes

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)553-577
Number of pages25
JournalJournal of Operations Management
Volume66
Issue number5
DOIs
StatePublished - Jul 1 2020

Keywords

  • contact center
  • customer routing
  • design science
  • machine learning
  • service quality management
  • text analytics

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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