Improving Customer Experience in Call Centers with Intelligent Customer-Agent Pairing

TL;DR

This paper presents a machine learning approach to optimize customer-agent pairing in call centers, significantly enhancing customer experience and retention for a major telecommunications provider.

Contribution

It introduces a novel learning-based method for customer-agent pairing, outperforming traditional rule-based approaches by approximately 215%.

Findings

215% performance improvement over rule-based methods

Enhanced customer satisfaction and retention

Effective collaboration with a major telecom provider

Abstract

Customer experience plays a critical role for a profitable organisation or company. A satisfied customer for a company corresponds to higher rates of customer retention, and better representation in the market. One way to improve customer experience is to optimize the functionality of its call center. In this work, we have collaborated with the largest provider of telecommunications and Internet access in the country, and we formulate the customer-agent pairing problem as a machine learning problem. The proposed learning-based method causes a significant improvement in performance of about $215\%$ compared to a rule-based method.