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

arXiv: https://arxiv.org/abs/2305.08594 · HTML: https://mlsanity.com/paper/2305.08594/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 2 of 2)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Customer churn prediction in telecom using machine learning and social network analysis in big data platform](https://mlsanity.com/paper/1904.00690/) (2019)

## Built on by (most cited first, 1 of 1)

- [Improving Customer Service with Automatic Topic Detection in User Emails](https://mlsanity.com/paper/2502.19115/) (2025)

## Abstract (arXiv)

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.