Caller-Agent Pairing in Call Centers Using Machine Learning Techniques with Imbalanced Data

Negin Mehrbod; Antonio Grilo; Aneesh Zutshi

doi:10.1109/ICE.2018.8436314

Caller-Agent Pairing in Call Centers Using Machine Learning Techniques with Imbalanced Data

Negin Mehrbod, Antonio Grilo, Aneesh Zutshi

UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

Call centers as the frontline of companies have high interaction with customers. Therefore, the call center performance is very important in the issue of customer satisfaction. Successful communications between agents and customers, satisfy customers and increase the performance of contact center. Call centers managers try to use historical data to improve the service to their clients. Pairing caller with the best suited agent using historical data, helps companies to reduce their costs and improve customer satisfaction. In this work, we proposed a model which optimize call centers outcome with using machine learning techniques to route the caller to the based-suited agent. The result shows using historical data of call center to find an intelligent pairing of callers and agents can improve the performance.

Original language	English
Title of host publication	2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	978-1-5386-1469-3
ISBN (Print)	978-1-5386-1470-9
DOIs	https://doi.org/10.1109/ICE.2018.8436314
Publication status	Published - 13 Aug 2018
Event	2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018 - Stuttgart, Germany Duration: 17 Jun 2018 → 20 Jun 2018

Conference

Conference	2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018
Country/Territory	Germany
City	Stuttgart
Period	17/06/18 → 20/06/18

Keywords

Call routing
Imbalanced learning
Intelligent pairing
SMOTE

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10.1109/ICE.2018.8436314Licence: Unspecified

Cite this

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title = "Caller-Agent Pairing in Call Centers Using Machine Learning Techniques with Imbalanced Data",

abstract = "Call centers as the frontline of companies have high interaction with customers. Therefore, the call center performance is very important in the issue of customer satisfaction. Successful communications between agents and customers, satisfy customers and increase the performance of contact center. Call centers managers try to use historical data to improve the service to their clients. Pairing caller with the best suited agent using historical data, helps companies to reduce their costs and improve customer satisfaction. In this work, we proposed a model which optimize call centers outcome with using machine learning techniques to route the caller to the based-suited agent. The result shows using historical data of call center to find an intelligent pairing of callers and agents can improve the performance.",

keywords = "Call routing, Imbalanced learning, Intelligent pairing, SMOTE",

author = "Negin Mehrbod and Antonio Grilo and Aneesh Zutshi",

note = "The authors would like to thank to COLLAB, SA for supporting the research activities. TOURIST COMPANION is a project partially funded by COMPETE and Portugal 2020.; 2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018 ; Conference date: 17-06-2018 Through 20-06-2018",

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doi = "10.1109/ICE.2018.8436314",

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Mehrbod, N , Grilo, A & Zutshi, A 2018, Caller-Agent Pairing in Call Centers Using Machine Learning Techniques with Imbalanced Data. in 2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018 - Proceedings., 8436314, Institute of Electrical and Electronics Engineers (IEEE), 2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018, Stuttgart, Germany, 17/06/18. https://doi.org/10.1109/ICE.2018.8436314

Caller-Agent Pairing in Call Centers Using Machine Learning Techniques with Imbalanced Data. / Mehrbod, Negin ; Grilo, Antonio ; Zutshi, Aneesh.
2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2018. 8436314.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Caller-Agent Pairing in Call Centers Using Machine Learning Techniques with Imbalanced Data

AU - Mehrbod, Negin

AU - Grilo, Antonio

AU - Zutshi, Aneesh

N1 - The authors would like to thank to COLLAB, SA for supporting the research activities. TOURIST COMPANION is a project partially funded by COMPETE and Portugal 2020.

PY - 2018/8/13

Y1 - 2018/8/13

N2 - Call centers as the frontline of companies have high interaction with customers. Therefore, the call center performance is very important in the issue of customer satisfaction. Successful communications between agents and customers, satisfy customers and increase the performance of contact center. Call centers managers try to use historical data to improve the service to their clients. Pairing caller with the best suited agent using historical data, helps companies to reduce their costs and improve customer satisfaction. In this work, we proposed a model which optimize call centers outcome with using machine learning techniques to route the caller to the based-suited agent. The result shows using historical data of call center to find an intelligent pairing of callers and agents can improve the performance.

AB - Call centers as the frontline of companies have high interaction with customers. Therefore, the call center performance is very important in the issue of customer satisfaction. Successful communications between agents and customers, satisfy customers and increase the performance of contact center. Call centers managers try to use historical data to improve the service to their clients. Pairing caller with the best suited agent using historical data, helps companies to reduce their costs and improve customer satisfaction. In this work, we proposed a model which optimize call centers outcome with using machine learning techniques to route the caller to the based-suited agent. The result shows using historical data of call center to find an intelligent pairing of callers and agents can improve the performance.

KW - Call routing

KW - Imbalanced learning

KW - Intelligent pairing

KW - SMOTE

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BT - 2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018 - Proceedings

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ER -

Caller-Agent Pairing in Call Centers Using Machine Learning Techniques with Imbalanced Data

Abstract

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Keywords

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