Abstract
At telecommunications companies, call-centers have the highest interaction with customers, and the operators’ performance is vital because an excellent service satisfies the customer and helps a better operation. Therefore, attempts are made to use customer data, call operator data, and historical service data to improve support. Pairing a customer with an operator who is comfortable with the problem to solve helps companies reducing costs, improves customer service, and increases employee productivity. In this article, we propose an approach based on machine learning and optimization, which predicts the problem for which the customer is calling and routes the call and the customer to the most appropriate call operator. The results show that using large amounts of business data along with innovative algorithms such as LightGBM can improve the customer support performance.
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References
DECO: Telecomunicações sempre no topo das reclamações (2019). https://www.deco.proteste.pt/institucionalemedia/imprensa/comunicados/2019/telecons-1705. Accessed 10 May 2020
Fly, A.: 5 Best Practices for AI- and Data-Driven Call Centers. https://towardsdatascience.com/5-best-practices-for-ai-and-data-driven-call-centers-647406b4234b. Accessed 7 June 2020
Hassan, R.S., Nawaz, A., Lashari, M.N., Zafar, F.: Effect of customer relationship management on customer satisfaction. Procedia Econ. Financ. 23(October 2014), 563–567 (2015)
Carneiro, D., Novais, P., Pêgo, J.M., Sousa, N., Neves, J.: Using mouse dynamics to assess stress during online exams. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 345–356. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19644-2_29
Tundjungsari, V.: Business intelligence with social media and data mining to support customer satisfaction in telecommunication industry. Int. J. Comput. Sci. Electron. Eng. 1(1), 1–4 (2013)
Mishra, K., Rani, R.: Churn prediction in telecommunication using machine learning. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017 (2012), pp. 2252–2257 (2018)
Marwaha, R.: Data mining techniques and applications in telecommunication industry. Artif. Intell. Med. 16(1), 1–2 (2014)
Ali, A.R.: Intelligent call routing: optimizing contact center throughput. In: Proceedings of the 11th International Workshop on Multimedia Data Mining, MDMKDD 2011 in Conjunction with SIGKDD 2011, pp. 34–43 (2011)
Mehrbod, N., Grilo, A., Zutshi, A.: Caller-agent pairing in call centers using machine learning techniques with imbalanced data. In: Proceedings of the 2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018 (2018)
Pete, C., et al.: CRISP-DM 1.0. CRISP-DM Consortium, p. 76 (2000)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information (2016)
Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space, pp. 1–12, January 2013
Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)
Scikit-learn: scikit-learn: f1\(\_\)score. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html. Accessed 19 Aug 2020
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
Nogueira, F.: Bayesian optimization: open source constrained global optimization tool for Python (2014). https://github.com/fmfn/BayesianOptimization. Accessed 9 June 2020
Team, S.: SimPy: Discrete-Event Simulation for Python. https://simpy.readthedocs.io/en/latest/. Accessed 9 June 2020
Acknowledgements
This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
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Jorge, S., Pereira, C., Novais, P. (2020). Intelligent Call Routing for Telecommunications Call-Centers. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_28
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