Schedule Planning Software for Call Center Using Machine Learning

Authors

  • Luis Alejandro Aguirre Robalino Instituto Superior Técnológico San Antonio

DOI:

https://doi.org/10.62465/riif.v3n1.2024.77

Keywords:

Machine learning, call-center, schedule planning

Abstract

Machine learning algorithms ease data analysis to obtain several objectives both of service quality and resource efficiency at the enterprise level. In this project we evaluate various algorithms of machine learning that facilitate the prediction of calls and subsequent scheduling of a call center in order to improve its level of service. Based on the call history and using the Erlang-C formula, the necessary agents per hour are estimated for a desired level of service. A Backend is developed in Python and it is integrated into the web application using PHP. The effectiveness of machine learning algorithms for these predictions, as well as their usefulness for scheduling, was tested. The level of call-center service obtained was higher than expected levels thanks to an adequate estimate of human resources.

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References

Ali, M. (Julio de 2020). PyCaret: An open source, low-code machine learning library in Python. Obtenido de https://pycaret.org/about

Deputechnologies Pty Ltd. (2021). Obtenido de https://www.deputy.com/industry/call-centre

Genesys. (2021). CALL CENTRE FORECASTING. Obtenido de https://www.genesys.com/en-gb/capabilities/forecasting-and-decisions

Kampakis, S. (3 de Enero de 2020). thedatascientist. Obtenido de https://thedatascientist.com/performance-measures-rmse-mae/

Nice. (2021). Workforce Management. Obtenido de https://www.nice.com/engage/workforce-management/

scikit-learn developers. (2021). Metrics and scoring: quantifying the quality of predictions. Obtenido de https://scikit-learn.org/stable/modules/model_evaluation.html

Shiftboard, Inc. (2021). Obtenido de https://www.shiftboard.com/industries/call-centers/

TixTime, Inc. (2021). Obtenido de https://www.tixtime.com/employee-scheduling-software/call-center-scheduling-software/

Verint. (2021). Verint Monet Workforce Engagement. Obtenido de https://www.verint.com/es/engagement-5/our-offerings/solutions/small-and-medium-sized-businesses/verint-monet/

Published

2024-04-19

How to Cite

Aguirre Robalino, L. A. (2024). Schedule Planning Software for Call Center Using Machine Learning . Revista Ingeniería E Innovación Del Futuro, 3(1), 54–72. https://doi.org/10.62465/riif.v3n1.2024.77

Issue

Section

Articles