Abstract
Call centre technology requires the assignment of a large volume of incoming calls to agents with the required skills to process them. In order to perform the right assignment of call types to agents in a production environment, an efficient prediction of call arrivals is needed. In this paper, we introduce a prediction approach to incoming phone calls forecasting in a multi-skill call centre by modelling and learning the problem with an Improved Backpropagation Neural Network which have been compared with other methods. This model has been trained and analysed by using a real-time data flow in a production system from our call centre, and the results obtained outperform other forecasting methods. The reader can learn which forecasting method to use in a real-world application and some guidelines to better adapt an improved backpropagation neural network to his needs. A comparison among techniques and some statistics are shown to corroborate our results.
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© 2009 Springer-Verlag Berlin Heidelberg
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Pacheco, J., Millán-Ruiz, D., Vélez, J.L. (2009). Neural Networks for Forecasting in a Multi-skill Call Centre. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_27
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DOI: https://doi.org/10.1007/978-3-642-03969-0_27
Publisher Name: Springer, Berlin, Heidelberg
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