Predicting the number of call center incoming calls using deep learning

One of the main problems in call centers is the call queue. This can lead to long waiting times for customers, increased frustration and call abandonment. The important role that predictive analytics plays in optimizing call center operations is increasingly recognized. Advanced models can be trained by training datasets such as the number of calls that have occurred throughout history, and by estimating how religious and public holidays have affected the weight of hours and the number of calls, and this study provides an analysis of 4 years. Call center data from Shatel, an Internet service provider. Predictive deep learning models, specifically the Bidirectional Short-Term Memory Model (BLSTM), were used to predict the number of incoming calls, predict the number of calls to centers, and prevent call queues with an accuracy of 90.56.