Estimation of Monthly Performance of Call Center Employees with Artificial Neural Networks Assistance

Rapid developments in information technologies have gone beyond innovation to a destructive dimension. This dimension has led to a broad research area for artificial intelligence applications. Today, significant progress has been made in the use of artificial intelligence technologies. Artificial Neural Network (ANN) technology has been developed by inspiring the human brain system. Artificial neural networks are considered to be the artificial intelligence technologies that have been acquired by computers and machines for their ability to learn from the functions of the human brain, and thus the theoretical framework of artificial neural networks such as the ability to make predictions and predictions of the future using learning through the past is studied. In this regard, ANN is the model of the biological nervous system in mathematical architecture. Superiority of forecasting performance ANN has been successfully used in many areas. In this study, call center employees' work performance for the next month was estimated with the aid of ANN. The performance of customer / citizen representatives working in call centers with ANN methodology was estimated by trying to avoid the findings and results. The basic condition for the success of the companies in the call center sector and for managers to make sound decisions is to know in advance what to do. For this purpose, the performance data of the personnel working in the call centers of the past month were obtained as hourly. Then, we use ANN as a predictor and use the backpropagation algorithm with the existing data obtained to construct the appropriate ANN architecture. The network parameters are determined by trial and error method. In the final stage, the successful performance of the personnel working at the call centers by using these appropriate ANNs which have been trained and tested successfully was predicted successfully and appropriate analyzes and evaluations were made. As a result, the methodology of forecasting modeling artificial neural networks in this study has been taken into consideration and the results have been successfully obtained with the findings of estimating the performance of the employees employed in the call center sector. According to the output obtained, this study has shown very serious possibilities for establishing optimum working conditions and maximizing efficiency, which will enable call centers or managers in similar sectors to make correct and healthy decisions for the future