Self-Organizing Map Based Wallboards To Interpret Sudden Call Hikes In Contact Centers

In a contact center, it is required to foresee and excavate any disturbance to the daily experiencing call pattern. Abnormal call pattern may be a result of a sudden change in the organization's external world. Expecting a methodological analysis prior to meet customers' demand may introduce a delay for queuing customers. It is required a fast and promising method to predict and reasoning any unwilling event. It is not possible to draw conclusions by considering one dimension such as total call count. Total call count may increase in same way due to a failure in any service. Research mainly focusses on reasoning multidimensional events based on historical records. In contrast to traditional wallboards, our approach is capable of clustering and predicting disturbances to the normal call patterns based on historical knowledge by considering many dimensions such as queue statistics of many service queues. Our approach showed improved results over traditional wallboards equipped with 2D or 3D graphs.