Customer service ticket evaluation using multiple time-based machine learning models customer

Abstract

Techniques are provided for customer service ticket prioritization using multiple time-based machine learning models. One method comprises obtaining a customer service ticket; collecting, in response to the obtaining, features related to the customer service ticket comprising dynamic information regarding activities performed to address the customer service ticket and textual information describing the customer service ticket; applying the collected features to one of multiple machine learning models to obtain a distress score indicating a likelihood that the customer service ticket will become a distressed customer service ticket, wherein the multiple machine learning models each correspond to different time periods and are trained on historical data and the collected features are applied to the one machine learning model based on an age of the customer service ticket; and processing multiple customer support tickets based on the distress score. The machine learning models are trained using cumulative corresponding historical training data associated with a respective one of hourly, daily, weekly and monthly time periods.