Abstract: An automated system and corresponding method is configured to predict a call duration of a customer service interaction between a caller and a customer-service agent of a call center, based at least in part on information provided orally by the caller to the automated system. The automated system transcribes the orally provided information, preprocesses the transcribed data, adds feature enrichment data to supplement the transcribed data, and executes a machine-learning model to predict the caller’s intent. If the predicted caller’s intent does not have an adequate confidence score associated therewith, the system requests additional data from the caller, and supplements the original data with newly provided data, and again determines a predicted call intent. This process may iterate until the confidence score satisfies applicable confidence criteria prior to utilizing two additional machine-learning models to predict a call duration of the interaction between the caller and a customer-service agent.

Abstract: To schedule a future interaction between a caller and a call-center, a management computing system associated with the call center is configured to determine an estimated call time for addressing the caller's issues, based at least in part on a predicted call intent as well as feature enrichment data received from one or more memory storage areas. Upon determining a predicted call duration for the call, the management computing entity accesses the caller's calendar, such as through an internet-based communication between the caller's user computing entity and the management computing entity, or via third-party access permissions provided by the caller. The management computing entity identifies one or more candidate timeslots based at least in part on the predicted call duration, and receives user input selecting a candidate timeslot for scheduling the callback.