Genesys Cloud Services, Inc.

Genesys Telecommunications Laboratories, Inc.

Greeneden U.S. Holdings Ii, LLC

Cliff Bell

Dan Stoops

Merijn Te Booij

Daniel Stoops

Sergey Fedorov

Adrian Lee-Kwen

David Beilis

A method for contact center agent incentivization via automated gamification according to an embodiment includes transferring an interaction between a contact center agent and a user to an automated agent, prompting, by the automated agent, the user to reward the contact center agent based on the interaction between the user and the contact center agent, receiving a selection of a reward option made by the user in response to prompting the user to reward the contact center agent, determining a point value associated with the user's selected reward option based on a mapping table, and associating the user's selected reward option with the contact center agent in an agent rewards table.

Technologies for contact center agent incentivization via automated gamification

A method according to an embodiment includes determining a group of subject matter experts in a back office with expertise to address a question from a user of a contact center system, determining an availability of one or more of the subject matter experts via a back-office platform executed by corresponding back-office devices of the one or more subject matter experts, assigning, via the back-office platform, a communication token to a selected subject matter expert determined to be available, wherein the communication token is associated with the question from the user, monitoring, via the back-office platform, for updates to the back-office platform associated with the communication token, receiving an update from the back-office platform associated with the communication token, and transmitting, via the contact center system, a message to the user device based on an update to the communication token.

Technologies for facilitating near real-time communication between a user device and a back-office device

A system for real-time agent assist in a crisis scenario according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to receive an information request from a contact center client, evaluate the information request using natural language understanding to determine a knowledge fragment associated with the information request, analyze the knowledge fragment based on a crisis knowledge base of the system with respect to at least one of a confidence in the knowledge fragment, a veracity of the knowledge fragment, a frequency of the knowledge fragment, or a proximity of the knowledge fragment to the crisis scenario, and generate a response to the contact center client in response to analysis of the knowledge fragment based on the crisis knowledge base.

Real-time agent assist

A system for utilizing conversational artificial intelligence (AI) to train contact center agents according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored therein that, in response to execution by the at least one processor, causes the system to place a virtual call from an automated training system to an agent device of an agent, connect the virtual call to a chatbot in response to establishing a communication connection with the agent device, transmit one or more statements from the chatbot, receive, from the agent device, one or more agent responses of the agent corresponding to the one or more statements, and analyze the one or more agent responses to determine one or more training characteristics associated with AI-based contact center training of the agent.

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Daly City, CA, United States of America

Cliff Bell