Genesys Telecommunications Laboratories, Inc.

Other

Bharath Aravamudhan

Herbert Willi Artur Ristock

Conor McGann

Yochai Konig

Petr Makagon

Vyacheslav I Zhakov

Gregory Duclos

Damjan Pelemis

Nikolay I Korolev

Andriy Vladimirovich Ryabchun

Josef Eric Eisner

Gordon L Bell

Anand Pai Krishnanand Nitin

Francisco Gutierrez

Bohdan Klepar

Stanislav Lypchanskyy

Stanislav Lypachanskyy

A system and method for making engagement offers. A processor monitors a navigation path of a user navigating a website. The processor makes an offer for engagement with the user on a first web page reached by the user upon following the navigation path. The processor monitors a reaction of the user to the offer for engagement, and updates tag data associated with a second web page encountered by the user while traversing the navigation path. The tag data is updated based on the reaction of the user to the offer for engagement.

System and method for making engagement offers based on observed navigation path

A system and method for proactively making knowledge offers. A processor is configured to gather information on interactions by a user with resources provided by an enterprise having a customer contact center. The processor anticipates need of the user based on the gathered information, and generates a query based on the anticipated need. Prior to the user expressly requesting knowledge relating to a particular topic, the processor proactively identifies and suggests the knowledge to the user based on the generated query. The processor receives feedback relating to the suggested knowledge and outputs based on the feedback, a relevance score for the suggested knowledge.

Proactive knowledge offering system and method

A system and method for routing interactions to contact center agents. The system is adapted to concurrently identify a plurality of interactions waiting to be routed, and identify a plurality of candidate agents viable for handling the plurality of interactions. The system is also adapted to calculate a predicted wait time associated with each of the candidate agents. For each agent of the plurality of candidate agents, the system is adapted to estimate an expected value to be obtained by routing each of the plurality of the interaction to the agent. The expected value is a function of the predicted wait time. The system is further adapted to assign each of the plurality of the interactions to one of the plurality of candidate agents based on the estimated reward, and signal a routing device for routing each of the plurality of the interactions to the agent assigned to the interaction.

Optimized routing of interactions to contact center agents based on forecast agent availability and customer patience

A system and method for muting interactions to contact center agents. The system is adapted to identify a network of agents and customers. Each node of the network represents either an agent or a customer, and a connection is made between the node representing the agent and the node representing the customer, in response to identifying a fit between the customer and the agent. The system is adapted to identify a current interaction to be routed, identify a first group of agents based on one or more constraints for generating one or more candidate agents, and determine the fit between a current customer and each of the candidate agents based on the network of agents and customers. The system is further adapted to select a particular agent of the candidate agents with a best fit, and transmit a signal for routing the current interaction to the particular agent.

System and method for generating a network of contact center agents and customers for optimized routing of interactions

A system that is adapted to route interactions to contact center agents. More specifically, the system is adapted to identify an interaction to be routed, and identify a group of agents based on one or more constraints for generating one or more candidate agents. The system is also adapted to gather context data surrounding the candidate agents. For each agent of the candidate agents, the system is adapted to estimate an expected value to be obtained by routing the interaction to the agent. The system is further adapted to select a particular agent of the candidate agents based on the estimates, and signal a routing device for routing the interaction to the particular agent.

Optimized routing of interactions to contact center agents based on machine learning

Growing community of inventors

Perungudi, India

Bharath Aravamudhan