Genesys Cloud Services, Inc.

David Farrell

Emir Munoz

Maciej Dabrowski

Manan Kalra

Greg Toth

Apostolos Galanpoulos

Emir Muñoz

Rory McTigue

Gergely Toth

A method for processing data for training a predictive routing model. The method includes receiving interaction data from previous interactions that includes audio data capturing a conversation and transcript data of the conversation. The method continues by performing speech analytics by processing the audio data to determine scores for speech metrics that include a measure of how much the agent or customer speaks during the conversation. The method continues by performing sentiment analysis to determine scores associated with sentiment metrics, the sentiment metrics including a measure of a sentiment based on classifying utterances appearing in the transcript data as being positive or negative. The method continues by performing feature engineering to generate feature data and generating a training dataset therefrom. The method continues by applying a machine learning algorithm to the training dataset to train a predictive routing model.

Generating data features from speech and sentiment analytics for enhanced predictive routing

A method of routing interactions to contact center agents according to an embodiment includes identifying an interaction to be routed to a contact center agent, determining a predictive routing score for each prospective contact center agent to which the interaction can be routed based on a historical performance of each prospective agent, determining a relative gain for each prospective agent based on an interaction class of the interaction, an agent class performance of the prospective agent, and an agent value of the prospective agent, wherein the relative gain of a respective agent is indicative of a relative optimization improvement of routing the interaction to the respective agent relative to another of the prospective agents, ranking the prospective agents based on the associated predictive routing score and the associated relative gain for each prospective agent, and routing the interaction to an agent selected based on the ranking of the prospective agents.

Systems and methods for relative gain in predictive routing

A method for adaptive predictive routing in a contact center system according to an embodiment includes identifying an interaction to be routed to a contact center agent, determining, for each agent cohort of a plurality of agent cohorts in sequential order and for a cohort time period associated with the respective agent cohort, whether a contact center agent within the respective cohort is available to be routed the interaction, wherein the plurality of agent cohorts is in sequential order based on descending agent performance scores for at least one key performance indicator, and routing the interaction to a first contact center agent determined to be available to be routed the interaction.

Technologies for adaptive predictive routing in contact center systems

A method of routing interactions to contact center agents according to an embodiment includes identifying an interaction to be routed to a contact center agent, identifying a group of contact center agents as candidates for routing of the interaction, retrieving agent performance data for each candidate agent of the group of contact center agents identified as candidates for routing of the interaction, determining a predicted score for a key performance indicator for each candidate agent based on the agent performance data, determining an occupancy rate of each candidate agent based on the agent performance data, generating a ranking of the candidate agents for routing prioritization based on the predicted score for the key performance indicator for each candidate agent and the occupancy rate of each candidate agent, and signaling a routing device to route the interaction to a selected candidate agent based on the ranking of the candidate agents.

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Galway, Ireland

David Farrell