Genesys Cloud Services, Inc.

Other

Emir Munoz

David Farrell

Maciej Dabrowski

Manan Kalra

Felix Immanuel Wyss

Aravind Ganapathiraju

Greg Toth

Sapna Negi

Gergely Toth

Veera Elluru Raghavendra

Rory McTigue

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 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.

Systems and methods relating to predictive routing and occupancy balancing

A system and method are presented for customer journey event representation learning and outcome prediction using neural sequence models. A plurality of events are input into a module where each event has a schema comprising characteristics of the events and their modalities (web clicks, calls, emails, chats, etc.). The events of different modalities can be captured using different schemas and therefore embodiments described herein are schema-agnostic. Each event is represented as a vector of some number of numbers by the module with a plurality of vectors being generated in total for each customer visit. The vectors are then used in sequence learning to predict real-time next best actions or outcome probabilities in a customer journey using machine learning algorithms such as recurrent neural networks.

System and method for customer journey event representation learning and outcome prediction using neural sequence models

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Galway, Ireland

Emir Munoz