Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.