Abstract: Systems and methods for generating a routing recommendation for an incoming interaction in a contact center are described. For example, the method includes receiving interaction data associated with the incoming interaction, determining candidate agents based on one or more constraints, obtaining agent data related to the candidate agents, determining, for each of the candidate agents, an expected performance score using a predictive routing model, the expected performance score indicating a predicted performance of the corresponding candidate agent relative to a predetermined performance metric for handling the incoming interaction, and generating a routing recommendation for the incoming interaction based on the expected performance scores of the candidate agents, the routing recommendation identifying one or more agents from the candidate agents predicted to achieve a predefined level of the predetermined performance metric.

Abstract: 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.

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 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.

Abstract: A method for classifying customers via operation of a probability score of a predictive model that expresses a probability of a customer achieving a predicted outcome. The method includes: recording previous probability scores and grouping within a dataset; sorting the dataset by an increasing value; deriving a set of reference quantiles based on the sorted previous probability scores of the dataset; determining action thresholds defining a range of target quantiles within the set of reference quantiles; receiving a probability score from the predictive model for a first customer in a current interaction; comparing the probability score of the first customer to the probability scores in the set of reference quantiles to determine a matching quantile; and determining if the matching quantile is within the range of target quantiles and, based thereon, selectively classifying the first customer in the target audience.

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.