Abstract: Systems and methods for evaluating and determining a root cause of a change in performance in a workforce management system are provided. Graphs can be created based on key performance indicators, behaviors and metrics to determined a root cause for agents who have a decrease in performance. The root cause can be transmitted to other computing systems which can select which interactions to record, selects a coaching program to run, or any combination.

Abstract: A computerized-method for determining an agent personalized coaching. The computerized-method includes for each agent in an agents database: (i) retrieving by one or more processors historical data.

Abstract: An artificial intelligence (AI)-based call response system and methods are provided that are configured to provide a context-based recommendation during a monitored conversation. The AI-based call response system includes a processor to perform conversation analysis operations, including determining transcribed words for the monitored conversation, analyzing the words using one or more machine learning (ML) models to produce a score associated with a model identifier (ID) identifying a ML model, comparing the score to a predefined threshold of the ML model, generating an alert when the score meets or exceeds the threshold, the alert including the model ID and a call identifier (ID) identifying the monitored conversation, creating one or more prompts with each prompt comprising an executable instruction that prompts, queries, or requests an output from a large language model for a response, retrieving the response for each of the prompts, and providing the response to a user.

Abstract: A computerized-method for training and applying a Machine Learning (ML) textual behavioral-identification-model to authenticate an agent, in a digital multi-channel environment, is provided herein. The computerized-method may include: (i) training a ML-textual-behavioral-identification-model using retrieved textual responses of each agent in one or more historical-interactions which were conducted in a controlled environment, as a training dataset.

Abstract: A computerized-method for calculating a level of friction within a customer and agent interaction, for quality improvement thereof, in a multichannel contact center. The computerized-method includes operating, for each interaction between the customer and the agent, in each channel, an Interaction Friction Score (IFS) calculation module. The IFS calculation module includes retrieving a transcript and interaction metadata of the interaction between the customer and the agent from the friction datastore and the database of interactions transcripts and metadata. The transcript includes ‘N’ sentences and calculating an IFS of the interaction between the customer and the agent then forwarding each interaction between the customer and the agent having a calculated IFS above a calculated Interaction Friction Threshold (IFT) for an intervention.

Abstract: Systems and methods for automatic real-time monitoring of interactions, carried out by at least one computer processor, including: producing a score for each text component of a text representation of an interaction; producing, based on the score for each text component, a score for each of a plurality of time periods of the interaction; producing a score history, including a plurality of the time period scores; and calculating, based on the score history, a real-time indication of the quality of the interaction.

Abstract: A computerized-method for training and applying a Machine Learning (ML) textual behavioral-identification-model to authenticate an agent, in a digital multi-channel environment, is provided herein. The computerized-method may include: (i) training a ML-textual-behavioral-identification-model using retrieved textual responses of each agent in one or more historical-interactions which were conducted in a controlled environment, as a training dataset.