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.