A method and a system for reporting metrics relating to customer calls for fungible agents that are utilizing multiple servicing applications in a contact center environment are provided. The method includes: receiving a service request call from a customer; determining applications that are usable for responding to the service request call from among a predetermined plurality of applications; monitoring the service request call in order to obtain information relating to call timing and events occurring during the call; when the service request call is completed, determining call-specific metrics such as hold time, number of transfers, and or idle time based on the information obtained during the monitoring; and reporting the metrics to a repository. Additional metrics that are specific to an agent assigned to handle the call may also be determined.

A computerized-method for identifying and utilizing effectiveness of agent handling multiple concurrent multi-channel interactions is provided herein. The computerized-method includes operating of a Multiple Multi-Channel Effectiveness (MME) module. The MME module includes: (a) operating an interaction module to retrieve one or more concurrent interactions of an agent from the data storage of interactions, according to a time range; (b) calculating an MME score for the agent based on metadata of the one or more concurrent interactions which defines the ability of the agent to handle multiple concurrent multi-channel interactions simultaneously; (c) storing the calculated MME score in the data storage of agents; and (d) sending the MME score to the one or more applications to take one or more follow-up actions based on the MME score.

The present invention is a system and method of continuous sentiment tracking and the determination of optimized agent actions through the training of sentiment models and applying the sentiment models to new incoming interactions. The system receives conversations comprising incoming interactions and agent actions and determines customer sentiment on a micro-interaction level for each incoming interaction. Based on interaction types, the system correlates the determined sentiment with the agent action received prior to the sentiment determination to create and train sentiment models. Sentiment models include agent action recommendations for a desired sentiment outcome. Once trained, the sentiment models can be applied to new incoming interactions to provide CSRs with actions that will yield a desired sentiment outcome.

A computer-implemented method of predicting customer satisfaction scores for a call center is disclosed, along with the use of the predicted customer satisfaction scores to perform various analytical functions, such as identifying changes to the predicted customer satisfaction score and identifying root causes of the predicted customer satisfaction scores. In some implementations, a pipeline includes an inference engine that includes an AI model trained on call transcripts and call attribute data to predict a customer satisfaction score.

A system and method for integrating audio data collected, such as audio data and analytical data, to perform behavioral analysis on the audio data, using an application of acoustic signal processing and machine learning algorithms, by converting the audio data to text data and performing behavioral analysis on the text data. The behavioral analysis data from the audio application of acoustic signal processing is combined with machine learning algorithms and speech to text data to provide a call agent with feedback to assist in the next best action or insight into customer behaviors.

Embodiments provide a method for providing a call center supplemental workflow tool, including: receiving from a database a workflow decisional matrix, a plurality of questions, and a plurality of scripts; displaying a web-based graphical user interface on a display providing at least one question or one script; receiving user input; determining, at least another question or at least another script, to be displayed, wherein determining is based upon the user input and the decisional matrix and advances the workflow; based upon the decisional matrix, determining that the workflow has been completed; providing an output of the questions displayed and the user input received; and the output being provided to a primary software application. Other aspects are described and claimed.

The present invention is a system and method of routing incoming communications to a CSR and providing guidance to the CSR based on the incoming communication using feedback information such as sentiment feedback, survey feedback, and feedback from actions taken by CSRs based on skill level. A CEC system receives an incoming communication, analyzes the communication and creates metadata based off of the analysis. The metadata is used by the RAE routing module to route the communication to an appropriate party. The metadata is also used by the GAE guidance module to determine the guidance to provide to the CSR. The CSR then performs an action based on the guidance. The CEC system continues to monitor the interaction until the interaction is completed. The communication metadata, the communication, the guidance, and the CSRs action are all provided to a RAS rules analysis module wherein the RAS analyzes the data and automatedly updates the rules (RAR and GAR) according to the analysis.

Disclosed embodiments may include a system that may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold. When the busyness score for the ATM does not exceed the busyness score threshold, the system may cause the ATM to present, via a first graphical user interface, a default ATM experience. When the busyness score for the ATM exceeds the busyness score threshold, the system may cause the ATM to present via, a second graphical user interface, a busy ATM experience.

There is thus provided a computerized-method for calculating a Repeated-Information-Request (RIR) score of an interaction in a contact center, by which a related interaction-recording is filtered for evaluation. The computerized-method is operating in a computerized-system which includes a processor, a data-storage and a memory to store the data-storage. The processor is operating a RIR score calculation module. The operating of the RIR score calculation module includes: (i) retrieving from the data-storage an interaction-recording and an interaction-recording-length thereof; (ii) operating a module on the retrieved interaction-recording to count a number of requests to repeat information; (iii) calculating a RIR score according to the number of requests to repeat information and the interaction-recording-length; and (iv) storing the RIR score of the retrieved interaction-recording in the data-storage. The RIR score is sent to a platform by which the platform is preconfigured to distribute the interaction-recording for evaluation, based on the RIR score.

A system obtains conversation data corresponding to conversations between users and agents of a client. The system identifies a set of intents from the conversations and identifies a set of contexts, explicit elements, and implied elements of these intents. The system identifies actions that can be performed to recognize new explicit and implied elements from new conversations and to address intents in these new conversations. Based on these actions, the system generates a set of recommendations that can be provided to the client. As agents communicate with users, the system monitors adherence to the set of recommendations.