A method and system matching contact center agents and back office staff with a customer inquiry. Exemplary systems include an expert term extraction engine, a customer term extraction engine, and a matching engine to compare customer request terms to the expert terms from the customer term extraction engine. The comparison determines whether there is a match or potential match between the customer request terms and the stored expert terms. An exemplary system may also include a timer that communicates with one or more communication servers. Back office staff may assist contact center agents when one or more conditions are met, such as when a customer wait time exceeds a predetermined period or when there is no match or potential match between the customer request terms and the stored expert terms for contact center agents.

Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Matching a customer with a representative is performed for assisting the customer with an issue. Information related to the customer, the issue, historical information, and other customer information may be used to determine a likely issue. A representative may be selected in real-time to assist the customer in resolving the issue based on the customers experience in resolve the same or similar issues to the likely issue and the information related to the customer and the representative may be updated dynamically in real-time.

Certain exemplary aspects of the present disclosure are directed to a data-communications system including a networked contact center for which, in an example embodiment, a communication is received by a networked contact center. In determining whether to allow the communication to reach contact center resources, a contact rate value is examined. The contact rate value may represent an allowable quantity of contact within a time interval. For some example embodiments, a determination of whether the network contact center is to accept or reject the communication is made based on the contact rate value.

Systems and methods forecast inbound telecommunications, and more particularly, analyze real-time and historical call center data, and apply a forecasting model to the data in order to predict inbound call volume. These systems and methods employ tools that manipulate call center data and generate visual representations of metrics pertaining to forecasting call center data via a dashboard.

Electronic communication methods and systems for rating agents and allowing customers for select agents are provided. Exemplary systems include a communication server for connecting a user to a communication with a contact center, a presence system to determine status of one or more agents, a wait-time determination engine to determine a wait time for one or more agents present for the communication, an agent scoring system for determining a score for one or more agents present for the communication, and a routing engine to connect a selected agent to the communication.

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.

Exemplary aspects involve a data-communications apparatus or system communicate over a broadband network with a plurality of remotely-located data-communications circuits respectively associated with a plurality of remotely-situated client entities. The system includes data-communications platform (e.g., UC-CC) that processes incoming data-communication interactions including different types of digitally-represented communications among which are incoming call, and that is integrated with a memory circuit including a database of information sets. Each of the information sets includes experience data corresponding to past incoming data-communication interactions processed by the platform, and with aggregated and organized data based on data collected in previous incoming interactions. The platform accesses the database and may: use past interactions and other data sources; and facilitate an automated self-service experience for users by resolving inquiries discerned through the incoming interactions; and/or effecting call-decision routing of incoming interactions to call-center agents or specialists.

Method starts with a processor receiving configuration settings including an identified task, a relationship data, and a criticality value. Processor initializes a communication session with an agent client device. The communication session is between a virtual caller associated with the system and the agent client device. Processor then processes an audio signal of the communication session to generate an agent utterance and generates a transcribed agent utterance based on the agent utterance using a speech-to-text processor. Processor generates a virtual caller utterance using a task-specific virtual caller neural network associated with the identified task. The virtual caller utterance can be generated based on the transcribed agent utterance. Processor then causes the virtual caller utterance to be played back in the communication session to the agent client device. Other embodiments are disclosed herein.

Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for pairing in a contact center including ordering one or more contacts, ordering one or more agents, comparing a first difference in ordering between a first contact and a first agent in a first pair with a second difference in ordering between a second contact and a second agent in a second pair, and selecting the first pair or the second pair for connection based on the comparing, wherein the first contact and the second contact are different or the first agent and the second agent are different.