A method of assisting an agent in real-time includes receiving a call interaction between a customer and an agent; identifying words spoken in the call interaction; providing the words to a behavioral models module; computing a score for a plurality of behavioral metrics; providing a phrase formed by the words to a knowledge article selection module; providing each score for the plurality of behavioral metrics to the knowledge article selection module; providing a plurality of knowledge selection rules to the knowledge article selection module; evaluating a combination of the phrase and the scores of the plurality of behavioral metrics against each of the plurality of knowledge selection rules; matching a knowledge selection rule to the combination; selecting a knowledge article associated with the matched knowledge selection rule; generating a visual representation of the selected knowledge article; and presenting in real-time the visual representation on a graphical user interface.

A method and related system provide for data tracking and generation for workforce management. The system has a database and tracks customers and conversations customers have with agents. The system tracks contact items each having a contact type and a channel. The system tracks work sessions each having one or more work bursts, based on tracking conversations and contact items. The system provides access to the database for users, reports or workforce management systems.

A computerized-method for recommending activities for an agent, in a contact center is provided herein. The computerized-method includes operating an activities-recommendation module, for agents in a contact center, during a work-shift of an agent. The operating of the activities-recommendation module includes: (i) operating a machine-learning-model to calculate a motivation-score; (ii) associating a preconfigured score-range for the calculated motivation score; (iii) collecting activities which were preassigned to the preconfigured score-range; (iv) retrieving one or more predetermined agent favorite activities; (v) retrieving one or more agent history activities, which were previously selected by the agent; (vi) calculating an activity-occurrence score for each activity of the collected activities, which were preassigned to the preconfigured score-range, by adding the: (a) motivation-score; (b) the one or more predetermined agent favorite activities; and (c) the one or more agent history activities; and (vii) displaying a rated list of recommended activities, according to the calculated activity-occurrence score.

Disclosed herein are embodiments of systems, methods, and products comprises an analytic server for electronic requests routing and distribution. The server receives a plurality of requests from a plurality of electronic user devices. Aiming to routing the plurality of requests to appropriate agents, the server trains an artificial intelligence model for each agent based on historical data. For each request, the server executes the artificial intelligence model to determine a score indicating the probability of the agent converting the request to a successful sale. The server determines an entropy value for each request based on the scores and order the requests into a queue based on the entropy values. The server also calculates a capacity for each agent based on historical agent data. For each request in the queue, the server routes the request to an agent based on at least one of the score and capacity of the agent.

A computer server system comprises a communications module; a processor coupled with the communications module; and a memory coupled to the processor and storing processor-executable instructions which, when executed by the processor, configure the processor to receive, via the communications module and from a first device, a signal including a request to schedule access to a shared resource within a time window; identify, based on a load projection for the shared resource, at least one time period within the time window for the access to the shared resource; send, via the communications module and to the first device, a signal including the at least one time period and requesting confirmation that a particular one of the at least one time period is acceptable; receive, via the communications module and from the first device, a signal including confirmation that the particular one of the at least one time period is acceptable; determine that a current time has reached the particular one of the at least one time period; and responsive to determining that the current time has reached the particular one of the at least one time period, provide access to a node of the shared resource, the node having available bandwidth.

The number of agents to which incoming queries to a customer interaction center agent group may be distributed is limited based on skip criteria. The skip criteria is defined based on information associated with agent devices, such as locked status of a device, in-memory status of a client application at the device, or whether a telephone number provisioned for use with the device is from an external public switched telephone network. Agents which fail to satisfy the skip criteria are excluded from distributions of queries to improve wait times for customer interaction center users. Thus, queries are distributed from a queue to agents which satisfy the skip criteria.

Systems and methods are provided to select and implement a resolution path, of a number of resolution paths, to resolve a work item prior to the expiration of a time constraint. The expiration of the time constraint, absent the resolution, results in a duplication of loads on resources that are necessary to re-establish and maintain the communication to obtain the resolution during a subsequent communication.

A computer server system comprises a communications module; a processor coupled with the communications module; and a memory coupled to the processor and storing processor-executable instructions which, when executed by the processor, configure the processor to receive, via the communications module and from a first device, a signal including a request to schedule access to a shared resource within a time window; identify, based on a load projection for the shared resource, at least one time period within the time window for the access to the shared resource; send, via the communications module and to the first device, a signal including the at least one time period and requesting confirmation that a particular one of the at least one time period is acceptable; receive, via the communications module and from the first device, a signal including confirmation that the particular one of the at least one time period is acceptable; determine that a current time has reached the particular one of the at least one time period; and responsive to determining that the current time has reached the particular one of the at least one time period, provide access to a node of the shared resource, the node having available bandwidth.

Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, a plurality of contacts available for connection to an agent; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the agent and the plurality of contacts; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings according to a probabilistic network flow model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for connection in the contact center system.

A system and method analyzes computer actions to identify computer-based processes (e.g. computer-user interactions) which are automation candidates. A data gathering process executed by a processor on a computer may collect low-level user action information or items, each low-level user action information or item including for example an input type description, a user name, and screen window information. At a computer sequential pattern mining may be applied to determine a set of subprocesses, each subprocess including a series of low-level user actions, each user action associated with a user action vector, and each subprocess associated with a subprocess vector generated from user action vectors associated with (typically generalized) low-level user actions comprised in the subprocess. The subprocess vectors may be grouped or clustered to create processes. For each process, an automation score may be calculated using the actions in the subprocesses in the process.