A telephone answering service support system including a task data DB 14g that stores a plurality of task data items, each task data item including a task name of a task in a telephone answering service and speech data indicating speech to be made in the task, a task list obtaining unit 14d that displays the task name on a display of an operator of the telephone answering service, and a task determining unit 14e that determines which task has been completed among the plurality of tasks corresponding to the plurality of task data items, based on voice of the operator and the speech data, and executes, in a case where it is determined that any of the tasks has been completed, display control for distinguishing the task name of the task that is determined to have been completed from other task names.

One or more embodiments related to a method that includes querying a data store for current interaction data between call center personnel and customers. The call center personnel are grouped into call center groups. The method further includes determining, for at least some call center groups, a current interaction metric specific to the call center group. The current interaction method is provided for each of the at least some call center groups.

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.

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.

A system for quickly deploying WFM systems in contact centers is provided. A user or administrator can quickly install a WFM application on a deployment server. The administrator can provide the WFM application access to the contact center data where the WFM application can import data from the contact center about one or more customers, agents, queues, teams, and any other items typically associated with contact centers. The imported data may also include presence data about the agents, teams, and customers. Based on the imported data, the WFM application may execute one or more workflows to automatically determine information such as maximum and minimum hours, break patterns, and shift data about the agents and teams.

A computerized-method for a personalized screen recording in a contact center is provided herein. The computerized-method includes, before each interaction between an agent and a customer, operating a predictive screen recording module to yield an Agent Recording Percentage (ARP) value; and based on the ARP value, operating a recording of screen events module for recording one or more voice or digital interactions, on one or more screens associated to a computing device of the agent.

A method of routing interactions to contact center agents according to an embodiment includes identifying an interaction to be routed to a contact center agent, identifying a group of contact center agents as candidates for routing of the interaction, retrieving agent performance data for each candidate agent of the group of contact center agents identified as candidates for routing of the interaction, determining a predicted score for a key performance indicator for each candidate agent based on the agent performance data, determining an occupancy rate of each candidate agent based on the agent performance data, generating a ranking of the candidate agents for routing prioritization based on the predicted score for the key performance indicator for each candidate agent and the occupancy rate of each candidate agent, and signaling a routing device to route the interaction to a selected candidate agent based on the ranking of the candidate agents.

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.

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving an input text data retrieved from a transcription associated with a previously recorded audio data file between a user of a plurality of users and an agent associated with a call center; identifying personal information associated with the user of the plurality of users from the input text data by inputting the input text data into a trained machine learning model; determining at least one key term within the personal information associated with the user of the plurality of users; automatically determining a confidence positivity score associated with the at least one key term; automatically extracting a plurality of tuples from the input text data; storing the plurality of tuples in an external database; and automatically generating a call script for conducting a subsequent call with the user.

A system for providing resource material to a customer service representative [CSR], including a database storing a plurality of resources associated with one or more customer service contexts, a memory storing instructions, and one or more processors configured to execute the instructions to analyze an on-going communication including a customer service request received from a customer; determine a context of the request based on the analysis and historical data associated with the customer determined to be associated with the communication, automatically identify, based on the context of the request, a resource in the database associated with the determined context; and display, via a user interface in real-time during the communication, the resource associated with the customer service request.