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 computerized method for automatic detection of an availability of an agent to attend a schedule of customer calls in a call center and reassignment thereof may include assessing an agent availability to attend a schedule in a predefined time interval before a starting time of the schedule. If the assigned agent is not available, candidate agent availability may be assessed from a plurality of schedules to participate in the schedule of the unavailable agent. One or more candidate agents from a plurality of agents may be identified to replace the unavailable assigned agent based on the candidate agent availability. A replacement agent chosen from the one or more candidate agents may be assigned based on skills or preferences for each of the one or more identified candidate agents. A supervisor may be notified that the chosen replacement agent will attend the schedule in place of the unavailable assigned agent.

A method for optimizing a workflow of deferrable work interactions in a contact center that includes: providing a NLP models and a priority model; using text derived from received deferrable working interactions as inputs to the NLP models to generate the NLP scores; using the LP scores as inputs to the priority model to generate the priority score; identifying candidate agents of the agents for handling the deferrable work interactions; receiving an inbound work forecast; receiving agent work schedule data; using an optimization process to generate an optimized workflow for the deferrable work interactions, where the optimized workflow includes assignments in which an agent from the candidate agents is selected to handle the deferrable work interaction and a target timeframe for handling is scheduled; and routing the deferrable work interactions in accordance with the assignments of the optimized workflow.

Various techniques described herein relate to building and using representative data systems to store and manage representative profile data within contact centers. A representative data system integrates with external communication services and other internal/external systems to provide a uniform data store and interface to create and manage representative profiles. The representative data system retrieves and analyzes representative profile data during runtime contact center operations such as contact queuing, contact management, and/or workforce management. The representative data system may be implemented using relational or graph-based data structures to support advanced predictive modeling and impact analysis of scenarios such as service outages, dynamic workload shifting, infrastructure changes, and workforce changes.

A method of assisting an agent in real-time includes 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.

An exclusive agent pool allocation method including collecting business data of agents; grouping agents according to the business data of the agents and forming multiple exclusive agent pools; calculating business skill values of agents according to the business data of the agents and classifying priorities of the agents; classifying priorities of agent pools according to the business data of the exclusive agent pools; and allocating calling user to the corresponding agent in the exclusive agent pool according to predetermined allocation strategy. The method solves the matching of the user and the agent in the region and the business level, allocates the agent resource according to the priority of the business skill, realizes the high match between the business skill of the agent and the business handled by the user, improves the pertinence and effectiveness of the agent service and promotes the satisfaction of the users.

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.

A method for providing anonymized contact information to a workforce management system includes receiving contact information from a plurality of customer business units; assigning each of the plurality of customer business units one or more industry classification codes or product classification codes; anonymizing, by a processor, the contact information to the one or more industry classification codes or product classification codes; receiving, by a processor, a query from a workforce management system for anonymized contact information in one or more industry classification codes or product classification codes; and providing the anonymized contact information to the workforce management system for use in a workforce management predictive model.

A method related to predicting agent attrition rate that includes: providing an attrition model; measuring and recording agent journey data of a first agent of a contact center, the agent journey data describing aspects related to an employment of the first agent and having data types that corresponds in kind to data types of inputs of the attrition model; determining that prediction of an attrition rate of the first agent is required; using the attrition model to predict the attrition rate by providing values for the inputs from applicable values taken from the agent journey data of the first agent, and calculating the attrition rate as the output of the attrition model; determining if the calculated current attrition rate indicates a high risk of attrition; and transmitting an alert communication to a supervisor of the first agent.

A computerized-method for increasing productivity of agents in a contact-center by improving an automatic-scheduling generation in a Workforce-Management (WFM) application, is provided herein. The computerized-method includes operating an Agent-Productivity-Score-Generator (APSG) module, for each agent. The APSG module includes: (i) receiving an activity-type and a period for agents shift-placement; (ii) retrieving historical-data of a preconfigured number of metrics for each scheduled-shift during a preconfigured period; (iii) calculating a weighted-sum of the retrieved historical-data of the preconfigured number of metrics and preconfigured attributed weight thereof to yield an Agent-Productivity-Score (APS) for each shift; and (iv) selecting a shift having a highest APS and adding the selected shift of the agent to a list-of-maximum-shifts. When the list-of-maximum-shifts is having all agents in a data-store then the list-of-maximum-shifts may be sent to the WFM for an automatic shift-schedule generation for the activity-type and a preconfigured period, based on the list-of-maximum-shifts and other input parameters.