Abstract: Methods of intelligent routing of work assignment includes indexing plurality of pending tasks and indexing a plurality of available employees. A first employee is retrieved from an index of available employees. A next available task assignable to the first employee is determined. A work item from the next available task is assigned to the first employee from the available employee list. The assigned work item is removed from pending task list. The first employee is removed from the available employee list. The next employee is retrieved from the index of available employees.

Abstract: Methods of intelligent routing of work assignment includes indexing plurality of pending tasks and indexing a plurality of available employees. A first employee is retrieved from an index of available employees. A next available task assignable to the first employee is determined. A work item from the next available task is assigned to the first employee from the available employee list. The assigned work item is removed from pending task list. The first employee is removed from the available employee list. The next employee is retrieved from the index of available employees.

Abstract: Methods of intelligent routing of work assignment includes indexing plurality of pending tasks and indexing a plurality of available employees. A first employee is retrieved from an index of available employees. A next available task assignable to the first employee is determined. A work item for the next available task is assigned to the first employee from the available employee list. The assigned work item is removed from pending task list. The first employee is removed from the available employee list. The next employee is retrieved from the index of available employees.

Abstract: Methods of intelligent routing of work assignment includes indexing plurality of pending tasks and indexing a plurality of available employees. A first employee is retrieved from an index of available employees. A next available task assignable to the first employee is determined. A work item from the next available task is assigned to the first employee from the available employee list. The assigned work item is removed from pending task list. The first employee is removed from the available employee list. The next employee is retrieved from the index of available employees.

Abstract: Methods of intelligent routing of work assignment includes indexing plurality of pending tasks and indexing a plurality of available employees. A first employee is retrieved from an index of available employees. A next available task assignable to the first employee is determined. A work item from the next available task is assigned to the first employee from the available employee list. The assigned work item is removed from pending task list. The first employee is removed from the available employee list. The next employee is retrieved from the index of available employees.

Abstract: Systems and methods are disclosed for scheduling a workforce. In one embodiment, the method comprises receiving a shift activity template; receiving an association between the shift activity template and at least one worker; and scheduling a plurality of schedulable objects. The scheduling is performed in accordance with a workload forecast and schedule constraints. Each of the schedulable objects is based on the shift activity template. The shift activity template describes a worker activity performed during a shift. The template has range of start times and a variable length for the activity. The activity is associated with a queue.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing the inquiries to an independent group of experts. Machine learning based systems are used in combination with contact center staff and/or the independent experts to resolve the customer service inquiries. The AI based systems can be configured to determine which of the customer service inquiries should be routed to the independent experts, and/or to generate content for inclusion in responses to the customer service inquiries. The machines systems are optionally trained based on scored responses to prior customer service inquiries. These scored responses can include content provided by a machine learning system and/or a human expert.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include methods of classifying customer service inquiries, training a machine learning system, and/or processing customer service requests.

Abstract: Systems and methods are disclosed for scheduling a workforce. In one embodiment, the method comprises receiving a shift activity template; receiving an association between the shift activity template and at least one worker; and scheduling a plurality of schedulable objects. The scheduling is performed in accordance with a workload forecast and schedule constraints. Each of the schedulable objects is based on the shift activity template. The shift activity template describes a worker activity performed during a shift. The template has range of start times and a variable length for the activity. The activity is associated with a queue.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include methods of classifying customer service inquiries, training a machine learning system, and/or processing customer service requests.

Abstract: Disclosed is a method of creating and updating schedules. The method comprises receiving a plurality of initial events associated with a plurality of entities, generating an initial schedule that identifies the plurality of initial events, receiving a plurality of subsequent events, processing each initial event of the plurality of initial events to identify a subset of the plurality of initial events that should be maintained based on which entity of the plurality of entities is associated with the initial event, and generating an updated schedule that identifies only the subset of the plurality of initial events and the plurality of subsequent events.

Abstract: Methods of intelligent routing of work assignment includes indexing plurality of pending tasks and indexing a plurality of available employees. A first employee is retrieved from an index of available employees. A next available task assignable to the first employee is determined. A work item fro the next available task is assigned to the first employee from the available employee list. The assigned work item is removed from pending task list. The first employee is removed from the available employee list. The next employee is retrieved from the index of available employees.

Abstract: A system for scheduling outbound agents comprises an instruction execution system. The instruction execution system is configured to identify a workload comprising a plurality of outbound contacts. The instruction execution system is further configured to receive historical information comprising connection rates of past outgoing contact attempts. The instruction execution system is further configured to schedule the outbound contacts based at least on the connection rates of the past outgoing contact attempts.

Abstract: Systems and methods are disclosed for scheduling a workforce. In one embodiment, the method comprises the steps of: collecting an agent activity of a first class and an agent activity of a second class; and displaying the agent activity of the first class and the agent activity of the second class along the same timeline axis. The agent activities are collected from a contact center data source. The second class is different from the first class. Both activities are associated with the same agent. Each activity is derived from a different virtual data source.

Abstract: Systems and methods for scheduling workforces are provided.

Abstract: Systems and methods of partial shift swapping are disclosed. An exemplary method comprises the steps of: receiving a partial shift swap request from an agent; determining a disposition of the partial shift swap request by applying a set of criteria associated with a validation rule to the partial shift swap request; and if the disposition indicates the partial shift swap request is approved, updating a schedule to reflect the partial shift swap request. An exemplary computer-readable medium has a computer program stored thereon. The computer program comprises computer-executable instructions for performing a computer-executed method of requesting a partial shift swap.

Abstract: Systems and methods are disclosed for scheduling overtime and time-off for a workforce. In one embodiment, a method of workforce scheduling to handle an expected workload comprises, in an instruction execution system, receiving a first workforce schedule describing existing assignments of a plurality of workers to a plurality of shifts, each of the shifts being associated with a time range and a day; in response to a variance in the expected workload, selecting a modification to the first workforce schedule required to handle the variance in the expected workload during the day; and producing a second workforce schedule that modifies the length of at least one of the plurality of shifts to accommodate the modification to the first workforce schedule.

Abstract: Methods and systems are presented for integrating workforce management and quality monitoring. In one embodiment, the method comprises the steps of: receiving information about a skill; capturing a plurality of contacts made by an agent; receiving an evaluation of the contacts; and updating the skill information based on the evaluation. The skill is associated with an agent, and the evaluation measures the agent skill. In another embodiment, the method comprises the steps of: receiving information about a skill; capturing a plurality of contacts made by an agent; receiving an evaluation form for the contacts; and updating the form based on the skill information. The skill is associated with an agent, and the form produces a measurement of the agent skill.

Abstract: A system for scheduling outbound agents comprises an instruction execution system. The instruction execution system is configured to identify a workload comprising a plurality of outbound contacts. The instruction execution system is further configured to receive historical information comprising connection rates of past outgoing contact attempts. The instruction execution system is further configured to schedule the outbound contacts based at least on the connection rates of the past outgoing contact attempts.

Abstract: Systems and methods for providing workforce optimization are provided. A representative method includes: planning a first campaign to implement business goals; scheduling and deploying a workforce in accordance with the campaign to produce a plurality of interactions, at least one of the interactions involving a customer and a first agent of a contact center, at least another of the interactions involving a customer and a second agent of a branch office or back office; measuring performance of the first agent and the second agent on at least a portion of the interactions to produce a set of quality metrics for the agents; combining at least a portion of quality metrics to produce performance indicators; and using the performance indicators in the planning step of a second campaign or another iteration of the first campaign.