Simon C. Borst - Convent Station NJ Andrew D. Flockhart - Thornton CO Francis C. Hymus - Bridgewater NJ Eugene P. Mathews - Barrington IL Martin I. Reiman - Maplewood NJ Judith B. Seery - Madison NJ John Z. Taylor - Bedminster NJ
Assignee:
Avaya Technology Corp. - Basking Ridge NJ
International Classification:
H04M 300
US Classification:
37926604, 37926511, 37922101
Abstract:
The alternate destination redirection (ADR) feature ( ) of telephone switching systems ( ) or an equivalent is used to implement a âpost-routeâ routing architecture having the benefits of a âpre-routeâ routing architecture in a network ACD (FIG. ). The ADR feature is administered in the network ( ) for individual ACD systems and individual call types at each ACD system to identify another ACD system as an alternative destination for calls of the individual call type rejected by the individual ACD system. The network distributes ( ) calls to the plurality of ACD systems ( ) on a basis (e. g. , fixed percentage, round-robin) that does not require the network to know the status of the individual ACD systems. Upon having a call of an individual type routed thereto, an individual ACD system checks ( ) the status of the ACD system that is administered as the alternative destination for its rejected calls of the individual type. If it determines that it can provide the better service, the individual ACD system services ( ) the call.
System For Integrating Agent Database Access Skills In Call Center Agent Assignment Applications
Keith R. McFarlane - Denver CO Andrew Derek Flockhart - Thornton CO Lucinda M. Sanders - Boulder CO Paul L. Richman - Boulder CO Darryl J. Maxwell - Lafayette CO
Assignee:
Avaya Technology Corp. - Basking Ridge NJ
International Classification:
H04M 300
US Classification:
37926505, 37926605
Abstract:
The system for integrating agent database access skills in call center agent assignment applications dynamically generates data indicative of an agents effective skill level by mapping the agents acquired skills into their augmented skills representative of their ability to use the various automated resources that are required to satisfy the customers request. The determined effective skill level is automatically updated as changes in the agents effective skills are measured. In order to distribute work among the agents based upon agent skill levels, there must be a measure of each agents competence with a particular skill. The pool of agents is divided into categories of those who must use guided problem solving tools to service a customer request, those who can address issues beyond the scope of the guided problem solving tool, and those who exhibit various levels of efficacy in using the guided problem solving tool. The system for integrating agent database access skills in call center agent assignment applications automatically computes an agents effective skill level, which is a term used herein to describe a metric indicative of the agents overall knowledge management ability consisting of both acquired skills and augmented skills.
Workflow-Scheduling Optimization Driven By Target Completion Time
Andrew D. Flockhart - Thornton CO Darryl J. Maxwell - Lafayette CO Keith Robert McFarlane - Aurora CO Paul L. Richman - Boulder CO Lucinda M. Sanders - Boulder CO
Assignee:
Avaya Technology Corp. - Basking Ridge NJ
International Classification:
G06F 1900
US Classification:
700102, 700100, 705 8, 37926601
Abstract:
The flow of work items ( ) through a workflow process ( ) is optimized by repeatedly reordering (FIG. ) work items enqueued in inbox queues ( ) of workflow process tasks ( ) to maximize results according to a given business strategy expressed through target times. Each enqueued work item has an associated in-queue rating (IQR ) that represents the number of queue positions ( ) that the work item can be retarded or needs to be advanced to meet its target time. When a work item enters a queue and whenever a work item changes its queue position, its IQR is computed. An optimization function is then performed ( ) on the queue to determine an order of the enqueued work items that optimizes a metric of those work items that may fail to meet their target times. The work items in the queue are then reordered ( ) accordingly.
Andrew Derek Flockhart - Thornton CO Robin H. Foster - Little Silver NJ Joylee E. Kohler - Northglenn CO Eugene P. Mathews - Barrington IL
Assignee:
Avaya Technology Corp. - Basking Ridge NJ
International Classification:
H04M 764
US Classification:
37926601, 3792661, 37926505, 37926512, 379307
Abstract:
Calls or other communications requiring a particular skill for handling are placed in a corresponding skill queue in a call center. One of a plurality of different values is assigned to each of the communications in the skill queue, with each of the values corresponding to a particular level of priority for access to the skill. For example, high, mid and low values may be assigned for communications placed in a technical support skill queue. A given communication is selected from the queue as a function of the assigned values, time advantages associated with the values, and the wait times of the communications in the queue. This communication selection process may include, for example, identifying communications in the queue which are candidates for out-of-order selection, computing an adjusted wait time for each of the identified communications, and selecting the communication with the highest adjusted wait time. The adjusted wait time for a given communication may be computed as, for example, a sum of the wait time for that communication and an advantage adjustment associated with the corresponding value. The selected communication may be placed in a call selection consideration pool for a multi-skill agent.
Methods And Apparatus For Processing Of Communications In A Call Center Based On Variable Rest Period Determinations
Andrew Derek Flockhart - Thornton CO Robin H. Foster - Little Silver NJ Joylee E. Kohler - Northglenn CO Eugene P. Mathews - Barrington IL John Z. Taylor - Bedminster NJ
A call center is configured to determine variable rest periods for one or more agents, based at least in part on factors such as call center service state and agent occupancy. The call center service states may include a number of designated service states associated with a particular skill or type of communication supported by one or more agents of the call center. A particular one of the states represents a branded service level, while other states represent over-service and under-service conditions. The rest period determined for one or more of the agents can be used to implement features such as many-to-many work assignment, just-in-time (JIT) delivery of work, next opportunity for service (NOS) indicators, thereby facilitating the processing of communications in the call center.
Methods And Apparatus For Service State-Based Processing Of Communications In A Call Center
Andrew Derek Flockhart - Thornton CO Robin H. Foster - Little Silver NJ Joylee E. Kohler - Northglenn CO Eugene P. Mathews - Barrington IL
Assignee:
Avaya Technology Corp. - Basking Ridge NJ
International Classification:
H04M 300
US Classification:
37926512, 37926506
Abstract:
A call center is configured to determine which of a number of designated service states is associated with a particular skill or type of communication supported by one or more agents of the call center. A particular one of the states represents a branded service level, while other states represent over-service and under-service conditions. If the particular skill or type of communication is determined to be associated with a service state other than that corresponding to the desired branded service level, a communication processing function of the call center is adjusted so as to return the skill or type of communication to the desired branded service level state. An example of such an adjustment is a dynamic agent pooling, in which a pool of agents available to perform work for the particular skill varies in accordance with the current service state of that skill. The call center processing operations may also perform appropriate tests to determine if predictors generated by certain predictive algorithms should be used in the service state determination process. A branded service metric may be used to characterize the performance of the call center with respect to the desired branded service level.
Methods And Apparatus For Analysis Of Load-Balanced Multi-Site Call Processing Systems
A multi-site call processing system includes multiple distributed call center sites, and utilizes a load balancing process to distribute calls among the sites for handling by agents. The system generates a multi-site performance score characterizing the performance of the load balancing process. Adjustments may be made in the load balancing process, such as selection of one type of load balancing over another for use at a particular time, based at least in part on the multi-site performance score. The multi-site performance score may be determined using single-site performance measures such as Average Speed of Answer (ASA) and agent occupancy generated across multiple time intervals. The multi-site performance score as generated for a given interval may be, e. g. , a ratio of the maximum and minimum values of a single-site performance measure for that interval. As another example, the multi-site performance score may be in the form of a standard deviation of a set of normalized scores generated for the multiple sites across the specified time intervals.
Methods And Apparatus For Multi-Variable Work Assignment In A Call Center
Andrew Derek Flockhart - Thornton CO Robin H. Foster - Little Silver NJ Joylee E. Kohler - Northglenn CO Eugene P. Mathews - Barrington IL
Assignee:
Avaya Technology Corp. - Basking Ridge NJ
International Classification:
H04M 300
US Classification:
37926505, 37926601
Abstract:
A multi-variable work assignment process is used to assign work items, such as voice calls, e-mails and other communications or tasks, to agents in a call center. The multi-variable work assignment process determines whether values of a particular variable characterizing the work items fall within a designated range, and if so utilizes at least one additional variable for making the work assignment decision. The work assignment process may also or alternatively consider a weighted combination of multiple variables in making the work assignment decision. Examples of variables which may be used in the multi-variable assignment process include current wait time, service objective, skill preference, skill level, anticipated wait time, predicted wait time, etc. The invention may be implemented in a one-to-many work assignment process which selects one of a set of agents available for handling a particular work item, in a many-to-one work assignment process which selects one of a set of work items for handling by a particular available agent, or in a many-to-many work assignment process in which multiple agents are each considered simultaneously for handling multiple work items.