Avaya Technology LLC

Lucent Technologies Inc.

Avaya Inc.

Avaya Technologies Corp

Robin Harris Foster

Eugene P Mathews

Andrew Derek Flockhart

Joylee E Kohler

Frank J Bogart

Lynn H Smith

Katherine Anthony Sobus

Roy A Jensen

John Z Taylor

Hongguang Li

Stephen L Skarzynski

Richard Alan Cohen

Patricia Ann Berkowitz

Rodney Allen Thomson

Gregory L Anderson

Darryl J Maxwell

David Abraham Horovitz

David Charles Bengtson

William Edward Gourlay

Mila Maximets

James E Ertel

Brigite M Babine

Jeffrey H Hagan

Myron Eugene Drapal

Jill K Ross

Methods for enabling call center agents to adapt their servicing of incoming calls based on the estimated value of the calls are disclosed. In accordance with the first illustrative embodiment, when there is a 'high-value' call waiting in the queue while one or more agents are currently servicing lower-value calls, an agent who is servicing a lower-value call is informed that there is a higher-value call waiting in the queue, and is prompted to provide an estimate of how much longer he or she will be servicing his or her current call. The call center system selects which agent the high-value call will be routed to based on: (i) the agents' responses to the prompts, and (ii) for each of the responding agents, a measure of how closely prior responses from the agent compared to the actual amount of time taken by the agent to complete servicing the call.

Servicing calls in call centers based on estimated call value

A data store (e.g., a relational database, an object-oriented database, an unstructured file system, an in-memory data structure, etc.) and associated methods are disclosed for predicting the accuracy of agent responses in a call center. In accordance with the illustrative embodiment, agents in a call center are prompted at different times during their workday for estimates of how much longer they will be servicing their current call, and the estimates are stored in the data store along with the actual times required for completion of the call. Statistics for each agent's prediction accuracy are computed and stored in the data store along with the raw data, and can then be used to assess the accuracy of subsequent agent responses to queries.

Data store for assessing accuracy of call center agent service time estimates

In a call center, a plurality of agents comprises at least a first group associated with a first skill and a second group associated with a second skill. The second group may have a substantially larger number of agents than the first group, and accordingly the first and second groups may be referred to as small and large groups, respectively, and the first and second skills may be referred to as small and large skills, respectively. In one embodiment, if a service level target for the small skill is being met, and a number of available agents in the small group exceeds a specified minimum available agent requirement, at least one of the available agents in the small group is selected and designated as eligible for handling a communication involving the large skill, without assigning that agent to the large group or removing that agent from the small group.

Utilizing small group call center agents to improve productivity without impacting service level targets

The present invention is directed to a contact center using presence information to direct work items to experts. More specifically, a system is provided that includes a feature that allows for subject matter experts, which are individuals other than customer service agents, to assist in incoming calls in at least three different modes: an assist mode, an intercept mode and an overflow mode. Thus, customer service efficiency and customer satisfaction are increased. In addition, it is contemplated that subject matter experts may either selectively accept or deny the request for assistance.

Prioritized service delivery based on presence and availability of interruptible enterprise resources with skills

Incoming communications received by call centers and the like are routed to one or more alternative call centers based on a comparison of Match Rates and Expected Wait Times. Such routing helps improve the overall load balancing of a call center or network of call centers while increasing the effectiveness and controlling the costs of handling incoming communications.

Methods and devices for balancing communications using match rates

In a blended inbound/outbound call center (), an outbound call-pacing function () decides whether or not to initiate an additional outbound call for a particular skill. It determines () whether the number of available agents for the skill plus the number of resting agents for the skill exceeds the number of available inbound calls for the skill plus the number of outstanding outbound calls for the skill. It also determines () whether a target service time for the skill exceeds an estimated wait time of each of the available inbound calls for the skill assuming that pending outbound calls for the skill plus one take precedence over the inbound calls. If both determinations are affirmative, the call pacing function initiates () an additional call for the skill. If either determination is negative, the function forbears () from initiating an additional call for the skill. The number of available and resting agents may be incremented () by (a) a difference between the target service time for the skill and the present waiting time of the last-enqueued inbound call for the skill divided by the average rate of advance through the skill's call queue, or (b) the average amount of time that a newly-initiated outbound call for the skill takes to become available divided by the average rate of advance, plus the number of agents for the skill who are busy but expected to become available within a time period equal to (a) or (b), less the number of those agents that will receive a rest period upon becoming idle. The number of calls may be incremented () by the number of inbound calls that are expected to arrive within the time period (a) or (b).

Intelligent inbound/outbound communications blending

A call selection process in a call center is configured to utilize a continuum of skill levels, in one or more categories, for a given multi-skill agent. In an illustrative embodiment, categories of levels are associated with skills supported by the given agent, with each of the categories including a level, e.g., a numerical value, for each of the skills. The levels are used in a call selection computation that determines which of a number of waiting calls should be delivered to the agent. The categories may include, for example, a speed category including levels indicative of the speed at which the agent can process calls for each of the corresponding skills, a yield category including levels indicative of results likely to be obtainable by the agent for each of the corresponding skills, and a preference category including levels indicative of preferences of the agent for calls requiring each of the corresponding skills. The process may involve selecting different subsets of one or more of the categories in a given set of categories for use at different times, depending on factors such as call volume. The call selection computation may involve, for example, computing a value for each of the skills, as a function of the corresponding level or levels and a wait time measure, e.g., a current wait time, a predicted wait time, or a ratio of predicted wait time to a service objective, for a call in a corresponding skill queue.

Call selection based on continuum skill levels in a call center

A call center ( ) rejects further calls when the sum of the present time, the anticipated call in-queue wait time ( ), and the anticipated call service time ( ) for this type ( ) of call exceeds the closing time of the call center. The call center may redirect the rejected calls to another call center if it determines that the other call center can service them by its closing time.

Timely shut-down of a real-time work center

A call center is configured such that voice prompts provided to a given caller are modified based on prior e-mail notifications or other non-telephonic communications between the call center and that caller. In an illustrative embodiment, information regarding at least one previous non-telephonic communication with a given caller is stored in a database or other memory accessible to the call center. When the given caller places a subsequent call to the call center, the call center retrieves the stored information for that caller, and selects voice prompts for the caller based at least in part on the e-mails or other non-telephonic communications previously sent to the caller. The call is then routed to an appropriately-skilled agent based at least in part on a response of the caller to the voice prompt. Additional information relating to the previous non-telephonic communication, e.g., a copy or summary of the communication, may be transmitted to the agent that receives the call.

Modification of voice prompting based on prior communication in a call center

A call center or other processing system receives an incoming communication over a computer network, determines at least a portion of a network source address of the originator, and utilizes information from the source address to select a particular type of treatment for the communication. The computer network may be an Internet Protocol (IP) network and the network source address may be an IP source address. The communication may be a service request delivered from the originator to a call center over the Internet. The source address may be processed to determine, e.g., a particular individual corresponding to the originator, or a company name, company size, Internet service provider, geographic region, or other characteristic associated with the originator. This information is then used to determine how the communication will be handled, e.g., provided with service within a designated time, handled by an agent having particular skills, etc. The source address may also be used to control subsequent activation of live agent support icons presented via a web page. Advantageously, the invention allows more efficient handling of communications received over the Internet by allowing a call center to provide appropriate customer treatment for such communications.

Method for providing customer treatment based on specified rules in conjunction with network source address of a request originator

To improve the prediction of wait times of calls ( ) waiting to be processed, or to improve the prediction of completion times of calls that are presently being processed, in low-volume call centers ( ) or splits/skills ( ), additional data points are created&mdash;either manually or automatically&mdash;during the processing of each call that is presently being processed, for use in effecting wait-time estimates of the waiting calls. At each of the plurality of points in the processing of each of the plurality of calls, information about the call that is relevant to how long completion of the processing of the call is likely to take is obtained ( ). This information is applied to historical information about previously-processed calls to estimate ( ) how long completion of the processing of each of those calls is likely to take, i.e., how long it will be before the agents ( ) who are processing those calls become available to handle the waiting calls. The estimated times are then used ( ) as the predicted wait times of that plurality of the waiting calls. The technique is applicable to workflow management in general.

Estimation of a work item's wait-time from the present stages of processing of preceding work items

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.

Methods and apparatus for multi-variable work assignment in a call center

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. Filtering operations may be applied to the data for one or more selected intervals in order to further improve the ability of the performance measure to distinguish relevant variations in the load balancing process.

Methods and apparatus for analysis of load-balanced multi-site call processing systems

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.

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Little Silver, NJ, United States of America

Robin Harris Foster