Avaya Technology LLC

Richard Alan Cohen

Robin Harris Foster

Andrew Derek Flockhart

Gokul Chander Prabhakar

William Edward Gourlay

Marcus Knotts

Mila Maximets

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

An arrangement ( ) for monitoring contact-center ( ) performance automates the monitoring process by using a database schema ( ) that records ( ) the contact center to be monitored, its skills, the locations where the skills are located (in the case of a distributed contact center), the objective or target value for a given skill attribute (such as average speed of answer, maximum delay, etc.), the acceptable deviation from the objective (tolerance), the maximum acceptable deviation between locations (in the case of a distributed contact center), the periodicity or frequency of the monitoring, and the comparison operator needed to determine how a given observed or actual value for the skill attribute is to be compared with its specified objective and tolerance. Monitoring is then effected ( ) as follows. Periodically, the contact center is automatically polled for performance data, actual values of the skill attributes are computed therefrom, and the actual values, the objective values, and the tolerances are used in a meta formula to determine the performance metrics. When skills are distributed across several locations of a distributed contact center, the arrangement determines the performance metrics for each location, and further determines whether the maximum deviation between the locations of their actual values exceeds the maximum acceptable deviation. Results indicating performance outages are sent to a party that is responsible for the contact center.

Arrangement for using dynamic metrics to monitor contact center performance

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.

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Wayland, MA, United States of America

Richard Alan Cohen