Examples are described of smart capacity workload routing. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is process and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model, and identifying an agent with capacity that is available to handle the requested user communication. A communication slot for the identified agent is activated and defined by the identified workload measurement. The request is then routed to the identified agent and updating available workload capacity in the system.

Systems and methods balance loads of traffic directed to various response entities. Systems and methods alternatively or complementarily generate simulated loads of traffic for handling by various response entities or load balancing algorithms.

Examples are described of smart capacity workload routing. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is process and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model, and identifying an agent with capacity that is available to handle the requested user communication. A communication slot for the identified agent is activated and defined by the identified workload measurement. The request is then routed to the identified agent and updating available workload capacity in the system.

Techniques for hybrid behavioral pairing in a contact center system are disclosed. In one embodiment, the techniques may be realized as a method for hybrid behavioral pairing in a contact center system comprising: determining a first ordering of a plurality of agents according to a behavioral pairing strategy with a balanced agent utilization; determining a second ordering of the plurality of agents according to a performance-based routing strategy with an unbalanced agent utilization; determining a third ordering of the plurality of agents according to a combination of the first ordering and the second ordering having a skewed agent utilization between the balanced agent utilization and the unbalanced agent utilization; and outputting a hybrid behavioral pairing model based on the third ordering for connecting a contact to an agent of the plurality of agents in the contact center system.

The present disclosure is directed to methods for determining an adaptive threshold, including specifying a variable, where the variable comprises a real-time state of a contact center; specifying a threshold, where the threshold is related to the variable; monitoring the variable; and adjusting the threshold in relation to a change in the variable.

Method, system and program product, comprising obtaining agent performance data; ranking, agents based the agent performance data; dividing agents into agent performance ranges; partitioning callers based on criteria into a set of partitions; determining for each partition an outcome value for a first agent performance range and a second agent performance range; calculating for the partitions a respective outcome value difference indicator based on the outcome value for the first agent performance range and the outcome value for the second agent performance range for the partition; matching a respective agent to a respective caller in one of the partitions, based on the outcome value difference indicators for the partitions.

Techniques for hybrid behavioral pairing in a contact center system are disclosed. In one embodiment, the techniques may be realized as a method for hybrid behavioral pairing in a contact center system comprising: determining a first ordering of a plurality of contacts according to a behavioral pairing strategy with a balanced contact utilization; determining a second ordering of the plurality of contacts according to a performance-based routing strategy with an unbalanced contact utilization; determining a third ordering of the plurality of agents according to a combination of the first ordering and the second ordering having a skewed contact utilization between the balanced contact utilization and the unbalanced contact utilization; and outputting a hybrid behavioral pairing model based on the third ordering for connecting an agent to a contact of the plurality of contacts in the contact center system.

Techniques for hybrid behavioral pairing in a contact center system are disclosed. In one embodiment, the techniques may be realized as a method for hybrid behavioral pairing in a contact center system comprising: determining a first ordering of a plurality of agents according to a behavioral pairing strategy with a balanced agent utilization; determining a second ordering of the plurality of agents according to a performance-based routing strategy with an unbalanced agent utilization; determining a third ordering of the plurality of agents according to a combination of the first ordering and the second ordering having a skewed agent utilization between the balanced agent utilization and the unbalanced agent utilization; and outputting a hybrid behavioral pairing model based on the third ordering for connecting a contact to an agent of the plurality of agents in the contact center system.

Systems and methods solve functions relating to load balancing, call routing, and costs in call center networks for specific parameters. Systems and methods can also utilize machine learning to provide specific parameters relating to load balancing, call routing, and costs in call center networks.

Network presence is used to assign a worker to a task. In today's networked environment, workers may be remotely located but still accomplish tasks. As long as workers have network access, the workers may be assigned tasks for completion. As tasks are completion, the network presences of the workers are determined, and the tasks may be assigned based on the network presences.