Abstract: Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, a plurality of agents available for connection to a contact; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the contact and the plurality of agents; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings according to a probabilistic model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for connection in the contact center system.

Abstract: Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, a plurality of agents available for connection to a contact; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the contact and the plurality of agents; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings according to a probabilistic model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for connection in the contact center system.

Abstract: Techniques for estimating expected performance of a task assignment strategy in a task assignment system are disclosed. In one particular embodiment, the techniques may be realized as a method comprising receiving, by at least one computer processor communicatively coupled to a task assignment system, a plurality of historical agent task assignments; determining, by the at least one computer processor, a sample of the plurality based on a strategy for pairing agents with tasks; determining, by the at least one computer processor, an expected performance of the strategy based on the sample; outputting, by the at least one computer processor, the expected performance; and optimizing, by the at least one computer processor, the performance of the task assignment system based on the expected performance.

Abstract: Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, a plurality of agents available for connection to a contact; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the contact and the plurality of agents; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings according to a probabilistic model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for connection in the contact center system.

Abstract: Techniques for benchmarking pairing strategies in a task assignment system are disclosed. In one particular embodiment, the techniques may be realized as a method for benchmarking pairing strategies in a task assignment system comprising determining a first base cohort of a first plurality of historical task assignments for at least two pairing strategies for a first base period, determining a first performance difference between the at least two pairing strategies after a first measurement period based on the first base cohort, and outputting the first performance difference.

Abstract: A method, system and program product, the method comprising: obtaining for each call in one set of calls a respective pattern representing multiple different respective data fields; obtaining performance data for the respective patterns of the calls; obtaining performance data for the respective agents; determining agent performance sensitivity to call pattern performance for agents in a set of agents comprising the agent performance data correlated to call performance data for the calls the agent handles; and matching a respective one of the agents from the set of agents to one of the calls based at least in part on the performance data for the respective pattern of the one call and on the agent sensitivity to call performance for the respective one agent of the set of agents.

Abstract: A method, system and program product, the method comprising: obtaining for each call in one set of calls a respective pattern representing multiple different respective data fields; obtaining performance data for the respective patterns of the calls; obtaining performance data for the respective agents; determining agent performance sensitivity to call pattern performance for agents in a set of agents comprising the agent performance data correlated to call performance data for the calls the agent handles; and matching a respective one of the agents from the set of agents to one of the calls based at least in part on the performance data for the respective pattern of the one call and on the agent sensitivity to call performance for the respective one agent of the set of agents.

Abstract: A method, system and program product, the method comprising: obtaining for each call in one set of calls a respective pattern representing multiple different respective data fields; obtaining performance data for the respective patterns of the calls; obtaining performance data for the respective agents; determining agent performance sensitivity to call pattern performance for agents in a set of agents comprising the agent performance data correlated to call performance data for the calls the agent handles; and matching a respective one of the agents from the set of agents to one of the calls based at least in part on the performance data for the respective pattern of the one call and on the agent sensitivity to call performance for the respective one agent of the set of agents.

Abstract: Techniques for case allocation are disclosed. In one particular embodiment, the techniques may be realized as a method for case allocation comprising receiving, by at least one computer processor, at least one case allocation allocated using a first pairing strategy, and then reassigning, by the at least one computer processor, the at least one case allocation using behavioral pairing.

Abstract: Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, a plurality of contacts available for connection to an agent; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the agent and the plurality of contacts; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings according to a probabilistic network flow model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for connection in the contact center system.

Abstract: Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, a plurality of agents available for connection to a contact; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the contact and the plurality of agents; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings according to a probabilistic model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for connection in the contact center system.

Abstract: Techniques for case allocation are disclosed. In one particular embodiment, the techniques may be realized as a method for case allocation comprising receiving, by at least one computer processor, at least one case allocation allocated using a first pairing strategy, and then reassigning, by the at least one computer processor, the at least one case allocation using behavioral pairing.

Abstract: Techniques for case allocation are disclosed. In one particular embodiment, the techniques may be realized as a method for case allocation comprising receiving, by at least one computer processor, at least one case allocation allocated using a first pairing strategy, and then reassigning, by the at least one computer processor, the at least one case allocation using behavioral pairing.

Abstract: A method, system and program product, the method comprising: obtaining for each call in one set of calls a respective pattern representing multiple different respective data fields; obtaining performance data for the respective patterns of the calls; obtaining performance data for the respective agents; determining agent performance sensitivity to call pattern performance for agents in a set of agents comprising the agent performance data correlated to call performance data for the calls the agent handles; and matching a respective one of the agents from the set of agents to one of the calls based at least in part on the performance data for the respective pattern of the one call and on the agent sensitivity to call performance for the respective one agent of the set of agents.

Abstract: Method, system and program product, comprising: obtaining agent parameter data; percentiling agents based on the agent parameter data, to obtain an agent distribution of agent percentiles; partitioning callers based on criteria into partitions; obtaining caller propensity data; percentiling the callers based on propensity for an outcome to obtain a caller distribution; performing distribution compensation using one algorithm selected from an edge compensation algorithm applied to the distribution of agent percentiles or the distribution of the caller percentiles, near at least one distribution edge to provide edge compensation, and a topology altering algorithm applied to either or both of the agent distribution and the caller distribution to change one or more of the distributions to a different topology; and matching an agent to a caller in one of the partitions with a closest respective percentile, where one of the caller percentile or the agent percentile has been distribution compensated.

Abstract: A method, system and program product, the method comprising: obtaining for each call in one set of calls a respective pattern representing multiple different respective data fields; obtaining performance data for the respective patterns of the calls; obtaining performance data for the respective agents; determining agent performance sensitivity to call pattern performance for agents in a set of agents comprising the agent performance data correlated to call performance data for the calls the agent handles; and matching a respective one of the agents from the set of agents to one of the calls based at least in part on the performance data for the respective pattern of the one call and on the agent sensitivity to call performance for the respective one agent of the set of agents.

Abstract: Method, system and program product, comprising: obtaining agent parameter data; percentiling agents based on the agent parameter data, to obtain an agent distribution of agent percentiles; partitioning callers based on criteria into partitions; obtaining caller propensity data; percentiling the callers based on propensity for an outcome to obtain a caller distribution; performing distribution compensation using one algorithm selected from an edge compensation algorithm applied to the distribution of agent percentiles or the distribution of the caller percentiles, near at least one distribution edge to provide edge compensation, and a topology altering algorithm applied to either or both of the agent distribution and the caller distribution to change one or more of the distributions to a different topology; and matching an agent to a caller in one of the partitions with a closest respective percentile, where one of the caller percentile or the agent percentile has been distribution compensated.

Abstract: Method, system and program product, comprising: obtaining agent parameter data; percentiling agents based on the agent parameter data, to obtain an agent distribution of agent percentiles; partitioning callers based on criteria into partitions; obtaining caller propensity data; percentiling the callers based on propensity for an outcome to obtain a caller distribution; performing distribution compensation using one algorithm selected from an edge compensation algorithm applied to the distribution of agent percentiles or the distribution of the caller percentiles, near at least one distribution edge to provide edge compensation, and a topology altering algorithm applied to either or both of the agent distribution and the caller distribution to change one or more of the distributions to a different topology; and matching an agent to a caller in one of the partitions with a closest respective percentile, where one of the caller percentile or the agent percentile has been distribution compensated.

Abstract: Method, system and program product, comprising: obtaining agent parameter data; percentiling agents based on the agent parameter data, to obtain an agent distribution of agent percentiles; partitioning callers based on criteria into partitions; obtaining caller propensity data; percentiling the callers based on propensity for an outcome to obtain a caller distribution; performing distribution compensation using one algorithm selected from an edge compensation algorithm applied to the distribution of agent percentiles or the distribution of the caller percentiles, near at least one distribution edge to provide edge compensation, and a topology altering algorithm applied to either or both of the agent distribution and the caller distribution to change one or more of the distributions to a different topology; and matching an agent to a caller in one of the partitions with a closest respective percentile, where one of the caller percentile or the agent percentile has been distribution compensated.