Abstract: Techniques for hybrid behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for hybrid behavioral pairing in a contact center system comprising: ordering a contact; ordering a plurality of agents; applying, by at least one processor, a hybridization function to the ordering of the plurality of agents to bias a first strategy for pairing toward a second strategy for pairing; comparing, by the at least one processor and based on the hybridization function, a first difference in ordering between the contact and a first agent in a first pair with a second difference in ordering between the contact and a second agent different from the first agent in a second pair; and selecting, by the at least one processor, the first pair or the second pair for connection based on the comparing.

Abstract: Techniques for hybrid behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for hybrid behavioral pairing in a contact center system comprising: ordering an agent; ordering a plurality of contacts; applying, by at least one processor, a hybridization function to the ordering of the plurality of contacts to bias a first strategy for pairing toward a second strategy for pairing; comparing, by the at least one processor and based on the hybridization function, a first difference in ordering between the agent and a first contact in a first pair with a second difference in ordering between the agent and a second contact different from the first contact in a second pair; and selecting, by the at least one processor, the first pair or the second pair for connection based on the comparing.

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 pairing in a contact center including ordering one or more contacts, ordering one or more agents, comparing a first difference in ordering between a first contact and a first agent in a first pair with a second difference in ordering between a second contact and a second agent in a second pair, and selecting the first pair or the second pair for connection based on the comparing, wherein the first contact and the second contact are different or the first agent and the second agent are different.

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 pairing in a contact center including ordering one or more contacts, ordering one or more agents, comparing a first difference in ordering between a first contact and a first agent in a first pair with a second difference in ordering between a second contact and a second agent in a second pair, and selecting the first pair or the second pair for connection based on the comparing, wherein the first contact and the second contact are different or the first agent and the second agent are different.

Abstract: Techniques for handling contacts and agents in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for handling contacts and agents in a contact center system including identifying a first contact available for assignment to a first agent, comparing information about the first contact with information about the first agent, and postponing assignment of the first contact and the first agent based on the comparing.

Abstract: Techniques for handling contacts and agents in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for handling contacts and agents in a contact center system including determining a first caller grade for a first call, determining a first agent grade for a first agent, and matching the first caller and the first agent based on a comparison of the first caller grade with the first agent grade.

Abstract: 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.

Abstract: 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.

Abstract: A method, system and program product, the method comprising: determining a distribution of real agent performance from previous real agent performance data; determining a set of hypothetical agents with respective hypothetical agent performances APi ranging from a worst performance to a best performance; calculating for each of the set of hypothetical agents a posterior distribution taking into account actual results of a respective actual agent in multiple skills, using the distribution of real agent performance and the set of hypothetical agents with respective hypothetical agent performances APi, to obtain a total probability for each hypothetical agent of the set of the hypothetical agents; repeating calculating the posterior distribution steps for multiple of the hypothetical agents to obtain the respective total probabilities for the respective hypothetical agents; determining one hypothetical agent with a better value of total probability as the actual agent's most probable global performance.

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

Abstract: Systems and methods are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. An exemplary method includes mapping a first portion (or fraction) of callers to agents according to a performance and/or pattern matching algorithm based on comparing caller data associated with the callers and agent data associated with the agents. The method further includes mapping a second portion of the callers (e.g., the remaining portion or fraction of all callers) to agents differently than the first portion of the callers (e.g., mapping based on queue order or performance based), which may provide a control group for monitoring or analyzing the effect of the pattern matching algorithm. The method may further include displaying the effect of the routing on at least one outcome variable, which may include revenue generation, cost, customer satisfaction, first call resolution, cancellation, or other variable outputs from the pattern matching algorithm of the system.

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: Methods and systems are provided for routing callers to agents in a call-center routing environment. An exemplary method includes pooling incoming callers, and causing a caller from the pool of callers to be routed. The caller may be routed from the pool of callers to an agent, placed in another pool of callers, or placed in a queue of callers. The caller data may include demographic or psychographic data. The caller may be routed from the pool of callers based on comparing the caller data with agent data associated with an agent via a pattern matching algorithm and/or computer model for predicting a caller-agent pair outcome. Additionally, if a caller is held beyond a hold threshold (e.g., a time, “cost” function, or the like) the caller may be routed to the next available agent.

Abstract: 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.

Abstract: Systems and methods are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. An exemplary method includes mapping a first portion (or fraction) of callers to agents according to a performance and/or pattern matching algorithm based on comparing caller data associated with the callers and agent data associated with the agents. The method further includes mapping a second portion of the callers (e.g., the remaining portion or fraction of all callers) to agents differently than the first portion of the callers (e.g., mapping based on queue order or performance based), which may provide a control group for monitoring or analyzing the effect of the pattern matching algorithm. The method may further include displaying the effect of the routing on at least one outcome variable, which may include revenue generation, cost, customer satisfaction, first call resolution, cancellation, or other variable outputs from the pattern matching algorithm of the system.

Abstract: Method, system and program product, for operating a call center system, the method comprising: obtaining performance data for agents in a set of agents; obtaining a respective abstracted data stream for multiple calls, each respective data stream having multiple different locations along the abstracted data stream representing multiple different respective fields, the meaning for the field data in the respective different locations for the different respective fields not known by the system; determining respective patterns for the respective data streams; obtaining performance data for the respective patterns; matching using a selected matching algorithm 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 call and on performance data for the respective agents of the set of agents.

Abstract: Systems and processes are disclosed for routing callers to agents in a contact center based on similar probabilities for an outcome variable. An exemplary probability multiplier process includes determining agent performance of a set of agents for an outcome variable (e.g., sales) and determining caller propensity of a set of callers for the outcome variable (e.g., the propensity or statistical chance of purchasing). Callers and agents are matched based on corresponding agent performance and propensity for the outcome variable of the caller, e.g., matching callers and agents having similar relative performance for the outcome variable, such as matching the highest ranked caller to the highest ranked agent, the worst ranked caller to the worst ranked agent, and so on. The performance and propensity of the callers and agents may be converted to percentile rankings, and callers and agents can be matched based on a closest match of percentile rankings.

Abstract: Methods and systems are disclosed for routing callers to agents in a contact center with an intelligent routing system. An exemplary method includes routing callers to agents based on a pattern matching algorithm utilizing caller data and agent data, where the agent data includes agent satisfaction data from past agent-caller pairings. The agent satisfaction data may be obtained via surveys of the agents regarding their satisfaction with past agent-caller contacts. The agent satisfaction data may be used by the pattern matching algorithm in an attempt to increase agent satisfaction for future calls, thereby potentially reducing attrition of agents and cost to the call center, increasing morale of the agents, and so on. The agent satisfaction data and output from past agent-caller pairings may be weighted by the contact center against other agent data and caller data for a desired mixing of output variables.

Abstract: Systems and processes are disclosed for routing callers to agents in a contact center based on predicted call handle times. An exemplary process includes using predicted call handle time as a variable for call routing along with a performance matching and/or psychodemograhpic matching process of caller-agent pairs to maximize sales, customer satisfaction, and so on. The process may allocate the highest performing agents and/or the most “demographic matchable” agents to those callers that are predicted have the shortest duration. The process may further allocate the lowest performing agents and or the least “demographic matchable” agents to those callers that are predicted have the longest duration, or may not allocate the lowest performing agents to any callers at all.

Abstract: Methods are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. One or more agents are graded on achieving an optimal interaction, such as increasing revenue, decreasing cost, or increasing customer satisfaction. Callers are then preferentially routed to a graded agent to obtain an increased chance at obtaining a chosen optimal interaction. In a more advanced embodiment, caller and agent demographic and psychographic characteristics can also be determined and used in a pattern matching algorithm to preferentially route a caller with certain characteristics to an agent with certain characteristics to increase the chance of an optimal interaction.