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 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: 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.

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: 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.

Abstract: Systems and methods are disclosed for routing callers to agents in a contact center utilizing a multi-layer processing approach to matching a caller to an agent. A first layer of processing may include two or more different computer models or methods for scoring or determining caller-agent pairs in a routing center. The output of the first layer may be received by a second layer of processing for balancing or weighting the outputs and selecting a final caller-agent match. The two or more methods may include conventional queue based routing, performance based routing, pattern matching algorithms, affinity matching, and the like. The output or scores of the two or more methods may be processed be the second layer of processing to select a caller-agent pair and cause the caller to be routed to a particular agent.

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.

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.

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.

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 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 and mapping a second portion of the callers (e.g., the remaining portion callers) to agents differently than the first portion of the callers (e.g., mapping based on queue order), which may provide a control group for monitoring or analyzing the effect and/or training of the pattern matching algorithm. The first and second portion may be varied separately for each agent within the contact center.

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: Systems and methods are disclosed for preprocessing caller-agent pairs in a contact routing center, along with an intelligent routing system. A method includes determining at least one agent data for each agent of a set of agents, determining at least one caller data for each caller of a set of callers, and determining a score for each possible caller-agent pair of the set of agents and the set of callers using the agent data and the caller data in a pattern matching algorithm. The scores for each possible caller-agent pair may then be stored, e.g., in a look-up table, for retrieval, where matching a caller of the set of callers to an agent of the set of agents is based on the previously determined or calculated scores. For example, the caller-agent pair having the best score of all potential caller-agent pairs may be connected.

Abstract: Systems and methods are disclosed for estimating and assigning agent performance characteristics in a call routing center. Performance characteristics (e.g., sales rate, customer satisfaction, duration of call, etc.) may be assigned to an agent when the agent has made few calls relative to other agents or otherwise has a large error in their measure of one or more performance characteristics used for matching callers to agents (e.g., via a performance based or pattern matching routing method). A method includes identifying agents of a plurality of agents having a number of calls fewer than a predetermined number of calls (or an error in the performance characteristic exceeding a threshold), assigning a performance characteristic to the identified agents (that is different than the agent's actual performance characteristic), and routing a caller to one of the plurality of agents based on the performance characteristics of the plurality of agents.

Abstract: Apparatus and methods are disclosed for routing callers to agents in a contact center. Exemplary methods and system include using one of a plurality of different computer models for matching callers to agents, the model selected based on a degree and/or type of caller data available. The models may include queue routing, performance based matching, adaptive pattern matching algorithms, or other computer models for matching callers to agents. In one example, similar adaptive models may be used for two or more different degrees/types of caller data, but are trained differently, e.g., based on the degree/type of caller data. Different models for routing callers to agents may perform differently for different degrees/types of caller data. Further, training correlation or adaptive pattern matching algorithms based on different degrees/types of caller data may improve their respective performance compared to a single algorithm for all degrees/types of caller data.

Abstract: Systems and methods are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. Exemplary methods include routing a caller from a set of callers to an agent from a set of agents based on a performance based routing and/or pattern matching algorithm(s) utilizing caller data associated with the caller and the agent data associated with the agent. For performance based routing, the performance or grading of agents may be associated with time data, e.g., a grading or ranking of agents based on time. Further, for pattern matching algorithms, one or both of the caller data and agent data may include or be associated with time effect data. Examples of time effect data include probable performance or output variables as a function of time of day, day of week, time of month, or time of year. Time effect data may also include the duration of the agent's employment.

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 routing a caller from a set of callers to an agent from a set of agents based on a pattern matching algorithm utilizing caller data associated with the caller from the set of callers and agent data associated with the agent from the set of agents. One or both of the caller data and agent data includes personality data, e.g., from a personality profile, associated with the caller or agent. The personality data and profile may be generated from administration of a personality test such as a Myers-Brigg Type Indicator questionnaire.

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 provided for routing callers to agents in a call-center routing environment. An exemplary method includes identifying caller data for a caller in a queue of callers, and jumping or moving the caller to a different position within the queue based on the caller data. The caller data may include one or both of demographic data and psychographic data. The caller can be jumped forward or backward in the queue relative to at least one other caller. Jumping the caller may further be based on comparing the caller data with agent data 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.