The Resource Group International LTD Patent applications |
Patent application number | Title | Published |
20140119533 | CALL MAPPING SYSTEMS AND METHODS USING VARIANCE ALGORITHM (VA) AND/OR DISTRIBUTION COMPENSATION - 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. | 05-01-2014 |
20140086404 | MATCHING USING AGENT/CALLER SENSITIVITY TO PERFORMANCE - 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. | 03-27-2014 |
20140086403 | USE OF ABSTRACTED DATA IN PATTERN MATCHING SYSTEM - 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. | 03-27-2014 |
20140086402 | MATCHING USING AGENT/CALLER SENSITIVITY TO PERFORMANCE - 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. | 03-27-2014 |
20140044255 | CALL MAPPING SYSTEMS AND METHODS USING VARIANCE ALGORITHM (VA) AND/OR DISTRIBUTION COMPENSATION - 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. | 02-13-2014 |
20130251138 | CALL MAPPING SYSTEMS AND METHODS USING BAYESIAN MEAN REGRESSION (BMR) - 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 AP | 09-26-2013 |
20130251137 | CALL MAPPING SYSTEMS AND METHODS USING VARIANCE ALGORITHM (VA) AND/OR DISTRIBUTION COMPENSATION - 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. | 09-26-2013 |
20130216036 | SYSTEMS AND METHODS FOR ROUTING CALLERS TO AN AGENT IN A CONTACT CENTER - 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. | 08-22-2013 |
20130101109 | SYSTEMS AND METHODS FOR ROUTING CALLERS TO AN AGENT IN A CONTACT CENTER - 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. | 04-25-2013 |
20120224680 | PREDICTED CALL TIME AS ROUTING VARIABLE IN A CALL ROUTING CENTER SYSTEM - 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. | 09-06-2012 |
20100142698 | SEPARATE PATTERN MATCHING ALGORITHMS AND COMPUTER MODELS BASED ON AVAILABLE CALLER DATA - 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. | 06-10-2010 |
20100020961 | ROUTING CALLERS TO AGENTS BASED ON TIME EFFECT DATA - 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. | 01-28-2010 |
20100020959 | ROUTING CALLERS TO AGENTS BASED ON PERSONALITY DATA OF AGENTS - 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. | 01-28-2010 |
20090323921 | PROBABILITY MULTIPLIER PROCESS FOR CALL CENTER ROUTING - 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. | 12-31-2009 |
20090190750 | Routing callers out of queue order for a call center routing system - 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 of a plurality of callers in a queue, and routing the caller from the queue out of queue order. For example, a caller that is not at the top of the queue may be routed from the queue based on the identified caller data, out of order with respect to the queue order. The caller may be routed to another queue of callers, a pool of callers, or an agent based on the identified caller data, where the caller data may include one or both of demographic and psychographic data. The caller may be routed from the queue 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. | 07-30-2009 |
20090190749 | Jumping callers held in queue for a call center routing system - 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. | 07-30-2009 |
20090190747 | CALL ROUTING METHODS AND SYSTEMS BASED ON MULTIPLE VARIABLE STANDARDIZED SCORING - Systems and methods are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. An exemplary method includes combining multiple output variables of a pattern matching algorithm (for matching callers and agents) into a single metric for use in the routing system. The pattern matching algorithm may include a neural network architecture, where the exemplary method combines output variables from multiple neural networks. The method may include determining a Z-score of the variable outputs and determining a linear combination of the determined Z-scores for a desired output. Callers may be routed to agents via the pattern matching algorithm to maximize the output value or score of the linear combination. The output variables may include revenue generation, cost, customer satisfaction performance, first call resolution, cancellation, or other variable outputs from the pattern matching algorithm of the system. | 07-30-2009 |
20090190745 | POOLING CALLERS FOR A CALL CENTER ROUTING SYSTEM - Methods and systems are provided for routing callers to agents in a call-center routing environment. An exemplary method includes routing a caller from a pool of callers based on at least one caller data associated with the caller, where a pool of callers includes, e.g., a set of callers that are not chronologically ordered and routed based on a chronological order or hold time of the callers. 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. | 07-30-2009 |
20090190744 | Routing callers from a set of callers based on caller data - 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. | 07-30-2009 |
20090190743 | SEPARATE MATCHING MODELS BASED ON TYPE OF PHONE ASSOCIATED WITH A CALLER - Systems and methods are disclosed for routing callers to agents in a contact center. Exemplary methods and systems include using one of a plurality of different methods or computer models for matching callers to agents, the method or model selected based on a type of phone or phone number associated with a caller (e.g., residential, business, or mobile). The models may include queue routing, performance based matching, adaptive pattern matching algorithms, or the like. In one example, similar adaptive models may be used for two or more different types of phones, but trained differently, e.g., based on data and outcomes for the particular type of phone. Different models for routing callers to agents may perform differently for different types of phones. Further, training correlation or adaptive pattern matching algorithms based on different types of phones may improve performance compared to a single algorithm for all types of phones. | 07-30-2009 |