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:
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 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. 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:
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 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.
Abstract:
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.
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.