Dynamic metric optimization in predictive behavioral routing

1. A system configured to optimize routing of customer communications comprising:

a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which comprise:

receiving a customer communication;

identifying a customer associated with the customer communication;

accessing a profile of the identified customer to determine customer data;

receiving normalized customer metric scores for a plurality of customer metrics;

identifying available agents;

accessing a profile of each available agent to determine agent data;

predicting, for each available agent, interaction outcome metric values for a plurality of customer metrics based on the customer data and the agent data;

normalizing, for each available agent, the predicted interaction outcome metric values;

calculating, in real-time using the normalized customer metric scores and the normalized predicted interaction outcome metric values, an aggregate agent-customer pairing score for each available agent, wherein the calculating comprises:

applying a mathematical operator to a normalized customer metric score and a normalized predicted interaction outcome metric value for each customer metric to provide a result for each customer metric, and

applying a mathematical operator to the result for each customer metric for a plurality of customer metrics to provide the aggregate agent-customer pairing score;

selecting a responding agent from the available agents with the highest aggregate agent-customer pairing score; and

providing a routing recommendation to a communication distributor to route the customer communication to the responding agent with the highest aggregate agent-customer pairing score.

2. The system of claim 1 , wherein the plurality of customer metrics comprises two or more of handling time, first call resolution, customer satisfaction, revenue retention, and sales.

3. The system of claim 1 , wherein the agent data comprises agent performance history.

4. The system of claim 1 , wherein the customer data comprises customer attributes and customer interaction history.

5. The system of claim 1 , wherein predicting the interaction outcome metric values for a plurality of customer metrics comprises inputting the customer data and the agent data into a predictive model specific for each customer metric and outputting an interaction outcome metric value for each customer metric.

6. The system of claim 1 , wherein the operations further comprise sorting the available agents in ascending or descending order based on the aggregate agent-customer pairing score.

7. A method for optimizing the routing of customer communications, which comprises:

receiving a customer communication;

identifying a customer associated with the customer communication;

accessing a profile of the identified customer to determine customer data;

receiving normalized customer metric scores for a plurality of customer metrics;

identifying available agents;

accessing a profile of each available agent to determine agent data;

predicting, by a processor for each available agent, interaction outcome metric values for a plurality of customer metrics based on the customer data and the agent data;

normalizing, by a processor for each available agent, the predicted interaction outcome metric values;

calculating, by a processor in real-time using the normalized customer metric scores and the normalized predicted interaction outcome metric values, an aggregate agent-customer pairing score for each available agent, wherein the calculating comprises:

applying a mathematical operator to a normalized customer metric score and a normalized predicted interaction outcome metric value for each customer metric to provide a result for each customer metric, and

applying a mathematical operator to the result for each customer metric for a plurality of customer metrics to provide the aggregate agent-customer pairing score;

selecting a responding agent from the available agents with the highest aggregate agent-customer pairing score; and

providing a routing recommendation to a communication distributor to route the customer communication to the responding agent with the highest aggregate agent-customer pairing score.

8. The method of claim 7 , wherein the plurality of customer metrics is selected from two or more of handling time, first call resolution, customer satisfaction, revenue retention, or sales.

9. The method of claim 7 , wherein the agent data comprises agent performance history, and the customer data comprises customer attributes and customer interaction history.

10. The method of claim 7 , wherein predicting the interaction outcome metric values for a plurality of customer metrics comprises inputting the customer data and the agent data into a predictive model specific for each customer metric and outputting an interaction outcome metric value for each customer metric.

11. The method of claim 7 , further comprising sorting the available agents in ascending or descending order based on the aggregate agent-customer pairing score.

12. A non-transitory computer-readable medium having stored thereon computer-readable instructions executable by a processor to perform operations which comprise:

receiving a customer communication;

identifying a customer associated with the customer communication;

accessing a profile of the identified customer to determine customer data;

receiving normalized customer metric scores for a plurality of customer metrics;

identifying available agents;

accessing a profile of each available agent to determine agent data;

predicting, for each available agent, interaction outcome metric values for a plurality of customer metrics based on the customer data and the agent data;

normalizing, for each available agent, the predicted interaction outcome metric values;

calculating, in real-time using the normalized customer metric scores and the normalized predicted interaction outcome metric values, an aggregate agent-customer pairing score for each available agent, wherein the calculating comprises:

applying a mathematical operator to a normalized customer metric score and a normalized predicted interaction outcome metric value for each customer metric to provide a result for each customer metric, and

applying a mathematical operator to the result for each customer metric for a plurality of customer metrics to provide the aggregate agent-customer pairing score;

selecting a responding agent from the available agents with the highest aggregate agent-customer pairing score; and

providing a routing recommendation to a communication distributor to route the customer communication to the responding agent with the highest aggregate agent-customer pairing score.

13. The non-transitory computer-readable medium of claim 12 , wherein the plurality of customer metrics comprises two or more of handling time, first call resolution, customer satisfaction, revenue retention, and sales.

14. The non-transitory computer-readable medium of claim 12 , wherein the agent data comprises agent performance history, and the customer data comprises customer attributes and customer interaction history.