Systems and methods for routing callers to an agent in a contact center

• US 8,737,595 B2
• Filed: 04/01/2013
• Issued: 05/27/2014
• Est. Priority Date: 01/28/2008
• Status: Active Grant

• Current Assignee: Afiniti Ltd
• Original Assignee: Satmap International Holdings, Ltd.
• Inventors: Chishti, Zia, Spottiswoode, S. James P., Jones, Chris W.
• Primary Examiner(s): AL AUBAIDI, RASHA S
• Application Number:
  US13/854,825
• Publication Number:
  US 20130216036A1
• Time in Patent Office: 421 Days
• Field of Search: 37926501-26502, 379/265.12
• US Class Current: 379/265.01
• CPC Class Codes: H04M 2201/16 Sequence circuits
• CPC Class Codes: H04M 2201/18 Comparators
• CPC Class Codes: H04M 2201/36 Memories
• CPC Class Codes: H04M 3/5158 in combination with automated outdialling systems devices for signalling identity of wanted subscriber H04M1/26
• CPC Class Codes: H04M 3/5175 Call or contact centers supervision arrangements
• CPC Class Codes: H04M 3/5183 Call or contact centers with computer-telephony arrangements
• CPC Class Codes: H04M 3/523 with call distribution or queueing
• CPC Class Codes: H04M 3/5232 Call distribution algorithms
• CPC Class Codes: H04M 3/5233 Operator skill based call distribution
• CPC Class Codes: H04M 3/5235 Dependent on call type or called number [DNIS]
• CPC Class Codes: H04M 3/5236 Sequential or circular distribution
• CPC Class Codes: H04M 3/5238 with waiting time or load prediction arrangements

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.

Claims

1. A method for routing callers to agents in a call-center routing environment, the method comprising:

determining, by one or more computers, agent data comprising agent performance data, demographic and psychographic data for an agent;

determining, by the one or more computers, caller data comprising multiple elements of demographic and/or psychographic data for each one from a set of callers; and

matching one or more of the agents to one or more of the callers, by the one or more computers, using a multi-element pattern matching algorithm, based at least in part on the agent data and the caller data and based at least in part on increasing the chance of revenue generation, to obtain one or more matching results; and

changing, by the one or more computers, a connection priority of one of the callers relative to the other callers in the set of callers or connecting one of the callers to one of the agents based at least in part on the one or more matching results from the matching step, to increase the chance of revenue generation.

2. The method as defined in claim 1, wherein the agent data comprises multiple elements of demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.

3. The method as defined in claim 1, wherein the caller data comprises multiple elements of demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.

4. The method as defined in claim 1, wherein the caller data comprises demographic data, and the demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.

5. The method as defined in claim 1, wherein the pattern matching algorithm comprises an adaptive correlation algorithm.

6. The method as defined in claim 1, further comprising training the pattern matching algorithm by correlating the chance of an optimal interaction against the agent data and the caller data, wherein the agent data and the caller data is determined from at least one historical contact between a caller and an agent.

7. The method as defined in claim 1, wherein the set of callers comprises a queue of callers.

8. The method as defined in claim 1, wherein the matching using the multi-element pattern matching algorithm is performed in a pair-wise fashion to obtain scores for a plurality of caller-agent pairs and wherein the matching results comprise the scores for the plurality of caller-agent pairs.

9. A system for routing callers to agents in a call-center routing environment, comprising:

one or more computers configured with computer-readable program code to perform, when executing the program code, the steps;

determining, by the one or more computers, agent data comprising agent performance data, demographic and psychographic data for an agent;

determining, by the one or more computers, caller data comprising multiple elements of demographic and/or psychographic data for each one from a set of callers; and

matching one or more of the agents to one or more of the callers, by the one or more computers, using a multi-element pattern matching algorithm, based at least in part on the agent data and the caller data and based at least in part on increasing the chance of revenue generation, to obtain one or more matching results; and

changing, by the one or more computers, a connection priority of one of the callers relative to the other callers in the set of callers or connecting one of the callers to one of the agents based at least in part on the one or more matching results from the matching step, to increase the chance of revenue generation.

10. The system as defined in claim 9, wherein the agent data comprises multiple elements of demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.

11. The system as defined in claim 9, wherein the caller data comprises multiple elements of demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.

12. The system as defined in claim 9, wherein the caller data comprises demographic data, and the demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.

13. The system as defined in claim 9, wherein the pattern matching algorithm comprises an adaptive correlation algorithm.

14. The system as defined in claim 9, further comprising the one or more computers configured with program code to perform, when executed, the step of training the pattern matching algorithm by correlating the chance of an optimal interaction against the agent data and the caller data, wherein the agent data and the caller data is determined from at least one historical contact between a caller and an agent.

15. The system as defined in claim 9, wherein the set of callers comprises a queue of callers.

16. The system as defined in claim 9, wherein the matching using the multi-element pattern matching algorithm is performed in a pair-wise fashion to obtain scores for a plurality of caller-agent pairs and wherein the matching results comprise the scores for the plurality of caller-agent pairs.

17. A program product for routing callers to agents in a call-center routing environment, comprising:

a non-transitory computer-readable medium configured with program code to perform, when executed by one or more computers, the steps;

determining, by the one or more computers, agent data comprising agent performance data, demographic and psychographic data for an agent;

determining, by the one or more computers, caller data comprising multiple elements of demographic and/or psychographic data for each one from a set of callers; and

matching one or more of the agents to one or more of the callers, by the one or more computers, using a multi-element pattern matching algorithm, based at least in part on the agent data and the caller data and based at least in part on increasing the chance of revenue generation, to obtain one or more matching results; and

changing, by the one or more computers, a connection priority of one of the callers relative to the other callers in the set of callers or connecting one of the callers to one of the agents based at least in part on the one or more matching results from the matching step, to increase the chance of revenue generation.

18. The program product as defined in claim 17, wherein the pattern matching algorithm comprises an adaptive correlation algorithm.

19. The program product as defined in claim 17, further comprising program code to perform, when executed, the step of training the pattern matching algorithm by correlating the chance of an optimal interaction against the agent data and the caller data, wherein the agent data and the caller data is determined from at least one historical contact between a caller and an agent.

20. The program product as defined in claim 17, wherein the matching using the multi-element pattern matching algorithm is performed in a pair-wise fashion to obtain scores for a plurality of caller-agent pairs and wherein the matching results comprise the scores for the plurality of caller-agent pairs.