PROBABILITY MULTIPLIER PROCESS FOR CALL CENTER ROUTING

• US 20090323921A1
• Filed: 06/24/2009
• Published: 12/31/2009
• Est. Priority Date: 01/28/2008
• Status: Active Grant

• Current Assignee: Afiniti Ltd
• Original Assignee: The Resource Group International Ltd
• Inventors: SPOTTISWOODE, S. James P., CHISHTI, Zia
• Granted Patent: US 8,781,100 B2
• Time in Patent Office: Days
• US Class Current: 379/265.12
• CPC Class Codes: H04M 2201/18 Comparators
• CPC Class Codes: H04M 2201/36 Memories
• CPC Class Codes: H04M 2203/551 Call history recording a computer conference H04L12/1831
• 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]

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.

Claims

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

determining agent performance of a set of agents for an outcome variable;

determining caller propensity of a set of callers for the outcome variable; and

matching a caller of the set of callers to an agent of the set of callers based on a determined agent performance and propensity for a caller.

2. The method of claim 1, wherein the agent performance and caller propensity further comprises percentile rankings for each agent of the set of agents and each caller of the set of callers for the outcome variable.

3. The method of claim 2, further comprising matching an agent to the closest matching percentile ranking caller.

4. The method of claim 2, further comprising matching a caller to the closest matching percentile ranking agent.

5. The method of claim 1, further comprising converting the agent performance and caller propensity into percentile rankings and matching the caller to the agent based on the closest corresponding percentile ranking.

6. The method of claim 1, wherein the highest performing agent is matched to the highest propensity caller.

7. The method of claim 1, wherein the outcome variable is associated with a sales rate.

8. The method of claim 1, wherein the caller propensity comprises a predicted value for the outcome variable based on at least one caller data.

9. The method of claim 1, wherein the agents are grouped into at least two subsets based on performance and the callers are grouped into at least two subsets based on propensity, and wherein callers from a first subset of callers are matched to agents from a first subset of agents and callers from a second subset of callers are matched to agents from a second subset of agents.

10. The method of claim 1, further comprising comparing demographic data of the agents to demographic data of the callers via a pattern matching algorithm to determine the chance of either or both of the first and second outcome variables, wherein the matching is further based on the comparison.

11. The method of claim 10, wherein the matching is based on a combination of the comparison via a pattern matching algorithm and the corresponding agent performance and caller propensity for the outcome variable.

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

determining agent performance of a set of agents for a first outcome variable;

determining caller propensity of a set of callers for a second outcome variable; and

matching a caller of the set of callers to an agent of the set of callers based on corresponding agent performance and propensity for a caller.

13. The method of claim 12, wherein the agent performance and caller propensity further comprises percentile rankings for each agent of the set of agents and each caller of the set of callers for the first and second outcome variables.

14. The method of claim 13, further comprising matching an agent to the closest matching percentile ranking caller.

15. The method of claim 13, further comprising matching a caller to the closest matching percentile ranking agent.

16. The method of claim 12, further comprising converting the agent performance and caller propensity into percentile rankings for the first and second outcome variables and matching the caller to the agent based on the closest corresponding percentile ranking.

17. The method of claim 12, wherein the highest performing agent is matched to the highest propensity caller.

18. The method of claim 12, wherein the first outcome variable and the second outcome variable are associated with a sales rate.

19. The method of claim 12, wherein one or both of the first outcome variable and the second outcome variable is associated with a sales rate.

20. The method of claim 12, wherein the first outcome variable comprises sales data and the second outcome variable comprises propensity to purchase data.

21. The method of claim 12, wherein the caller propensity comprises a predicted value for the second outcome variable based on at least one caller data.

22. The method of claim 12, wherein the agents are grouped into at least two subsets based on performance and the callers are grouped into at least two subsets based on propensity, and wherein callers from a first subset of callers are matched to agents from a first subset of agents and callers from a second subset of callers are matched to agents from a second subset of agents.

23. The method of claim 12, further comprising comparing demographic data of the agents to demographic data of the callers via a pattern matching algorithm to determine the chance of either or both of the first and second outcome variables, wherein the matching is further based on the comparison.

24. The method of claim 23, wherein the matching is based on a combination of the comparison via a pattern matching algorithm and the corresponding agent performance and caller propensity.

25. Computer-readable storage medium comprising computer-readable instructions for matching callers to agents based on predicted outcome performance, the computer readable instructions comprising:

determining agent performance of a set of agents for an outcome variable;

determining caller propensity of a set of callers for the outcome variable; and

matching a caller of the set of callers to an agent of the set of callers based on a determined agent performance and propensity for a caller.

26. The computer readable storage medium of claim 25, wherein the agent performance and caller propensity further comprises percentile rankings for each agent of the set of agents and each caller of the set of callers for the outcome variable.

27. The computer readable storage medium of claim 25, further comprising instructions for matching an agent to the closest matching percentile ranking caller.

28. The computer readable storage medium of claim 25, further comprising instructions for matching a caller to the closest matching percentile ranking agent.

29. The computer readable storage medium of claim 25, further comprising instructions for converting the agent performance and caller propensity into percentile rankings and matching the caller to the agent based on the closest corresponding percentile ranking.

30. The computer readable storage medium of claim 25, wherein the highest performing agent is matched to the highest propensity caller.

31. The computer readable storage medium of claim 25, wherein the outcome variable is associated with a sales rate.

32. The computer readable storage medium of claim 25, wherein the caller propensity comprises a predicted value for the outcome variable based on at least one caller data.

33. The computer readable storage medium of claim 25, further comprising instructions for comparing demographic data of the agents to demographic data of the callers via a pattern matching algorithm to determine the chance of either or both of the first and second outcome variables, wherein the matching is further based on the comparison.

34. Apparatus for routing callers to agents in a call center routing environment based on predicted outcome performance, the apparatus comprising logic configured for:

determining agent performance of a set of agents for an outcome variable;

determining caller propensity of a set of callers for the outcome variable; and

matching a caller of the set of callers to an agent of the set of callers based on a determined agent performance and propensity for a caller.

35. The apparatus of claim 34, wherein the agent performance and caller propensity further comprises percentile rankings for each agent of the set of agents and each caller of the set of callers for the outcome variable.

36. The apparatus of claim 34, wherein further comprising logic for matching an agent to the closest matching percentile ranking caller.

37. The apparatus of claim 34, further comprising logic for matching a caller to the closest matching percentile ranking agent.

38. The apparatus of claim 34, further comprising logic for converting the agent performance and caller propensity into percentile rankings and matching the caller to the agent based on the closest corresponding percentile ranking.

39. The apparatus of claim 34, wherein the highest performing agent is matched to the highest propensity caller.

40. The apparatus of claim 34, wherein the outcome variable is associated with a sales rate.