SEPARATE PATTERN MATCHING ALGORITHMS AND COMPUTER MODELS BASED ON AVAILABLE CALLER DATA

• US 20100142698A1
• Filed: 12/09/2008
• Published: 06/10/2010
• Est. Priority Date: 12/09/2008
• Status: Active Grant

• Current Assignee: Afiniti Ltd
• Original Assignee: The Resource Group International Ltd
• Inventors: Chishti, Zia, Spottiswoode, S. James P.
• Granted Patent: US 8,634,542 B2
• Time in Patent Office: Days
• US Class Current: 379/265.110
• CPC Class Codes: H04M 2203/408 Customer-specific call routing plans
• CPC Class Codes: H04M 3/42068 where the identifier is used to access a profile
• CPC Class Codes: H04M 3/4211 where the identifier is used to access a profile
• CPC Class Codes: H04M 3/5232 Call distribution algorithms

Abstract

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.

Claims

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

selecting a model from at least two different models for matching a caller to an agent, the model selected based on one or both of degree and type of caller data associated with the caller; and

causing the caller to be routed based on the selected model.

2. The method of claim 1, further comprising selecting the model based on one or both of a degree and type of agent data associated with the agent.

3. The method of claim 1, wherein the caller is routed to an agent, a pool of callers, or a queue of callers.

4. The method of claim 1, wherein the agent is matched to one of a plurality of callers via the selected model.

5. The method of claim 1, wherein at least two of the at least two different models each comprise an adaptive correlation algorithm, and each of the adaptive correlation algorithms is trained differently.

6. The method of claim 5, wherein each adaptive correlation algorithm is trained on past caller data and agent data for agent-caller pairs matched according to the adaptive correlation algorithm.

7. The method of claim 5, wherein at least one of the two adaptive correlation algorithms comprises a neural network algorithm.

8. The method of claim 1, wherein at least two of the at least two different models each comprise an adaptive correlation algorithm, the adaptive correlation algorithms different from each other.

9. The method of claim 1, wherein one of the at least two models comprises a performance based matching algorithm and one of the at least two models comprises an adaptive correlation algorithm.

10. The method of claim 1, wherein one of the at least two models comprises an adaptive correlation algorithm and at least one of the at least two models does not comprise an adaptive correlation algorithm.

11. The method of claim 1, further comprising estimating an additional caller data based on the determined caller data.

12. The method of claim 1, wherein a first degree of caller data comprises no caller data and a second degree of caller data comprises a portion of a predefined set of caller data.

13. The method of claim 1, wherein the degree of caller data includes one or more of no caller data and Billing Telephone Number (BTN) data.

14. The method of claim 1, wherein a type of caller data includes one or more types of Billing Telephone Number (BTN) data.

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

selecting a model from at least two different models for matching a caller to an agent, the model selected based on one or both of degree and type of agent data associated with an agent; and

causing the caller to be routed based on the selected model.

16. The method of claim 15, further comprising selecting the model based on one or both of a degree and type of caller data associated with the caller.

17. The method of claim 15, wherein at least two of the at least two different models each comprise an adaptive correlation algorithm, and each of the adaptive correlation algorithms is trained differently.

18. The method of claim 17, wherein each adaptive correlation algorithm is trained on past caller data and agent data for agent-caller pairs matched according to the adaptive correlation algorithm.

19. The method of claim 17, wherein at least one of the two adaptive correlation algorithms comprises a neural network algorithm.

20. The method of claim 15, wherein at least two of the at least two different models each comprise an adaptive correlation algorithm, the adaptive correlation algorithms different from each other.

21. The method of claim 15, wherein one of the at least two models comprises a performance based matching algorithm and one of the at least two models comprises an adaptive correlation algorithm.

22. The method of claim 15, wherein one of the at least two models comprises an adaptive correlation algorithm and at least one of the at least two models does not comprise an adaptive correlation algorithm.

23. Apparatus for routing callers to agents in a call center routing environment, the apparatus comprising logic for:

selecting a model from at least two different models for matching a caller to an agent, the model selected based on one or both of degree and type of caller data associated with the caller; and

causing the caller to be routed based on the selected model.

24. The apparatus of claim 23, further comprising selecting the model based on one or both of a degree and type of agent data associated with the agent.

25. The apparatus of claim 23, wherein the caller is routed to an agent, a pool of callers, or a queue of callers.

26. The apparatus of claim 23, wherein at least two of the at least two different models each comprise an adaptive correlation algorithm, and each of the adaptive correlation algorithms is trained differently.

27. The apparatus of claim 26, wherein each adaptive correlation algorithm is trained on past caller data and agent data for agent-caller pairs matched according to the adaptive correlation algorithm.

28. The apparatus of claim 26, wherein at least one of the two adaptive correlation algorithms comprises a neural network algorithm.

29. The apparatus of claim 23, wherein at least two of the at least two different models each comprise an adaptive correlation algorithm, the adaptive correlation algorithms different from each other.

30. The apparatus of claim 23, wherein one of the at least two models comprises a performance based matching algorithm and one of the at least two models comprises an adaptive correlation algorithm.

31. The apparatus of claim 23, wherein one of the at least two models comprises an adaptive correlation algorithm and at least one of the at least two models does not comprise an adaptive correlation algorithm.

32. The apparatus of claim 23, further comprising logic for estimating an additional caller data based on the determined caller data.

33. The apparatus of claim 23, wherein a first degree of caller data comprises no caller data and a second degree of caller data comprises a portion of a predefined set of caller data.

34. The apparatus of claim 23, wherein the degree of caller data includes one or more of no caller data and Billing Telephone Number (BTN) data.

35. The apparatus of claim 23, wherein the type of caller data includes one or more types of Billing Telephone Number (BTN) data.

36. Computer readable storage medium comprising computer readable instructions for carrying out the method of:

selecting a model from at least two different models for matching a caller to an agent, the model selected based on one or both of degree and type of caller data associated with the caller; and

causing the caller to be routed based on the selected model.

37. The computer readable storage medium of claim 36, further comprising program code for selecting the model based on one or both of a degree and type of agent data associated with the agent.

38. The computer readable storage medium of claim 36, wherein the caller is routed to an agent, a pool of callers, or a queue of callers.

39. The computer readable storage medium of claim 36, wherein at least two of the at least two different models each comprise an adaptive correlation algorithm, and each of the adaptive correlation algorithms is trained differently.

40. The computer readable storage medium of claim 39, wherein each adaptive correlation algorithm is trained on past caller data and agent data for agent-caller pairs matched according to the adaptive correlation algorithm.

41. The computer readable storage medium of claim 39, wherein at least one of the two adaptive correlation algorithms comprises a neural network algorithm.

42. The computer readable storage medium of claim 36, wherein at least two of the at least two different models each comprise an adaptive correlation algorithm, the adaptive correlation algorithms different from each other.

43. The computer readable storage medium of claim 36, wherein one of the at least two models comprises a performance based matching algorithm and one of the at least two models comprises an adaptive correlation algorithm.

44. The computer readable storage medium of claim 36, wherein one of the at least two models comprises an adaptive correlation algorithm and at least one of the at least two models does not comprise an adaptive correlation algorithm.

45. The computer readable storage medium of claim 36, further comprising program code for estimating an additional caller data based on the determined caller data.

46. The computer readable storage medium of claim 36, wherein a first degree of caller data comprises no caller data and a second degree of caller data comprises a portion of a predefined set of caller data.

47. The computer readable storage medium of claim 36, wherein the degree of caller data includes one or more of no caller data and Billing Telephone Number (BTN) data.

48. The computer readable storage medium of claim 36, wherein the type of caller data includes one or more types of Billing Telephone Number (BTN) data.