Systems and Methods for Routing Callers to an Agent in a Contact Center

• US 20090190740A1
• Filed: 01/28/2008
• Published: 07/30/2009
• Est. Priority Date: 01/28/2008
• Status: Active Grant

• Current Assignee: Afiniti Ltd
• Original Assignee: Afiniti Europe Technologies Limited (Afiniti Ltd)
• Inventors: Jones, Christopher W., Chishti, Zia, Spottiswoode, Stuart J.
• Granted Patent: US 9,712,679 B2
• Time in Patent Office: Days
• US Class Current: 379/265.06
• 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 of operating an inbound contact center, the method comprising:

grading two agents on an optimal interaction; and

matching a caller with at least one of the two graded agents to increase the chance of the optimal interaction.

2. The method of claim 1, further comprising connecting the caller to one of the two graded agents.

3. The method of claim 1, further comprising connecting the caller to one of the two graded agents with a minimally-acceptable grade for the optimal interaction.

4. The method of claim 1, further comprising connecting the caller to one of the two graded agents with the highest grade for the optimal interaction.

5. The method of claim 1, further comprising determining an order of agent suitability for matches between the caller and at least two of the two graded agents.

6. The method of claim 5, further comprising determining the availability of the two graded agents, and connecting the caller to an available agent from the two graded agents.

7. The method of claim 1, further comprising partially randomizing the matches between the caller and at least two of the two graded agents, wherein the matches are partially randomized by a factor between about 0.1% and 99.9%

8. The method of claim 1, wherein the optimal interaction comprises one of improved revenue generation, decreased cost, or improved customer satisfaction.

9. The method of claim 1, wherein the two agents are graded for the optimal interaction over a period of at least 10 days.

10. The method of claim 1, wherein the two agents are each graded on two optimal interactions.

11. The method of claim 10, further comprising weighting one optimal interaction against another optional interaction, and connecting the caller with one of the two graded agents to increase the chance of a more heavily-weighted optimal interaction

12. The method of claim 11, wherein weighting one optimal interaction against the other optimal interaction takes place in real-time.

13. The method of claim 11, wherein weighting one optimal interaction against the other optimal interaction is designated to take effect at a future point in time.

14. The method of claim 1, further comprising determining from an affinity database a caller affinity data for the caller, and using the caller affinity data to match the caller with at least one of the two graded agents.

15. The method of claim 14, wherein the caller affinity data comprises one of purchase history, contact time history, or customer satisfaction history.

16. The method of claim 1, further comprising determining from an affinity database an agent affinity data for at least one of the two graded agents, and using the agent affinity data to match the caller with at least one of the two graded agents.

17. The method of claim 16, wherein the agent affinity data comprises agent performance data for one of revenue generation, cost, or customer satisfaction.

18. The method of claim 17, wherein the agent performance data comprises caller demographic data or caller psychographic data.

19. The method of claim 1, wherein the caller is in a queue of callers, further comprising increasing a connection priority of the caller based on a favorable match between the caller and at least one of the two graded agents.

20. A method of operating an outbound contact center, the method comprising:

identifying a group of at least two callers;

grading two agents on an optimal interaction; and

matching at least one of the two graded agents with at least one caller from the group.

21. The method of claim 20, further comprising connecting one caller from the group to one of the two graded agents.

22. The method of claim 20, further comprising connecting one caller from the group to one of the two graded agents with a minimally-acceptable grade for the optimal interaction.

23. The method of claim 20, further comprising connecting one caller from the group to one of the two graded agents with the highest grade for the optimal interaction.

24. The method of claim 20, further comprising partially randomizing the matches between at least one caller and at least two of the two graded agents, wherein the matches are partially randomized by a factor between about 0.1% and 99.9%

25. The method of claim 20, wherein the optimal interaction comprises one of improved revenue generation, decreased cost, or improved customer satisfaction.

26. The method of claim 20, wherein the two agents are graded for the optimal interaction over a period of at least 10 days.

27. The method of claim 20, wherein the two agents are each graded on two optimal interactions.

28. The method of claim 27, further comprising weighting one optimal interaction against another optional interaction, and connecting one caller from the group to one of the two graded agents to increase the chance of a more heavily-weighted optimal interaction

29. The method of claim 28, wherein weighting one optimal interaction against the other optimal interaction takes place in real-time.

30. The method of claim 28, wherein weighting one optimal interaction against the other optimal interaction is designated to take effect at a future point in time.

31. The method of claim 20, further comprising generating a lead list for one of the two graded agents, wherein the lead list comprises contact information for one caller from the group.

32. The method of claim 31, further comprising one of the two graded agents using the contact information to contact the one caller from the group.

33. The method of claim 20, further comprising determining an order of agent suitability for matches between at least one caller from the group and at least two of the graded agents.

34. The method of claim 20, wherein the group of at least two callers is identified from a lead list.

35. The method of claim 34, further comprising using a dialer to contact at least one caller from the group.

36. The method of claim 20, further comprising determining from an affinity database a caller affinity data for at least one caller from the group, and using the caller affinity data to match the at least one caller with at least one of the two graded agents.

37. The method of claim 36, wherein the caller affinity data is one of purchase history, contact time history, or customer satisfaction history.

38. The method of claim 20, further comprising determining from an affinity database one agent affinity data for at least one of the two graded agents, and using the agent affinity data to match at least one of the two graded agents with at least one caller from the group.

39. The method of claim 38, wherein the agent affinity data comprises agent performance data for one of revenue generation, cost, or customer satisfaction.

40. The method of claim 39, wherein the performance data is derived from one of caller demographic data or caller psychographic data.

41. The method of claim 20, wherein the group of at least two callers is identified from a queue of callers.

42. The method of claim 41, wherein the connection priority of at least one of the two callers is increased.

43. A method for operating an inbound contact center, the method comprising:

determining at least one agent data for each of two agents;

determining at least one caller data for a caller;

using the agent data and the caller data in a pattern matching algorithm; and

matching the caller to one of the two agents to increase the chance of an optimal interaction.

44. The method of claim 43, wherein the agent data comprises one of demographic data or psychographic data.

45. The method of claim 43, wherein the caller data comprises one of demographic data or psychographic data.

46. The method of claim 44, wherein the agent data comprises demographic data, wherein the demographic data comprises one of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, or credit score.

47. The method of claim 45, wherein the caller data comprises demographic data, wherein the demographic data comprises one of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, or credit score.

48. The method of claim 43, wherein the caller data is determined from a commercial database.

49. The method of claim 43, wherein the agent data is demographic data determined from a survey.

50. The method of claim 43, wherein determining the caller data further comprises determining the caller'"'"'s CallerID.

51. The method of claim 43, wherein determining the caller data further comprises determining the caller'"'"'s account number.

52. The method of claim 43, further comprising grading the two agents on an optimal interaction, and wherein the agent data, caller data, and agent grades are used in the pattern matching algorithm.

53. The method of claim 43, further comprising connecting the caller to one of the two agents.

54. The method of claim 43, further comprising connecting the caller to one of the two agents with a minimally-acceptable predicted chance of an optimal interaction.

55. The method of claim 43, further comprising connecting the caller to one of the two agents with the highest predicted chance of an optimal interaction.

56. The method of claim 43, further comprising determining an order of agent suitability for matches between the caller and at least two of the two agents.

57. The method of claim 56, further comprising determining the availability of the two agents, and connecting the caller to an available agent from the two agents.

58. The method of claim 43, further comprising partially randomizing the matches between the caller and at least two of the two agents, wherein the matches are partially randomized by a factor between about 0.1% and 99.9%

59. The method of claim 43, wherein the pattern matching algorithm comprises one of a neural network algorithm or a genetic algorithm.

60. The method of claim 43, wherein the optimal interactions comprises one of improved revenue generation, decreased cost, or improved customer satisfaction.

61. The method of claim 43, 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.

62. The method of claim 61, wherein the training occurs periodically.

63. The method of claim 43, further comprising using the pattern matching algorithm to create a computer model reflecting the predicted chances of an optimal interaction occurring between the caller and each of the two agents.

64. The method of claim 63, further comprising weighting the optimal interaction against another optimal interaction, modifying the computer model to generate suitability scores for matches between the caller and each of the two agents, and connecting the caller with one of the two agents to increase the chance of the more heavily-weighted optimal interaction.

65. The method of claim 43, further comprising determining from an affinity database a caller affinity data for the caller, and using the caller affinity data to match the caller with at least one of the two agents.

66. The method of claim 65, wherein the caller affinity data comprises one of purchase history, contact time history, or customer satisfaction history.

67. The method of claim 43, further comprising determining from an affinity database an agent affinity data for at least one of the two agents, and using the agent affinity data to match the caller with at least one of the two agents.

68. The method of claim 67, wherein the agent affinity data comprises agent performance data for one of revenue generation, cost, or customer satisfaction.

69. The method of claim 68, wherein the performance data is derived from one of caller demographic data or caller psychographic data.

70. The method of claim 43, wherein the caller is a caller in a queue of callers, further comprising increasing the connection priority of the caller based on a favorable match between the caller and at least one of the two agents.

71. A method for operating an outbound contact center, the method comprising:

determining at least one agent data for each of two agents;

identifying a group of at least two callers;

determining at least one caller data for at least one caller from the group;

using the agent data and the caller data in a pattern matching algorithm; and

matching at least one caller from the group to one of the two agents to increase the chance of an optimal interaction.

72. The method of claim 71, wherein the agent data comprises one of demographic data or psychographic data.

73. The method of claim 71, wherein the caller data comprises one of demographic data or psychographic data.

74. The method of claim 72, wherein the agent data comprises demographic data, wherein the demographic data comprises one of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, or credit score.

75. The method of claim 73, wherein the caller data comprises demographic data, wherein the demographic data comprises one of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, or credit score.

76. The method of claim 71, wherein the caller data is determined from a commercial database.

77. The method of claim 71, wherein the agent data is demographic data determined from a survey.

78. The method of claim 71, wherein determining the caller data further comprises determining the CallerID of at least one caller from the group.

79. The method of claim 71, wherein determining the caller data further comprises determining the account number of at least one caller from the group.

80. The method of claim 71, further comprising grading the two agents on an optimal interaction, and wherein the agent data, caller data, and agent grades are used in the pattern matching algorithm.

81. The method of claim 71, further comprising connecting one of the callers from the group to one of the two agents.

82. The method of claim 71, further comprising connecting one of the callers from the group to one of the two agents with a minimally-acceptable predicted chance of an optimal interaction.

83. The method of claim 71, further comprising connecting one of the callers from the group to one of the two agents with the highest predicted chance of an optimal interaction.

84. The method of claim 71, further comprising determining an order of agent suitability for matches between one of the callers from the group and at least two of the two agents.

85. The method of claim 84, further comprising determining the availability of the two agents, and connecting one of the callers from the group to an available agent from the two agents.

86. The method of claim 71, further comprising partially randomizing the matches between one of the callers from the group and at least two of the two agents, wherein the matches are partially randomized by a factor between the ranges of 0.1% and 99.9%

87. The method of claim 71, wherein the pattern matching algorithm comprises one of a neural network algorithm or a genetic algorithm.

88. The method of claim 71, wherein the optimal interactions comprises one of improved revenue generation, decreased cost, or improved customer satisfaction.

89. The method of claim 71, 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.

90. The method of claim 89, wherein the training occurs periodically.

91. The method of claim 71, further comprising using the pattern matching algorithm to create a computer model reflecting the predicted chances of an optimal interaction occurring between one of the callers from the group and each of the two agents.

92. The method of claim 91, further comprising weighting the optimal interaction against another optimal interaction, modifying the computer model to generate suitability scores for matches between one of the callers from the group and each of the two agents, and connecting one of the callers from the group with one of the two agents to increase the chance of the more heavily-weighted optimal interaction.

93. The method of claim 71, further comprising determining from an affinity database a caller affinity data for one of the callers from the group, and using the caller affinity data to match one of the callers from the group with at least one of the two agents.

94. The method of claim 93, wherein the caller affinity data comprises one of purchase history, contact time history, or customer satisfaction history.

95. The method of claim 71, further comprising determining from an affinity database an agent affinity data for at least one of the two agents, and using the agent affinity data to match one of the callers from the group with at least one of the two agents.

96. The method of claim 95, wherein the agent affinity data comprises agent performance data for one of revenue generation, cost, or customer satisfaction.

97. The method of claim 96, wherein the performance data is derived from one of caller demographic data or caller psychographic data.

98. The method of claim 71, further comprising generating a lead list for one of the two agents, wherein the lead list comprises contact information for one caller from the group.

99. The method of claim 71, further comprising one of the two agents using the contact information to contact the one caller from the group.

100. The method of claim 71, further comprising determining an order of agent suitability for matches between at least one caller from the group and at least two of the agents.

101. The method of claim 71, wherein the group of at least two callers is identified from a lead list.

102. The method of claim 101, further comprising using a dialer to contact at least one caller from the group.

103. The method of claim 71, wherein the group of at least two callers is identified from a queue of callers.

104. The method of claim 103, wherein the connection priority of at least one of the two callers is increased.

105. An intelligent routing system for a contact center, the system comprising means for grading two or more agents on an optimal interaction;

and

means for matching a caller with at least one of the two or more graded agents to increase the chance of an optimal interaction.

106. The system of claim 105, wherein the means for grading an agent comprises a survey.

107. The system of claim 105, wherein the means for grading an agent comprises a computational device for recording one of an agent'"'"'s revenue generation performance or the agent'"'"'s contact time per caller.

108. The system of claim 105, wherein the means for matching a caller comprises a computational device.

109. The system of claim 105, further comprising a switching system.

110. The system of claim 105, further comprising a dialer.

111. The system of claim 105, further comprising a callerID device.

112. The system of claim 105, further comprising connecting the caller with one of the two or more graded agents.

113. The system of claim 105, wherein the caller is an inbound caller.

114. The system of claim 105, wherein the caller is an outbound caller.

115. An intelligent routing system for a contact center, the system comprising means for determining one agent data for each of two or more agents;

means for determining one caller data for a caller;

means for using the agent data and the caller data in a pattern matching algorithm;

means for matching the caller to one of the two or more agents to increase the chance of an optimal interaction.

116. The system of claim 115, wherein the means for determining agent data comprises a survey.

117. The system of claim 115, wherein the means for determining agent data comprises a computational device for recording one of an agent'"'"'s revenue generation performance or the agent'"'"'s contact time per caller.

118. The system of claim 115, wherein the means for using the agent data and caller data in a pattern matching algorithm comprises a computational device.

119. The system of claim 115, further comprising a switching system.

120. The system of claim 115, further comprising a dialer.

121. The system of claim 115, further comprising a callerID device.

122. The system of claim 115, further comprising connecting the caller with one of the two or more agents.

123. The system of claim 115, wherein the caller is an inbound caller.

124. The system of claim 115, wherein the caller is an outbound caller.