System and method for managing routing of customer calls to agents
地域：
MA
MA Springfield
摘要
A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.
說明書
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
This application is a continuation-in-part of U.S. patent application Ser. No. 16/773,805, entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Jan. 27, 2020, which is a continuation of U.S. patent application Ser. No. 16/541,046, entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Aug. 14, 2019, which is a continuation of U.S. patent application Ser. No. 16/276,165, entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Feb. 14, 2019, which is a continuation of U.S. patent application Ser. No. 16/110,940, entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Aug. 23, 2018, which claims the benefit of U.S. Provisional App. No. 62/551,690, filed Aug. 29, 2017, claims the benefit of U.S. Provisional App. No. 62/648,325, filed Mar. 26, 2018, claims the benefit of U.S. Provisional App. No. 62/648,330, filed Mar. 26, 2018, and claims the benefit of U.S. Provisional App. No. 62/687,130, filed Jun. 19, 2018, all of which are incorporated by reference in their entirety.
權(quán)利要求
What is claimed is:1. A processor-based method, comprising:in response to receiving a customer call from an identified customer at an inbound call receiving device:retrieving, by a processor, a set of customer data for the identified customer in the customer call; executing, by the processor, a predictive machine learning model configured to output a signal representative of likelihood of a business outcome by inputting the customer data for the identified customer to determine, for each of a plurality of customer records, the signal representative of the likelihood of the business outcome, the predictive machine learning model classifying the identified customer into a first value group or into a second value group based on the signal determined; and directing, by the processor, the inbound call receiving device,in the event the processor classifies the identified customer into the first value group, to route the identified customer to a first call queue assignment; in the event the processor classifies the identified customer into the second value group, to route the identified customer to a second call queue assignment. 2. The processor based method according to claim 1 , wherein the set of customer data for the identified customer is associated with one or more customer groups selected from the group consisting of prospects, leads, new business applicants, and purchasers. 3. The processor based method according to claim 2 , wherein the respective signal targets the one or more customer groups. 4. The processor based method according to claim 1 , wherein the customer data for the identified further comprises events data associating the identified customer with one or more customer acquisition or marketing events. 5. The processor based method according to claim 4 , wherein the events data is selected from the group consisting of a promotional activity, customer prospecting activity, and call center activity. 6. The processor based method according to claim 1 , further comprising the step of retrieving customer demographic data associated with the identified customer, wherein the predictive machine learning model is configured to output the signal upon inputting the customer data for the identified customer and the customer demographic data associated with the identified customer. 7. The processor based method according to claim 4 , wherein the customer demographic data for the identified customer comprises external third-party customer demographic data associated with a customer identifier for the identified customer. 8. The processor based method according to claim 1 , further comprising the step of selecting, by the processor, the predictive machine learning model from a plurality of predictive machine learning models, wherein each of the plurality of predictive machine learning models is configured to determine a respective signal representative of likelihood of a respective business outcome. 9. The processor based method according to claim 6 , wherein the selected predictive machine learning model is the one of the plurality of predictive machine learning models for which the set of enterprise customer data for the identified customer has a highest importance in determining the respective signal. 10. The processor based method according to claim 1 , wherein the first call queue assignment is a priority call queue assignment, and the second call queue assignment is a subordinate call queue assignment. 11. The processor-based method according to claim 10 , wherein the priority call queue assignment is a queue position in a hold list for callers on hold for live connection to an agent, and the subordinate call queue assignment is a queue position in a call-back queue. 12. The processor based method according to claim 1 , wherein the first value group comprises customers having a first set of modeled lifetime values, and the second value group comprises customers having a second set of modeled lifetime values, wherein modeled lifetime values in the first set of modeled lifetime values are higher than modeled lifetime values in the second set of modeled lifetime values. 13. The processor based method according to claim 1 , wherein the predictive machine learning model is comprises one of a logistic regression model with l1 regularization or a logistic regression model with l2 regularization. 14. The processor based method according to claim 1 , wherein the predictive machine learning model is a random forests ensemble learning method for classification. 15. A system for managing customer calls within a call center, comprising:an inbound telephone call receiving device for receiving a customer call to the call center; and a non-transitory computer-readable medium storing a customer database and storing a computer program instructions, and a processor coupled to the non-transitory computer-readable medium and configured to execute the instructions to:upon receiving the customer call at the inbound telephone call receiving device from an identified customer, retrieve from the customer database a set of customer data for the identified customer in the customer call; output a signal representative of likelihood of a business outcome for the identified customer via applying a predictive machine learning model to the set of retrieved customer data to determine the signal representative of the likelihood of the business outcome for the identified customer, and classify the identified customer into one of a first value group and a second value group based on the signal determined; and direct the inbound telephone call receiving device:in the event the inbound queue management module classifies the identified customer into the first value group, to route the customer call of the identified customer to a first call queue assignment; in the event the inbound queue management module classifies the identified customer into the second value group, to route the customer call of the identified customer to a second call queue assignment. 16. The system according to claim 15 , wherein the processor is further configured to execute the instructions to:retrieve customer demographic data associated with the identified customer; and determine the signal representative of likelihood of a business outcome for the identified customer via applying the predictive machine learning model to the retrieved set of customer data and the customer demographic data associated with the identified customer. 17. The system according to claim 15 , wherein the processor is further configured to execute the instructions to:select one of a first predictive machine learning model or a second predictive machine learning model based upon the retrieved set of customer data for the identified customer; and determine the signal representative of likelihood of the business outcome for the identified customer via applying the selected predictive machine learning model to the retrieved set of customer data. 18. The system according to claim 15 , wherein the predictive machine learning model comprises a logistic regression model. 19. The system according to claim 15 , wherein the predictive machine learning model comprises a random forests ensemble learning method for classification.
意見反饋
使用該功能遇到問題
該功能需要專業(yè)版企業(yè)版VIP權(quán)限，您可以：
您也可以聯(lián)系官方QQ: 2157717237 電話: 13264338900