System and method for managing routing of customer calls to agents

Patent metadata

Field	Value
Publication number	US-10909463-B1
Application number	US-202016773805-A
Country	US
Kind code	B1
Filing date	Jan 27, 2020
Priority date	Aug 29, 2017
Publication date	Feb 2, 2021
Grant date	Feb 2, 2021

Abstract

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.

First claim

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 enterprise customer data for the identified customer in the customer call;
retrieving, by the processor, customer demographic data for the identified customer;
executing, by the processor, a predictive machine-learning model configured to determine a business outcome signal representative of likelihood of a business outcome by inputting customer demographic data to determine, for each of a plurality of customer records, the business outcome 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; 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 , 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 business outcome signal representative of likelihood of a respective business outcome, wherein the selected predictive machine-learning model is selected based upon the set of enterprise customer data for the identified customer, wherein in the executing step the selected predictive machine-learning model is configured to determine the respective business outcome signal representative of a likelihood of the respective business outcome.

3. The processor based method according to claim 2 , 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 business outcome signal.

4. 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.

5. The processor based method of claim 4 , wherein the priority call queue assignment is a priority queue position in a call queue, and the subordinate call queue assignment is a subordinate queue position in the call queue.

6. The processor-based method according to claim 4 , 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.

7. The processor based method according to claim 2 , wherein the set of enterprise customer data for the identified customer is associated with one or more of prospects, leads, and purchasers of an enterprise.

8. The processor based method according to claim 7 , wherein the respective business outcome signal targets the one or more of the prospects, leads, and purchasers of the enterprise associated with the set of enterprise customer data for the identified customer.

9. The processor based method according to claim 2 , wherein the respective business outcome signal is representative of likelihood of accepting an offer to purchase a product.

10. The processor based method according to claim 2 , wherein the set of enterprise customer data for the identified customer is representative of one or more of promotional activities, customer prospecting activities, and call center CRM activities.

11. 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.

12. The processor based method according to claim 1 , wherein the predictive machine-learning model is comprises a logistic regression model.

13. The processor-based method according to claim 12 , wherein the logistic regression model is one of a logistic regression model with l 1 regularization or a logistic regression model with l 2 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. The method of claim 1 , 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.

16. 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;
non-transitory machine-readable memory that stores a customer database including enterprise customer data associated with customers of an enterprise serviced by the call center;
a predictive modeling module that stores a first predictive model of customer value, wherein the first predictive model is configured to determine, for each of a plurality of customer records, a first business outcome signal representative of a likelihood of a first business outcome, and that stores a second predictive model of customer value, wherein the second predictive model is configured to determine is configured to determine, for each of a plurality of customer records, a second business outcome signal representative of a likelihood of a second business outcome; and
a processor, wherein the processor in communication with the non-transitory machine-readable memory and the predictive modeling module executes a set of instructions instructing the processor 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 the enterprise customer data associated with the identified customer in the customer call;
retrieve customer demographic data associated with the identified customer;
select one of the first predictive model of customer value or the second predictive model of customer value based upon the retrieved set of the enterprise customer data associated with the identified customer in the customer call;
determine a value prediction signal for the identified customer via applying the selected predictive model to the retrieved customer demographic data, and classify the identified customer into one of a first value group and a second value group based on the value prediction 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.

17. The system according to claim 16 wherein the selected predictive machine-learning model is the one of the first predictiv

Assignees

• Massachusetts Mutual Life Insurance Co

Inventors

• Merritt Sears

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06Q30/0601 Primary

  Electronic shopping [e-shopping] · CPC title

• H04M3/5231

  with call back arrangements · CPC title

• H04M3/5235

  Dependent on call type or called number [DNIS] · CPC title