System and method for managing routing of customer calls to agents

• US 10,346,750 B1
• Filed: 02/04/2019
• Issued: 07/09/2019
• Est. Priority Date: 08/29/2017
• Status: Active Grant

• Current Assignee: Massachusetts Mutual Life Insurance Company
• Original Assignee: Massachusetts Mutual Life Insurance Company
• Inventors: Merritt, Sears
• Primary Examiner(s): Hong, Harry S
• Application Number:
  US16/267,331
• Time in Patent Office: 155 Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: H04M 2203/556 Statistical analysis and interpretation
• CPC Class Codes: H04M 2203/558 Databases
• CPC Class Codes: H04M 3/42059 Making use of the calling party identifier
• CPC Class Codes: H04M 3/42195 Arrangements for calling back a calling subscriber when the wanted subscriber ceases to be busy H04M3/48
• CPC Class Codes: H04M 3/4365 based on information specified by the calling party, e.g. priority or subject
• CPC Class Codes: H04M 3/5183 Call or contact centers with computer-telephony arrangements
• CPC Class Codes: H04M 3/5231 with call back arrangements
• 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

Upon receiving an inbound call, a call management system retrieves from a customer database enterprise customer data associated with an identified customer. The customer database tracks prospects, leads, new business and purchasers of an enterprise. Enterprise customer data may include customer event data, activity event data and attributions data. The system retrieves customer demographic data associated with the identified customer. A group of agents is selected from a plurality of groups of agents based on retrieved enterprise customer data. A predictive 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 queue position, and routes a customer call classified in the second value group to a second queue position. for connection to an agent from the selected group of agents.

Claims

1. A processor-based method for managing customer calls within a call center, comprising:

upon receiving a customer call at a call center from an identified customer, retrieving, by a processor, a set of the enterprise customer data associated with the identified customer from a customer database that stores enterprise customer data;

retrieving, by the processor, customer demographic data associated with the identified customer in the customer call;

selecting, by the processor, a group of agents from a plurality of groups of agents of the call center wherein the group of agents implements a predetermined customer care interaction;

wherein the group of agents is selected based upon consistency of the predetermined customer care interaction with the set of enterprise customer data;

executing, by the processor, a predictive model to determine a value prediction signal in real time by applying a logistic regression model in conjunction with a tree-based model to the retrieved customer demographic data, the value prediction signal comprising one or more of a first signal representative of a likelihood that the identified customer will accept an offer to purchase a product, a second signal representative of a likelihood that the identified customer will lapse in payments for a purchased product, and a third signal representative of a likelihood that the identified customer will accept an offer to purchase the product and will not lapse in payments for the purchased product;

classifying, by the predictive model executing on the processor based on the value prediction signal determined by the selected predictive model, the identified customer into one of a first value group and a second value group;

when the classifying step classifies the identified customer into the first value group, routing, by the processor, the customer call for the identified customer to a priority queue assignment for connection to an agent from the selected group of agents; and

when the classifying step classifies the identified customer into the second value group, routing, by the processor, the customer call for the identified customer to a subordinate queue assignment.

2. The processor-based method according to claim 1, wherein the priority queue assignment is a relatively favorable queue position in a hold list for callers on hold for connection to an agent from the selected group of agents, and the subordinate queue assignment is a relatively unfavorable queue position in the hold list for callers on hold for connection to an agent from the selected group of agents.

3. The processor-based method according to claim 1, wherein the priority queue assignment is a relatively favorable queue position in a call-back queue for connection to an agent from the selected group of agents, and the subordinate queue assignment is a relatively unfavorable queue position in the call-back queue for connection to an agent from the selected group of agents.

4. The processor-based method according to claim 1, wherein the priority queue assignment is a queue position in a hold list for callers on hold for live connection to an agent from the selected group of agents, and the subordinate queue assignment is a queue position in a call-back queue.

5. The processor-based method according to claim 1, wherein the priority queue assignment is a queue assignment for connection to an agent from the group of agents assigned to implement the predetermined customer care interaction, and the subordinate queue assignment is a queue assignment for connection to an agent from a group of agents assigned to implement a customer care interaction other than the predetermined customer care interaction.

6. The processor-based method according to claim 1, wherein the predetermined customer care interaction is at least one customer care interaction selected from the group consisting of generating customer leads, screening prospects to generate qualified leads, screening leads for prospective customers, closing product sales, cross-selling of products, promoting a given product, closing sales of a given product, and servicing existing customers.

7. The processor-based method according to claim 1, wherein the predetermined customer care interaction comprises closing product sales, wherein the priority queue assignment is a queue assignment for connection to an agent from a group of agents assigned to close product sales, and the subordinate queue assignment is a queue assignment for connection to an agent from a group of agents assigned to screen leads for prospective customers.

8. The processor-based method according to claim 1, wherein the predetermined customer care interaction comprises screening prospects to generate qualified leads, and the set of enterprise customer data comprises customer event data associated with a prospect.

9. The processor-based method according to claim 1, wherein the predetermined customer care interaction comprises cross-selling of products, and the set of enterprise customer data comprises customer event data associated with a purchaser and activity event data indicating likelihood of an additional purchase.

10. The processor-based method according to claim 1, wherein the predetermined customer care interaction comprises closing sales of a given product, and the set of enterprise customer data comprises activity event data indicating customer interest in a marketing campaign for the given product.

11. The processor-based method according to claim 1, wherein the step of selecting the group of agents from the plurality of groups of agents based upon consistency of the predetermined customer care interaction with the set of enterprise customer data comprises selecting the group of agents from three or more groups of agents.

12. The processor-based method according to claim 1, wherein the customer database further stores enterprise customer data associated with new business applicants of the enterprise, wherein the predetermined customer care interaction comprises closing product sales, and the set of enterprise customer data comprises customer event data associated with a new business applicant or attributions data associated with the new business applicant.

13. 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.

14. The processor-based method according to claim 1, wherein the likelihood that the identified customer will lapse in payments for a purchased product comprises a likelihood that the identified customer will fail to make a payment for the purchased product during a predetermined time period following purchase of the purchased product, wherein the predetermined time period was previously determined by modeling lifetime value over varying durations of the time period following purchase of the purchased product.

15. The processor-based method according to claim 1, wherein the logistic regression model is one of a logistic regression model with l₁ regularization or a logistic regression model with l₂ regularization, and the tree-based model is a random forests ensemble learning method for classification.

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 prospects, leads, and purchasers of an enterprise serviced by the call center; and

a processor configured to execute an inbound queue management module and a predictive modeling module, the predictive modeling module is configured to store a predictive model of customer value, wherein the predictive model comprises a logistic regression model operating in conjunction with a first tree-based model, wherein the processor in communication with the non-transitory machine-readable memory 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;

retrieve customer demographic data associated with the identified customer;

select a group of agents that implements a predetermined customer care interaction from a plurality of groups of agents of the call center, based upon consistency of the predetermined customer care interaction with the set of enterprise customer data;

determine a value prediction signal for the identified customer via applying the predictive model to the set of the enterprise customer data;

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;

when the inbound queue management module classifies the identified customer into the first value group, to route the customer call for the identified customer to a priority queue assignment for connection to an agent from the selected group of agents; and

when the inbound queue management module classifies the identified customer into the second value group, to route the customer call for the identified customer to a subordinate queue assignment.

17. The system according to claim 16, wherein the priority queue assignment is a relatively favorable queue position in a hold list for live connection to an agent from the selected group of agents, and the subordinate queue assignment is a relatively unfavorable queue position in the hold list for live connection to an agent from the selected group of agents.

18. The system according to claim 16, wherein the priority queue assignment is a relatively favorable queue position in a call-back queue for connection to an agent from the selected group of agents, and the subordinate queue assignment is a relatively unfavorable queue position in the call-back queue for connection to an agent from the selected group of agents.

19. The system according to claim 16, wherein the priority queue assignment is a queue position in a hold list for live connection to an agent from the selected group of agents, and the subordinate queue assignment is a queue position in a call-back queue.

20. The system according to claim 16, wherein the priority queue assignment is a queue assignment for connection to an agent from the group of agents assigned to implement the predetermined customer care interaction, and the subordinate queue assignment is a queue assignment for connection to an agent from a group of agents assigned to implement a customer care interaction other than the predetermined customer care interaction.