Patent Issued for System and method for managing routing of customer calls to agents (USPTO 11551108): Massachusetts Mutual Life Insurance Company

2023 JAN 27 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Merritt, Sears (Groton, MA, US), filed on November 8, 2021, was published online on January 10, 2023.

The patent’s assignee for patent number 11551108 is Massachusetts Mutual Life Insurance Company (Springfield, Massachusetts, United States).

News editors obtained the following quote from the background information supplied by the inventors: “Customer contact centers provide an important interface for customers/partners of an organization to contact the organization. The contact can be for a request for a product or service, for trouble reporting, service request, etc. The contact mechanism in a conventional call center is via a telephone, but it could be via a number of other electronic channels, including e-mail, online chat, etc.

“The contact center consists of a number of human agents, each assigned to a telecommunication device such as a phone or a computer for conducting email or Internet chat sessions that is connected to a central switch. Using these devices, the agents generally provide sales, customer service, or technical support to the customers or prospective customers of a contact center or of a contact center’s clients. Conventionally, a contact center operation includes a switch system that connects callers to agents. In an inbound contact center, these switches route inbound callers to a particular agent in a contact center, or, if multiple contact centers are deployed, to a particular contact center for further routing. When a call is received at a contact center (which can be physically distributed, e.g., the agents may or may not be in a single physical location), if a call is not answered immediately, the switch will typically place the caller on hold and then route the caller to the next agent that becomes available. This is sometimes referred to as placing the caller in a call queue. In conventional methods of routing inbound callers to agents, high business value calls can be subjected to a long wait while the low business value calls are often answered more promptly, possibly causing dissatisfaction on the part of the high business value caller.

“In many call centers, the agents answering calls are organized into a plurality of groups or teams, with each group having primary responsibility of the calls in one or more call queues. Different agent groups often have responsibility for different goals or functions of the call center, such as generating customer leads, closing sales with prospects, and servicing existing customers. Routing an inbound caller to an appropriate group or team of the call center to address the needs of that caller can be a burdensome, time-consuming process.

“There is a need for a system and method for identifying high business value inbound callers at a call center during a time period in which inbound callers are awaiting connection to an agent. Additionally, there is a need to improve traditional methods of routing callers, such as “round-robin” caller routing, to improve allocation of limited call center resources to high business value inbound callers. Further, there is a need to improve traditional methods of routing callers to a group or team of agents appropriate to the caller’s needs from a plurality of agent groups that implement different functions or goals of the call center.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “Embodiments described herein can automatically route a call from a customer to one of a plurality of call queue assignments based on predicted value of the telephone call. Embodiments described herein can automatically route a call from a customer to one of a plurality of call queues associated with a plurality of groups of agents, based on information concerning an identified customer of an enterprise and predicted value of the telephone call to that enterprise.

“In various embodiments, the method retrieves from a customer database a set of enterprise customer data associated with an identified customer in a customer call. The customer database stores enterprise customer data associated with prospects, leads, and purchasers of an enterprise, such as a sponsoring organization or client of the call center. In various embodiments, the enterprise customer data comprises one or more of customer event data, activity event data, and attributions data.

“In an embodiment, the process then retrieves customer demographic data associated with the identified customer. In various embodiments, the customer demographic data may be associated with the identified customer by two or more identifying data including name of the identified customer, address of the identified customer, and zip code of the identified customer. In an embodiment, the customer management system obtains a customer identifier for the received customer call from caller information associated with the inbound call.

“Disclosed embodiments select a predictive model from a plurality of predictive models. Each of the plurality of predictive models is configured to determine a respective business outcome signal representative of one or more of: likelihood of accepting an offer to purchase a product, likelihood of accepting an offer to purchase a product, likelihood of not lapsing in payments for a purchased product, and likelihood of accepting an offer to purchase a product and not lapsing in payments for the purchased products. The method and system selects the one of the plurality of predictive models for which the set of enterprise customer data has a highest importance in determining the respective business outcome signal.

“A method for managing routing of customer calls to agents executes a selected predictive model to determine a value prediction signal. In various embodiments, the value prediction signal includes one or more of a first signal representative of the likelihood that the identified customer will accept an offer to purchase a product, a second signal representative of the likelihood that the identified customer will lapse in payments for a purchased product, and a third signal representative of the likelihood that the identified customer will accept an offer to purchase the product and will not lapse in payments for the purchased product. In an embodiment, the selected predictive model determines the value prediction signal in real time by applying a logistic regression model in conjunction with a tree-based model to the set of the enterprise customer data and the retrieved customer demographic data.”

The claims supplied by the inventors are:

“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, customer data associated with 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 retrieved customer data, wherein the predictive machine learning model is configured to determine, for each of a plurality of customer records, the signal representative of the likelihood of the business outcome, classifying the identified customer into a first value group or into a second value group based on the output signal representative of the likelihood of the business outcome; and transmitting, by the processor, to a device in operative communication with the processor, information representative of the retrieved customer data and information representative of the classification of the identified customer into the first value group or into the second value group.

“2. The processor based method of claim 1, wherein the transmitting step comprises, upon routing the customer call for live connection to an agent associated with the device, transmitting to the device the information representative of the retrieved customer data and the information representative of the classification of the identified customer into the first value group or the second value group.

“3. The processor based method of claim 1, further comprising the step, upon receiving the customer call at the call center from the identified customer, of retrieving from a customer database that stores enterprise customer data associated with customers of an enterprise, a set of the enterprise customer data associated with the identified customer in the customer call, wherein the transmitting step further comprises transmitting to the device information representative of the set of the enterprise customer data.

“4. The processor based method of claim 3, wherein the executing step further comprises executing the predictive machine learning model by inputting the set of the enterprise customer data to determine, for each of the plurality of customer records, the signal representative of the likelihood of the business outcome.

“5. The method according to claim 1, wherein the customer data comprises customer demographic data, wherein the retrieving step retrieves the customer demographic data from a third-party data source.

“6. The method according to claim 5, wherein the processor retrieves the customer data from the third-party data source via a lookup tool executing on the processor to perform real time matching of the customer demographic data to a customer identifier for the identified customer in the customer call.

“7. The method according to claim 1, wherein the predictive machine learning model is configured to output the signal representative of likelihood of the business outcome by applying a logistic regression model in conjunction with a tree-based model to the retrieved customer data.

“8. The method according to claim 1, wherein the first value group comprises customers having a first set of modeled values of the business outcome, and the second value group comprises customers having a second set of modeled values of the business outcome, wherein modeled values in the first set of modeled values are higher than modeled values in the second set of modeled values.

“9. 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, customer demographic data associated with a customer identifier for an identified customer in a customer call, via a lookup tool executing on the processor to perform real time matching of customer demographic data to the customer identifier 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 retrieved customer demographic data, wherein the predictive machine learning model is configured to determine, for each of a plurality of customer records, the signal representative of the likelihood of the business outcome, classifying the identified customer into a first value group or into a second value group based on the output signal representative of the likelihood of the business outcome; and transmitting, by the processor, to a device in operative communication with the processor, information representative of the retrieved customer demographic data and information representative of the classification of the identified customer into the first value group or into the second value group.

“10. The processor based method of claim 9, wherein the transmitting step comprises, upon routing the customer call for live connection to an agent associated with the device, transmitting to the device the information representative of the retrieved customer data and the information representative of the classification of the identified customer into the first value group or the second value group.

“11. The method according to claim 9, wherein the predictive machine learning model is configured to output the signal representative of likelihood of the business outcome by applying a logistic regression model in conjunction with a tree-based model to the retrieved customer data.

“12. The method according to claim 9, wherein the first value group comprises customers having a first set of modeled values of the business outcome, and the second value group comprises customers having a second set of modeled values of the business outcome, wherein modeled values in the first set of modeled values are higher than modeled values in the second set of modeled values.

“13. A system for managing customer calls, comprising: non-transitory, machine-readable memory that stores customer data; and a computer configured to execute a predictive machine learning model, wherein the computer in communication with the non-transitory machine-readable memory executes a set of instructions instructing the computer to: in response to receiving a customer call associated with an identified customer, retrieve from the non-transitory, machine-readable memory a set of customer data associated with the identified customer; output a signal representative of likelihood of a business outcome by applying the predictive machine learning model to the retrieved customer data, wherein the predictive machine learning model is configured to determine, for each of a plurality of customer records, the signal representative of the likelihood of the business outcome; classify the identified customer into one of a first value group and a second value group based on the output signal representative of likelihood of the business outcome; and transmit to a device in operative communication with the computer, information representative of the retrieved customer data and information representative of the classification of the identified customer into the first value group or into the second value group.

“14. The system of claim 13, further comprising an inbound telephone call-receiving device for receiving the customer call and for associating the customer call with the identified customer.

“15. The system of claim 14, wherein the set of instructions further instruct the computer to direct the inbound telephone call-receiving device to the route the customer call associated with the identified customer for live connection to an agent associated with the device.

“16. The system of claim 13, wherein the set of instructions further instruct the computer, in response to receiving the customer call associated with the identified customer, to retrieve from a customer database that stores enterprise customer data associated with customers of an enterprise, a set of the enterprise customer data associated with the identified customer in the customer call.

“17. The system of claim 16, wherein output the signal representative of likelihood of the business outcome further comprises applying the predictive machine learning model to the retrieved set of enterprise customer data.

“18. The system of claim 13, wherein the set of instructions further instruct the computer, in response to receiving the customer call associated with the identified customer, to retrieve customer demographic data from a third party source, wherein output the signal representative of likelihood of the business outcome further comprises applying the predictive machine learning model to the retrieved customer demographic data.

“19. The system of claim 18, wherein retrieve the customer demographic data from the third party source applies a lookup tool executing on the computer to perform real time matching of the customer demographic data to a customer identifier for the identified customer.

“20. The system of claim 13, wherein the first value group comprises customers having a first set of modeled values of the business outcome, and the second value group comprises customers having a second set of modeled values of the business outcome, wherein modeled values in the first set of modeled values are higher than modeled values in the second set of modeled values.”

For additional information on this patent, see: Merritt, Sears. System and method for managing routing of customer calls to agents. U.S. Patent Number 11551108, filed November 8, 2021, and published online on January 10, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11551108)&db=USPAT&type=ids

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