Patent Issued for System And Method For Managing Customer Call-Backs (USPTO 10,582,060)

2020 MAR 16 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventor Merritt, Sears (Groton, MA), filed on April 8, 2019, was published online on March 16, 2020.

The patent’s assignee for patent number 10,582,060 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, which 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.

“Being placed on hold for more than a few seconds can be an unpleasant and frustrating experience for many people. As a consequence, a significant number of inbound callers who are put on hold for more than a brief time up abandon their calls and hang up. In some cases, depending on the call center’s communication system, before abandoning the call the caller may leave a message requesting a call-back. Many call centers maintain call-back systems to returned abandoned calls and such systems often place such callers in a queue for call-back by a call center agent. In conventional methods of agent call-backs to abandoned callers, high business value callers may not receive a call-back for an extended period of time, while the low business value calls often receive call-backs more promptly, possibly causing additional dissatisfaction on the part of the high business value caller.

“There is a need for a system and method for identifying high business value inbound callers at a call center that have left a message requesting a call-back. There is a related need for a system and method for identifying high business value inbound callers at a call center as to those inbound callers that have abandoned an inbound call. Additionally, there is a need to improve traditional methods of arranging call-backs to inbound callers who have abandoned a call to improve allocation of limited call center resources to high business value callers.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventor’s summary information for this patent: “Embodiments described herein can automatically call back a customer following termination of the customer’s inbound call based on predicted value of the call-back. The present systems and methods include call-back of customers that have terminated a customer call by exercising a call-back option of an interactive voice response unit during the inbound call, as well as customers that have abandoned the inbound call. By identifying high value customers for prioritized or personalized call-back, the present methods improve allocation of limited call center resources and can improve customer satisfaction of high business value inbound callers.

“In a first step of a processor-based method, upon receiving a customer call from an inbound caller, the processor opens an inbound call record and automatically includes in that call record any automatic number identifier information included with the customer call.

“In an embodiment, the call management system monitors the customer call and a call evaluation module automatically collects call related information and updates the inbound call record with this call related information. In an embodiment, the inbound caller interacts with an interactive voice response unit, and the call evaluation module collects IVR data provided by the inbound caller. In an embodiment, the call management system monitors an inbound call queue including the inbound caller, and the call evaluation module collects related inbound queue data.

“In various embodiments, the call evaluation module analyzes one or more of the IVR data and the inbound queue data to detect any termination of the customer call. In one embodiment, the call evaluation module detects termination of the customer call by the inbound caller’s exercising a call-back option of the interactive voice response unit. In another embodiment, the call evaluation module detects the inbound caller’s abandonment of the customer call by monitoring the inbound caller’s interactions with the IVR and presence in the inbound queue.

“In various embodiments, upon detecting termination of the inbound call, a call-back module opens a call-back record for the terminated customer call and includes in that call-back record information from the inbound call record, including, e.g., any automatic number identifier information included with the customer call. The call-back module queries one or more database, including databases of the enterprise and external databases, to retrieve customer identifier data. In an embodiment, the customer identifier data comprise two or more of name of the identified customer, address of the identified customer, and zip code of the identified customer. The call-back module associates the call-back record with an identified customer via the customer identifier data, and then retrieves customer demographic data associated with the identified customer.

“In various embodiments, the call-back module determines a value prediction signal for the identified customer via a predictive module includes a logistic regression module operating in conjunction with a tree-based module. The value prediction signal includes 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.

“Based on the value prediction signal determined, the call-back module classifies the identified customer into a first call-back group or a second call-back group. In various embodiments, an identified customer classified in the first call-back group is assigned a priority call-back queue assignment while an identified customer classified in the second call-back group is assigned a subordinate call-back queue assignment. An automatic calling device of the call center calls back the identified customer based on priority call-back queue assignment or subordinate call-back queue assignment for that customer.

“In another embodiment, in the event the call-back module classifies the identified customer in the first call-back group, the automatic calling device calls back the identified customer for connection to an agent of the call center who is selected based on one or more of the customer identifier data and the customer demographic data. In the event the call-back module classifies the identified customer in the second call-back group, the automatic calling device calls back the identified customer via a subordinate call-back procedure.

“In an embodiment, a processor based method for managing customer calls within a call center comprises, upon receiving a customer call at a call center of an enterprise from an inbound caller, opening, by the processor, an inbound call record for the inbound caller including any automatic number identifier information delivered with the customer call; monitoring, by the processor, the customer call of the inbound caller to retrieve one or more of IVR data received from the inbound caller via interaction with an interactive voice response unit, and inbound queue data retrieved by monitoring an inbound call queue including the inbound caller, and updating the inbound call record for the inbound caller with the one or more of the IVR data and the inbound queue data retrieved; analyzing, by the processor, the one or more of the IVR data and the inbound queue data to detect any termination of the inbound call by exercising a call-back option of the interactive voice response unit or by abandoning the customer call, and in the event of detecting the termination of the customer call: opening, by the processor, a call-back record for the terminated customer call including any automatic number identifier information and delivered the one or more of the IVR data and the inbound queue data from the inbound call record; querying, by the processor, one or more database to retrieve customer identifier data and associating the call-back record with an identified customer via the customer identifier data; retrieving, by the processor, customer demographic data associated with the identified customer; determining, by a predictive model executing on the processor, a 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; wherein the predictive model comprises a logistic regression model operating in conjunction with a tree based model; classifying, by the predictive model executing on the processor based on the value prediction signal determined by the predictive model, the identified customer into one of a first value group and a second value group; in the event the classifying step classifies the identified customer into the first value group, assigning, by the processor, the identified customer to a priority call-back queue assignment; wherein the priority call-back queue assignment comprises one or more of a queue position in a priority call-back queue, and a priority queue position in a call-back queue; in the event the classifying step classifies the identified customer into the second value group, assigning, by the processor, the identified customer to a subordinate call-back queue assignment; wherein the subordinate call-back queue assignment comprises one or more of a queue position in a subordinate call-back queue, and a subordinate queue position in a call-back queue; and automatically calling back the identified customer, by an automatic calling device in communication with the processor, based on the priority call-back queue assignment or the subordinate call-back queue assignment for the identified customer.

“In an embodiment, a processor based method for managing customer calls within a call center comprises, upon receiving a customer call at a call center of an enterprise from an inbound caller, opening, by the processor, an inbound call record for the inbound caller, the inbound call record including any automatic number identifier information delivered with the customer call; monitoring, by the processor, the customer call to detect any termination of the customer call by exercising a call-back option of the interactive voice response unit or by abandoning the customer call, and in the event of detecting the termination of the customer call: opening, by the processor, a call-back record for the terminated customer call including any automatic number identifier information delivered; querying, by the processor, one or more database to retrieve customer identifier data and associating the call-back record with an identified customer via the customer identifier data; retrieving, by the processor, customer demographic data associated with the identified customer; determining, by a predictive model executing on the processor, a 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; wherein the predictive model comprises a logistic regression model operating in conjunction with a tree based model; classifying, by the predictive model executing on the processor based on the value prediction signal determined by the predictive model, the identified customer into one of a first value group and a second value group; in the event the classifying step classifies the identified customer into the first value group, automatically calling back the identified customer, via an automatic calling device in communication with the processor, for connection to a selected agent of the call center; in the event the classifying step classifies the identified customer into the second value group, automatically executing a subordinate call-back procedure via an automatic calling device in communication with the processor.

“In an embodiment, a system for managing customer calls within a call center comprises an inbound telephone call receiving device for receiving a customer call from an inbound caller to the call center; a telephone calling device for placing outbound customer calls from the call center; an interactive voice response unit; non-transitory machine-readable memory that stores call history information and customer profile information for customers of the call center; a predictive modeling module that stores a predictive model of customer value, wherein the predictive model comprises a logistic regression model operating in conjunction with a tree based model; and a processor, configured to execute a call-back management module, 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: generate a customer call customer call record including any automatic number identifier information delivered with the customer call received by the inbound telephone call receiving device; monitor the customer call to detect any termination of the customer call by exercising a call-back option of the interactive voice response unit or by abandoning the customer call, and in the event of detecting the termination of the customer call: opening a call-back record for the terminated customer call including any automatic number identifier information delivered; query one or more database to retrieve customer identifier data, and associate the call-back record with an identified customer via the customer identifier data; retrieve from the non-transitory machine-readable memory any of the call history information and the customer profile information associated with the identified customer; determine a 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; classify the identified customer into one of a first value group and a second value group based on the value prediction signal; and direct the telephone calling device for placing outbound customer calls: in the event the call-back management module classifies the identified customer into the first value group, to automatically call back the identified customer for connection to a selected agent of the call center; in the event the call-back management module classifies the identified customer into the second value group, to automatically execute a subordinate call-back procedure.

“Other objects, features, and advantages of the present disclosure will become apparent with reference to the drawings and detailed description of the illustrative embodiments that follow.”

The claims supplied by the inventors are:

“What is claimed is:

“1. A processor based method for managing customer calls within a call center, comprising: in response to receiving a customer call from an identified customer at an inbound call receiving device: opening, by a processor, an inbound call record for the customer call including any automatic number identifier information delivered with the customer call; monitoring, by the processor, the customer call of the identified customer to retrieve interactive voice response (IVR) data received from the identified customer via interaction with an IVR unit, and updating the inbound call record for the identified customer with IVR data retrieved; detecting, by the processor, a termination of the customer call; in the event of detecting the termination of the customer call: executing, by the processor, a predictive machine learning model configured to determine a value prediction signal representative of a likelihood that the identified customer will accept an offer for a product purchase, by inputting information from the inbound call record for the customer call the call-back record for the identified customer into a logistic regression model operating in conjunction with a tree based model; classifying, by the predictive model executing on the processor based on the value prediction signal determined by the predictive model, the identified customer into one of a first value group and a second value group; in the event the classifying step classifies the identified customer into the first value group, assigning, by the processor, the identified customer to a priority call-back queue assignment; in the event the classifying step classifies the identified customer into the second value group, assigning, by the processor, the identified customer to a subordinate call-back queue assignment; and automatically calling back the identified customer, by an automatic calling device in communication with the processor, based on the priority call-back queue assignment or the subordinate call-back queue assignment for the identified customer.

“2. The processor based method of claim 1, wherein the predictive machine-learning model is continually trained by inputting updated customer demographic data, payment data, marketing costs data, and lapse data into the logistic regression model operating in conjunction with a tree based model.

“3. The processor based method of claim 1, further comprising the step of opening, by the processor, a call-back record for the terminated customer call including any automatic number identifier information delivered and any IVR data in the inbound call record, wherein the executing step inputting the call-back record for the terminated customer call into the logistic regression model operating in conjunction with the tree based model.

“4. The processor based method of claim 3, further comprising the step, after opening the call-back record for the terminated customer call, of retrieving customer demographic data associated with the identified customer; wherein the predictive machine learning model is configured to determine the value prediction signal by inputting the call-back record for the identified customer and the customer demographic data associated with the identified customer into the logistic regression model operating in conjunction with the tree based model.

“5. The processor based method of claim 3, further comprising the step, after opening the call-back record for the terminated customer call, of retrieving call history information associated with the identified customer; wherein the predictive machine learning model is configured to determine the value prediction signal by inputting the call-back record for the identified customer and the call history information associated with the identified customer into the logistic regression model operating in conjunction with the tree based model.

“6. The processor based method of claim 5, wherein the automatically calling back the identified customer by the automatic calling device based the priority call-back queue assignment comprises call-back by a selected agent of the call center, wherein the selected agent is selected based on the call history information associated with the identified customer.

“7. The processor based method of claim 1, wherein the detecting step comprises detecting any termination of the customer call by exercising a call-back option via the interactive voice response unit or by abandoning the customer call, wherein the identified customer is associated with one or more of an ANI or DNIS delivered with the customer call, and a call-back number provided by the inbound caller while exercising the call-back option.

“8. The processor based method of claim 1, wherein the step of monitoring the customer call of the identified customer further updates the inbound call record with inbound queue data retrieved by monitoring an inbound call queue including the identified customer, and the step of opening the call-back record for the terminated customer call further includes in the call-back record any inbound queue data in the inbound call record.

“9. The processor based method of claim 8, wherein the inbound queue data include one or more of waiting time of the customer call spent in the inbound queue, and identity of the inbound queue among multiple inbound queues of the call center.

“10. The processor based method of claim 1, wherein the priority call-back queue assignment is a queue position in a priority call-back queue, and the subordinate call-back queue assignment is a queue position in a subordinate call-back queue.

“11. The processor based method of claim 10, wherein the priority call-back queue is a queue for immediate call-back, and the subordinate call-back queue is a queue for deferred call-back.

“12. The processor based method of claim 10, wherein the priority call-back queue is a queue for call-back by higher-skill agents, and the subordinate call-back queue is a queue for call-back by lower-skill agents.

“13. The processor based method of claim 1, wherein the automatically calling back the identified customer by the automatic calling device based the priority call-back queue assignment comprises call-back by a selected agent of the call center.

“14. The processor based method of claim 13, wherein the selected agent of the call center is selected by matching customer identifier data to private call-back data associated with the selected agent.

“15. A system for managing customer calls within a call center, comprising: an inbound telephone call receiving device for receiving a customer call from an inbound caller to the call center; a telephone calling device for placing outbound customer calls from the call center; an interactive voice response unit; a predictive modeling module that stores a predictive model of customer value, wherein the predictive model comprises a logistic regression model operating in conjunction with a tree based model; and a processor, configured to execute a call-back management module, 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: generate an inbound call record including any automatic number identifier information delivered with the customer call received by the inbound telephone call receiving device; monitor the customer call to detect a termination of the customer call, and in the event of detecting the termination of the customer call: open a call-back record for the terminated customer call and input the call-back record into the predictive modeling module; determine a value prediction signal representative of representative of a likelihood that the identified customer will accept an offer for a product purchase; classify the identified customer into one of a first value group and a second value group based on the value prediction signal; and direct the telephone calling device for placing outbound customer calls: in the event the call-back management module classifies the identified customer into the first value group, to automatically call back the identified customer based on a priority call-back queue assignment; in the event the call-back management module classifies the identified customer into the second value group, to automatically call back the identified customer based on a subordinate call-back queue assignment.

“16. The system according to claim 15, wherein the set of instructions further comprise an instruction to retrieve customer demographic data associated with the identified customer and input the retrieved customer demographic data into the predictive modeling module.

“17. The system according to claim 15, wherein the predictive machine-learning model is continually trained by inputting updated customer demographic data, payment data, marketing costs data, and lapse data into the logistic regression model operating in conjunction with a tree based model.

“18. The system according to claim 15, further comprising non-transitory machine-readable memory that stores call history information for customers of the call center, wherein the set of instructions further comprise an instruction to retrieve from the non-transitory machine-readable memory any of the call history information associated with the identified customer and input the retrieved call history information into the predictive modeling module.

“19. The system according to claim 15, wherein the priority call-back queue assignment is a queue position in a priority call-back queue, and the subordinate call-back queue assignment is a queue position in a subordinate call-back queue.

“20. The system according to claim 15, wherein priority call-back queue assignment comprises call-back by a selected agent of the call center.”

For additional information on this patent, see: Merritt, Sears. System And Method For Managing Customer Call-Backs. U.S. Patent Number 10,582,060, filed April 8, 2019, and published online on March 16, 2020. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=10,582,060.PN.&OS=PN/10,582,060RS=PN/10,582,060

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