System and method for managing routing of customer calls to agents
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.
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.