System and method for direct mailing insurance solicitations utilizing hierarchical bayesian inference for prospect selection

• US 8,543,445 B2
• Filed: 12/21/2009
• Issued: 09/24/2013
• Est. Priority Date: 12/21/2009
• Status: Active Grant

• Current Assignee: Hartford Fire Insurance Company (The Hartford Financial Services Group, Inc.)
• Original Assignee: Hartford Fire Insurance Company (The Hartford Financial Services Group, Inc.)
• Inventors: Hall, Arlest Bryon III
• Primary Examiner(s): Jarrett, Scott L
• Application Number:
  US12/643,344
• Publication Number:
  US 20110153419A1
• Time in Patent Office: 1,373 Days
• Field of Search: 705/7.29, 705/7.31
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0254 based on statistics

Abstract

A computer system selects prospects for insurance policy marketing activities. The computer system stores at least one equation that defines a predictive model responsive to a plurality of independent variables and a plurality of parameters and having an associated error component. The error component includes a spatio-temporal error component. The computer system also evaluates the parameters in order to parameterize the predictive model in such a way as to reduce the error component. Further, the computer system applies the parameterized predictive model to a data set that represents a universe of potential prospects for insurance marketing activities. Still further, the computer system generates a list of selected prospects, based on results of applying the parameterized predictive model to the data set. The list of selected prospects represents a subset of the universe of potential prospects. The computer system also outputs the list of prospective prospects.

Claims

1. A computer system for selecting prospects for insurance marketing activities, the computer system comprising:

a processor; and

a memory in communication with the processor and storing program instructions, the processor operative with the program instructions to;

store at least one equation, the at least one equation defining a predictive model responsive to a plurality of independent variables and a plurality of parameters, and having an associated spatio-temporal error component, the predictive model including a first dependent variable that represents a probability that a respective prospect will respond to a marketing offer, a second discrete dependent variable that represents a probability that the respective prospect will be retained as a customer, and a third dependent variable that represents a predicted insurance premium amount that would be paid by the respective prospect, wherein the second discrete dependent variable has a value of noncontinuation for prospects expected to cancel a policy within a first time period, continuation for prospects expected to cancel the policy within a second time period that is longer than the first time period, and renewal for prospects expected to continue as policy-holders for a third time period that is longer than the second time period;

evaluate the parameters to parameterize the predictive model in such a way as to reduce the error component;

apply the parameterized predictive model to a data set, the data set representing a universe of potential prospects for insurance marketing activities and including, for each potential prospect, a spatial component represented as a distance matrix identifying a distance between each of the potential prospects;

generate, based on results of applying the parameterized predictive model to the data set, a list of selected prospects, the list of selected prospects representing a subset of the universe of potential prospects, wherein the list is ranked according to a score based on (1) the first dependent variable that represents the probability that the respective selected prospect will respond to the marketing offer, (2) the second discrete dependent variable that represents the probability that the respective selected prospect will be retained as a customer, and (3) the third dependent variable that represents the predicted insurance premium amount that would be paid by the respective selected prospect, the predicted insurance premium determined based at least in part on the spatial component; and

output the list of selected prospects.

2. The computer system of claim 1, wherein the evaluation of the parameters includes iteratively applying Bayesian inference using a training sample data set.

3. The computer system of claim 2, wherein hierarchical Bayesian inference is applied in evaluating the parameters.

4. The computer system of claim 1, wherein the spatio-temporal error component is separable.

5. The computer system of claim 3, wherein the spatio-temporal error component is nonseparable.

6. The computer system of claim 1, wherein the predictive model has a fourth dependent variable that represents a probability that the respective prospect would submit a claim under the policy written for the respective prospect and wherein the score used to rank the selected prospects is further based upon the fourth dependent variable.

7. The computer system of claim 1, wherein the processor is further operative with the program instructions to:

receive text input that represents a solicitation for an insurance product;

generate mailing materials that contain the solicitation;

print the mailing materials; and

dispatch the printed mailing materials to the selected prospects.

8. A computer system for selecting prospects for insurance marketing activities, the computer system comprising:

a processor; and

a memory in communication with the processor and storing program instructions, the processor operative with the program instructions to;

store at least one equation, the at least one equation defining a predictive model responsive to a plurality of independent variables and a plurality of parameters, and having an associated spatio-temporal error component, wherein the predictive model has a first dependent variable that represents a probability that a respective prospect will respond to a marketing offer, a second dependent variable that represents a probability that a respective prospect will be retained as a customer, and a third dependent variable that represents a predicted insurance premium amount that would be paid by a potential prospect, wherein the second discrete dependent variable has a value of noncontinuation for prospects expected to cancel a policy within a first time period, continuation for prospects expected to cancel a policy within a second time period that is longer than the first time period, and renewal for prospects expected to continue as policy-holders for a third time period that is longer than the second time period;

evaluate the parameters to parameterize the predictive model in such a way as to reduce the error component;

apply the parameterized predictive model to a data set, the data set representing a universe of potential prospects for insurance marketing activities and including, for each potential prospect, a spatial component represented as a distance matrix identifying a distance between each of the potential prospects;

generate, based on results of applying the parameterized predictive model to the data set, a list of selected prospects, the list of selected prospects representing a subset of the universe of potential prospects, wherein the list is ranked according to a score based on (1) the first dependent variable that represents the probability that the respective selected prospect will respond to the marketing offer, (2) the second discrete dependent variable that represents the probability that the respective selected prospect will be retained as a customer, and (3) the third dependent variable that represents the predicted insurance premium that would be paid by the respective selected prospect, the predicted insurance premium determined based at least in part on the spatial component; and

output the list of selected prospects;

wherein the evaluation of the parameters includes iteratively applying Bayesian inference using a training sample data set.

9. The computer system of claim 8, wherein the spatio-temporal error component is nonseparable.

10. The computer system of claim 8, wherein the predictive model has a fourth dependent variable that represents a probability that the respective prospect would submit a claim under a policy written for the respective prospect and wherein the score used to rank the selected prospects is further based upon the fourth dependent.

13. The computer system of claim 8, wherein the spatio-temporal error component is separable.

14. The computer system of claim 8, wherein the processor is further operative with the program instructions to:

receive text input that represents a solicitation for an insurance product;

generate mailing materials that contain the solicitation;

print the mailing materials; and

dispatch the printed mailing materials to the selected prospects.

11. A method for selecting prospects for insurance marketing activities, the method comprising:

storing at least one equation in a computer, the at least one equation defining a predictive model responsive to a plurality of independent variables and a plurality of parameters, and having an associated spatio-temporal error component, wherein the predictive model has a first dependent variable that represents a probability that a respective prospect will respond to a marketing offer, a second dependent variable that represents a probability that a respective prospect will be retained as a customer, and a third dependent variable that represents a predicted insurance premium amount that would be paid by a potential prospect, wherein the second discrete dependent variable has a value of noncontinuation for prospects expected to cancel a policy within a first time period, continuation for prospects expected to cancel a policy within a second time period that is longer than the first time period, and renewal for prospects expected to continue as policy-holders for a third time period that is longer than the second time period;

evaluating the parameters by the computer to parameterize the predictive model in such a way as to reduce the error component;

applying the parameterized predictive model by the computer to a data set, the data set representing a universe of potential prospects for insurance marketing activities and including, for each potential prospect, a spatial component represented as a distance matrix identifying a distance between each of the potential prospects;

generating by the computer, based on results of applying the parameterized predictive model to the data set, a list of selected prospects, the list of selected prospects representing a subset of the universe of potential prospects, wherein the list is ranked according to a score based on (1) the first dependent variable that represents the probability that the respective selected prospect will respond to the marketing offer, (2) the second discrete dependent variable that represents the probability that the respective selected prospect will be retained as a customer, and (3) the third dependent variable that represents the predicted insurance premium that would be paid by the respective selected prospect, the predicted insurance premium determined based at least in part on the spatial component; and

outputting the list of selected prospects.

12. The computer system of claim 11, wherein the predictive model has a fourth dependent variable that represents a probability that the respective prospect would submit a claim under a policy written for the respective prospect and wherein the score used to rank the selected prospects is further based upon the fourth dependent.

15. The method of claim 11, wherein evaluating the parameters by the computer comprises iteratively applying Bayesian inference using a training sample data set.

16. The method of claim 15, wherein hierarchical Bayesian inference is applied in evaluating the parameters.

17. The method of claim 11, wherein the spatio-temporal error component is separable.

18. The method of claim 16, wherein the spatio-temporal error component is nonseparable.

19. The method of claim 11, further comprising:

receiving by the computer text input that represents a solicitation for an insurance product;

generating mailing materials that contain the solicitation;

printing the mailing materials; and

dispatching the printed mailing materials to the selected prospects.