SYSTEM AND METHOD FOR INTELLIGENT MODELING FOR INSURANCE MARKETING

• US 20110153419A1
• Filed: 12/21/2009
• Published: 06/23/2011
• 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
• Granted Patent: US 8,543,445 B2
• Time in Patent Office: Days
• US Class Current: 705/14.52
• 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, said at least one equation defining a predictive model responsive to a plurality of independent variables and a plurality of parameters, and having an associated error component, the error component including a spatial error component;

evaluate said 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;

generate, based on results of applying the parameterized predictive model to the data set, a list of selected prospects, said list of selected prospects representing a subset of said universe of potential prospects; and

output said 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 spatial error component is a spatio-temporal error component.

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 dependent variable that represents a predicted insurance policy premium amount.

7. The computer system of claim 1, wherein the predictive model has a dependent variable that represents a probability that a respective prospect will respond to a marketing offer.

8. The computer system of claim 1, wherein the predictive model has a dependent variable that represents a probability that a respective prospect will be retained as a customer.

9. 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

10. 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, said at least one equation defining a predictive model responsive to a plurality of independent variables and a plurality of parameters, and having an associated error component, the error component including a temporal error component;

evaluate said 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;

generate, based on results of applying the parameterized predictive model to the data set, a list of selected prospects, said list of selected prospects representing a subset of said universe of potential prospects; and

output said list of selected prospects.

11. The computer system of claim 10, wherein the evaluation of the parameters includes iteratively applying Bayesian inference using a training sample data set.

12. The computer system of claim 11, wherein hierarchical Bayesian inference is applied in evaluating the parameters.

13. The computer system of claim 10, wherein the predictive model has a dependent variable that represents a predicted insurance policy premium amount.

14. The computer system of claim 10, wherein the predictive model has a dependent variable that represents a probability that a respective prospect will respond to a marketing offer.

15. The computer system of claim 10, wherein the predictive model has a dependent variable that represents a probability that a respective prospect will be retained as a customer.

16. 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, said at least one equation defining a predictive model responsive to a plurality of independent variables and a plurality of parameters, and having an associated error component;

evaluate said 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;

generate, based on results of applying the parameterized predictive model to the data set, a list of selected prospects, said list of selected prospects representing a subset of said universe of potential prospects; and

output said list of selected prospects;

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

17. The computer system of claim 16, wherein the predictive model has a dependent variable that represents a predicted insurance policy premium amount.

18. The computer system of claim 16, wherein the predictive model has a dependent variable that represents a probability that a respective prospect will respond to a marketing offer.

19. The computer system of claim 16, wherein the predictive model has a dependent variable that represents a probability that a respective prospect will be retained as a customer.

20. 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, said at least one equation defining a predictive model responsive to a plurality of independent variables and a plurality of parameters, and having an associated error component, the error component including a spatio-temporal error component;

evaluate said 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;

generate, based on results of applying the parameterized predictive model to the data set, a list of selected prospects, said list of selected prospects representing a subset of said universe of potential prospects; and

output said list of selected prospects;

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

21. The computer system of claim 20, wherein the spatio-temporal error component is nonseparable.

22. The computer system of claim 20, wherein the predictive model has a dependent variable that represents a predicted insurance policy premium amount.

23. The computer system of claim 20, wherein the predictive model has a dependent variable that represents a probability that a respective prospect will respond to a marketing offer.

24. The computer system of claim 20, wherein the predictive model has a dependent variable that represents a probability that a respective prospect will be retained as a customer.

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

storing at least one equation in a computer, said at least one equation defining a predictive model responsive to a plurality of independent variables and a plurality of parameters, and having an associated error component;

evaluating said 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;

generating by the computer, based on results of applying the parameterized predictive model to the data set, a list of selected prospects, said list of selected prospects representing a subset of said universe of potential prospects; and

outputting said list of selected prospects.

26. The computer system of claim 25, wherein the predictive model has a dependent variable that represents a predicted insurance policy premium amount.

27. The computer system of claim 25, wherein the predictive model has a dependent variable that represents a probability that a respective prospect will respond to a marketing offer.

28. The computer system of claim 25, wherein the predictive model has a dependent variable that represents a probability that a respective prospect will be retained as a customer.

29. A computer implemented method for targeted insurance marketing communications comprising the steps of:

defining, in a processing module, a predictive model based on a desirable customer characteristic, the model further having time and space components;

receiving, via a communications module, a data set representative of a plurality of potential customers, the potential customers represented in part by at least some time and space data;

applying, via the processing module, the predictive model to a potential customer data set to determine one or more candidate customers;

selecting, via the processing module, one or more candidate customers having the desirable customer characteristics in a certain time and space;

composing, via the user interface module, at least one targeted insurance marketing communication for the one or more candidate customers; and

transmitting, via the communications module, the targeted insurance marketing communication to the one or more candidate customers.

30. The computer implemented method of claim 29, wherein the desirable customer characteristic is at least one of highest predicted premium, probability of response and probability of retention.

31. The computer implemented method of claim 29, wherein the targeted insurance marketing communication is at least one of an SMS text message, an MMS message and an electronic mail message.