System and method for identifying a targeted consumer

• US 8,650,184 B2
• Filed: 02/04/2011
• Issued: 02/11/2014
• Est. Priority Date: 02/04/2011
• Status: Active Grant

• Current Assignee: Twenty-Ten Incorporated
• Original Assignee: Twenty-Ten Incorporated
• Inventors: Kowalchuk, Craig, Smith, Sheldon, Diamond, David, Ferris, Raymond
• Primary Examiner(s): Ruiz, Angelica
• Application Number:
  US13/020,967
• Publication Number:
  US 20120203780A1
• Time in Patent Office: 1,103 Days
• Field of Search: None
• US Class Current: 707/722
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A method. The method includes receiving, at a computing device, data associated with a first plurality of consumers. The data comprises a combination of behavioral data and attitudinal data. The method also includes assigning a consumer of the first plurality of consumers to a first respective segments based on the received data, wherein the assigning is performed by the computing device. The method further includes calculating a goodness-of-fit score for the consumer of the first plurality of consumers for the first segment, wherein the calculating is performed by the computing device. Additionally, the method includes calculating a predicted goodness-of-fit score for a consumer of a second plurality of consumers for the first segment, the second plurality of consumers including at least the first plurality of consumers, wherein the calculating is performed by the computing device.

Claims

1. A system, comprising:

a computing device, wherein the computing device comprises;

a processor;

a subgroup selection module communicably connected to the processor, wherein the subgroup selection module is configured to select a subgroup of consumers from a list of consumers;

a placement module communicably connected to the processor, wherein the placement module is configured to assign a consumer of the subgroup to a first segment based on information associated with the consumer, the information comprising a combination of behavioral data and attitudinal data, wherein;

the behavioral data is associated with behavior of the consumer; and

the attitudinal data is associated with an attitude of the consumer expressed in one or more survey responses;

a scoring module communicably connected to the processor, wherein the scoring module is configured to calculate a goodness-of-fit score for the consumer of the subgroup for the first segment; and

a scoring prediction module communicably connected to the processor, wherein the scoring prediction module is configured to calculate a predicted goodness-of-fit score for another consumer from the list of consumers.

2. The system of claim 1, wherein the behavioral data comprises data indicative of an actual past behavior associated with the consumer.

3. The system of claim 1, wherein the behavioral data comprises data indicative of a self-reported future predisposition associated with the consumer.

4. The system of claim 1, wherein the behavioral data comprises a combination of:

data indicative of an actual past behavior associated with the consumer; and

data indicative of a self-reported future predisposition associated with the consumer.

5. The system of claim 1, wherein the placement module is further configured to identify:

at least one behavioral dimension based on the behavioral data; and

at least one attitudinal dimension based on the attitudinal data.

6. The system of claim 5, wherein the placement module is further configured to define different segments based on different combinations of behavioral and attitudinal dimensions.

7. The system of claim 1, further comprising a survey module communicably connected to the processor, wherein the survey module is configured to perform at least one of the following:

create statements and questions which are to be presented to the consumers selected for the subgroup; and

receive data associated with responses to the statements and questions.

8. The system of claim 1, further comprising a significance module communicably connected to the processor, wherein the significance module is configured to determine a correlation between the goodness-of-fit score and one or more non-attitudinal variables associated with the consumer of the subgroup.

9. The system of claim 1, further comprising a predictive algorithm module communicably connected to the processor, wherein the predictive algorithm module is configured to generate an algorithm which predicts the goodness-of-fit score calculated for the consumer of the first subgroup for the first segment.

10. The system of claim 1, further comprising a validation module communicably connected to the processor, wherein the validation module is configured to determine a performance of a predictive algorithm.

11. The system of claim 1, further comprising a ranking module communicably connected to the processor, wherein the ranking module is configured to rank, on a segment by segment basis, the consumers on the list of consumers based on the predicted goodness-of-fit scores calculated by the scoring prediction module.

12. A method, comprising:

receiving, at a computing device, data associated with a first plurality of consumers, wherein said data comprises a combination of behavioral data and attitudinal data, wherein;

the behavioral data is associated with one or more behaviors of the consumers; and

the attitudinal data is associated with one or more attitudes of the consumers expressed in one or more survey responses;

assigning a consumer of the first plurality of consumers to a first segment based on the received data, wherein the assigning is performed by the computing device;

calculating a goodness-of-fit score for the consumer of the first plurality of consumers for the first segment, wherein the calculating is performed by the computing device; and

calculating a predicted goodness-of-fit score for a consumer of a second plurality of consumers for the first segment, the second plurality of consumers including at least the first plurality of consumers, wherein the calculating is performed by the computing device.

13. The method of claim 12, wherein receiving the combination of behavioral data and attitudinal data comprises receiving:

data indicative of an actual past behavior associated with the first plurality of consumers; and

attitudinal data associated with the first plurality of consumers.

14. The method of claim 12, wherein receiving the combination of behavioral data and attitudinal data comprises receiving:

data indicative of a self-reported future predisposition associated with the first plurality of consumers; and

attitudinal data associated with the first plurality of consumers.

15. The method of claim 12, wherein receiving the combination of behavioral data and attitudinal data comprises receiving:

data indicative of an actual past behavior associated with the first plurality of consumers;

data indicative of a self-reported future predisposition associated with the first plurality of consumers; and

attitudinal data associated with the first plurality of consumers.

16. The method of claim 12, wherein assigning the consumer comprises assigning the consumer based on the following:

at least one behavioral dimension associated with the received data; and

at least one attitudinal dimension associated with the received data.

17. The method of claim 12, wherein calculating the goodness-of-fit score comprises calculating the goodness-of-fit score based on at least one dimension associated with the received data.

18. The method of claim 12, wherein calculating the predicted goodness-of-fit score comprises calculating the predicted goodness-of-fit score utilizing a predictive algorithm.

19. The method of claim 12, further comprising defining:

at least one behavioral dimension based on the received data; and

at least one attitudinal dimension based on the received data, wherein the defining is performed by the computing device.

20. The method of claim 12, further comprising defining the first segment based on the following:

the received behavioral data; and

the received attitudinal data, wherein the defining is performed by the computing device.

21. The method of claim 12, further comprising determining a correlation between the goodness-of-fit score and one or more non-attitudinal variables associated with the consumer of the first plurality of consumers, wherein the determining is performed by the computing device.

22. The method of claim 21, wherein determining the correlation comprises determining a cross-correlation between different non-attitudinal variables.

23. The method of claim 12, further comprising validating a performance of a predictive algorithm, wherein the validating is performed by the computing device.