Consumer profiling and advertisement selection system

• US 20060230053A1
• Filed: 06/09/2006
• Published: 10/12/2006
• Est. Priority Date: 04/19/2001
• Status: Abandoned Application

• Current Assignee: Prime Research Alliance E, LLC
• Original Assignee: Prime Research Alliance E., Inc.
• Inventors: Eldering, Charles
• Application Number:
  US11/450,252
• Publication Number:
  US 20060230053A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface
• CPC Class Codes: Y10S 707/99944 Object-oriented database structure
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing
• CPC Class Codes: Y10S 707/99948 Application of database or data structure, e.g. distributed, multimedia, or image

Abstract

A consumer profiling and advertisement selection system is presented in which consumers or subscribers can be characterized based on their purchase or viewing habits. The result of this process is a consumer characterization vector describing the probabilistic demographics and product preferences of the subscriber or viewer. Advertisement characterization vectors describing an actual or hypothetical market for a product or desired viewing audience can be determined. The ad characteristics including an ad demographic vector, an ad product category and an ad product preference vector is transmitted along with a consumer ID. The consumer ID is used to retrieve a consumer characterization vector which is correlated with the ad characterization vector to determine the suitability of the advertisement to the consumer. A price for displaying the advertisement can be determined based on the results of the correlation of the ad characteristics with the consumer characterization vector.

Claims

1. A method of profiling users in an computer environment, said method comprising:

(a) receiving a purchase history corresponding to a unique user and including information related to the purchase of at least one item by said unique user;

(b) receiving demographic information corresponding to said unique user;

(c) receiving product characterization information describing a statistical relationship between a particular product and demographic characteristics of purchasers of the product;

(d) extrapolating additional demographic information from said purchase history and said product characterization information; and

(e) updating said demographic information based on said additional demographic information.

2. The method of claim 1, wherein said product characterization information is developed based on a market study of a user population that visits a particular website.

3. The method of claim 1, wherein said demographic information is received from a user demographic profile corresponding to said unique user.

4. The method of claim 3, further comprising:

(f) identifying missing demographic information in said user demographic profile.

7. The method of claim 1, wherein said product characterization information does not include information about specific users.

8. The method of claim 1, further comprising:

(f) targeting advertisements based on said updated demographic information.

9. The method of claim 1, further comprising:

(f) comparing said updated demographic information to a target expression;

(g) generating a score based on said comparing; and

(h) delivering an advertisement based on said score.

5. The method of claim 5, wherein at least a portion of said missing demographic information in said user demographic profile is obtained through said additional demographic information.

6. The method of claim 5, further comprising:

(g) returning said updated demographic information of step (e) to said user demographic profile.

10. A method of targeting ads in an computer environment based on user segmentation, said method comprising:

(a) receiving user profile information corresponding to a unique user;

(b) assigning, based on said user profile information, said unique user to a population segment; and

(c) comparing said population segment of said unique user to an ad segment characterization corresponding to at least one advertisement.

11. The method of claim 10, further comprising:

(d) calculating a correlation factor between said ad segment characterization and said population segment of said unique user; and

(e) targeting an ad based on said correlation factor.

12. The method of claim 10, wherein said assigning is realized by creating a population segment vector that describes the population segment of said unique user.

13. The method of claim 12, wherein said ad segment characterization is represented by an ad segment vector.

14. The method of claim 13, wherein said comparing is realized by comparing said population segment vector to said ad segment vector.

15. The method of claim 14, further comprising:

(d) calculating a correlation factor between said ad segment vector and said population segment vector; and

(e) targeting an ad based on said correlation factor.

16. The method of claim 10, wherein said user profile information includes purchase history information.

17. The method of claim 16, wherein said purchase history information includes a record of at least one purchase.

18. The method of claim 10, wherein said user profile information includes demographic information.

19. A method of presenting cross-sell products in a computer environment, said method comprising:

(a) receiving a purchase history corresponding to a unique user and including information related to the purchase of at least one product;

(b) receiving product characterization information for said at least one product wherein said product characterization describes a relationship between said at least one product and demographic characteristics of purchasers of said at least one product;

(c) calculating a consumer characterization vector based on said purchase history and said product characterization information; and

(d) suggesting items based on said consumer characterization vector.

20. The method of claim 19, wherein said consumer characterization vector is based on a combination of a demographic characterization vector and a product preference vector.

21. The method of claim 20, wherein the suggestion made in step (d) is based on the product preference vector.

22. A method of profiling users in a computer environment, said method comprising:

(a) receiving a purchase history wherein said purchase history corresponding to a unique user and said purchase history including information related to the purchase of at least one item by said unique user;

(b) receiving demographic information corresponding to said unique user;

(c) receiving product characterization information describing a statistical relationship between a particular product and demographic characteristics of purchasers of the product;

(d) calculating additional demographic information from said purchase history and said product characterization information; and

(e) updating said demographic information based on said additional demographic information.