System and Method for Deep Targeting Advertisement Based on Social Behaviors

• US 20100262456A1
• Filed: 04/08/2009
• Published: 10/14/2010
• Est. Priority Date: 04/08/2009
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Oath Inc. (Verizon Communications Inc.)
• Inventors: Feng, Jun, Wei, Jianbin
• Application Number:
  US12/420,638
• Publication Number:
  US 20100262456A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0229 Multi-merchant loyalty card systems
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

A method and system to display a targeted online advertisement to a targeted online user based on offline behavior profiles. The method include techniques for identifying offline behavior aspects of the online user through a variety of databases, including databases of physical sites visited, databases of transactions and amounts, databases pertaining to retail loyalty cards and databases hosting occurrence of real world events. Techniques employed for codifying offline behavior include classifying behavior into categories or groups, categorizing behavior by location of an event, size of expenditure, nature of event, frequency of event, and/or periodicity of event. Having established some codified offline behavior of the online user, the system proceeds by correlating some aspect of user'"'"'s offline behavior to some aspect of an online advertisement. Given the correlation between the user'"'"'s offline behavior and the targeting characteristics of the advertisement, the advertisement is optionally customized and displayed to the targeted user.

Claims

1. A method to display an online advertisement to an online user based on offline behavior profiles, the method comprising:

identifying offline behavior of the online user;

matching the online user'"'"'s online profile to at least one online user'"'"'s offline profile;

correlating at least one first aspect of the online user'"'"'s offline profile to at least one second aspect of an online advertisement profile; and

using at least one third aspect of the online user'"'"'s offline profile to select at least one online advertisement for online display.

2. The method of claim 1, wherein identifying offline behavior includes at least one of, site visited, a transaction amount, a stated retail preference, retail loyalty card, an offline event, a periodicity of an event, a social network setting.

3. The method of claim 1, wherein identifying offline behavior includes classifying behavior into at least one category selected from the group, location of event, size of expenditure, nature of event, frequency of event, periodicity of event.

4. The method of claim 1, wherein identifying offline behavior includes predicting behavior based on correlated users.

5. The method of claim 1, wherein matching the online user'"'"'s online profile to at least one online user'"'"'s offline profile includes matching at least one of, a name, an address, a social security number, a user ID, a payment method, a event pattern, brand-derived behavior.

6. The method of claim 1, wherein matching the online user'"'"'s online profile to at least one online user'"'"'s offline profile includes using at least one of, a rule, a rule set, a predictive model, a plurality of predictive models.

7. The method of claim 1, wherein the first aspect is the same aspect as the third aspect.

8. A computer readable medium comprising a set of instructions which, when executed by a computer, cause the computer to display an online advertisement to an online user based on offline behavior profiles, the instructions for:

identifying offline behavior of the online user;

matching the online user'"'"'s online profile to at least one online user'"'"'s offline profile;

correlating at least one first aspect of the online user'"'"'s offline profile to at least one second aspect of an online advertisement profile; and

using at least one third aspect of the online user'"'"'s offline profile to select at least one online advertisement for online display.

9. The computer readable medium of claim 8, wherein identifying offline behavior includes at least one of, site visited, a transaction amount, a stated retail preference, retail loyalty card, an offline event, a periodicity of an event, a social network setting.

10. The computer readable medium of claim 8, wherein identifying offline behavior includes classifying behavior into at least one category selected from the group, location of event, size of expenditure, nature of event, frequency of event, periodicity of event.

11. The computer readable medium of claim 8, wherein identifying offline behavior includes predicting behavior based on correlated users.

12. The computer readable medium of claim 8, wherein matching the online user'"'"'s online profile to at least one online user'"'"'s offline profile includes matching at least one of, a name, an address, a social security number, a user ID, a payment method, a event pattern, brand-derived behavior.

13. The computer readable medium of claim 8, wherein matching the online user'"'"'s online profile to at least one online user'"'"'s offline profile includes using at least one of, a rule, a rule set, a predictive model, a plurality of predictive models.

14. The computer readable medium of claim 8, wherein the first aspect is the same aspect as the third aspect.

15. An ad server network for display of an online advertisement to an online user based on offline behavior profiles, the server network comprising:

a module for identifying offline behavior of the online user;

a module for matching the online user'"'"'s online profile to at least one online user'"'"'s offline profile;

a module for correlating at least one first aspect of the online user'"'"'s offline profile to at least one second aspect of an online advertisement profile; and

a module for using at least one third aspect of the online user'"'"'s offline profile to select at least one online advertisement for online display.

16. The server network of claim 15, wherein identifying offline behavior includes at least one of, site visited, a transaction amount, a stated retail preference, retail loyalty card, an offline event, a periodicity of an event, a social network setting.

17. The server network of claim 15, wherein identifying offline behavior includes classifying behavior into at least one category selected from the group, location of event, size of expenditure, nature of event, frequency of event, periodicity of event.

18. The server network of claim 15, wherein identifying offline behavior includes predicting behavior based on correlated users.

19. The server network of claim 15, wherein matching the online user'"'"'s online profile to at least one online user'"'"'s offline profile includes matching at least one of, a name, an address, a social security number, a user ID, a payment method, a event pattern, brand-derived behavior.

20. The server network of claim 15, wherein matching the online user'"'"'s online profile to at least one online user'"'"'s offline profile includes using at least one of, a rule, a rule set, a predictive model, a plurality of predictive models.