Behavioral targeting system that generates user profiles for target objectives

• US 20070239535A1
• Filed: 03/29/2006
• Published: 10/11/2007
• Est. Priority Date: 03/29/2006
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Yin, Hongfeng, Lin, Long-Ji, Koran, Joshua, Chung, Christina
• Granted Patent: US 8,438,170 B2
• Time in Patent Office: Days
• US Class Current: 705/14.250
• CPC Class Codes: G06F 16/212 with details for data modelling support
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/28 Databases characterised by their database models, e.g. relational or object models
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0251 Targeted advertisements

Abstract

A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Claims

1. A method for determining user behavior from online activity, said method comprising:

storing a model comprising a plurality of parameters for determining a user score for a targeting objective;

receiving, at an entity, event information from at least one event, wherein said event comprises on-line activity between said user and said entity; and

generating at least one user interest score using said model and said event information to predict user propensity for success in said targeting objective.

2. The method as set forth in claim 1, wherein said targeting objective comprises a marketing objective.

3. The method as set forth in claim 2, wherein said marketing objective comprises brand awareness.

4. The method as set forth in claim 2, wherein said marketing objective comprises direct response advertising.

5. The method as set forth in claim 4, wherein said direct response advertising marketing objective comprises user acquisition.

6. The method as set forth in claim 4, wherein said direct response advertising marketing objective comprises user retention.

7. The method as set forth in claim 4, wherein said direct response advertising marketing objective comprises engagement.

8. The method as set forth in claim 4, wherein said direct response advertising marketing objective comprises monetization.

9. A system for determining user behavior from online activity, said system comprising:

storage for storing a model comprising a plurality of parameters for determining a user score for a targeting objective; and

at least one server, coupled to said storage, for receiving event information from at least one event, wherein said event comprises on-line activity between said user and said entity, and for generating at least one user interest score using said model and said event information to predict user propensity for success in said targeting objective.

10. The system as set forth in claim 9, wherein said targeting objective comprises a marketing objective.

11. The system as set forth in claim 10, wherein said marketing objective comprises brand awareness.

12. The system as set forth in claim 10, wherein said marketing objective comprises direct response advertising.

13. The system as set forth in claim 12, wherein said direct response advertising marketing objective comprises user acquisition.

14. The system as set forth in claim 12, wherein said direct response advertising marketing objective comprises user retention.

15. The system as set forth in claim 12, wherein said direct response advertising marketing objective comprises engagement.

16. The system as set forth in claim 12, wherein said direct response advertising marketing objective comprises monetization.

19. The computer readable medium as set forth in claim 13, wherein said marketing objective comprises brand awareness.

20. The computer readable medium as set forth in claim 13, wherein said marketing objective comprises direct response advertising.

17. A computer readable medium comprising a set of instructions which, when executed by a computer, cause the computer to perform user behavior from online activity, said instructions for:

storing a model comprising a plurality of parameters for determining a user score for a targeting objective;

receiving, at an entity, event information from at least one event, wherein said event comprises on-line activity between said user and said entity; and

generating at least one user interest score using said model and said event information to predict user propensity for success in said targeting objective.

18. The computer readable medium as set forth in claim 17, wherein said targeting objective comprises a marketing objective.

21. A method for determining user interests from online activity, said method comprising:

receiving, at an entity, event information from at least one event, wherein said event comprises on-line activity between said user and said entity;

selecting at least one targeting objective; and

generating at least one user interest score to predict user propensity for success in said targeting objective.

22. The method as set forth in claim 21,

wherein:

selecting at least one targeting objective from a plurality of targeting objectives comprises;

storing a plurality of models at an entity, wherein a model defines a plurality of parameters for a targeting objective;

selecting a model that corresponds to said marketing objective; and

generating at least one user interest score by applying said parameters from said model selected to said user event information.