Targeting Using Historical Data

• US 20090063268A1
• Filed: 09/04/2007
• Published: 03/05/2009
• Est. Priority Date: 09/04/2007
• Status: Abandoned Application

• Current Assignee: Yahoo Holdings, Inc. (Verizon Communications Inc.)
• Original Assignee: Yahoo Holdings, Inc. (Verizon Communications Inc.)
• Inventors: Kapur, Shyam, Burgess, David A.
• Application Number:
  US11/849,986
• Publication Number:
  US 20090063268A1
• Time in Patent Office: Days
• US Class Current: 705/14.390
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0239 Online discounts or incentives
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

A method of advertising receives a data log that includes the activities of users. The users have unique identifiers and associated profiles that form a user base. The method segments the user base into user segments by types of users. Hence, a first user segment is formed. The users within the first user segment have a profile similarity. The method groups publisher inventory, and forms a first publisher group. The publishers provide content to the users. The method categorizes advertisements and thereby generates a first ad category. The advertisements relate to a marketer, which has various marketer data. The method targets a first advertisement within the first ad category based on at least one of the first ad category, the publisher grouping, and the user segments. A system for ad targeting includes a user module, a publisher module, a marketer and/or advertisement module, and a matching engine. The user module is for receiving a plurality of users and segmenting the users into user segments including a first user segment. The publisher module is for receiving several publishers'"'"' inventory and grouping the publishers'"'"' inventory into publisher groups that include a first publisher group that has a first inventory location for the presentation of advertising. The marketer-ad module is for receiving advertisements and categorizing the advertisements into ad categories that include a first ad category. The matching engine is for matching the first publisher group and/or the first user segment to the first ad category. The matching engine is also for ranking ads and placing within the first inventory location a first advertisement from the first ad category.

Claims

1. A method of advertising comprising:

receiving a data log comprising the activities of users, the users having unique identifiers and associated profiles, the users comprising a user base;

segmenting the user base into user segments by types of users, thereby generating a first user segment, the users within the first user segment having a profile similarity;

grouping publishers, thereby generating a first publisher group, the publishers having a plurality of pages for providing content to the users;

categorizing advertisements thereby generating a first ad category, the advertisements relating to a marketer having marketer data; and

targeting a first advertisement within the first ad category based on at least one of the first ad category, the publisher grouping, and the user segments.

2. The method of claim 1, the publisher sites comprising inventory for the presentation of advertisements, the targeting further comprising:

selecting an advertisement, and

placing the selected advertisement in a first inventory location.

3. The method of claim 1, wherein targeting further comprises:

determining and using an attribute value for a user action, the user action comprising one or more of an impression, a click, a lead, and an acquisition.

4. The method of claim 3, the attribute value comprising a propensity score for the user action, the propensity based on an advertisement and at least one of a user segment, a publisher category, and a marketer category, the targeting based on the propensity score.

5. The method of claim 4, further comprising:

determining an average propensity by using the propensity score.

6. The method of claim 1, wherein the first advertisement comprises a relevance to the user, the targeting based on the relevance to the user.

7. The method of claim 1, wherein grouping publishers comprises a first context, wherein the first context comprises one of a predetermined web page and a predetermined time.

8. The method of claim 1, further comprising:

by using an optimization algorithm, optimizing a combination for at least two of an advertisement a user segment, a publisher group context.

9. The method of claim 8, the optimizing further comprising:

matching the first advertisement to the first user segment by using an attribute value.

10. The method of claim 8, the optimizing further comprising:

matching the first advertisement to inventory within the first publisher group by using an attribute value.

11. The method of claim 1, the first ad category comprising an ad campaign, the method further comprising:

determining a campaign performance metric for one or more of;

each user segment, each publisher category, and each type of ad.

12. The method of claim 1, the marketer data comprising one or more of:

advertisement purchase data including ad placement, ad targeting, time and ad cost; and

advertisement performance data including one or more of rate of impression, click rate, lead generation rate, and acquisition rate.

14. The method of claim 1, the advertising data for multiple forms of advertising including graphical ads, precision match, content match and domain match.

13. (canceled)

15. A method of advertising comprising:

segmenting users based on user information, the user information stored in a user profile, the user profile including at least one of demographic information, geographic information, behavior information, and information that is representative of a user segment;

grouping publisher web pages based on intrinsic content within the web pages;

categorizing advertisements based on an ad campaign;

identifying a set of attributes that relate at least two of;

the user segments,

the publisher groups,

the ad categories, and

time of day;

determining values for each attribute, the values representing the strength of the relationships between;

the user segments, the publisher groups, and the ad categories, for a user activity;

generating a hierarchical structure for the attributes based on the relationships; and

targeting a first advertisement by using the hierarchical structure and the attribute values.

16. The method of claim 15, the user activity comprising one or more of an impression, a click, a lead, and an acquisition, wherein the users, the publishers, the marketers, and the advertisements each comprise associated data that form inputs to a matching problem, the method further comprising:

organizing the inputs by using the hierarchical structure thereby reducing the problem size.

17. The method of claim 15, the generating a hierarchical grouping of attributes further comprising;

matching two or more of a user segment, a publisher group, and an ad category, thereby generating one or more hierarchical clusters;

organizing the hierarchical clusters into rows; and

normalizing the values of an attribute for the relationship.

18. The method of claim 15, further comprising:

determining a propensity score for a user action, the propensity based on an advertisement and at least one of a user segment, a publisher group, and an ad category.

determining an average propensity by using the propensity score.

20. The method of claim 15, wherein targeting the first advertisement comprises one or more of;

a first user segment and a relevance to the first user segment; and

a first context, wherein the first context comprises one of a web page, content and time.

19. (canceled)

21. A system for ad targeting comprising:

a user module for receiving a plurality of users and segmenting the users into user segments comprising a first user segment;

a publisher module for receiving a plurality of publishers and grouping the context into publisher groups comprising a first publisher group, the first publisher group having a first inventory location for the presentation of advertising;

a marketer-ad module for receiving advertisements and categorizing the advertisements into ad categories comprising a first ad category; and

a matching engine for;

matching the first ad category to one or more of the first publisher group and the first user segment; and

placing within the first inventory location a first advertisement from the first ad category.

22. The system of claim 21, the matching engine configured to perform one or more of:

behavioral, match;

demographic match;

technographic match;

geographic match;

domain match; and

content match.

23. The system of claim 21, wherein a marketer has a budget for per day advertising spend, the system further configured for assigning the first advertisement a cost, wherein the cost comprises the cost of placing the first advertisement within the first inventory location.

24. The system of claim 21, the system further configured for assigning the first advertisement a weight, wherein one or more of the user segments, publisher groups, and ad categories are ranked.

26. The system of claim 21, further comprising a time component, wherein the users and the publishers are categorized by time of day, wherein the first user segment comprises users who have a higher propensity to perform an action during particular times of day.

28. The system of claim 21, wherein multiple advertisements are linked to a single campaign, wherein each advertisement comprises an associated attribute value wherein the attribute values associated with the multiple advertisements linked to the campaign are one or more of averaged and weighted for the campaign.

29. The system of claim 21, wherein multiple campaigns are linked to a single advertisement, wherein at least one of weighting and averaging is performed for the attribute values of the advertisement.

25. (canceled)

27. (canceled)