Method and system for electronic advertising

• US 8,527,342 B2
• Filed: 06/04/2010
• Issued: 09/03/2013
• Est. Priority Date: 06/04/2009
• Status: Active Grant

• Current Assignee: Black CROW AI Incorporated, Hercules Capital, Inc.
• Original Assignee: Intent Media Incorporated
• Inventors: Feuerstein, Joshua, Harris, Richard, Hartmann, Joshua, Pritchard, Adam, Rajan, Arun, Schrader, Kurt, Taqqu, Jonathan, Tassone, Damon
• Primary Examiner(s): Weiss, John G
• Assistant Examiner(s): Macasiano, Marilyn G
• Application Number:
  US12/793,904
• Publication Number:
  US 20110054998A1
• Time in Patent Office: 1,187 Days
• Field of Search: 705/14.49, 725/46
• US Class Current: 705/14.42
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0243 Comparative campaigns
• CPC Class Codes: G06Q 30/0247 Calculate past, present or future revenues
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0253 During e-commerce, i.e. online transactions
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/08 Auctions

Abstract

A method of delivering advertising in an online environment includes determining an intent of a user interacting with an e-commerce website and determining a hurdle rate, based at least on the intent of the user, that identifies a threshold amount to be bid by an advertiser in order to display an advertisement to the user interacting with the e-commerce website. The method may further include selecting, from a plurality of advertisements, one or more optimal advertisements having a combined advertiser bid that exceeds the determined hurdle rate. The method may further include providing one or more controls in a user interface of a client computer system, each of the controls being associated with a respective advertisement of the one or more optimal advertisements and adapted to enable the user to select one or more of the controls, and providing an element, responsive to an input of the user, that is adapted to display, to the user, the respective advertisements selected by the user.

Claims

1. A method of delivering advertising in an online environment comprising acts of:

determining an intent of a user interacting with an e-commerce website;

determining, by a publisher of the e-commerce website, a hurdle, based at least on the intent of the user, that identifies a threshold amount to be bid by an advertiser in order to display an advertisement to the user interacting with the e-commerce website;

selecting, from a plurality of advertisements, one or more optimal advertisements having a combined advertiser bid that exceeds the determined hurdle rate;

providing one or more controls in a user interface of a client computer system, each of the one or more controls being associated with a respective advertisement of the one or more optimal advertisements and adapted to enable the user to select one or more of the one or more controls; and

providing an element, responsive to an input of the user, that is adapted to display, to the user, the one or more respective advertisements selected by the user.

2. The method according to claim 1, further comprising receiving, from the publisher of the e-commerce website, an advertisement request.

3. The method according to claim 2, wherein the advertisement request includes one or more keywords, and wherein the intent of the user is based at least in part on the one or more keywords.

4. The method according to claim 2, further comprising translating the advertisement request into one or more intent targets.

5. The method according to claim 4, wherein translating the advertisement request includes selecting each of the one or more intent targets from a plurality of normalized intent targets based on information included in the advertisement request, and wherein the plurality of normalized intent targets is stored in an intent target taxonomy database.

6. The method according to claim 4, wherein determining the hurdle rate includes adjusting the hurdle rate in relation to the one or more intent targets.

7. The method according to claim 4, wherein determining the hurdle rate includes adjusting the hurdle rate based on a historical average conversion rate of each of the one or more intent targets.

8. The method according to claim 4, wherein determining the hurdle rate includes adjusting the hurdle rate based on an average contribution margin of a completed transaction for the one or more intent targets.

9. The method according to claim 1, wherein determining the intent of the user further includes translating the advertisement request into one or more process contexts.

10. The method according to claim 9, wherein translating the advertisement request includes selecting each of the one or more process contexts from a plurality of normalized process contexts based on information included in the advertisement request, and wherein the plurality of normalized process contexts is stored in a process context taxonomy database.

11. The method according to claim 9, wherein determining the hurdle rate includes adjusting the hurdle rate based on the one or more process contexts.

12. The method according to claim 1, further comprising selecting, from the plurality of advertisements, one or more candidate advertisements, and wherein the one or more optimal advertisements are selected from the one or more candidate advertisements.

13. The method according to claim 12, wherein each of the one or more candidate advertisements is selected based on the hurdle rate.

14. The method according to claim 12, wherein each of the one or more candidate advertisements is selected based on a relationship between the user and the publisher.

15. The method according to claim 12, further comprising determining, for each of the one or more candidate advertisements, an advertisement revenue potential, and wherein the one or more optimal advertisements have an advertisement revenue potential exceeding the determined advertisement revenue potential of each of the others of the one or more candidate advertisements.

16. The method according to claim 15, wherein the advertisement revenue potential is based on a return to visit rate.

17. The method according to claim 15, wherein the advertisement revenue potential is based on an expected return to purchase value.

18. The method according to claim 15, wherein the advertisement revenue potential is based on a fully loaded expected transaction value.

19. The method according to claim 1, wherein the one or more optimal advertisements are associated with a relevance type selected by the publisher.

20. The method according to claim 19, wherein the relevance type is one of retailer discovery, substitute discovery, and complement discovery.

21. The method according to claim 1, further comprising generating one or more dynamic uniform resource locators based on the intent of the user, and including the one or more dynamic uniform resource locators within a respective one of the one or more optimal advertisements.

22. The method according to claim 1, further comprising configuring one or more creative contents for one or more of the one or more optimal advertisements such that each of the one or more creative contents targets the intent of the user.

23. The method according to claim 1, wherein the hurdle rate is based on one or more outcomes related to displaying the one or more optimal advertisements to the user.

24. The method according to claim 1, wherein the one or more controls are located within a rail of the user interface.

25. A distributed computer system comprising:

at least one memory device;

an interface configured to display a list of advertisements to a user; and

at least one processor coupled to the at least one memory device and the interface, wherein the at least one processor is configured to determine a context of the user operating a client computer to interact with an e-commerce site, translate the context into one or more context targets, and identify a hurdle rate of the e-commerce site, the hurdle rate being determined by a publisher of the e-commerce website based on the one or more context targets, and the hurdle rate identifying a threshold amount to be bid by one or more advertisers in order to display one or more advertisements to the user via the interface;

one or more controls, each of which is associated with an advertisement of the list, the controls being responsive to the user selecting one or more advertisements of the list; and

an element, responsive to an input of the user, that is adapted to display each of the advertisements selected by the user from the list.

26. The system according to claim 25, wherein the at least one processor includes a decision engine operable to generate, from a plurality of advertisements each having a respective bid, the list of advertisements to be displayed to the user, wherein a combined bid including the respective bid for each advertisement in the list of advertisements exceeds the hurdle rate.

27. The system according to claim 26, wherein the at least one processor includes an optimization engine operable to determine a contribution per visitor outcome for each of one or more advertisements of the plurality of advertisements.

28. The system according to claim 27, wherein the contribution per visitor outcome is based the hurdle rate.

29. The system according to claim 27, wherein the contribution per visitor outcome is based on an expected return to purchase value.

30. The system according to claim 27, further comprising a segmentation engine operable to identify a relationship between the user and a publisher.

31. The system according to claim 30, wherein the contribution per visitor outcome is based on the relationship between the user and the publisher.

32. The system according to claim 25, wherein the at least one processor includes a candidate advertisement engine operable to generate a list of candidate advertisements based on the context of the user.

33. The system according to claim 25, further comprising an outcome engine operable to analyze an outcome of displaying the list of advertisements to the user.

34. The system according to claim 25, wherein the at least one processor includes a bid component operable to receive, from an advertiser, at least one bid for each advertisement in the list of advertisements.

35. A computer readable medium having stored thereon sequences of instructions including instructions that will cause a processor to perform a method of delivering advertising comprising acts of:

determining an intent of a user interacting with an e-commerce website;

identifying a hurdle rate of the e-commerce website, the hurdle rate determined by a publisher of the e-commerce website based at least on the intent of the user, and the hurdle rate identifying a threshold amount to be bid by an advertiser in order to display an advertisement to the user interacting with the e-commerce website;

selecting, from a plurality of advertisements, one or more optimal advertisements having a combined advertiser bid that exceeds the determined hurdle rate;

providing one or more controls in a user interface of a client computer system, each of the one or more controls being associated with a respective advertisement of the one or more optimal advertisements and adapted to enable the user to select one or more of the one or more controls; and

providing an element, responsive to an input of the user, that is adapted to display, to the user, the one or more respective advertisements selected by the user.