AD PERFORMANCE OPTIMIZATION FOR RICH MEDIA CONTENT

• US 20080228576A1
• Filed: 11/20/2007
• Published: 09/18/2008
• Est. Priority Date: 03/13/2007
• Status: Abandoned Application

• Current Assignee: Powerreviews Incorporated
• Original Assignee: ScanScout, Inc. (Magnite, Inc.)
• Inventors: Yonezaki, Tadashi
• Application Number:
  US11/943,357
• Publication Number:
  US 20080228576A1
• Time in Patent Office: Days
• US Class Current: 705/14
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0244 Optimization
• CPC Class Codes: G06Q 30/0254 based on statistics

Abstract

In one embodiment, a method for optimizing advertisement performance is provided. In one embodiment, advertisements may be clustered together into different buckets of advertisements. Rich media content may also be clustered together. A performance model may then be generated that is based on previous performance of ads with content. The performance data may be used to predict which ads may provide the best performance when shown with a target piece of content that is going to be displayed. Particular embodiments use performance data for advertisements in an ad bucket to determine which ad bucket out of multiple ad buckets may provide the best performance for a content bucket that includes the target content. The ad bucket includes a plurality of ads and the method determines which ad should be displayed with the target content. In one embodiment, performance data is used to determine which ad to display.

Claims

1. A method for optimizing performance of advertisements, the method comprising:

determining a content bucket for target rich media content, the content bucket including a plurality of rich media content pieces;

determining an ad bucket in a plurality of ad buckets based on performance data for ads in the ad bucket as applied to features for the content bucket, the ad bucket including a plurality of advertisements; and

determining an advertisement in the plurality of advertisements in the ad bucket to render with the target rich media content based on the performance data for the plurality of advertisements.

2. The method of claim 1, wherein the performance data includes data on how advertisements in the plurality of advertisements performed with respect to the plurality of rich media content pieces in the content bucket.

3. The method of claim 1, wherein the performance data includes a probability determined based on the previous performance of an ad with a rich media content piece in the plurality of rich media content pieces.

4. The method of claim 3, wherein the previous performance of the ad for the rich media content affects the probability of another ad.

5. The method of claim 1, wherein determining the ad bucket comprises:

determining a performance model for the ad bucket using the performance data;

determining features for the content bucket; and

determining a probability for the ad bucket based on the features and the performance model.

6. The method of claim 5, further comprising:

determining the probabilities for the plurality of ad buckets; and

determining the ad bucket based on the determined probabilities.

7. The method of claim 6, further comprising determining the ad bucket that provides a highest probability for optimal performance if rendered with the target content.

8. The method of claim 1, wherein determining the advertisement comprises:

determining a distance from features in the plurality of advertisements to features in the target content; and

determining the advertisement based on its having a smallest determined distance as compared to other advertisements in the plurality of advertisements.

9. The method of claim 1, further comprising sending the advertisement to a client for rendering with the target content.

10. An apparatus configured to optimize performance of advertisements comprising:

one or more processors; and

logic encoded in one or more tangible media for execution by the one or more processors and when executed operable to;

determine a content bucket for target rich media content, the content bucket including a plurality of rich media content pieces;

determine an ad bucket in a plurality of ad buckets based on performance data for ads in the ad bucket as applied to features for the content bucket, the ad bucket including a plurality of advertisements; and

determine an advertisement in the plurality of advertisements in the ad bucket to render with the target rich media content based on the performance data for the plurality of advertisements.

11. The apparatus of claim 10, wherein the performance data includes data on how advertisements in the plurality of advertisements performed with respect to the plurality of rich media content pieces in the content bucket.

12. The apparatus of claim 10, wherein the performance data includes a probability determined based on the previous performance of an ad with a rich media content piece in the plurality of rich media content pieces.

13. The apparatus of claim 12, wherein the previous performance of the ad for the rich media content affects the probability of another ad.

14. The apparatus of claim 10, wherein the logic when executed is further operable to:

determine a performance model for the ad bucket using the performance data;

determine features for the content bucket; and

determine a probability for the ad bucket based on the features and the performance model.

15. The apparatus of claim 14, wherein the logic when executed is further operable to:

determine the probabilities for the plurality of ad buckets; and

determine the ad bucket based on the determined probabilities.

16. The apparatus of claim 15, wherein the logic when executed is further operable to determine the ad bucket that provides a highest probability for optimal performance if rendered with the target content.

17. The apparatus of claim 10, wherein the logic when executed is further operable to:

determine a distance from features in the plurality of advertisements to features in the target content; and

determine the advertisement based on its having a smallest determined distance as compared to other advertisements in the plurality of advertisements.

18. The apparatus of claim 10, wherein the logic when executed is further operable to send the advertisement to a client for rendering with the target content.

19. An apparatus configured to optimize performance of advertisements, the method comprising:

means for determining a content bucket for target rich media content, the content bucket including a plurality of rich media content pieces;

means for determining an ad bucket in a plurality of ad buckets based on performance data for ads in the ad bucket as applied to features for the content bucket, the ad bucket including a plurality of advertisements; and

means for determining an advertisement in the plurality of advertisements in the ad bucket to render with the target rich media content based on the performance data for the plurality of advertisements.

20. The apparatus of claim 19, wherein the performance data includes data on how advertisements in the plurality of advertisements performed with respect to the plurality of rich media content pieces in the content bucket.