System and Method for Adaptive Bidding for Display Advertising

• US 20100250332A1
• Filed: 03/30/2009
• Published: 09/30/2010
• Est. Priority Date: 03/30/2009
• Status: Abandoned Application

• Current Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Original Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Inventors: Rubinstein, Benjamin I.P., Vassilvitskii, Sergei, Ghosh, Arpita, Zinkevich, Martin
• Application Number:
  US12/414,166
• Publication Number:
  US 20100250332A1
• 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 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0249 based upon budgets or funds
• CPC Class Codes: G06Q 30/0275 Auctions
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/08 Auctions

Abstract

A system for performing adaptive bidding to secure Internet advertising impressions in an auction. Included are systems for analyzing advertising campaign objectives, including a campaign period, a target number of impressions, a target budget. An exemplary technique defines a bidding agent for performing the adaptive bidding seeking the minimum target spending of the budget. Objective results of the campaign such as average cost per won impression, total campaign duration relative to desired campaign period, and total expenditure relative to campaign budget can be optimized based on an empirically determined forecast. Techniques for adapting bids include statistically modeling winning bids during an exploration bidding phase, performing iterations for adjustment of the bid amounts using learn-while-bid adaptive bidding, learn-then-bid adaptive bidding, and guess-double-adjust adaptive bidding. Bidding tactics, especially those used to identify a minimum target bid and a maximum target bid used in bidding on an exchange are employed, and the results analyzed using statistical models.

Claims

1. A method for adaptive bidding for display advertising in an online advertising campaign comprising:

storing advertising campaign objectives, including at least one of, a campaign period, a target number of impressions to be served during the campaign, a target budget to be spent during the campaign;

performing exploration bidding of ad offers for advertisement ad calls for determining a minimum required bid for winning an advertisement ad call auction and a maximum required bid for achieving at least one campaign objective;

calculating a likelihood of achieving at least one campaign objective; and

performing exploitation bidding for optimizing campaign results based on bidding a bid amount between the minimum required bid and the maximum required bid.

2. The method of claim 1, wherein performing exploration bidding includes performing iterations for adjustment of the exploration bid amount using at least one of, learn-while-bid adaptive bidding, learn-then-bid adaptive bidding, guess-double-adjust adaptive bidding, an exploration bid amount calculation;

3. The method of claim 1, wherein performing exploration bidding phase includes retrieving a supply forecast.

4. The method of claim 1, wherein performing bidding phase includes calculating the campaign average cost per impression.

5. The method of claim 1, wherein performing exploration bidding phase includes bidding an amount greater than twice the calculated target average cost per impression.

6. The method of claim 1, wherein performing exploration bidding phase includes calculating the feasibility of winning the target number of impressions based on bids equal to average cost per impression.

7. The method of claim 1, wherein calculating a likelihood includes predicting how close an empirically determined distribution function will be to the distribution from which the empirical samples are drawn.

8. The method of claim 7, wherein calculating a likelihood includes calculating a Dvoretzky-Kiefer-Wolfowitz inequality.

9. The method of claim 1, wherein calculating a likelihood includes calculating an amount twice the calculated target average cost per impression.

10. The method of claim 1, wherein calculating a likelihood increases the exploration bid by a factor greater than 1.

11. The method of claim 1, wherein calculating a likelihood includes a test if continued use of the exploration bid amount is feasible to achieve the desired number of remaining needed impressions for the campaign.

12. The method of claim 1, wherein calculating a likelihood includes a test to determine if the current bid is too low.

13. The method of claim 1, wherein calculating a likelihood includes a test to determine if more observed samples need to be recorded in order to more accurately predict winnable impression opportunities.

14. The method of claim 1, wherein performing exploitation bidding includes a test if the campaign has ended.

15. The method of claim 1, wherein performing exploitation bidding includes a test if there are no more matching impressions available.

16. The method of claim 1, wherein performing exploitation bidding includes a test if the desired number of impression opportunities have been won.

17. The method of claim 1, wherein performing exploitation bidding includes a test if there remains sufficient budget to continue bidding.

18. The method of claim 1, wherein optimizing campaign objectives includes optimizing to achieve the lowest average cost per impression.

19. The method of claim 1, wherein optimizing campaign results includes optimizing to exhaust the budget by the end of the campaign period.

20. An apparatus for implementing a method for adaptive bidding for display advertising in an online advertising campaign comprising:

a module for storing advertising campaign objectives, including at least one of, a campaign period, a target number of impressions to be served during the campaign, a target budget to be spent during the campaign;

a module for performing exploration bidding of ad offers for advertisement ad calls for determining a minimum required bid for winning an advertisement ad call auction and a maximum required bid for achieving at least one campaign objective;

a module for calculating a likelihood of achieving at least one campaign objective; and

a module for performing exploitation bidding for optimizing campaign results based on bidding a bid amount between the minimum required bid and the maximum required bid.