SOURCE OF DECISION CONSIDERATIONS FOR MANAGING ADVERTISING PRICING

• US 20120130828A1
• Filed: 12/31/2010
• Published: 05/24/2012
• Est. Priority Date: 11/23/2010
• Status: Abandoned Application

• Current Assignee: Optimine Software Incorporated
• Original Assignee: Optimine Software Incorporated
• Inventors: Cooley, Robert W., Bryan, Douglas
• Application Number:
  US12/982,928
• Publication Number:
  US 20120130828A1
• Time in Patent Office: Days
• US Class Current: 705/14.71
• CPC Class Codes: G06Q 30/0273 Determination of fees for advertising
• CPC Class Codes: G06Q 30/0275 Auctions

Abstract

Methods and systems for training predictive models for an advertising price management system. One method includes storing information to a database about a past price determined by the advertising price management system and a source used to set the past price, the source including at least one of a constraint-based optimizer, a testing routine, and a manual override; assigning a weight to the information about the past price based on the source used to set the past price; and using the stored information about the past price and the weight to train the predictive models for the advertising price management system. The method also includes using the predictive models to determine a price for an advertising placement and communicating the price.

Claims

1. A computer-implemented method for training a predictive model for an advertising price management system, the method comprising:

storing information to a database about a past price determined by the advertising price management system and a source used to set the past price, the source including at least one of a constraint-based optimizer, a testing routine, and a manual override;

assigning a weight to the information about the past price based on the source used to set the past price;

using the stored information about the past price and the weight to train the predictive model for the advertising price management system;

using the predictive model to determine a price for an advertising placement; and

communicating the price.

2. The method of claim 1, wherein the assigning the weight to the information about the past price includes assigning the information a first weight when the source used to set the past price included the testing routine and assigning the information a second weight when the source used to set the past price included the constraint-based optimizer, wherein the first weight is greater than the second weight.

3. The method of claim 1, wherein the assigning the weight to the information about the past price includes assigning the information a first weight when the source used to set the past price included the manual override and assigning the information a second weight when the source used to set the past price included the constraint-based optimizer, wherein the first weight is greater than the second weight.

4. The method of claim 1, wherein the storing information about the past price includes storing information about a past bid determined by a bid management system and wherein the using the predictive model includes using the predictive model to determine a bid for the advertising placement.

5. The method of claim 4, wherein the communicating the price includes outputting the bid to at least one paid search server.

6. The method of claim 1, wherein the storing the information about the past price includes storing at least one of a placement location, a position, a day of the week, a day of the month, a weekend flag, and a days-to-next holiday value for the past price.

7. The method of claim 1, wherein the storing the information about the past price includes storing at least one of a predicted views, a predicted cost, a predicted cost-per-view, a predicted events, a predicted events-per-view, a predicted value, and a predicted value-per-view for the past price determined by the advertising price management system.

8. The method of claim 1, wherein the storing the information about the past price includes storing at least one of a moving seven-day average and a moving day-of-the-week average for the past price.

9. A system for training a predictive model for an advertising price management system, the system comprising:

a database storing information about a past price determined by the advertising price management system and a source used to set the past price, the source including at least one of a constraint-based optimizer, a testing routine, and a manual override;

a predictive model training module assigning a weight to the information about the past price based on the source used to set the past price and using the stored information about the past price and the weight to train the predictive model of the advertising price management system; and

the constraint-based optimizer using the predictive model to determine a price for an advertising placement and communicating the price.

10. The system of claim 9, wherein the predictive model training module assigns the information a first weight when the source used to set the past price included the testing routine and assigns the information a second weight when the source used to set the past price included the constraint-based optimizer, wherein the first weight is greater than the second weight.

11. The system of claim 9, wherein the predictive model training module assigns the information a first weight when the source used to set the past price included the manual override and assigns the information a second weight when the source used to set the past price included the constraint-based optimizer, wherein the first weight is greater than the second weight.

12. The system of claim 9, wherein the information about the past price includes information about a past bid determined by a bid management system and wherein the constraint-based optimizer uses the predictive model to determine a bid for the advertising placement.

13. The system of claim 12, further comprising an output module outputting the bid to at least one paid search server.

14. The system of claim 9, wherein the information about the past price includes at least one of a placement location, a position, a day of the week, a day of the month, a weekend flag, and a days-to-next holiday value for the past price.

15. The system of claim 9, wherein the information about the past price includes at least one of a predicted views, a predicted cost, a predicted cost-per-view, a predicted events, a predicted events-per-view, a predicted value, and a predicted value-per-view for the past price determined by the advertising price management system.

16. The system of claim 9, wherein the information about the past price includes at least one of a moving seven-day average and a moving day-of-the-week average for the past price.

17. Non-transitory computer readable medium encoded with a plurality of processor-executable instructions for:

storing information about a past bid determined by a paid search bid management system and a source used to set the past bid to a database, the source including at least one of a constraint-based optimizer, a testing routine, and a manual override;

assigning a weight to the information about the past bid based on the source used to set the past bid;

using the stored information about the past bid and the weight to train at least one predictive model for an advertising price management system;

using the at least one predictive model to determine a bid for an advertisement; and

communicating the bid to at least one paid search server.

18. The computer-readable medium of claim 17, wherein the instructions for assigning the weight to the information about the past price includes instructions for assigning the information a first weight when the source used to set the past price included the testing routine and assigning the information a second weight when the source used to set the past price included the constraint-based optimizer, wherein the first weight is greater than the second weight.

19. The computer-readable medium of claim 17, wherein the instructions for assigning the weight to the information about the past price includes assigning the information a first weight when the source used to set the past price included the manual override and assigning the information a second weight when the source used to set the past price included the constraint-based optimizer, wherein the first weight is greater than the second weight.