METHOD AND SYSTEM FOR SELECTING AND OPTIMIZING BID RECOMMENDATION ALGORITHMS

• US 20110055004A1
• Filed: 09/02/2009
• Published: 03/03/2011
• Est. Priority Date: 09/02/2009
• Status: Abandoned Application

• Current Assignee: The Search Agency
• Original Assignee: The Search Agency
• Inventors: Libby, Bradd Elden
• Application Number:
  US12/552,797
• Publication Number:
  US 20110055004A1
• Time in Patent Office: Days
• US Class Current: 705/14.45
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0246 Traffic
• CPC Class Codes: G06Q 30/0247 Calculate past, present or future revenues
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0275 Auctions

Abstract

A system and method are provided for evaluating the performance of at least one algorithm that generates bid recommendations for keyword advertising on search engines. According to one example, metric data associated with a plurality of internet advertisement objects is divided into a plurality of similar object groups. In one example, these object groups are provided to one or more client devices. In one example, the client devices generate bid recommendations according to at least one algorithm and place the bids with search engines. In still another example, the performance of the at least one bid recommendation algorithm is later measured or compared to the performance of another bid recommendation algorithm.

Claims

1. A method for evaluating at least one bid recommendation algorithm, comprising acts of:

retrieving a first metric data set associated with a plurality of internet advertisement objects;

dividing the plurality of internet advertisement objects and the first metric set into a plurality of object groups;

providing a first object group to a first client device;

to providing a second object group to a second client device;

retrieving a second metric data set associated with the plurality of internet advertisement objects; and

measuring the performance of the at least one bid recommendation algorithm with respect to the second metric data set.

2. The method of claim 1, wherein the at least one bid recommendation algorithm includes a first bid recommendation algorithm and a second bid recommendation algorithm, further comprising acts of:

applying the first bid recommendation algorithm to the first object group; and

applying the second bid recommendation algorithm to the second object group.

3. The method of claim 2, further comprising the act of placing a bid for an internet advertisement object in accordance with a result of the first bid recommendation algorithm and a result of the second bid recommendation algorithm.

4. The method of claim 1, wherein the at least one bid recommendation algorithm includes a first bid recommendation algorithm, further comprising acts of:

applying the first bid recommendation algorithm to the first object group, the bid recommendation algorithm operating on a first parameter; and

applying the first bid recommendation algorithm to the second object group, the bid recommendation algorithm operating on a second parameter.

5. The method of claim 1, where the plurality of internet advertisement objects is a plurality of keywords.

6. The method of claim 1, wherein the first metric set and the second metric set comprise measurements of clicks.

7. The method of claim 1, wherein the first metric set and the second metric set comprise measurements of impressions.

8. The method of claim 1, wherein the first metric set and the second metric set comprise measurements of conversions.

9. The method of claim 1, wherein the first client device and the second client device are the same client device.

10. The method of claim 1, wherein the act of measuring the performance of at least one bid recommendation algorithm with respect to the second metric data set includes identifying a quality score of an advertisement.

11. The method of claim 1, wherein the act of measuring the performance of at least one bid recommendation algorithm with respect to the second metric data set includes identifying a profit margin of an advertisement.

12. A computer-readable medium comprising computer-executable instructions that, when executed on a processor of a server, perform a method for evaluating at least one bid recommendation algorithm, the method comprising acts of:

retrieving a first metric data set associated with a plurality of internet advertisement objects;

dividing the plurality of internet advertisement objects and the first metric set into a plurality of object groups;

providing a first object group to a first client device;

providing a second object group to a second client device;

retrieving a second metric data set associated with the plurality of internet advertisement objects; and

measuring the performance of the at least one bid recommendation algorithm with respect to the second metric data set.

13. The computer-readable medium of claim 12, the method further comprising acts of:

applying a first bid recommendation algorithm to the first object group; and

applying a second bid recommendation algorithm to the second object group.

14. The computer-readable medium of claim 13, the method further comprising the act of placing a bid for an internet advertisement object in accordance with a result of the first bid recommendation algorithm and a result of the second bid recommendation algorithm.

15. The computer-readable medium of claim 12, the method further comprising acts of:

applying a bid recommendation algorithm to the first object group, the bid recommendation algorithm operating on a first parameter; and

applying the bid recommendation algorithm to the second object group, the bid recommendation algorithm operating on a second parameter.

16. The computer-readable medium of claim 12, wherein the plurality of internet advertisement objects is a plurality of keywords.

17. The computer-readable medium of claim 12, wherein the first metric set and the second metric set comprise measurements of clicks.

18. The computer-readable medium of claim 12, wherein the first metric set and the second metric set comprise measurements of impressions.

19. The computer-readable medium of claim 12, wherein the first metric set and the second metric set comprise measurements of conversions.

20. The computer-readable medium of claim 12, wherein the first client device and the second client device are the same client device.

21. The method of claim 12, wherein the act of measuring the performance of at least one bid recommendation algorithm with respect to the second metric data set includes identifying a quality score of an advertisement.

22. The method of claim 12, wherein the act of measuring the performance of at least one bid recommendation algorithm with respect to the second metric data set includes identifying a profit margin of an advertisement.

23. A system comprising:

a grouping engine configured to divide the plurality of internet advertisement objects and associated metric data sets into a plurality of object groups;

a data transfer engine configured to send and receive the plurality of internet advertisement objects, the associated metric data sets, and the plurality of object groups;

an analysis engine configured to measure the performance of at least one bid recommendation algorithm with respect to a metric data set;

a metric database configured to store metric data; and

at least one interface configured to allow interaction with the grouping engine, the data transfer engine, the analysis engine, and the metric database.

24. The system of claim 23, further comprising:

at least one client device configured to receive an object group from the data transfer engine and perform a bid recommendation algorithm on the object group.

25. The system of claim 24, wherein the at least one client device is further configured to place a bid on an internet advertisement object.

26. The system of claim 25, wherein the at least one client device further comprises a user interface configured to allow a participant to interact with the bid recommendation algorithm.