CONTENT ADVERTISING PERFORMANCE OPTIMIZATION SYSTEM AND METHOD

• US 20080288328A1
• Filed: 12/20/2007
• Published: 11/20/2008
• Est. Priority Date: 05/17/2007
• Status: Abandoned Application

• Current Assignee: ScienceOps, Inc. (Web.com Group, Inc.)
• Original Assignee: ScienceOps, Inc. (Web.com Group, Inc.)
• Inventors: Frajka, Tamas, Minor, Bryan Michael, Samuels, Corey L., Pettigrew, Michel F., Clarkson, Douglas B., Johnson, William T. G.
• Application Number:
  US11/961,485
• Publication Number:
  US 20080288328A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0243 Comparative campaigns

Abstract

A content targeted advertising performance optimization system and method are provided herein.

Claims

1. A computer-implemented method of generating optimized content ad groups, the method comprising:

obtaining a first keyword list for a target ad campaign;

obtaining target performance metric criteria for the target ad campaign;

creating a control ad group comprising the first keyword list; and

performing an iterative keyword optimization routine, wherein each iteration includes;

creating a plurality of test ad groups, each of said test ad groups comprising a test subset of keywords selected from said first keyword list, wherein;

on a first iteration, each test subset of keywords is selected by a random process; and

on subsequent iterations, each test subset of keywords is generated by an iterative refinement process in accordance with a plurality of better-performing complementary ad groups selected in the preceding iteration;

running each of said test ad groups for a period of time;

in accordance with said target performance metric criteria, tracking a test performance metric for each of said test ad groups;

in accordance with said test performance metrics, selecting a new plurality of better-performing complementary ad groups from among test ad groups.

2. The method of claim 1, further comprising ending the optimization routine when the test performance metrics of the plurality of better-performing complementary ad groups meet said target performance metric criteria.

3. The method of claim 2, further comprising running the plurality of better-performing complementary ad groups in the target ad campaign.

4. The method of claim 1, further comprising continuously optimizing said target ad campaign.

5. The method of claim 1, wherein said test performance metric is obtained from at least one of a content targeted advertising service provider and an advertiser.

6. The method of claim 1, wherein the method operates without knowledge of the ad network'"'"'s content ad selection algorithm.

7. The method of claim 1, wherein said iterative refinement process includes at least one of artificial neural network, genetic algorithm, adaptive logistics algorithm, and simulated annealing.

8. The method of claim 1, wherein said iterative refinement process comprises:

selecting a plurality of parent pairs from said test ad groups in accordance with said target performance metric criteria;

creating a pair of offspring from each of said plurality of parent pairs; and

mutating said pair of offspring in accordance with a mutation probability.

9. The method of claim 8, wherein the probability that each of said test ad groups will be selected as a parent pair is directly proportional to its fitness in accordance with said target performance metric criteria.

10. The method of claim 8, wherein creating a pair of offspring from each of said plurality of parent pairs comprises:

selecting a first group of keywords from a first ad group of the parent pair;

selecting a second group of keywords from a second ad group of the parent pair; and

in accordance with a crossover probability, swapping said first and second groups of keywords.

11. The method of claim 10, wherein said crossover probability is approximately 0.7.

12. The method of claim 8, wherein said mutation probability is approximately 0.01.

13. The method of claim 8, wherein said iterative refinement process further comprises:

determining a most fit test ad group from a previous iteration; and

selecting said most fit test group for said new plurality of better-performing complementary ad groups.

14. The method of claim 8, wherein said iterative refinement process further comprises replacing a duplicate ad group within said test ad groups with a replacement ad group comprising a randomly selected list of keywords from said first keyword list.

15. The method of claim 1, wherein said iterative keyword optimization routine further comprises obtaining new target performance metric criteria.

16. The method of claim 1, wherein said iterative keyword optimization routine further comprises obtaining new target performance metric criteria if said test performance metric exceeds a threshold.

17. The method of claim 1, further comprising incorporating a new keyword into at least one of said first keyword list and said test ad groups.

18. The method of claim 1, further comprising:

obtaining a new keyword;

adding said new keyword to said first keyword list; and

randomly incorporating said new keyword into at least one of said test ad groups.

19. The method of claim 1, further comprising:

removing a keyword from said first keyword list; and

deleting said removed keyword from at least one of said test ad groups.

20. The method of claim 1, wherein said target performance metric criteria comprise at least one of a number of impressions, a number of clicks, a number of conversions, an impression rate, a clickthrough rate, a conversion rate, a cost per impression, a cost per click, and a cost per conversion.

21. The method of claim 1, wherein said target ad campaign is run in accordance with at least one of a product launch, a marketing campaign, a holiday, and a range of dates.

22. The method of claim 1, wherein said iterative keyword optimization routine further comprises at least one of changing a bid for a keyword, changing a landing page of said target ad campaign, and changing a content of an ad of said target ad campaign.

23. The method of claim 1, wherein said first keyword list and said target performance metric criteria are obtained via a network.

24. A computing apparatus comprising a processor and a memory having executable instructions for performing the method of claim 1.

25. A computer readable medium comprising executable instructions for performing the method of claim 1.