ESTIMATING EXPECTED PERFORMANCE OF ADVERTISEMENTS

• US 20080249832A1
• Filed: 04/04/2007
• Published: 10/09/2008
• Est. Priority Date: 04/04/2007
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Ragno, Robert J., Dominowska, Ewa, Richardson, Matthew R.
• Application Number:
  US11/696,467
• Publication Number:
  US 20080249832A1
• 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 30/0247 Calculate past, present or future revenues
• CPC Class Codes: G06Q 30/0256 User search

Abstract

Systems, methods, and computer-readable media for estimating expected advertisement performance of advertisements are provided. An advertisement performance prediction model is developed using features extracted from a sample set. Once developed, advertisements that are not a part of the sample set are identified and features are extracted there from. The features are then input into the advertisement performance prediction model and expected performance of the corresponding advertisement is estimated. In embodiments, the estimated expected advertisement performance may be used to appropriately rank the advertisement relative to a plurality of other advertisements such that the advertisement will be displayed according to the advertisement ranking.

Claims

1. A computerized system for developing an advertisement performance prediction model configured to estimate expected performance of advertisements, the system comprising:

an extracting component configured to extract at least one feature from a plurality of advertisements, one or more of the plurality of advertisements having at least one historical measure of performance; and

a developing component configured to develop the advertisement performance prediction model utilizing the extracted at least one feature and the at least one historical measure of performance for each of the plurality of advertisements.

2. The computerized system of claim 1, further comprising an implementing component configured to implement the advertisement performance prediction model to estimate expected performance of advertisements.

3. The computerized system of claim 1, further comprising a sample set identifying component configured for identifying the plurality of advertisements and creating a sample set of advertisements including at least a portion of the plurality of advertisements.

4. The computerized system of claim 1, wherein the at least one feature comprises one or more of a user gender, a user age, a user location, a user interest, a user previous search, a user previous advertisement click behavior, a request day, a request time, or a combination thereof.

5. The computerized system of claim 1, wherein the at least one feature comprises at least one datum, at least one value, or a combination thereof that represents at least one of an advertising information item or the at least one historical measure of performance.

6. The computerized system of claim 1, wherein the at least one feature comprises one or more of an advertisement copy feature, an advertisement breadth feature, an advertisement consistency feature, an advertisement page feature, a specific advertisement feature, a click-through rate advertisement feature, a performance-based feature, a query-based feature, and a search data feature.

7. The computerized system of claim 1, wherein the advertisement performance prediction model comprises a logistic regression model, a decision tree, a regression tree, a neural network, a boosted tree, or a support vector machine.

8. The computerized system of claim 1, wherein the advertisement performance prediction model is developed via machine learning, an algorithm, or a combination thereof.

9. A method for estimating expected performance of advertisements, the method comprising:

receiving an advertisement;

extracting at least one feature from the received advertisement;

inputting the at least one extracted feature into an advertisement performance prediction model, wherein the advertisement performance prediction model is based upon extracted features, recognized information items and/or historical performance of a plurality of advertisements; and

utilizing the advertisement performance prediction model to estimate expected performance of the received advertisement.

10. The method of claim 9, further comprising ranking the received advertisement relative to at least a portion of the plurality of advertisements utilizing the estimated expected performance of the received advertisement.

11. The method of claim 9, further comprising:

measuring the actual performance of the received advertisement; and

updating the estimated expected performance of the received advertisement in accordance with the measured actual performance thereof.

12. The method of claim 9, further comprising developing the advertisement performance prediction model.

13. The method of claim 12, wherein developing the advertisement prediction model comprises:

identifying the plurality of advertisements,

extracting at least one feature from each of the plurality of advertisements; and

developing the advertisement prediction model utilizing the extracted at least one feature and at least one of a recognized information item and/or a historical performance measure associated with each of the plurality of advertisements.

14. The method of claim 9, wherein each of the extracted features comprises one or more of an advertisement copy feature, an advertisement breadth feature, an advertisement consistency feature, an advertisement page feature, a specific advertisement feature, a click-through rate advertisement feature, a performance-based feature, a query-based feature, and a search data feature.

15. The method of claim 9, wherein the estimated expected performance of the received advertisement comprises a probability a user will perform an action with respect to the received advertisement.

16. The method of claim 15, wherein the probability a user will perform an action with respect to the received advertisement comprises a click-through rate.

17. One or more computer-readable media having computer-executable instructions embodied thereon that, when executed, perform a method for estimating expected performance of advertisements, the method comprising:

identifying at least one advertisement having one or more recognized information items and/or performance measures associated therewith;

extracting one or more features from the at least one advertisement;

developing an advertisement performance prediction model based upon the one or more extracted features and at least one of the one or more recognized information items and/or performance measures;

extracting at least one feature from a first advertisement that is different than the at least one advertisement; and

estimating the expected performance of the first advertisement utilizing the advertisement performance prediction model.

18. The one or more computer-readable media of claim 17, wherein the method further comprises ranking the first advertisement relative to a plurality of other advertisements utilizing the estimated expected advertisement performance measure.

19. The one or more computer-readable media of claim 17, wherein the method further comprises:

measuring the actual performance of the first advertisement; and

updating the estimated expected performance of the first advertisement in accordance with the measured actual performance thereof.

20. The one or more computer-readable media of claim 17, wherein each of the one or more features comprises an advertisement copy feature, an advertisement breadth feature, an advertisement consistency feature, an advertisement page feature, a specific advertisement feature, a click-through rate advertisement feature, a performance-based feature, a query-based feature, a search data feature, or a combination thereof.