Metric Conversion for Online Advertising

• US 20080275757A1
• Filed: 02/04/2008
• Published: 11/06/2008
• Est. Priority Date: 05/04/2007
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Heilig, Joerg, Chen, Kai, Sharma, Abhinay, Kniaz, Rob
• Application Number:
  US12/025,642
• Publication Number:
  US 20080275757A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• 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 30/0273 Determination of fees for advertising

Abstract

Methods, systems and computer program products for estimating a CPC bid (eCPC) as a function of a target CPA bid based on predictive data (e.g., predicted conversion rate) have been described. The eCPC parameter can be used to develop a model that could be used to charge advertisers on a CPA basis while crediting publishers on a CPC basis.

Claims

1. A method comprising:

obtaining input specifying a first metric value associated with an advertisement;

determining a predicted conversion rate for a potential impression of the advertisement;

estimating a second metric value based on the first metric value and the predicted conversion rate;

compensating based on the second metric value; and

debiting based on the first metric value.

2. The method of claim 1, wherein the first metric value and the second metric value are based on different bidding models.

3. The method of claim 2, wherein the bidding models include Cost-Per-Action, Cost-Per-Click and Cost-Per-Impression models.

4. The method of claim 2, wherein the first metric value is a value based on a Cost-Per-Action model, and the second metric value is based on a Cost-Per-Click model.

5. The method of claim 2, wherein the first metric value is a value based on one of Cost-Per-Click model or Cost-Per-Action model, and the second metric value is based on a Cost-Per-Impression model.

6. The method of claim 2, wherein determining a predicted conversion rate include mapping one or more impression context features to the predicted conversion rate using a learning model.

7. The method of claim 6, wherein the learning model is a machine learning system model that includes predetermined rules for mapping the one or more impression context features to the predicted conversion rate.

8. The method of claim 6, wherein the learning model is built using conversion data.

9. The method of claim 2, wherein estimating the second metric value includes multiplying the first metric value with the predicted conversion rate.

10. A method comprising:

receiving an advertiser input specifying a first metric value for a conversion event associated with an online advertisement;

determining a predicted conversion rate for a potential impression of the advertisement based on historical data;

determining a correction factor for the predicted conversion rate; and

automatically computing a second metric value using the first metric value, the predicted conversion rate and the correction factor.

11. The method of claim 10, wherein determining a correction factor includes:

monitoring a deviation error associated with the predicted conversion rate within a bidding period; and

automatically updating the correction factor in a subsequent bidding period.

12. The method of claim 11, wherein updating the correction factor includes incrementing or decrementing the correction factor to equalize the deviation error.

13. The method of claim 10, wherein the correction factor includes:

a first parameter indicative of an aggregate total paid to a publisher within a bidding period; and

a second parameter indicative of an aggregate total received from an advertiser within the bidding period.

14. The method of claim 13, wherein the first parameter and the second parameter are based on one of a number of clicks, impressions or cost accrued over the bidding period.

15. The method of claim 13, further comprising:

selecting the bidding period such that an optimum of data is available for determining the first parameter and the second parameter.

16. The method of claim 13, wherein computing a second metric value includes:

correcting the second metric value by adjusting the correction factor in a subsequent bidding period if the first parameter is greater or less than the second parameter.

17. The method of claim 13, wherein adjusting the correction factor includes:

adjusting the bidding period such that a difference between the first parameter and the second parameter is optimally reduced.

18. The method of claim 10, wherein computing a second metric value includes multiplying the first metric value with the predicted conversion rate and the correction factor.

19. The method of claim 10, wherein the first metric is a value based on a Cost-Per-Action model, and the second metric value is based on a Cost-Per-Click model

20. The method of claim 10, wherein the first metric value is a value based on a one of Cost-Per-Click model or Cost-Per-Action model, and the second metric value is based on a Cost-Per-Impression model.

21. A system comprising:

a processor;

a computer-readable medium operatively coupled to the processor and including instructions, which, when executed by the processor, causes the processor to perform operations comprising;

obtaining input specifying a first metric value associated with an advertisement;

determining a predicted conversion rate for a potential impression of the advertisement;

estimating a second metric value based on the first metric value and the predicted conversion rate;

compensating based on the second metric value; and

debiting based on the first metric value.

22. A system comprising:

a processor;

a computer-readable medium operatively coupled to the processor and including instructions, which, when executed by the processor, causes the processor to perform operations comprising;

receiving an advertiser input specifying a first metric value for a conversion event associated with an online advertisement;

determining a predicted conversion rate for a potential impression of the advertisement based on historical data;

determining a correction factor for the predicted conversion rate; and

automatically computing a second metric value using the first metric value, the predicted conversion rate and the correction factor.

23. A computer-readable medium having instructions stored thereon, which, when executed by a processor, causes the processor to perform operations comprising:

obtaining input specifying a first metric value associated with an advertisement;

determining a predicted conversion rate for a potential impression of the advertisement;

estimating a second metric value based on the first metric value and the predicted conversion rate;

compensating based on the second metric value; and

debiting based on the first metric value.

24. A computer-readable medium having instructions stored thereon, which, when executed by a processor, causes the processor to perform operations comprising:

a processor;

a computer-readable medium operatively coupled to the processor and including instructions, which, when executed by the processor, causes the processor to perform operations comprising;

receiving an advertiser input specifying a first metric value for a conversion event associated with an online advertisement;

determining a predicted conversion rate for a potential impression of the advertisement based on historical data;

determining a correction factor for the predicted conversion rate; and

automatically computing a second metric value using the first metric value, the predicted conversion rate and the correction factor.

25. A system comprising:

means for obtaining input specifying a first metric value associated with an advertisement;

means for determining a predicted conversion rate for a potential impression of the advertisement;

means for estimating a second metric value based on the first metric value and the predicted conversion rate;

means for compensating based on the second metric value; and

means for debiting based on the first metric value.

26. A system comprising:

means for receiving an advertiser input specifying a first metric value for a conversion event associated with an online advertisement;

means for determining a predicted conversion rate for a potential impression of the advertisement based on historical data;

means for determining a correction factor for the predicted conversion rate; and

means for automatically computing a second metric value using the first metric value, the predicted conversion rate and the correction factor.