Forecasting Ad Traffic Based on Business Metrics in Performance-based Display Advertising

• US 20120158456A1
• Filed: 12/20/2010
• Published: 06/21/2012
• Est. Priority Date: 12/20/2010
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Oath Inc. (Verizon Communications Inc.)
• Inventors: Wang, Xuerui, Zhang, Ruofei (Bruce)
• Application Number:
  US12/973,637
• Publication Number:
  US 20120158456A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0241 Advertisements

Abstract

A method, advertising network, and computer readable medium for forecasting ad traffic based on business metrics in performance-based display advertising. The method commences by defining a set of advertising campaign parameters, the advertising campaign parameters comprising target predicates, a campaign pricing model, and a campaign performance model. The method continues by forecasting a supply of impressions satisfying target predicates, based on a statistical analysis of a historical dataset containing impressions satisfying target predicates. Once a measure of forecasted supply is known, an auction model serves for calculating the likelihood of winning the forecasted impression at auction, based the campaign pricing model and the campaign performance model. Having a forecasted supply, and also an assessment of the likelihood of winning at auction, the method proceeds by determining values for various performance metrics. The performance metrics are displayed; the user makes changes to any one or more of the advertising campaign parameters.

Claims

1. A method for forecasting ad traffic based on business metrics in performance-based display advertising, the method comprising:

defining, in memory, advertising campaign parameters, the advertising campaign parameters comprising at least one target predicate, at least one campaign pricing model, and at least one campaign performance model;

forecasting, using a computer, at least one forecasted impression satisfying the at least one target predicate, the forecasting comprising a statistical analysis of a historical dataset;

modeling, using a computer, a likelihood of winning the forecasted impression in an auction model, the auction model for predicting at least the highest bid for the forecasted impression; and

determining, using a computer, a performance metric using the forecasted impression and a likelihood of winning the forecasted impression.

2. The method of claim 1, further comprising:

reporting, using a display device, the performance metric.

3. The method of claim 1, wherein the defining advertising campaign parameters comprises user input of at least one campaign parameter using a screen device.

4. The method of claim 1, wherein the modeling includes a bidding agent training model, the training model for learning a distribution of winning bids based on historical events.

5. The method of claim 1, wherein the performance metric is one of, a click-through-rate, a click-response rate, a number of won impressions.

6. The method of claim 1, wherein the campaign pricing model is at least one of, cost-per-impression, cost-per-click, cost-per-action.

7. The method of claim 1, wherein the campaign performance model is at least one of, a minimize cost-per-click model, a minimize cost-per-action model.

8. An advertising server network forecasting ad traffic based on business metrics in performance-based display advertising, comprising:

a module, comprising at least one processor and memory, for defining advertising campaign parameters, the advertising campaign parameters comprising at least one target predicate, at least one campaign pricing model, and at least one campaign performance model;

a module, comprising at least one processor and memory, for forecasting at least one forecasted impression satisfying the at least one target predicate, the forecasting comprising a statistical analysis of a historical dataset;

a module, comprising at least one processor and memory, for modeling a likelihood of winning the forecasted impression in an auction model, the auction model for predicting at least the highest bid for the forecasted impression; and

a module, comprising at least one processor and memory, for determining a performance metric using the forecasted impression and a likelihood of winning the forecasted impression.

9. The advertising server network of claim 8, further comprising:

reporting, using a display device, the performance metric.

10. The advertising server network of claim 8, wherein the defining advertising campaign parameters comprises user input of at least one campaign parameter using a screen device.

11. The advertising server network of claim 8, wherein the modeling includes a bidding agent training model, the training model for learning a distribution of winning bids based on historical events.

12. The advertising server network of claim 8, wherein the performance metric is one of, a click-through-rate, a click-response rate, a number of won impressions.

13. The advertising server network of claim 8, wherein the campaign pricing model is at least one of, cost-per-impression, cost-per-click, cost-per-action.

14. The advertising server network of claim 8, wherein the campaign performance model is at least one of, a minimize cost-per-click model, a minimize cost-per-action model.

15. A computer readable medium comprising a set of instructions which, when executed by a computer, cause forecasting of ad traffic based on business metrics in performance-based display advertising, said instructions for:

defining, in memory, advertising campaign parameters, the advertising campaign parameters comprising at least one target predicate, at least one campaign pricing model, and at least one campaign performance model;

forecasting, using a computer, at least one forecasted impression satisfying the at least one target predicate, the forecasting comprising a statistical analysis of a historical dataset;

modeling, using a computer, a likelihood of winning the forecasted impression in an auction model, the auction model for predicting at least the highest bid for the forecasted impression; and

determining, using a computer, a performance metric using the forecasted impression and a likelihood of winning the forecasted impression.

16. The computer readable medium of claim 15, wherein the defining advertising campaign parameters comprises user input of at least one campaign parameter using a screen device.

17. The computer readable medium of claim 15, wherein the modeling includes a bidding agent training model, the training model for learning a distribution of winning bids based on historical events.

18. The computer readable medium of claim 15, wherein the performance metric is one of, a click-through-rate, a click-response rate, a number of won impressions.

19. The computer readable medium of claim 15, wherein the campaign pricing model is at least one of, cost-per-impression, cost-per-click, cost-per-action.

20. The computer readable medium of claim 15, wherein the campaign performance model is at least one of, a minimize cost-per-click model, a minimize cost-per-action model.