Traffic estimation

• US 8,024,447 B1
• Filed: 12/30/2009
• Issued: 09/20/2011
• Est. Priority Date: 08/31/2001
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: AOL Advertising Incorporated (Apollo Global Management, Inc.)
• Inventors: Raines, Taylor, Garcia-Franco, Jose Carlos, Kamprath, Michael F., Luenberger, Rob
• Primary Examiner(s): Walsh; John B.
• Application Number:
  US12/650,200
• Time in Patent Office: 629 Days
• Field of Search: None
• US Class Current: 709/223
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level

Abstract

Methods, systems, and articles of manufacture of the present invention may assist in planning, execution, and evaluation of advertising campaigns on the Internet. Particularly, methods, systems, and articles of manufacture of the present invention may help evaluate and/or predict traffic volume on the Internet. An exemplary method for predicting traffic may comprise receiving historical traffic data for a location, and computing a prediction of traffic volume for a particular time at the location using the historical traffic data and at least one prediction algorithm.

Claims

1. A method comprising:

comparing, via one or more processors, a predictability of traffic for a first location and a predictability of traffic for a second location; and

generating, via the one or more processors, an advertising campaign for at least the first location,

wherein generating the advertising campaign for the first location is based at least in part on the predictability of traffic for the first location.

2. The method of claim 1, wherein the generating of the advertising campaign for the first location comprises

selecting the first location for delivery of advertisements in the advertising campaign based at least in part on the predictability of traffic for the first location.

3. The method of claim 1, wherein the first location is a website.

4. The method of claim 1, further comprising

determining the predictability of traffic for the second location.

5. The method of claim 1, wherein the generating of the advertising campaign for the first location is based at least in part on the comparison of the predictability of traffic for the first location and the predictability of traffic for the second location.

6. The method of claim 1, wherein the first location is a first website and the second location is a second website.

7. The method of claim 1, further comprising

receiving historical traffic data for the first location, and

observing traffic volume data from the first location,

wherein the determining of the predictability of traffic for the first location is based at least in part on the historical traffic data for the first location and the observed traffic volume data from the first location.

8. A method comprising:

generating an advertising campaign for at least a first location;

conducting the advertising campaign for the first location;

determining a predictability of traffic for the first location;

comparing, via one or more processors, the predictability of traffic for the first location and a predictability of traffic for a second location; and

adjusting, via the one or more processors, an advertisement delivery schedule of the advertising campaign for the first location based at least in part on the predictability of traffic for the first location.

9. The method of claim 8, wherein the first location is a website.

10. The method of claim 8, further comprising

determining the predictability of traffic for the second location.

11. The method of claim 8, wherein the adjusting of the advertisement delivery schedule of the advertising campaign for the first location is based at least in part on the comparison of the predictability of traffic for the first location and the predictability of traffic for the second location.

12. The method of claim 8, further comprising

adjusting an advertisement delivery schedule for the second location based at least in part on the comparison of the predictability of traffic for the first location and the predictability of traffic for the second location.

13. The method of claim 8, wherein the first location is a first website and the second location is a second website.

14. The method of claim 8, further comprising

receiving historical traffic data for the location, and

observing traffic volume data from the location,

wherein the determining of the predictability of traffic for the location is based at least in part on the historical traffic data for the location and the observed traffic volume data from the location.

15. A method comprising:

generating an advertising campaign for at least one location;

receiving historical traffic data for the location;

computing, via one or more processors, a prediction of traffic volume for a particular time at the location using the historical traffic data;

conducting the advertising campaign for the location;

receiving observed traffic volume data for the particular time at the location;

comparing, via the one or more processors, the prediction of traffic volume with the observed traffic volume data;

computing, via the one or more processors, a predictability score for the location from the comparison; and

adjusting, via the one or more processors, an advertisement delivery schedule of the advertising campaign for the location based at least in part on the predictability score for the location.

16. The method of claim 15, wherein the particular time at the location is a particular hour of day at the location and the computing of the prediction of traffic volume uses historical hourly traffic data from the same particular hour over a plurality of days.

17. The method of claim 15, wherein the location is a first location, and the adjusting of the advertisement delivery schedule comprises scheduling delivery of an advertisement in the campaign to a second location based on the predictability score for the first location.

18. The method of claim 15, wherein the location is a first location and the predictability score is a first predictability score, the method further comprising:

computing a second predictability score for a second location; and

comparing the first predictability score and the second predictability score,

wherein the adjusting of the advertisement delivery schedule comprises scheduling delivery of an advertisement in the campaign to the second location based on the comparison of the first predictability score and the second predictability score.