Accurately Estimating the Audience of Digital Content

• US 20140244566A1
• Filed: 12/22/2011
• Published: 08/28/2014
• Est. Priority Date: 12/22/2011
• Status: Active Grant

• Current Assignee: Adobe Inc.
• Original Assignee: Adobe Systems Incorporated (Adobe Inc.)
• Inventors: Hewett, Edward L., George, William Brandon, Smith, Kevin G., Anderson, Eric V.
• Granted Patent: US 9,477,966 B2
• Time in Patent Office: Days
• US Class Current: 706/52
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06N 7/04 Physical realisation
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements

Abstract

A method, system, and computer-readable storage medium are disclosed for accurately estimating the audience of digital content. A plurality of user interactions occurring on a digital device are received, wherein digital content is displayed on the digital device. Based on the plurality of user interactions, a total number of unique viewers of the digital content on the digital device is estimated.

Claims

1. A computer-implemented method of estimating a circulation of a digital publication, comprising:

receiving a plurality of indications of user interactions occurring on each of a plurality of digital devices, wherein the digital publication is displayed on each of the digital devices, and wherein the digital publication includes one or more advertisements;

analyzing the user interactions from each particular digital device to distinguish individual viewers of the digital publication from other viewers of the digital publication using the particular digital device; and

generating an estimate of the circulation of the digital publication, wherein the estimate is based on a total number of unique viewers of the digital publication calculated from the analysis of the user interactions from each of the digital devices.

2. The method as recited in claim 1, wherein generating the estimate comprises:

assigning a score to each of a plurality of usage factors based on the plurality of user interactions, wherein each of the plurality of user interactions is classified into a respective one of the plurality of usage factors;

assigning a weight to each of the plurality of usage factors; and

determining a plurality of behavioral fingerprints based on the respective score and respective weight assigned to each of the plurality of usage factors.

3. The method as recited in claim 1, wherein generating the estimate comprises:

assigning a score to each of a plurality of usage factors based on the plurality of user interactions, wherein each of the plurality of user interactions is classified into a respective one of the plurality of usage factors; and

determining a plurality of behavioral fingerprints based on the respective score assigned to each of the plurality of usage factors.

4. The method as recited in claim 1, wherein the plurality of user interactions comprise user interactions with a touch-screen display device.

5. The method as recited in claim 1, further comprising:

determining a plurality of usage factors based on capabilities of the digital device, wherein each of the plurality of user interactions is classified into a respective one of the plurality of usage factors.

6. The method as recited in claim 1, further comprising:

assigning a first viewer of the unique viewers to a usage segment based on a similarity between the plurality of user interactions for the first viewer and a plurality of additional user interactions for a plurality of additional viewers in the usage segment.

7. The method as recited in claim 6, further comprising:

modifying the digital content on the digital device in response to assigning the first viewer to the usage segment.

8. A system, comprising:

at least one processor; and

a memory coupled to the at least one processor, wherein the memory stores program instructions, wherein the program instructions are executable by the at least one processor to;

receive a plurality of indications of user interactions occurring on each of a plurality of digital devices, wherein the digital publication is displayed on each of the digital devices, and wherein the digital publication includes one or more advertisements;

analyze the user interactions from each particular digital device to distinguish individual viewers of the digital publication from other viewers of the digital publication using the particular digital device; and

generating an estimate of the circulation of the digital publication, wherein the estimate is based on a total number of unique viewers of the digital publication calculated from the analysis of the user interactions from each of the digital devices.

9. The system as recited in claim 8, wherein, in generating the estimate, the program instructions are executable by the at least one processor to:

assign a score to each of a plurality of usage factors based on the plurality of user interactions, wherein each of the plurality of user interactions is classified into a respective one of the plurality of usage factors;

assign a weight to each of the plurality of usage factors; and

determine a plurality of behavioral fingerprints based on the respective score and respective weight assigned to each of the plurality of usage factors.

10. The system as recited in claim 8, wherein, in generating the estimate, the program instructions are executable by the at least one processor to:

assign a score to each of a plurality of usage factors based on the plurality of user interactions, wherein each of the plurality of user interactions is classified into a respective one of the plurality of usage factors; and

determine a plurality of behavioral fingerprints based on the respective score assigned to each of the plurality of usage factors.

11. The system as recited in claim 8, wherein the plurality of user interactions comprise user interactions with a touch-screen display device.

12. The system as recited in claim 8, wherein the program instructions are further executable by the at least one processor to:

determine a plurality of usage factors based on capabilities of the digital device, wherein each of the plurality of user interactions is classified into a respective one of the plurality of usage factors.

13. The system as recited in claim 8, wherein the program instructions are further executable by the at least one processor to:

assign a first viewer of the unique viewers to a usage segment based on a similarity between the plurality of user interactions for the first viewer and a plurality of additional user interactions for a plurality of additional viewers in the usage segment.

14. The system as recited in claim 13, wherein the program instructions are further executable by the at least one processor to:

modify the digital content on the digital device in response to assigning the first viewer to the usage segment.

15. A non-transitory computer-readable storage medium storing program instructions computer-executable to perform:

receiving a plurality of indications of user interactions occurring on each of a plurality of digital devices, wherein the digital publication is displayed on each of the digital devices, and wherein the digital publication includes one or more advertisements;

analyzing the user interactions from each particular digital device to distinguish individual viewers of the digital publication from other viewers of the digital publication using the particular digital device;

generating an estimate of the circulation of the digital publication, wherein the estimate is based on a total number of unique viewers of the digital publication calculated from the analysis of the user interactions from each of the digital devices.

16. The computer-readable storage medium as recited in claim 15, wherein generating the estimate comprises:

assigning a score to each of a plurality of usage factors based on the plurality of user interactions, wherein each of the plurality of user interactions is classified into a respective one of the plurality of usage factors;

assigning a weight to each of the plurality of usage factors; and

determining a plurality of behavioral fingerprints based on the respective score and respective weight assigned to each of the plurality of usage factors.

17. The computer-readable storage medium as recited in claim 15, wherein the plurality of user interactions comprise user interactions with a touch-screen display device.

18. The computer-readable storage medium as recited in claim 15, wherein the program instructions are further computer-executable to perform:

determining a plurality of usage factors based on capabilities of the digital device, wherein each of the plurality of user interactions is classified into a respective one of the plurality of usage factors.

19. The computer-readable storage medium as recited in claim 15, wherein the program instructions are further computer-executable to perform:

assigning a first viewer of the unique viewers to a usage segment based on a similarity between the plurality of user interactions for the first viewer and

a plurality of additional user interactions for a plurality of additional viewers in the usage segment.

20. The computer-readable storage medium as recited in claim 15, wherein the program instructions are further computer-executable to perform:

modifying the digital content on the digital device in response to assigning the first viewer to the usage segment.

21. A computer-implemented method of estimating a circulation of a digital publication, comprising:

receiving a plurality of indications of user interactions occurring on each of a plurality of digital devices, wherein the digital publication is displayed on each of the digital devices, and wherein the digital publication includes one or more advertisements;

determining a plurality of behavioral fingerprints based on the plurality of user interactions;

differentiating between a plurality of users based on differences between the plurality of behavioral fingerprints; and

generating an estimate of the circulation of the digital publication, wherein the estimate is based on a total number of unique viewers of the digital publication calculated from the differentiated users.

22. The method as recited in claim 21, further comprising:

detecting a first one of the plurality of behavioral fingerprints in use on the digital device;

and

modifying the digital content on the digital device in response to detecting the first one of the plurality of behavioral fingerprints in use on the digital device.

23. The method as recited in claim 21, further comprising:

determining a plurality of usage segments, wherein each of the behavioral segments comprises one or more of the plurality of behavioral fingerprints, and wherein the plurality of usage segments are determined based on similarities in usage patterns.

24. The method as recited in claim 21, wherein the plurality of user interactions comprise a plurality of inputs representing user behaviors.