System and method for measuring television audience engagement

• US 8,904,419 B2
• Filed: 03/19/2013
• Issued: 12/02/2014
• Est. Priority Date: 03/19/2012
• Status: Active Grant

• Current Assignee: comScore, Inc.
• Original Assignee: comScore, Inc.
• Inventors: Vinson, Michael, Goerlich, Bruce, Loper, Maria, Martin, Melissa, Yazdani, Amir
• Primary Examiner(s): Parry, Chris
• Application Number:
  US13/847,476
• Publication Number:
  US 20130247081A1
• Time in Patent Office: 623 Days
• Field of Search: None
• US Class Current: 725/14
• CPC Class Codes: H04N 21/254 Management at additional data server, e.g. shopping server, rights management server arrangements for maintenance or administration in data networks H04L41/00; network services using third party service providers H04L67/53
• CPC Class Codes: H04N 21/44204 Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched monitoring of user activities for profile generation for accessing a video database G06F16/739; protecting generic digital content where the protection is independent of the precise nature of the content G06F21/10; arrangements for monitoring the use made of the broadcast services in broadcast systems H04H60/31
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02

Abstract

A system and method are disclosed that measures audience engagement with a program based on the time a user spends viewing the program. A program'"'"'s ability to retain viewers is referred to as the “stickiness” of the program. The viewership for a program is determined by the system and then compared to viewership of various other programs in order to assess the audience retention for the analyzed program as compared to the retention of the other programs. The system calculates a “stickiness index” for the program, which is a numerical representation of the analyzed program'"'"'s stickiness as compared to the average of the other programs. The system may generate a report including the analyzed data and calculated stickiness index for advertisers, agencies, media sellers, or other parties that are interested in assessing viewership of the analyzed program.

Claims

1. A computer implemented method for evaluating audience engagement with broadcast content, the method comprising:

receiving, on a computing system, tune data from one or more clients, each client associated with a video playback device;

analyzing the tune data in order to determine viewership data for program content that is viewed by an audience, the program content having a program length and the viewership data including an average percentage of the program content viewed by the audience;

comparing the viewership data to historical viewership data, the historical viewership data associated with other program content having an equivalent program length; and

calculating an index based on the historical viewership data, wherein the index reflects an engagement level of the program content relative to other program content.

2. The method of claim 1, wherein the historical viewership data is a regression curve constructed from viewership data for program content having one or more differing program lengths.

3. The method of claim 2, wherein the regression curve is constructed based on daypart, the daypart indicating a specific time frame in which program content is broadcast.

4. The method of claim 2, wherein two or more curves are constructed, each curve representing a particular percentile characterizing the program content.

5. The method of claim 4, wherein the index is determined by interpolating between the two or more curves in order to estimate historical viewership data for the program content.

6. The method of claim 1, wherein the received tune data is associated with broadcast content from one or more delivery platforms.

7. The method of claim 6, further comprising scaling the index for the one or more delivery platforms.

8. The method of claim 1, wherein the historical viewership data is determined for a set of program content, the set of program content sharing one or more attributes.

9. The method of claim 8, wherein the attributes include any one or more of genre, broadcast platform, video playback device, time of day, day of week, length of program, broadcast channel, or network provider.

10. A non-transitory computer-readable medium containing instructions that, when executed by a processor, cause a computing system to implement a method for evaluating audience engagement with broadcast content comprising:

receiving, on a computing system, tune data from one or more clients, each client associated with a video playback device;

analyzing the tune data in order to determine viewership data for program content that is viewed by an audience, the program content having a program length and the viewership data including an average percentage of the program content viewed by the audience;

comparing the viewership data to historical viewership data, the historical viewership data associated with other program content having an equivalent program length; and

calculating an index based on the historical viewership data, wherein the index reflects an engagement level of the program content relative to other program content.

11. The non-transitory computer-readable medium of claim 10, wherein the historical viewership data is a regression curve constructed from viewership data for program content having one or more differing program lengths.

12. The non-transitory computer-readable medium of claim 11, wherein the regression curve is constructed based on daypart, the daypart indicating a specific time frame in which program content is broadcast.

13. The non-transitory computer-readable medium of claim 11, wherein two or more curves are constructed, each curve representing a particular percentile characterizing the program content.

14. The non-transitory computer-readable medium of claim 13, wherein the index is determined by interpolating between the two or more curves in order to estimate historical viewership data for the program content.

15. The non-transitory computer-readable medium of claim 10, wherein the received tune data is associated with broadcast content from one or more delivery platforms.

16. The non-transitory computer-readable medium of claim 15, further comprising instructions that cause the computing system to scale the index for the one or more delivery platforms.

17. The non-transitory computer-readable medium of claim 10, wherein the historical viewership data is determined for a set of program content, the set of program content sharing one or more attributes.

18. The non-transitory computer-readable medium of claim 17, wherein the attributes include any one or more of genre, broadcast platform, video playback device, time of day, day of week, length of program, broadcast channel, or network provider.