METHODS AND APPARATUS TO CORRECT ERRORS IN AUDIENCE MEASUREMENTS FOR MEDIA ACCESSED USING OVER THE TOP DEVICES

• US 20180234730A1
• Filed: 04/18/2018
• Published: 08/16/2018
• Est. Priority Date: 07/02/2015
• Status: Active Grant

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: Nagaraja Rao, Kumar, Toker Yildiz, Kamer, Haskell, Jennifer, Ion, Cristina, Zhang, Mimi
• Granted Patent: US 10,368,130 B2
• Time in Patent Office: Days
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25883 being end-user demographical data, e.g. age, family status or address arrangements for identifying locations of users in broadcast systems H04H60/52
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; 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 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/44226 on social networks
• 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 21/4661 Deriving a combined profile for a plurality of end-users of the same client, e.g. for family members within a home user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4663 involving probabilistic networks, e.g. Bayesian networks
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02

Abstract

Methods and apparatus to correct errors in measuring audiences of over the top media are disclosed. An example apparatus includes a demographic corrector to identify first impression data received from a computer, the first impression data including demographic data of users, the computer producing a misattribution error in the first impression data, and the misattribution error corresponding to a difference between reported demographics in the first impression data and actual demographics corresponding to the first impression data. The example demographic corrector further is to generate a model based on a difference between the first impression data and second data and generate corrected demographic data by applying the model to the first impression data. In addition, the example apparatus includes a viewership assigner to correct the misattribution error produced by the computer by assigning viewership to an impression associated with the first impression data using the corrected demographic data, at least one of the demographic corrector or the viewership assigner is a logic circuit.

Claims

1. An apparatus comprising:

a demographic corrector to;

identify first impression data received from a computer, the first impression data including demographic data of users, the computer producing a misattribution error in the first impression data, the misattribution error corresponding to a difference between reported demographics in the first impression data and actual demographics corresponding to the first impression data;

generate a model based on a difference between the first impression data and second data; and

generate corrected demographic data by applying the model to the first impression data; and

a viewership assigner to correct the misattribution error produced by the computer by assigning viewership to an impression associated with the first impression data using the corrected demographic data, at least one of the demographic corrector or the viewership assigner is a logic circuit.

2. The apparatus as defined in claim 1, wherein the second data is from a database proprietor, the demographic corrector is to generate the second data by:

identifying a demographic bucket;

determining whether the demographic bucket exists in a household based on the model;

determining a number of members in the household associated with the demographic bucket based on a demographic distribution associated with third data from the database proprietor; and

determining the second data based on the number of members in the household associated with the demographic bucket.

3. The apparatus as defined in claim 1, wherein the viewership assigner is to assign viewership to the impression by:

identifying viewing history associated with the second data;

determining a first time associated with a first demographic viewing a media presentation in a first household associated with the second data;

determining a second time associated with the first demographic and a second demographic viewing the media presentation in the household;

determining a first probability that the first demographic viewed the media presentation by dividing the first time by the second time;

identifying a first person in the first household associated with the second data having a second probability similar to the first probability; and

imputing a viewing history of the first person to a second person in a second household associated with the first impression data.

4. The apparatus as defined in claim 3, wherein to identify the first person in the first household associated with the second data having the second probability similar to the first probability, the viewership assigner is to:

identify a third person in the first household associated with the second data;

rank the first and third person based on a first highest probability;

identify a fourth person in the second household associated with the first impression data;

rank the second and fourth person based on a second highest probability; and

identify the first person has the second probability similar to the first probability when the first person has the first highest probability and the second person has the second highest probability.

5. The apparatus as defined in claim 1, further including a demographic predictor to linearly scale the first impression data to accommodate for fourth data missing from the first impression data.

6. The apparatus as defined in claim 1, further including a demographic predictor to differentially scale the first impression data to accommodate for fourth data missing from the first impression data.

7. The apparatus as defined in claim 1, wherein the first impression data represents impressions collected from an over-the-top device and the second data is from a database proprietor.

8. The apparatus as defined in claim 1, wherein the model is to determine a demographic dependency between two demographic categories and the demographic corrector is to generate the corrected demographic data based on the demographic dependency.

9. A tangible computer readable storage medium comprising instructions that, when executed, cause a machine to at least:

identify first impression data received from a computer, the first impression data including demographic data of users;

identify a misattribution error in the first impression data produced by the computer, the misattribution error corresponding to a difference between reported demographics in the first impression data and actual demographics corresponding to the first impression data;

generate a model based on a difference between the first impression data and second data;

generate corrected demographic data error by applying the model to the first impression data; and

correct the misattribution error by assigning viewership to an impression associated with the first impression data using the corrected demographic data.

10. The storage medium as defined in claim 9, wherein the second data is from a database proprietor, and the instructions, when executed, further cause the machine to generate the second data by:

identifying a demographic bucket;

determining whether the demographic bucket exists in a household based on the model;

determining a number of members in the household associated with the demographic bucket based on a demographic distribution associated with third data from the database proprietor; and

determining the second data based on the number of members in the household associated with the demographic bucket.

11. The storage medium as defined in claim 9, wherein the instructions, when executed, further cause the machine to assign viewership to the impression by:

identifying viewing history associated with the second data;

determining a first time associated with a first demographic viewing a media presentation in a first household associated with the second data;

determining a second time associated with the first demographic and a second demographic viewing the media presentation in the household;

determining a first probability that the first demographic viewed the media presentation by dividing the first time by the second time;

identifying a first person in the first household associated with the second data having a second probability similar to the first probability; and

imputing a viewing history of the first person to a second person in a second household associated with the first impression data.

12. The storage medium as defined in claim 11, wherein the instructions, when executed, further cause the machine to identify the first person in the first household associated with the second data having the second probability similar to the first probability by:

identifying a third person in the first household associated with the second data;

ranking the first and third person based on a first highest probability;

identifying a fourth person in the second household associated with the first impression data;

ranking the second and fourth person based on a second highest probability; and

identifying the first person has the second probability similar to the first probability when the first person has the first highest probability and the second person has the second highest probability.

13. The storage medium as defined in claim 9, wherein the instructions, when executed, further cause the machine to linearly scale the first impression data to accommodate for fourth data missing from the first impression data.

14. The storage medium as defined in claim 9, wherein the instructions, when executed, further cause the machine to differentially scale the first impression data to accommodate for fourth data missing from the first impression data.

15. The storage medium as defined in claim 9, wherein the first impression data represents impressions collected from an over-the-top device and the second data is from a database proprietor.

16. The storage medium as defined in claim 9, wherein the instructions, when executed, further cause the machine to further:

use the model to determine a demographic dependency between two demographic categories; and

generate the corrected demographic data based on the demographic dependency.

17. A method to correct a misattribution error produced by a computer, the method comprising:

identifying, by executing an instruction with a processor, first impression data received from the computer, the first impression data including demographic data of users;

identifying, by executing an instruction with the processor, a misattribution error in the first impression data produced by the computer, the misattribution error corresponding to a difference between reported demographics in the first impression data and actual demographics corresponding to the first impression data;

generating, by executing an instruction with the processor, a model based on a difference between the first impression data and second data;

generating, by executing an instruction with the processor, corrected demographic data by applying the model to the first impression data; and

correcting, by executing an instruction with the processor, the misattribution error by assigning viewership to an impression associated with the first impression data using the corrected demographic data.

18. The method of claim 17, wherein the second data is from a database proprietor and the generating the second data includes:

identifying, by executing an instruction with the processor, a demographic bucket;

determining, by executing an instruction with the processor, whether the demographic bucket exists in a household based on the model;

determining, by executing an instruction with the processor, a number of members in the household associated with the demographic bucket based on a demographic distribution associated with third data from the database proprietor; and

determining, by executing an instruction with the processor, the second data based on the number of members in the household associated with the demographic bucket.

19. The method as defined in claim 17, wherein the assigning viewership to the impression includes:

identifying by executing an instruction with the processor, viewing history associated with the second data;

determining, by executing an instruction with the processor, a first time associated with a first demographic viewing a media presentation in a first household associated with the second data;

determining, by executing an instruction with the processor, a second time associated with the first demographic and a second demographic viewing the media presentation in the household;

determining, by executing an instruction with the processor, a first probability that the first demographic viewed the media presentation by dividing the first time by the second time;

identifying, by executing an instruction with the processor, a first person in the first household associated with the second data having a second probability similar to the first probability; and

imputing, by executing an instruction with the processor, a viewing history of the first person to a second person in a second household associated with the first impression data.

20. The method as defined in claim 17, wherein the identifying the first person in the first household associated with the second data having the second probability similar to the first probability includes:

identifying, by executing an instruction with the processor, a third person in the first household associated with the second data;

ranking, by executing an instruction with the processor, the first and third person based on a first highest probability;

identifying, by executing an instruction with the processor, a fourth person in the second household associated with the first impression data;

ranking, by executing an instruction with the processor, the second and fourth person based on a second highest probability; and

identifying, by executing an instruction with the processor, the first person has the second probability similar to the first probability when the first person has the first highest probability and the second person has the second highest probability.