Methods and apparatus to correct errors in audience measurements for media accessed using over-the-top devices

Patent metadata

Field	Value
Publication number	US-10045082-B2
Application number	US-201514967355-A
Country	US
Kind code	B2
Filing date	Dec 13, 2015
Priority date	Jul 2, 2015
Publication date	Aug 7, 2018
Grant date	Aug 7, 2018

Abstract

Methods and apparatus to correct errors in measuring audiences of over-the-top media are disclosed. In some examples, the methods and apparatus identify a first set of data from a first data source, the first set of data different from a second set of data from a second data source. In some examples, the methods and apparatus generate a third set of data based on the second set of data from the second data source. In some examples, the methods and apparatus generate a model based on a difference between the first set of data and the third set of data. In some examples, the methods and apparatus apply the model to the first set of data. In some examples, the methods and apparatus assign viewership to an impression associated with the first set of data by imputing viewership associated with the second set of data to the first set of data.

First claim

What is claimed is:

1. A method comprising:
identifying, by executing an instruction via a processor, a first set of impression data received from a computer at a first data source, the first set of impression data having matched demographic data from users registered with both an over-the-top (OTT) device and a database proprietor, the first set of impression data different from a second set of data from a second data source, the computer producing a misattribution error in the first set of impression data, the misattribution error based on a demographic data error in the first set of impression data, the demographic data error based on a difference between reported demographic data in the first set of impression data and actual demographic data corresponding to the first set of impression data;
generating, by executing an instruction via the processor, a third set of data based on the second set of data from the second data source;
generating, by executing an instruction via the processor, an independent binary model based on a difference between the first set of impression data and the third set of data;
correcting the demographic data error in the first set of impression data by applying, by executing an instruction via the processor, the independent binary model to the first set of impression data to generate corrected demographic data; and
correcting the misattribution error produced by the computer by assigning, by executing an instruction via the processor, viewership to an impression associated with the first set of impression data using the corrected demographic data.

2. The method as defined in claim 1 , wherein the generating of the third set of data includes:
identifying a demographic bucket;
determining whether the demographic bucket exists in a household based on the independent binary model; and
determining the number of members in the household associated with the demographic bucket based on a demographic distribution associated with the second set of data.

3. The method as defined in claim 2 , wherein the independent binary model is a machine learning algorithm generated based on at least one of a household size, an age, a gender, a person status, an income, an education, or an ethnicity.

4. The method as defined in claim 3 , wherein the machine learning algorithm is at least one of a classification and regression tree, a log it function, a conditional inference tree, a random forest, a neural network, or a bootstrap aggregate decision tree.

5. The method as defined in claim 1 , wherein the first set of impression data from the first data source has a fourth set of data missing, further including linearly scaling the first set of impression data to accommodate for the missing fourth set of data.

6. A method comprising:
identifying, by executing an instruction via a processor, a first set of impression data received from a computer at a first data source, the first set of impression data different from a second set of data from a second data source, the computer producing a misattribution error in the first set of impression data, the misattribution error based on a demographic data error in the first set of impression data, the demographic data error based on a difference between reported demographic data in the first set of impression data and actual demographic data corresponding to the first set of impression data;
generating, via the processor, a third set of data based on the second set of data from the second data source;
generating, via the processor, an independent binary model based on a difference between the first set of impression data and the third set of data; and
correcting the demographic data error in the first set of impression data by applying, via the processor, the independent binary model to the first set of impression data to generate corrected demographic data; and
correcting the misattribution error produced by the computer by assigning, via the processor, viewership to an impression associated with the first set of impression data using the corrected demographic data, the assigning of the viewership to the impression includes:
identifying viewing history associated with the second set of data;
determining a first time associated with a first demographic viewing a media presentation in a first household associated with the second set of 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 set of 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 set of impression data.

7. The method as defined in claim 6 , wherein the identifying of the first person in the first household associated with the second set of data having the second probability similar to the first probability includes:
identifying a third person in the first household associated with the second set of 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 set of 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.

8. An apparatus comprising:
a demographic corrector to:
identify a first set of impression data received from a computer at a first data source, the first set of impression data having matched demographic data from users registered with both an over-the-top (OTT) device and a database proprietor, the first set of impression data different from a second set of data from a second data source, the computer producing a misattribution error in the first set of impression data, the misattribution error based on a demographic data error in the first set of impression data, the demographic data error based on a difference between reported demographic data in the first set of impression data and actual demographic data corresponding to the first set of impression data;
generate a third set of data based on the second set of data from the second data source;
generate an independent binary model based on a difference between the first set of impression data and the third set of data; and
correct the demographic data error in the first set of impression data by applying the independent binary model to the first set of impression data to generate corrected demographic data; and

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

9. The apparatus as defined in claim 8 , wherein to generate the third set of data, the demographic corrector is to:
identify a demographic bucket;
determine whether the demographic bucket exists in a household based on the independent binary model; and
determine the number of members in the household associated with the demographic bucket based on a demographic distribution associated with the second set of data.

10. The apparatus as defined in claim 9 , wherein the independent binary model is a machine learning algorithm genera

Assignees

• Nielsen Co Us Llc

Inventors

• Nagaraja Rao Kumar
• Toker Yildiz Kamer
• Haskell Jennifer
• Ion Cristina
• Zhang Mimi

Classifications

• G06Q10/40

  Business processes related to social networking or social networking services · CPC title

• G06Q30/02

  Marketing; Price estimation or determination; Fundraising · CPC title

• H04N21/252

  Processing of multiple end-users' preferences to derive collaborative data · CPC title

• G06Q30/0241

  Advertisements · CPC title

• G06Q30/00

  Commerce · CPC title