The Nielsen Comapny (us), LLC

Cristina Ion

Kumar Nagaraja Rao

Mimi Zhang

Jennifer Haskell

Kamer Toker Yildiz

Seema Varma Srivastava

Gilles Duterque

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. 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.

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

An example system comprising at least one memory, programmable circuitry, and instructions to cause the programmable circuitry to obtain first time information corresponding to one or more first times that a panelist accessed first media via a television, obtain second time information corresponding to one or more second times that a household accessed second media via an over-the-top device, the second time information matching the first time information and associated with corrected demographic information of members of the household, determine that a first genre of the first media matches a second genre of the second media, determine that a member of the household accessed the second media via the over-the-top device based on historical media access events of the panelist, and attribute the access of the second media to the member of the household to create a corrected demographic impression record.

An example to predict demographics for impressions includes a prediction manager to: determine that first demographic data corresponding to a first database proprietor subscriber does not match second demographic data corresponding to a media delivery device, both the first and second demographic data corresponding to an impression; obtain third demographic data corresponding to an Internet protocol address, the third demographic data obtained from a second database proprietor; and generate matched demographic data based on comparing the third demographic data to the first demographic data; and a modeler to generate a prediction model based on the matched demographic data, the prediction model to predict fourth demographic data for the impression.

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

Methods and apparatus to predict demographics are disclosed. An example apparatus includes a prediction manager to access matched demographic impression data when a database proprietor subscriber does not match user data corresponding to a media delivery device, the matched demographic impression data including first demographic data stored by a database proprietor; a linear scaler to linearly scale the matched demographic impression data when the matched demographic impression data covers all demographic categories under consideration; a differential scaler to differentially scale the matched demographic impression data when the matched demographic impression data does not cover at least one of the demographic categories; and a modeler to: generate a model representative of the matched demographic impression data linearly scaled or the matched demographic impression data differentially scaled; and generate predicted demographic impression data by applying the model to second impression data to correct for the database proprietor not storing second demographic data.

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.

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.

Methods and apparatus are disclosed to identify session users with cookie information. An example method includes identifying a most recently used cookie based on a comparison of timestamps associated with a plurality of cookies, comparing an identifier of the most recently used cookie to a cookie table, the cookie table associating cookies with the plurality of different users, and when the identifier of the most recently used cookie is associated with one of the plurality of different users, associating subsequent usage activity of the computing device with the one of the plurality of different users.

Methods and apparatus to identify session users with cookie information

Methods and apparatus are disclosed to identify session users with cookie information. An example method includes locating, with a processor, a first storage location associated with a first browser and a second storage location associated with a second browser, identifying, with the processor, a first cookie in the first storage location having a most recent timestamp, when the first storage location includes the first cookie of a first type, establishing a first user associated with the first cookie as a current user of the computing device. The example method includes searching, with the processor, the second storage location for a second cookie having the same type as the first cookie, when the second storage location includes the second cookie, comparing, with the processor, the timestamp of the first cookie and a timestamp of the second cookie to determine a most recent cookie, when the first cookie is the most recent, confirming the first user as the current user of the computing device, and when the second cookie is the most recent, replacing the first user with a user associated with the second cookie as the current user of the computing device.

Methods and apparatus are disclosed to identify session users with cookie information. An example method includes identifying activity associated with a first cookie having a first type, in response to identifying the first, parsing the first cookie to identify a first panelist, when the first cookie includes an identifier associated with a first panelist, associating current usage activity of the computing device with the first panelist, and when the first cookie includes an identifier unassociated with the first panelist, associating current usage activity of the computing device with a guest.

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Belmont, CA, United States of America

Cristina Ion