The Nielsen Comapny (us), LLC

Other

The Nielson Company (us), LLC

Michael D Morgan

Jonathan Lee Sullivan

Michael R Sheppard

Edward Murphy

Ludo Daemen

Balachander Shankar

Frank Downing

Kumar Nagaraja Rao

Neung Soo Ha

Billie Jo Kline

William Rene Waldron

Chris Samuel Redino

Logan Thomas

Andrew Stannard

Kali Bogovich

Calvin James Bissett

Cesar Arocho

Peter Quoc Nguyen

Gary P Sorensen

Raphael Gordon

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to assign respondents to geographic locations for audience measurement are disclosed. Example apparatus disclosed herein are to determine a set of constraints based on aggregate values of demographic features associated with respective ones of the geographic locations. Disclosed example apparatus are also to construct a model to return probabilities that respective ones the respondents are associated with the respective ones of the geographic locations, the model to have a set of parameters, respective ones of the parameters to be associated with respective ones of the constraints. Disclosed example apparatus are further to evaluate the model based on values of the set of parameters and values of the demographic features for a first one of the respondents to determine a set of probabilities that the first respondent is to be assigned to respective ones of the geographic locations.

Assigning synthetic respondents to geographic locations for audience measurement

Disclosed examples determine a duplicated audience size representative of panelists exposed to television media and digital media; determine a panel duplication reach based on the duplicated audience size and a panelist population; determine a did-not-view reach based on a television audience size, a digital audience size, the duplicated audience size, and the panelist population; obtain an overlap multiplier as a ratio of (1) a product of the panel duplication reach and the did-not-view reach and (2) a product of a television panel reach and a digital panel reach; determine a duplication factor for a media item based on a television audience reach, a digital audience reach, and the overlap multiplier; and determine a total audience for the media item based on the television audience reach, the digital audience reach, and the duplication factor.

Methods and apparatus to estimate deduplicated total audiences in cross-platform media campaigns

Example methods, apparatus, systems, and articles of manufacture are disclosed to estimate population reach for different unions based on marginal ratings. An example apparatus includes memory and processor circuitry to determine a population reach estimate of a union of time intervals for which media ratings data is available, the population reach estimate based on a pseudo universe estimate of a population audience corresponding to the union of the time intervals; determine a pseudo universe estimate of a recorded audience corresponding to the union of the time intervals; determine the pseudo universe estimate of the population audience based on the pseudo universe estimate of the recorded audience; and in response to a decision to update the pseudo universe estimate of the population audience to reduce an error of the population reach estimate output the population reach estimate of the union when the error of the population reach estimate satisfies a threshold.

Methods and apparatus to estimate population reach from different marginal rating unions

Methods, apparatus, systems, and articles of manufacture are disclosed to generate synthetic respondent level data. Example apparatus disclosed herein include means for generating a synthetic panel corresponding to a duration of time, the means for generating the synthetic panel to: generate a transition matrix corresponding to a first sub-duration of the duration of time and a second sub-duration of the duration of time; generate, based on the transition matrix, a plurality of synthetic panelists and associated viewing data; remove first ones of the synthetic panelists associated with one or more weights that do not satisfy a threshold to generate the synthetic panel corresponding to the duration of time, the synthetic panel representative of audiences of media presented by a plurality of media devices during the duration of time; and generate synthetic respondent level data based on the viewing data associated with remaining second ones of the synthetic panelists.

Methods and apparatus to determine synthetic respondent level data using constrained Markov chains

Example methods, apparatus, systems, and articles of manufacture are disclosed to estimate population reach for different unions based on marginal ratings. An example method includes performing first parallel computations to determine first multipliers corresponding to a total number of panelists exposed to media at (a) a first margin of time, (b) a second margin of time, and (c) a union corresponding to the first and second margins of time, keeping a subset of the first multipliers corresponding to unknown census data; performing second parallel computation to determine second multipliers corresponding to a total audience exposed to the media at the first margin of time and the second margin of time, the second parallel computations to be performed by solving second equations corresponding to a tree association; and determining an estimate for a population reach of the union based on the second multipliers.

Disclosed examples obtain an overlap multiplier as a ratio of (1) a first product of a panel duplication reach and a did-not-view reach and (2) a second product of a television panel reach and a digital panel reach, the panel duplication reach based on a duplicated audience size and a panelist population, the did-not-view reach based on a television audience size, a digital audience size, the duplicated audience size, and the panelist population, the television panel reach corresponding to the television audience size, the digital panel reach corresponding to the digital audience size; and determine a total audience for a media item based on a television audience reach, a digital audience reach, and a duplication factor, the duplication factor based on the television audience reach, the digital audience reach, and the overlap multiplier, the television audience reach corresponding to television media, the digital audience reach corresponding to digital media.

Methods, apparatus, systems, and articles of manufacture are disclosed to generate synthetic respondent level data. An example apparatus includes at least one memory; processor circuitry to execute instructions to: identify locations corresponding to monitored panelists; when a location of the identified locations corresponds to two or more affiliates, sum tuning minutes of the two or more affiliates; generate an affiliate probability based on the sum; convert first data of households in the location to a candidate receivability session based on the affiliate probability; generate a seed panel, the seed panel to include at least one of the monitored panelists selected based on a population associated with return path data; and donate second data associated with a donor pool of first panelists to second panelists included in the seed panel when the second panelists are included in the seed panel, the second panelists corresponds to faulty media monitoring equipment.

Methods and apparatus to determine synthetic respondent level data

Methods, systems, articles of manufacture, and apparatus are disclosed to map client specifications to standardized characteristics. An example apparatus includes a cluster identifier to cluster client databases into client clusters based on a threshold quantity of overlapping universal product codes (UPCs) between respective ones of the client databases, a characteristic analyzer to identify custom characteristics from the respective ones of the client clusters, ones of the custom characteristics having dissimilar nomenclature, and a graph builder to cluster the ones of the custom characteristics based on a similarity metric, and normalize the ones of the custom characteristics as a proxy characteristic, the proxy characteristic having a common nomenclature to represent the ones of the custom characteristics, the characteristic analyzer to enable improved product marketing analysis by replacing dissimilar nomenclature with the proxy characteristic.

Methods, systems, articles of manufacture, and apparatus to map client specifications with standardized characteristics

Example methods, apparatus, and articles of manufacture are disclosed to estimate population reach. An example apparatus includes processor circuitry to determine first multipliers corresponding to a panelist impression count and panelist audience size totals of at least one of a first margin of media, a second margin of the media, or a union of the first margin and the second margin, the first margin, the second margin, and the union included in a tree association; concurrently determine second multipliers using the tree association and the first multipliers; determine third multipliers corresponding to a total audience size exposed to the media at at least one of the first margin, the second margin, or the union based on the tree association using database proprietor impression totals; and determine, based on the third multipliers, an estimate for the population reach of the media for at least one of the first margin, the second margin, or the union.

Methods and apparatus to estimate population reach from different marginal ratings and/or unions of marginal ratings based on impression data

Example methods and apparatus to replicate panelists using a local minimum solution of an integer least squares problem are disclosed. An example apparatus includes memory; and processor circuitry to execute computer readable instructions to: determine weight adjustments based on a ratio of (A) a first dot product of (i) attribute data and (ii) a difference between first aggregate panelist data and second seed panelist data, and (B) a second dot product of the attribute data and the attribute data, the first aggregate panelist data based on the attribute data, the attribute data corresponding to a seed panel; determine weight estimates based on the weight adjustments, the weight estimates to replicate respective seed panelists in the seed panel; and replicate respective ones of the seed panelists based on the weight estimates to generate a synthetic audience representative of return path data reported by a plurality of media devices.

Growing community of inventors

Bartlett, IL, United States of America

Michael D Morgan