The Nielsen Comapny (us), LLC

Paul Donato

Michael R Sheppard

Peter Campbell Doe

Alejandro Terrazas

Jonathan Lee Sullivan

Molly Poppie

David J Kurzynski

Peter Lipa

Albert T Borawski

Balachander Shankar

Kumar Nagaraja Rao

Brian Fuhrer

Mainak Mazumdar

Joseph Gregory Milavsky

John Charles Coughlin

Seema Varma Srivastava

Marie Kramer

Benjamin Wood

Albert Ronald Perez

David R Miller

Josh Gaunt

James R Oliver

Juliette Aurisset

Michael Himmelfarb

Matt Reid

Yutao Peng

David N Wong

Peng Fei Yi

Methods, apparatus, systems to determine a conditional probability based on audience member probability distributions for media audience measurement are disclosed. Disclosed example methods for media audience measurement include determining a first audience probability distribution for a first member of a household and determining a second audience probability distribution for a second member of the household. Disclosed example methods also include calculating probabilities for audience combinations of the first member and the second member of the household based on the first audience probability distribution and the second audience probability distribution. Disclosed example methods further include determining a household audience characteristic probability based on the calculated probabilities of the audience combinations of the household. The household audience characteristic indicates likelihoods of different possible audience compositions of the household for a media event.

Methods and apparatus to determine a conditional probability based on audience member probability distributions for media audience measurement

Example methods disclosed herein include accessing a query requesting a metric associated with a number of unique members of an audience of media over an aggregate monitoring interval corresponding to a plurality of component monitoring intervals. Disclosed example methods also include determining respective aggregate interval probability distributions modeling likelihoods of respective monitored individuals being exposed to the media during the aggregate monitoring interval, a first one of the aggregate interval probability distributions for a first one of the monitored individuals being determined by combining parameters of respective component interval probability distributions modeling likelihoods of the first one of the monitored individuals being exposed to the media during respective ones of the component monitoring intervals. Disclosed example methods further include evaluating an audience-level probability distribution determined from the aggregate interval probability distributions to determine the metric to transmit to the computing device in response to the query.

Determining metrics characterizing numbers of unique members of media audiences

Methods and apparatus to solve for a minimum variance solution for a constrained weight problem in a scalable and memory efficient manner are disclosed. An example method includes storing a constraint matrix and a population matrix into a memory, the constraint matrix representing demographics associated with a plurality of panelists in a population, the population matrix representing second demographics of the population; determining, by executing an instruction with a processor, a linear system based on a set of constraints including the constraint matrix and the population matrix; determining, by executing an instruction with the processor, a solution to the linear system by determining an inverse of a first matrix of the linear system, the determination of the inverse including breaking down the first matrix into blocks, dimensions of the blocks being no larger than dimensions of the constraint matrix and outputting a set of weights based on the inverse of the linear system, the set of weights satisfying the set of constraints.

Methods and apparatus to determine weights for panelists in large scale problems

Methods, apparatus, systems and articles of manufacture are disclosed to improve reach calculation efficiency. An example method includes estimating, with a processor, a sample distribution of marketing data to generate a maximum entropy distribution, generating, with the processor, a geometric distribution based on estimating a minimum cross entropy of (a) the maximum entropy distribution and (b) the sample distribution of marketing data, and improving calculation efficiency of the public reach of the sample distribution of marketing data by generating, with the processor, conserved quantity expressions of the geometric distribution.

Methods and apparatus to improve reach calculation efficiency

Methods and apparatus to solve for a minimum variance solution for a constrained weight problem in a scalable and memory efficient manner are disclosed. An example method of reducing memory usage of a computing device disclosed herein comprises storing a constraint matrix and a population matrix into a memory, the population matrix representing a population related to data of the constraint matrix, determining, via a processor, a linear system based on a set of constraints including the constraint matrix and the population matrix, determining, via a processor, an inverse of a matrix of the linear system, the determination of the inverse including breaking down the matrix into blocks, the dimensions of the blocks being no larger than dimensions of the constraint matrix, and outputting a set of weights based on the inverse of the linear system, the set of weights satisfying the set of constraints.

Methods, apparatus, systems and articles of manufacture are disclosed to calculate video-on-demand and dynamically inserted advertisement viewing probability. An example apparatus disclosed herein includes a category manager to identify a targeting strategy for advertisements associated with media presented in a first set of households and a second set of households, a probability engine to calculate a baseline viewing probability for a non-time shifted presentation of the media in the first set of households and the second set of households, and in response to identifying presentations of the media having a subsequent temporal sequence from the non-time shifted presentation, calculate corresponding time-shifted viewing probabilities of the media for the first set of households and the second set of households, and an imputation engine to generate viewing probability trend information of the media for the first set of households and the second set of households, the viewing probability trend information based on the respective time-shifted viewing probabilities, and adjust the targeting strategy to reduce wasteful targeting of the advertisements, the adjustment to select one of the first set of households or the second set of households to insert the advertisements based on the viewing probability trend information.

Methods and apparatus to calculate video-on-demand and dynamically inserted advertisement viewing probability

Methods and apparatus to estimate demography based on aerial images are disclosed. An example method includes analyzing a first aerial image of a first geographic area to detect a first plurality of objects, analyzing a second aerial image of a second geographic area to detect a second plurality of objects, associating first demographic information to the second plurality of objects, the first demographic information obtained by a sampling of the second geographic area, and comparing the second plurality of objects to the first plurality of objects to estimate a demographic characteristic of the first geographic area based on the comparison.

Methods and apparatus to estimate demography based on aerial images

Methods, apparatus, systems and articles of manufacture are disclosed to calculate video-on-demand viewing probability. Example methods disclosed herein include identifying a first set of households associated with a target set of categories that presented a first media having a first telecast delay, calculating first total tuning minutes and first total exposure minutes for the first set of households, identifying a second set of households that presented the first media having the first telecast delay and having a portion of the target set of categories, the second set of households having second total tuning minutes and second total exposure minutes for each category in the second set of households, calculating a household tuning proportion based on the first and second total tuning minutes, and calculating a household exposure proportion based on the first and second total exposure minutes, and calculating the panelist viewing probability associated with the first telecast delay based on a ratio of the household exposure proportion and the household tuning proportion.

Examples to determine media impressions are disclosed. An example method includes determining an exposure of first media, wherein the exposure occurs after a cookie identifier is received at a computing device, wherein the cookie identifier is received from a database proprietor, determining a first panelist identifier associated with the exposure based on the cookie identifier, determining a second panelist identifier associated with the exposure based on a determination of a user identity by a panelist meter associated with the computing device, determining an adjustment factor by comparing the first panelist identifier and the second panelist identifier, determining a number of impressions of second media from data logged by the database proprietor, the data logged in response to requests transmitted to the database proprietor in response to clients accessing the second media, and applying the adjustment factor to the number of impressions to determine an adjusted number of impressions.

Methods and apparatus to determine an adjustment factor for media impressions

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New York, NY, United States of America

Paul Donato