The Nielsen Comapny (US), LLC

The Nielson Company (US), LLC

Balachander Shankar

David J Kurzynski

Jonathan Lee Sullivan

Molly Poppie

Samantha M Mowrer

Richard Peters

Neung Soo Ha

John Charles Coughlin

Michael R Sheppard

Edward Murphy

Michael D Morgan

Remy Spoentgen

Rachel Worth Olson

Frank Downing

Paul J Chimenti

Xiaoqi Cui

Choongkoo Lee

Katya Sotiris

Ieok Hou Wong

Fatemehossadat Miri

Joshua Ivan Friedman

Adam E Hasinski

Albert T Borawski

Peter Campbell Doe

Brian Fuhrer

Paul Donato

Joseph Gregory Milavsky

Marie Kramer

Christine Bourquin

Timothy Dolson

Benjamin Wood

Jarrett Garcia

Lukasz Chmura

Huaxin You

Michael Evan Anderson

Joshua Zinzer

Jone Mary Burr

Michael Grotelueschen

Robert Gottesman

Arijit Jha

Lisa G Rossi

Sharan Senthilvasan

Hariprasath Balasubramani

Mike Anderson

Abhignya Goje

Jonathan Ouegnin

Christine Heiss

Lauren Howard

Michelle Gelman

Shuangxing Chen

Peter Schnatz

Zachary Zofrea

Anthony P Venetucci

Methods, apparatus, systems, and articles of manufacture are disclosed to determine total audience ratings. An example apparatus includes metrics generator circuitry to generate a first audience size for media accessed by first devices of a first media platform at a first level of aggregation, the first level of aggregation corresponding to the media accessed on a first television network and on a first website, generate a second audience size for the media accessed by the first devices of the first media platform at a second level of aggregation, the second level of aggregation corresponding to the media accessed on the first television network and accessed on the first website and a second website, comparator circuitry to compare the first audience size to the second audience size, adjustor circuitry to reduce the first audience size based on the second audience size, and audience determination circuitry to determine a total audience size.

Methods and apparatus to structure processor systems to determine total audience ratings

Methods and apparatus disclosed herein can be used to perform co-viewer adjustment. An example apparatus or viewership adjustment includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to determine compliant and non-compliant household panel meter data based on household metrics for past household viewing behavior, adjust undercounted viewership data based on an active media session interval associated with the household metrics, adjust overcounted viewership data based on a viewership model, the viewership model trained using the compliant household panel meter data, and output adjusted viewership data based on the adjusted undercounted viewership data and adjusted overcounted viewership data.

Methods and apparatus for co-viewing adjustment

Methods, apparatus, systems and articles of manufacture to impute media consumption behavior are disclosed. An example system includes one or more media meters to obtain tuning data, one or more people meters to obtain viewing data, and one or more servers to, in response to a determination that a difference satisfies a first threshold, determine that a first subset of the tuning data associated with first panelist households having tuned to first media in a first area exhibits local bias, determine that a second subset of the viewing data associated with second panelist households having viewed the first media in the second area represents heavy viewing, and impute the second subset of the viewing data for the first subset of the tuning data in response to the second subset of the viewing data representing heavy viewing.

Methods and apparatus to impute media consumption behavior

Methods, apparatus, systems, and articles of manufacture are disclosed for editing tuning data collected via automated content recognition. Examples include determining whether a time conflict exists between first tuning data corresponding to a first tuning event and second tuning data corresponding to a second tuning event. Examples also include that, in response to determining that the time conflict exists, creating a third tuning event based on the first tuning data, the second tuning data, and one or more criteria. Examples also include that modifying at least one of the first tuning event or the second tuning event based on the third tuning event. Examples also include that crediting a media presentation by the presentation device based on edited tuning data, the edited tuning data including the first modified tuning event, the second modified tuning event, and the third tuning event.

Methods, articles of manufacture, and apparatus to edit tuning data collected via automated content recognition

Methods and apparatus to model on/off states of media presentation devices based on return path data are disclosed. An apparatus includes a memory and processor circuitry to execute instructions stored in the memory to: generate a probability distribution of actual durations of first tuning segments, the first tuning segments representative of lengths of time during which panelists accessed first media; modify the lengths associated with ones of the first tuning segments to generate second tuning segments having second durations; and estimate a time when a media device associated with a return path data (RPD) device is powered on based on (i) the probability distribution, (ii) the second tuning segments, and (iii) a third tuning segment during which the RPD device accessed second media, the third tuning segment reported from the RPD device.

Methods and apparatus to model on/off states of media presentation devices based on return path data

Methods, apparatus, systems and articles of manufacture to assign viewers to media meter data are disclosed. An apparatus includes processor circuitry to execute computer readable instructions to at least: identify a candidate household from a plurality of second households to associate with a first household based on an analysis of a first duration of time first media was presented by a first media presentation device and a second duration of time second media was presented by second media presentation devices; match different ones of first panelists of the first household with matching ones of second panelists of the candidate household; and impute respective portions of the first duration of time to the different ones of the first panelists based on portions of the second duration of time for which the matching ones of the second panelists of the candidate household were exposed to the second media.

Methods and apparatus to assign viewers to media meter data

Methods, apparatus, systems, and articles of manufacture are disclosed to rectify false set top box tuning data. Disclosed examples methods include identifying, by executing an instruction with a processor, in the return path data, first tuning data corresponding to a first group of set top boxes, the first group of set top boxes classified as associated with machine events, determining, by executing an instruction with a processor, a ratio between first tuning events in the return path data and second tuning events in the return path data, the first tuning events attributed to the first group of the set top boxes, the second tuning events attributed to a second group of the set top boxes classified at not associated with machine events, and in response to the ratio satisfying a threshold during a time interval, removing second tuning data associated with the time interval from the first tuning data.

Methods and apparatus to detect and rectify false set top box tuning data

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 apparatus disclosed herein include a return path data classifier to classify a first viewing period associated with segments of return path data received from a set top box into tuning classifications based on the segments of the return path data; calculate a total reported tuning duration for the first viewing period when the first viewing period is classified as live or playback tuning; and compare the total reported tuning duration to a duration threshold to determine whether the segments of return path data associated with the first viewing period are valid. The example apparatus also includes a return path data rectifier to rectify missing tuning data associated with a second viewing period based on tuning data included in the segments of return path data associated with the first viewing period when the segments of the return path data associated with the first viewing period are determined to be valid.

Methods and apparatus to calibrate return path data for audience measurement

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Tampa, FL, United States of America

Balachander Shankar