The Nielsen Comapny (us), LLC

Denis Voytenko

Paul J Chimenti

David J Kurzynski

Joshua Zinzer

Joshua T Deragon

Thomas Harrington

Jack Barbey

Richard Peters

Rachel Worth Olson

William Deshong

Meghan LePage Beeman

Elisabeth Weber

Horalia Armas

Brett Gebhardt

Inh Jee

Naveen Kumar Vakati

Methods, apparatus, systems, and articles of manufacture to determine an additive reach adjustment factor for audience measurement are disclosed. An example apparatus for additive reach adjustment includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to identify a first probability, the first probability associated with a population tuning to a marketing campaign, the tuning including missing data, identify a second probability, the second probability associated with the population not tuning to the marketing campaign or the tuning including missing data, determine an additive reach adjustment based on a compound probability and a no-tuning probability, the compound probability and the no-tuning probability determined using the first probability and the second probability, and credit a population exposed to the marketing campaign to include missing impressions based on the additive reach adjustment.

Methods and apparatus to determine additive reach adjustment factors for audience measurement

Methods, apparatus, systems and articles of manufacture are disclosed for a window-metric rule for return path data (RPD). In some examples, a media monitor is to determine a first count of a number of devices in a first household that reported RPD for a first day, and compare the first count to a current window-metric, the current window-metric associated with a window of time for the first household, the window of time associated with a content provider and having N number of days. In some examples, the media monitor is also to exclude the RPD from the first household for the first day when the first count does not satisfy the current window-metric, and generate a media exposure report using the RPD from the first household for the first day when the first count satisfies the current window-metric, independent of if the first household has heartbeat data available.

Methods and apparatus for a window-metric rule

An example apparatus includes a distance matrix generator to determine a number of remapping combinations corresponding to a first and a second household demographic model, generate (a) a first age-based household demographic model and a second age-based household demographic model and (b) a third age-based household demographic model and a fourth age-based household demographic model, generate a first distance matrix corresponding to the first and the third age-based household demographic model, and generate a second distance matrix corresponding to the second and the fourth age-based household demographic model, a distance matrix identifier to determine a first and a second reassignment distance and a person identification assigner to assign a person identification number of a first person corresponding to the first household to a second person corresponding to the second household based on the first distance matrix or the second distance matrix.

Methods, systems, articles of manufacture and apparatus to remap household identification

Methods, apparatus, systems, and articles of manufacture are disclosed to generate audience metrics. An example apparatus includes online panel factor generator circuitry to generate an online panel adjustment factor based on television panel data and online panel data; over-the-top (OTT) factor generator circuitry to generate an OTT adjustment factor based on the television panel data and a portion of the online panel data, the portion of the online panel data corresponding to OTT panel data; independence probability factor generator circuitry to generate an independence probability adjustment factor; and a convergence circuitry to: generate an aggregated adjustment factor based on the online panel adjustment factor, the OTT adjustment factor, and the independence probability adjustment factor; and generate a deduplicated reach corresponding to a media item based on a comparison of the aggregated adjustment factor to historical data.

Methods and apparatus to generate audience metrics

An example apparatus includes a distance matrix generator to determine a number of remapping combinations corresponding to a first and a second household demographic model, generate (a) a first age-based household demographic model and a second age-based household demographic model and (b) a third age-based household demographic model and a fourth age-based household demographic model, generate a first distance matrix corresponding to the first and the third age-based household demographic model, and generate a second distance matrix corresponding to the second and the fourth age-based household demographic model; a distance matrix identifier to determine a first and a second reassignment distance; and a person identification assigner to assign a person identification number of a first person corresponding to the first household to a second person corresponding to the second household based on the first distance matrix or the second distance matrix.

Methods, apparatus, systems and articles of manufacture are disclosed for a window-metric rule for return path data (RPD). In some examples, a media monitor includes a count calculator to determine a first count of a number of devices in a first household that reported RPD for a first day. The example media monitor also includes an RPD selector to compare the first count to a current window-metric, the current window-metric associated with a window of time for the first household, the window of time associated with a content provider and having N number of days. The example RPD selector the RPD from the first household for the first day when the first count does not satisfy the current window-metric. The example media monitor also includes a report generator to generate a media exposure report using the RPD from the first household for the first day when the first count satisfies the current window-metric, independent of if the first household has heartbeat data available.

Methods, apparatus, systems and articles of manufacture to build viewing sessions from ping-level data are disclosed herein. An example apparatus includes a threshold calculator to determine a threshold based on reference sessions represented in panel data obtained from panel meters. The example apparatus also includes a session partitioner to determine if a first ping from a device corresponds to a start ping of the session and a second ping from the device corresponds to an end ping of the session based on (i) the threshold, (ii) first payload data from the first ping, and (iii) second payload data from the second ping. The example apparatus includes the session partitioner to also determine a duration of the session based on a difference between a first time associated with the first ping and a second time associated with the second ping.

Methods and apparatus for session building from ping-level data

Apparatus, systems, articles of manufacture, and methods are disclosed for correcting systematic tuning defects. An example apparatus includes a defect analyzer to identify a suspect time window in tuning volume data of return path data reported by set top boxes. The example apparatus further includes a return path data transformer to identify a first return path data entry indicating a first set top box reported a first transition to an off state at a first time during the suspect time window and to modify the first return path data entry to (1) eliminate the first transition to the off state at the first time and (2) assign the first return path data entry a first duration to remain in an on state after the first time, where the first duration is determined based on monitoring data reported from media device meters monitoring media presentation devices.

Correcting systematic tuning defects

Methods, apparatus, systems and articles of manufacture are disclosed to reduce a remapping error. An example apparatus includes a distance matrix generator to generate a distance matrix corresponding to a first household demographic model and a second household demographic model. The example apparatus also includes a distance matrix identifier to determine a reassignment distance of the distance matrix. The example apparatus also includes a person identification assigner to, in response to the reassignment distance being less than a reassignment threshold, assign a person identification number of a first person of the first household to a second person of the second household based on the distance matrix.

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Oldsmar, FL, United States of America

Denis Voytenko