The Nielsen Comapny (us), LLC

John Charles Coughlin

Molly Poppie

David J Kurzynski

Balachander Shankar

Samantha M Mowrer

Paul J Chimenti

Jonathan Lee Sullivan

Rachel Worth Olson

Neung Soo Ha

Joshua Ivan Friedman

Adam E Hasinski

Albert T Borawski

Brian Fuhrer

Paul Donato

Joseph Gregory Milavsky

Marie Kramer

Benjamin Wood

Narasimha Reddy Goli

Gangadhar Jakkula

Remy Spoentgen

Jean Guerrettaz

Christine Heiss

Shuangxing Chen

Kimberly I Gilberti

Jiji Sadasivakurup

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

Example methods, apparatus, systems and articles of manufacture to implement under-addressable advertisement measurement are disclosed. Example apparatus disclosed herein determine a tuned under-addressable advertisement duration and a total tuned advertisement duration for a first television broadcast at a first monitored site, the tuned under-addressable advertisement duration and the total tuned advertisement duration determined based on (i) return-path tuning data associated with the first monitored site, (ii) linear advertisement log data associated with a broadcast area including the first monitored site, and (iii) addressable advertisement log data associated with the first monitored site. Disclosed example apparatus also determine an under-addressable advertisement factor for the first television broadcast based on the tuned under-addressable advertisement duration and the total tuned advertisement duration, and adjust an input advertisement rating for the first television broadcast based on the under-addressable advertisement factor to determine an under-addressable advertisement rating for the first television broadcast.

Under-addressable advertisement measurement

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

Methods, apparatus, systems, and articles of manufacture are disclosed to rectify false set top box tuning data. Disclosed examples methods include identifying in return path data a first group of set top boxes classified as likely to exhibit machine events in tuning data of the return path data more frequently than a second group of set top boxes represented in the return path data. Additionally, in some examples, the method includes determining whether the first group of set top boxes includes a machine event based on a pattern of the tuning data in the return path data for respective ones of the first group of set top boxes and improving an accuracy of return path data by rectifying the machine event.

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, 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.

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Oldsmar, FL, United States of America

John Charles Coughlin