The Nielsen Comapny (us), LLC

Jean Guerrettaz

Samantha M Mowrer

Molly Poppie

Narasimha Reddy Goli

Gangadhar Jakkula

Paul J Chimenti

John Charles Coughlin

Kimberly I Gilberti

Jiji Sadasivakurup

David J Kurzynski

Michael Grotelueschen

Demetrios Fassois

Vince Tambellini

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 methods and apparatus to onboard return path data providers for audience measurement are disclosed herein. Example apparatus disclosed herein to predict return path data quality include a classification engine to compute a first data set of model features from validation tuning data reported from media metering devices and a second data set of model features from return path data reported from return path data devices. The example apparatus also include a prediction engine to train a machine learning algorithm based on the first data set, apply the trained machine learning algorithm to the second data set to predict quality of the return path data reported from the return path data devices, and determine an onboarding status for a return path data provider based on an aggregate predicted quality of the return path data reported from the return path data devices.

Onboarding of return path data providers for audience measurement

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Chicago, IL, United States of America

Jean Guerrettaz