The Nielsen Comapny (us), LLC

Austin William Albino

Achilleas Papakostas

Albert Ronald Perez

Michael Andrew Yonker

Kevin Michael Hannan

Chao Wang

Sarah Macdonald

Jana Dodson

Alexander Fragapane

Rohit Bhatia

Amilcar Pérez

Michael Greenawald

Kunal Barai

Joel Ullmann

Methods, apparatus, systems, and articles of manufacture to calibrate payload information are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry. The processor circuitry is to execute the instructions to obtain (a) passive measurement data and (b) background active measurement (BAM) data from panelists in a market of interest, the BAM data associated with network usage metrics of panelist wireless devices undisturbed by panelist behavior. The processor circuitry is also to execute the instructions to assign a first share of the passive measurement data and a second share of the BAM data to particular areas within the market of interest, the first share based on a number of passive measurement occurrences in a first one of the particular areas, the second share based on a number of BAM occurrences in the first one of the particular areas. The processor circuitry is also to execute the instructions to remove a bias between the passive measurement data and the BAM data by calibrating the BAM data for the market of interest using weights determined for the first one of the particular areas based on the first and second shares.

Methods, systems, articles of manufacture and apparatus to calibrate payload information

Methods, apparatus, systems and articles of manufacture are disclosed to calibrate payload information. An example apparatus a data acquirer to obtain (a) passive measurement data and (b) background active measurement (BAM) data from panelists in a market of interest, the BAM data associated with network usage metrics of panelist wireless devices undisturbed by panelist behavior. The example apparatus further includes a share assignor to assign a first share of the passive measurement data and a second share of the BAM data to particular areas within the market of interest, the first share based on a number of passive measurement occurrences in a first one of the particular areas and the second share based on a second number of BAM occurrences in the first one of the particular areas, a weight calculator to determine weights for the first area based on the first and second shares, and a BAM calibrator to remove a bias between the passive measurement data and the BAM data by calibrating the BAM data for the market of interest using the weights.

Methods, apparatus, systems and articles of manufacture are disclosed to generate an overall performance index. The overall performance index is generated from data values from multiple different datasources that measure the same aspect of network performance of wireless providers of interest. The data values are used to generate metrics that measure the same aspect of network performance. The metrics are indexed and combined to generate an overall performance index.

Methods and apparatus to generate an overall performance index

Example audience measurement methods disclosed herein include accessing demographic information and identification information for subscribers of a service provider based on first data received from an audience measurement entity. Such disclosed example methods also include accessing network log data stored by the service provider for the subscribers using the identification information. Such disclosed example methods further include determining audience measurement data for the subscribers based on the network log data, the demographic information and the identification information, and removing the identification information from the audience measurement data to determine anonymous audience measurement data to send to the audience measurement entity.

Methods and apparatus to obtain anonymous audience measurement data from network server data for particular demographic and usage profiles

Methods and apparatus to obtain anonymous audience measurement data from network server data for particular demographic and usage profiles are disclosed. An example method to provide anonymous audience measurement data to an audience measurement entity disclosed herein comprises obtaining a demographic profile and a network usage profile, sampling customer data stored in a customer database not accessible by the audience measurement entity to generate a customer sample representative of the demographic profile and the network usage profile without customer intervention, the customer sample including customer identification information, processing log data obtained from a network server not accessible by the audience measurement entity using the customer identification information to determine audience measurement data associated with customers in the customer sample, and removing the customer identification information from the audience measurement data to prepare the anonymous audience measurement data for the audience measurement entity.

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San Francisco, CA, United States of America

Austin William Albino