The Nielsen Comapny (us), LLC

Albert Ronald Perez

Kumar Nagaraja Rao

Josh Gaunt

Mimi Zhang

Stephen S Bell

Tianjue Luo

Jonathan Lee Sullivan

Michael R Sheppard

Alejandro Terrazas

Achilleas Papakostas

David N Wong

Michael Andrew Yonker

Jennifer Haskell

Kevin Michael Hannan

Austin William Albino

Peter Lipa

Paul Donato

Mainak Mazumdar

Antonia Toupet

Seema Varma Srivastava

Shi Lu

James R Oliver

Juliette Aurisset

Michael F Younge

Yutao Peng

A disclosed example includes accessing computer-generated impression records, the computer-generated impression records based on network communications received at a server of a database proprietor from client devices, the computer-generated impression records indicative of accesses to media at the client devices; accessing self-reported demographic data and behavioral data from a database of the database proprietor, the self-reported demographic data and the behavioral data corresponding to user accounts registered with the database proprietor and associated with the client devices; comparing the self-reported demographic data with a probability distribution of higher-accuracy demographic data; determining different adjustments for corresponding ones of self-reported demographics of the self-reported demographic data based on the comparison; adjusting the corresponding ones of the self-reported demographics based on corresponding ones of the different adjustments to generate adjusted user demographic data; and assigning the adjusted user demographic data to corresponding ones of the computer-generated impression records.

Methods and apparatus to analyze and adjust demographic information

An example apparatus includes an impression collector to receive, at a first internet domain, a first request from a client device, the first request indicative of access to media at the client device; and send, from the first internet domain, a response to the client device, the response to instruct the client device to send a second request to a second internet domain; an audience adjustment factor determiner to determine an audience adjustment factor for a demographic group based on first impressions reported by the client device to the first internet domain and second impressions reported by the client device to the second internet domain; and a unique audience corrector to determine a misattribution-corrected unique audience size for the demographic group based on the audience adjustment factor and based on a second unique audience size determined at the second internet domain for the demographic group.

Methods and apparatus to correct misattributions of media impressions

Methods and apparatus to compensate impression data for misattribution and non-coverage are disclosed. An example method includes receiving a first request from a first type of computing device; sending a request for demographic information corresponding to requests received at a first internet domain from the first type of computing device; generating an aggregated audience distribution including a first audience distribution of a first household aggregated with a second audience distribution of a second household; normalizing the aggregated audience distribution to generate a misattribution correction matrix, the misattribution correction matrix including a probability that an impression of the media is attributable to a first demographic group when the database proprietor determines the impression corresponds to a person in a second demographic group; and compensating misattribution error in the impressions by re-assigning the impressions from the second demographic group to the first demographic group using the misattribution correction matrix.

Methods and apparatus to compensate for server-generated errors in database proprietor impression data due to misattribution and/or non-coverage

An example apparatus includes at least one memory, instructions, and at least one processor to execute the instructions to generate electronic mobile measurement data based on network communications received from first client devices, select attributes associated with the electronic mobile measurement data to include in a model, generate the model based on the attributes and a first portion of the electronic mobile measurement data, determine a percentage of a second portion of the electronic mobile measurement data that the model correctly associates with corresponding first users of the first client devices, and when the percentage satisfies a threshold determine: (a) when a second user operating a second client device is a primary user, and (b) when the user operating the second client device is a third user, and associate demographic information of the second user with the electronic mobile measurement data to reduce a misattribution error.

Methods and apparatus to generate electronic mobile measurement census data

An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to access first cookies and a plurality of user identifiers, the first cookies and the user identifiers corresponding to devices accessing media, determine an error between a first demographic estimate based on the first cookies forming a pattern and based on a second demographic estimate corresponding to the user identifiers, obtain impression information from a database proprietor, the impression information including second cookies, identify a subset of the second cookies that are associated with a same person based on the pattern, and associate impressions corresponding to the subset with the same person.

Methods and apparatus to de-duplicate impression information

Examples apparatus disclosed herein are to determine a plurality of weights based on a data structure having elements corresponding to pairings of ones of a plurality of demographic partition statistics and ones of a plurality of baseline demographic statistics obtained for a target population, the demographic partition statistics corresponding to a plurality of demographic partitions of a sample population, a first element of the data structure to combine a first one of the demographic partition statistics with a first one of the baseline demographic statistics of the target population based on a first value corresponding to a numerator term of an expression and a second value corresponding to a denominator term of the expression, the weights corresponding respectively to the demographic partitions of the sample population. Disclosed example apparatus are also to adjust the attribute data based on the weights to determine ratings data for the target population.

Reducing processing requirements to correct for bias in ratings data having interdependencies among demographic statistics

An example includes generating panelist-user data based on reference demographic information and self-reported demographic information, the reference demographic information and the self-reported demographic information corresponding to audience members of an audience member entity panel that are also registered users of a database proprietor, the reference demographic information from a panelist database of an audience measurement entity, and the self-reported demographic information from a user database of the database proprietor; generating a first training model and a second training model, the first training model based on a first portion of the panelist-user data, the second training model based on a second portion of the panelist-user data; selecting the first training model based on outputs of the first and second training models; and generating a third model by making an adjustment to a demographic category of the first training model, the third model to adjust third demographic information.

Methods and apparatus to adjust media impressions based on media impression notification loss rates in network communications are disclosed. An example method includes determining a first network media impression adjustment factor based on a quantity of lost test media impression notifications for a first network in a geographic region; determining an adjusted quantity of first network user-accessed media impression notifications for the first network by applying the first network media impression adjustment factor to a quantity of successfully received user-accessed media impression notifications to generate a product and weighting the product based on a market share of first subscribers corresponding to the first network in the geographic region; and storing the weighted adjusted quantity of the user-accessed media impression notifications in association with a media identifier to credit corresponding media with a quantity of media accesses corresponding to the weighted adjusted quantity of the user-accessed media impression notifications.

Methods and apparatus to adjust media impressions based on media impression notification loss rates in network communications

An example apparatus includes means for extracting characters from first cookies stored in a first memory space, the first cookies collected from client devices via network communications, and means for storing in a second memory space, a pattern of characters in association with a first demographic to indicate that the pattern of characters and the first demographic are both representative of a same first unique audience member, the storing being in response to determining that at least two of the first cookies (1) include the pattern of characters, and (2) correspond to the first demographic.

An example apparatus includes at least one processor and first memory comprising computer readable instructions that, when executed, cause the at least one processor to log an impression in second memory at an audience measurement entity based on a communication received from a computing device. The example computer readable instructions, when executed, cause the at least one processor to determine that a primary user of the computing device is not the user operating the computing device when a first probability score for the primary user does not satisfy a threshold, and in response to determining that the primary user is not the user operating the computing device, identify one of a plurality of secondary users as the user operating the computing device. The example computer readable instructions, when executed, cause the at least one processor to store demographic information of the user operating the computing device.

An example method involves determining an impressions adjustment factor for a first demographic group based on first impressions reported by a client device to a first internet domain and second impressions reported by the client device to a second internet domain. The first and second impressions correspond to same media accessed on the client device. The example also involves determining a misattribution-corrected impressions count for the first demographic group based on the impressions adjustment factor and based on a second impressions count determined at the second internet domain for the first demographic group. The second impressions count has an error based on some of the second impressions being misattributed at the second internet domain to the first demographic group when the some of the second impressions correspond to a second demographic group.

An example includes generating a first model based on reference demographic data corresponding to panelists, and based on second demographic data and behavioral data from a database proprietor that is separate from a first entity, the reference demographic data collected from the panelists by the first entity, and the second demographic data and the behavioral data corresponding to ones of the panelists having user accounts with the database proprietor; analyzing an output of the first model including the second demographic data partitioned into a plurality of nodes, each node representing a respective subset of the second demographic data; redistributing some of the second demographic data between some of the nodes to generate a second model; applying the second model to third demographic data of the database proprietor; and determining whether to adjust at least some of the third demographic data based on an output of the second model.

Growing community of inventors

San Francisco, CA, United States of America

Albert Ronald Perez