Methods and apparatus for multi-account adjustment in third-party privacy-protected cloud environments are disclosed herein. A disclosed example apparatus includes memory and processor circuitry to execute computer readable instructions to select a type of client device, determine a number of panelists associated with a demographic characteristic and the type of client device, the demographic characteristic defining a segment of the panelists, determine a number of user accounts associated with the number of panelists, determine a multi-account adjustment factor for the demographic characteristic and the client device based on a ratio of the number of the panelists to the number of user accounts, and adjust an initial estimate of a unique audience size based on the multi-account adjustment factor.

An example system disclosed herein includes programmable circuitry to identify donor adjustment factors and recipient adjustment factors used for correction of media impressions logged by a database proprietor, the donor adjustment factors including first donor adjustment factors associated with a first geographic region and second donor adjustment factors associated with a second geographic region, determine a first reduced donor factor set corresponding to ones of the first donor adjustment factors that satisfy a threshold, determine a second reduced donor factor set corresponding to ones of the second donor adjustment factors that satisfy the threshold, and generate imputation factors based on an aggregation of retained ones of the donor adjustment factors, the retained ones of the donor adjustment factors selected based on the first reduced donor factor set and the second reduced donor factor set, the imputation factors to reduce error in the correction.

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.

Television is the largest advertising category in the United States with over 65 billion spent by advertisers per year. A variety of different targeting algorithms are compared, ranging from the traditional age-gender targeting methods employed based on Nielsen ratings, to new approaches that attempt to target high probability buyers using Set Top Box data. The performance of these different algorithms on a real television campaign is shown, and the advantages and limitations of each method are discussed. In contrast to other theoretical work, all methods presented herein are compatible with targeting the existing 115 million Television households in the United States and are implementable on current television delivery systems.

In general, embodiments of the present invention provide systems, methods and computer readable media for data record compression using graph-based techniques.

Methods and apparatus to correct for deterioration of a demographic model to associate demographic information with media impression information are disclosed. An example method includes estimating first and second ages of audience members based on demographic information; estimating a third age of an audience member who is not included in the audience members; applying a window function to the second ages to determine a distribution of ages based on the third age; multiplying window values by the first ages to determine corrected first age components; dividing a total of the corrected first age components by a sum of the window values to determine an estimated age of the audience member at a first time; and determining the corrected age of the audience member at a second time based on the estimated age of the audience member at the first time and a time difference between the first and second times.

Systems and methods of dynamically varying the intensity of providing content items in a remarketing campaign based on tracking client device interactions are provided. The system can assign an account identifier to a first segment for a pre-conversion model, responsive to receiving a first interaction associated with a content provider from a client device. The system can assign the account identifier to a second segment for the pre-conversion model, responsive to receiving a second interaction. The system can assign the account identifier to a third segment, responsive to receiving a third interaction. The third interaction can include a conversion event. The system can generate a post-conversion model based on the third segment and the pre-conversion model. The system can determine an intent index for the account identifier based on the post-conversion model. The system can store the account identifier into an interest cluster based on the intent index.

Systems and methods are disclosed for determining an estimate of available user impressions on a network, comprising receiving a request for an estimate of available user impressions for viewing one or more media elements on a network, the request comprising one or more viewer demographic group limitations. A request may be received to include deterministic users and probabilistic users in the estimate of available user impressions. A number of deterministic users may be determined based on query results from a deterministic user data set. A number of probabilistic users may be determined based on query results from a probabilistic user data set, and the estimate of available user impressions may be determined based on the number of deterministic users and the number of probabilistic users.

Techniques and systems are described to enable users to optimize a digital marketing content system by analyzing an effect of components of digital marketing content on audience segments, environments of consumption, and channels of consumption. A computing device of an analytics system receives user interaction data describing an effect of user interaction with multiple items of digital marketing content on achieving an action for multiple audience segments. The analytics system identifies which of a plurality of components are included in respective items of digital marketing content. The analytics system generates data identifying different aspects that likely had an effect on the achieving an action on the items of digital marketing content, such as components of the items of digital marketing content, environments of consumption, channels of consumption. The analytics system outputs a result based on the data in a user interface.

Protected audience selection system. Media consumption histories of browsers which have converted are received at a modeling system where targeting of browsers is prohibited. A model is built by determining a frequency of each respective media consumption event among the histories and comparing each determined frequency of a respective media consumption event to a frequency of the respective media consumption event among a population of browsers without the conversion event. The model is sent to a targeting system which excludes conversion events. A description of the conversion event is received at the targeting system. A history of a targetable browser is received at the targeting system. The model is applied to the history of the targetable browser at the targeting system, where conversion events have been excluded from the history. Advertising content is sent to the targetable browser according to a result of applying the model.