Disclosed herein are systems and methods for protecting user privacy in networked data collection. One embodiment takes the form of a method that includes obtaining a user-data request that is associated with a requesting party. The method also includes preparing a first candidate response to the user-data request, where the first candidate response is based at least in part on data that is associated with a first user. The method also includes receiving additional candidate responses that are respectively based on data that is respectively associated with a plurality of additional users. The method also includes determining a privacy level of the first candidate response based at least in part on the received plurality of additional candidate responses. The method also includes determining that the privacy level exceeds a privacy threshold, and responsively sending, to the requesting party, a user-data response associated with the user-data request.

Aspects of the subject disclosure may include, for example, receiving an indication of a selection of a first advertisement included within a first content item from a communication device, transmitting terms of purchase associated with the first advertisement responsive to the receiving of the indication of the selection of the first advertisement, selecting a second content item based on an indication of the first content item and/or a context associated with the communication device, transmitting the second content item to the communication device responsive to the selecting of the second content item, obtaining an indication of a transaction completed for a purchase from the communication device, selecting a second advertisement responsive to the indication of the transaction, and transmitting the second advertisement to the communication device. Other embodiments are disclosed.

An example system disclosed herein includes an analyzer to determine a first priming characteristic of media at a first location based on first neuro-response data and determine a second priming characteristic of the media at the second location based on second neuro-response data. The example system includes a selector to select a third location in the media after the first location or a fourth location in the media after the second location as a candidate location for introduction of advertising material based on the first priming characteristic and the second priming characteristic. The selector is to select the third location when the first priming characteristic indicates increased receptivity to the advertising material at the first location relative to the second priming characteristic and select the fourth location when the second priming characteristic indicates increased receptivity to the advertising material at the second location relative to the first priming characteristic.

Systems and methods are disclosed for targeting of advertising content for a consumer product, by obtaining consumer demographic data, the consumer demographic data including a plurality of demographic attributes for each person; identifying a plurality of media slots; and obtaining program information for a respective identified program aired in each media slot among the plurality of media slots, the program information including viewing data of a plurality of viewing persons viewing the program and each viewing person being among the plurality of persons. The methods also include enriching the viewing data with the consumer demographic data; identifying a plurality of advertiser industries; enriching the product purchaser data with the consumer demographic data; calculating a relevance of each advertiser industry among the plurality of advertiser industries for each identified program based on demographic attributes of the product purchasers in each advertiser industry and demographic attributes of the viewing persons.

The present technology generally relates to metadata based generation and management of event presentations. The technology may include selecting a plurality of target audiences, programmatically generating a plurality of presentations and a presentation plan, programmatically executing the presentation plan, and programmatically adjusting the presentation plan based on monitored efficiency. The adjusting of the presentation plan may be based, for example, on performance of constituent elements of particular presentations relative to other constituent elements, e.g., from other presentations.

A facility providing systems and methods for targeting advertisements based on a polling system is disclosed. The facility provides techniques for interleaving advertisements within a stream of “split queries.” The ads are themselves split queries and are inserted sparsely into the split stream. An ad campaign may include “branch” nodes, with decision logic over the targeting variables representative of a program, complete with dynamic variables and flow control (branching logic). Via this paradigm, the ad campaign can direct highly specific users toward specialized content and offers. In addition to their value as ad impressions, splits may have associated with them custom advertiser-defined targeting variables and actions for how user responses update those variables. These variables may then be used as targeting variables.

A system and methods for providing data to interested entities (e.g., publisher or advertiser) on consumer behavior when viewing advertising displayed on web pages is described. In some implementations, a JavaScript tag or other code is designated with the purpose of examining and communicating the consumer behaviors of web page consumers to an external server. The JavaScript tag is equipped with a customized pixel or similar signaling software, which is invoked under certain pre-defined conditions associated by any one of the interested entities with the JavaScript Tag and known to all users (e.g. publisher or advertiser) of the JavaScript Tag.

Cross-channel and cross-source data are aggregated into an aggregated data store. Custom segmentation is generated from the aggregated data. A campaign is monitored for the custom segmentation with successes and failures provided as dynamic feedback to a machine learning process that dynamically adjusts the segmentation and the campaign for optimal performance. In an embodiment, a final recommendation is provided identifying a final optimal segmentation and campaign.

A data system can maintain a targeted incentive database mapping product categories to targeted incentive levels and mapping the targeted incentive levels to a plurality of user segments corresponding to observable features of users. The data system can receive a user query from a user computer device, the query comprising product configuration information, apply a set of segment matching rules to match the user to a user segment from the targeted incentive database based on a set of observable features associated with the user, identify a targeted incentive level for a product category from a plurality of targeted incentive levels for the product category according to a mapping between the determined user segment and the identified targeted incentive level in the targeted incentive database and generate a responsive web page to display the identified targeted incentive level at the user computing device.

A method including receiving a digital promotion payload from a brand manufacturer for at least one branded consumer packaged good, the digital promotion payload including a digital promotion value associated with the branded consumer packaged good, is provided. The method includes receiving a bid request to the digital promotion engine, providing a bid response to the bid request, the bid response including the digital promotion payload, and receiving, from the supply side platform, a confirmation that the bid response has been selected from one or more bids from different digital advertising entities. The method includes providing a command to the supply side platform to deliver the digital promotion payload to a mobile device accessing a resource from the mobile display publisher, and loading the digital promotion value to a frequent shopper identification in response to a consumer interaction with the digital promotion payload detected from the mobile device.