A method includes determining a plurality of harvest content items. The harvest content items are ranked based on a performance metric. Matching criterion aspects of the harvest content items are determined. Aspects of a candidate content item are compared with the plurality of harvest content items according to the matching criterion aspects. A subset of the harvest content items that are similar to the candidate content item is determined. A transformation for the candidate content item is selected and applied to the candidate content item to generate a transformed content item.

Disclosed embodiments relate to performing an advertisement campaign filtering process while protecting the privacy of both an advertiser and a user of a personal computing device. Techniques include maintaining a plurality of sets of advertising competition rules, the plurality of sets of advertising competition rules being associated with a plurality of discrete advertising campaigns; for a set of advertising competition rules from the plurality of sets of advertising competition rules: identifying advertisement targeting criteria associated with the set of advertising competition rules, differentiating, from within the advertisement targeting criteria, between advertisement-sensitive targeting criteria and advertiser-insensitive criteria, and transforming the advertisement-sensitive sensitive targeting criteria; and transmitting, to the personal computing device, at least a portion of the transformed advertisement-sensitive targeting criteria.

Disclosed embodiments relate to conducting a real-time selection of a targeted advertisement or campaign on a personal computing device. Techniques include receiving, on the personal computing device, a plurality of sets of advertising competition rules associated with a plurality of discrete advertising campaigns. Further techniques include identifying an opportunity to display a targeted advertisement to a user of the personal computing device, accessing sensitive data associated with the user stored locally on the personal computing device, the sensitive data not being made available outside of the personal computing device without authorization from the user, and conducting, based on the identified opportunity and using the accessed sensitive data, a real-time selection of at least one of the plurality of discrete advertising campaigns, the real-time selection is based on one or more of the plurality of sets of advertising competition rules.

Disclosed embodiments relate to efficiently providing advertising competition rules to a personal computing device of a user. Techniques include maintaining a plurality of sets of advertising competition rules, the plurality of sets of advertising competition rules being associated with a plurality of discrete advertising campaigns; receiving non-personal advertisement targeting data from the personal computing device; filtering the plurality of sets of advertising competition rules using a filtering technique, based on the non-personal advertisement targeting data, to identify a subset of the plurality of sets of advertising competition rules; and transmitting the subset of the plurality of sets of advertising competition rules to the personal computing device.

Disclosed embodiments relate to receiving a targeted advertisement on a personal computing device without revealing sensitive data of a user of the personal computing device. Techniques include maintaining sensitive data associated with the user; identifying, while the user is interacting with an application running on the personal computing device, an opportunity to display a targeted advertisement to the user of the personal computing device; receiving a prompt to request a targeted advertisement for display on the personal computing device, the targeted advertisement being part of a discrete advertising campaign selected based on the sensitive data associated with the user; and requesting, based on the prompt, the targeted advertisement for display on the personal computing device.

The present invention uses micro and nano segmentation to create targeted digital content, in the form of segment specific static ads, pictures, carousel, mobile new feeds, video, canvas and other ad types. For example, if the motivation for a health change in an individual is for the sake of their family, specific images or video from social media may be effective in tying the rationale for the change to the messaging. This content can then be delivered is static ads, in carousels or other media options. The content is targeted to a specific user using conventional and unconventional social media targeting systems to assist with the creation of custom campaign delivered over social media for the purposes of influencing positive health behavior. Data such as including browsing history, social media posts, Interests, Gender, Relationship Status, Educational Status, Age, Location, Language as well as user specific medical and non medical data sources including but not limited to demographics, medical history, treatments, credit score data and report information etc. will be used with a machine learning algorithm to create digital patient phenotypes or cohorts and associate them with the probability that a given digital media campaign will be maximally affect to influence individuals in that cohort to affect the desired behavior change. Example may include what graphic elements are included in the digital materials, the frequency of delivering that content, the channels use etc.

One embodiment is a method that predicts a probability of users responding to a second email campaign based on data gathered from a first email campaign. Data from the first email campaign is applied to different models to identify attributes that are predictive of users who responded to the first email campaign. These attributes are used to predict a probability of users responding to the second email campaign.

Systems and methods are disclosed for planning, executing, reviewing, and reporting the results of an advertising campaign to be run on TV. A demand-side platform receives ad slot opportunities from TV programming sources, and analyzes the ad slots to produce a prioritized list of placement opportunities for the advertising campaign to be presented to advertiser/clients. Each ad slot is analyzed with respect to past viewership data and with respect to desired targeting characteristics that may include conventional age and gender targeting, or additionally strategic targeting characteristics. Scores are established for each ad slot with respect to numbers of projected on-target impressions and/or a cost for projected on-target impressions. The scores are sorted to produce the prioritized list. Projected results can be viewed with respect to any or all of network, day, and daypart. After a campaign has completed, viewership data representing actual results is acquired, processed, and reported.

A computer-implemented method for determining a micro-moment value that indicates an optimal time for a customer to receive a targeted advertisement. The method includes receiving, via a network, customer data associated with behavior of a plurality of customers. The method includes determining, via one or more processors, a micro-moment value predicting an optimal time and network location to engage a customer based on the customer data.

Machine learning techniques for multi-objective content item selection are provided. In one technique, resource allocation data is stored that indicates, for each campaign of multiple campaigns, a resource allocation amount that is assigned by a central authority. In response to receiving the content request, a subset of the campaigns is identified based on targeting criteria. Multiple scores are generated, each score reflecting a likelihood that a content item of the corresponding campaign will be selected. Based on the scores, a particular campaign from the subset is selected and the corresponding content item transmitted over a computer network to be displayed on a computing device. A resource allocation amount that is associated with the particular campaign is identified. A resource reduction amount associated with displaying the content item of the particular campaign is determined. The particular resource allocation is reduced based on the resource reduction amount.