A campaign profile specifies products and/or content items associated with a campaign. A target group selection engine applies an affinity attribute model to user information of a user. The affinity attribute model is used to determine the user's affinity towards (a) product attributes of the products associated with the campaign and/or (b) content attributes of the content items associated with the campaign. The affinity attribute model may be generated using machine learning. A user interface accepts target user tuning parameters that specify weights to be applied to the affinity attributes determined by the affinity attribute model. Based at least on applying the weights to the affinity attributes, an inclusion score and/or exclusion score for the user is determined. The user is included in a target group, for engaging with the campaign, based on the inclusion score and/or exclusion score.

A campaign profile specifies products and/or content items associated with a campaign. A target group selection engine applies an affinity attribute model to user information of a user. The affinity attribute model is used to determine the user's affinity towards (a) product attributes of the products associated with the campaign and/or (b) content attributes of the content items associated with the campaign. The affinity attribute model may be generated using machine learning. A user interface accepts target user tuning parameters that specify weights to be applied to the affinity attributes determined by the affinity attribute model. Based at least on applying the weights to the affinity attributes, an inclusion score and/or exclusion score for the user is determined. The user is included in a target group, for engaging with the campaign, based on the inclusion score and/or exclusion score.

Various embodiments of an apparatus, method(s), system(s) and a computer program product(s) described herein are directed to a Offer Engine. The Offer Engine extracts one or more features from data associated with a first user requesting access to a portion of content of a content corpus. The Offer Engine feeds at least one of the features of the first user into a decision tree. The decision tree has multiple levels, wherein at least one level comprises a plurality of leaves and each respective leaf implements at least one machine learning model. The Offer Engine determines whether to provide the first user with a subscription fee offer first option or a non-subscription fee offer second option based at least in part on output of the decision tree.

Systems and methods for allocating cells within a virtual grid to content providers according to various priority and selection schemes are used to target content delivery to information playback devices in a geographically and/or application selective manner. The priority schemes, geographical selectivity, and application selectivity of the system and methods of the invention allow a content provider to specifically target a desired demographic with high cost efficiency and flexibility.

In a computer apparatus, at least processor is arranged to determine values for two or more candidate advertisements. Based on the determined values, one of the candidate advertisements is selected. The selected advertisement is displayed on a user interface.

A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.

A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.

When a server serves web pages and/or software application pages with digital ads to client devices, a system determines viewability scores for individual ad insertion spaces on the pages. The system determines viewability scores for each field based on the time at which at least a threshold percentage or ratio of the field's pixels where viewable and not off-screen or obscured by another open window. The system or the server may then select digital ads to serve to each field based on the field's viewability score.

Systems and methods are disclosed that measure web activity bursts after ad broadcasts that may be sent to multiple persons. One system uses a cookie-less/cookie-optional, anonymous/personal-identification-not-required, method for web-based conversion tracking that will work on broadcast media systems such as television, and could also be applied to measuring spikes from email, radio, and other forms of advertising where an episodic ad event is broadcast to multiple parties, and where responses occur in a batch after the broadcast.

A health care provider (HCP) engagement engine is disclosed. The HCP engagement engine facilitates effective communication between pharmaceutical sales representatives (medical reps) and HCPs by generating, using machine learning algorithms, messages for the medical reps to send to HCPs. The recommended messages may be sent over a network and may include email messages, text messages, or online chat messages. The recommended messages may be drafted entirely by the engagement engine or may be drafted as addenda to messages already used by the medical reps. The HCP engagement engine uses historical data on actions performed by the HCPs and medical reps, as well as data collected from historical message recommendation events, in order to produce message recommendations.