A computer method and system for intercepting email messages, scanning the email messages for key words, determining whether the key words match or relate to key words determined to relate to advertising content, and enhancing the email message by routing the emails to recipients in a manner so that highly relevant, highly targeted advertising tag lines or other content are displayed together with the emails when the emails are accessed and viewed by email recipients.

A system that analyzes a user's communications to select a promotion that is presented to the user. The analysis may occur in two stages: a first stage analyzes a single communication from a user to determine whether the user is a potential target for a promotion; for potential targets, a second stage analyzes a history of communications from the user to generate a user profile. The system may then select a promotion based on the profile. The profile may include a set of profile tags that are considerably more detailed and granular than traditional demographic data; tags may for example indicate user affiliations with groups or ideas (such as religions or political parties), or user life cycle stages. Using these rich, detailed user profile tags, the system may achieve promotion response rates far above those from traditional advertising, which relies on cookies or simple demographic categories.

A computer receives a request for graphical display source code for a computerized graphical advertisement display, and retrieves seed images including a plurality of seed image features. The computer generates candidate images based on the one or more seed images, where the computer alters a first aspect of a seed image to generate an altered seed image having a plurality of altered seed image features and the computer alters a second aspect of the altered seed image to generate a candidate image having a plurality of candidate image features. The computer generates candidate image scores based upon a context of the advertisement display and the plurality of candidate image features. The computer selects an image from the candidate images based on the candidate image scores and generates the graphical display source code based on the selected image, a size of the advertisement display, and display capabilities of the user device.

The subject disclosure relates to employing a computer-implemented method that sources, by a system operatively coupled to a processor, a set of personalized data comprising at least one of biometric data, statistical data, or contextual data. The method also includes determining, by the system, predictive relationships based on an evaluation of the set of personalized data. In another aspect, the method includes generating, by the system, a personal dynamic decision grid comprising a set of decision data coupled to a set of scores based on the predictive relationships, wherein the set of scores represent a probability of performing respective decisions of the set of decisions.

A method of an attribution server. The method determines publishing channels for advertisements in a marketing campaign to analyze their marketing effectiveness for purchasable items using a processor and a memory of the attribution server. Data points are associated with users. A K-th order attribution model is constructed. Independent and dependent variables of the attribution model are associated with various types of marketing data. An observation matrix and a conversion vector are determined. A regression analysis is performed with refining steps. Insignificant second order cross terms of the attribution model are identified and removed. A modified K-th order attribution model is constructed. Another regression analysis is performed to find optimal model parameters. The attribution server computes attribution scores associated with the publishing channels based on the attribution models and the optimal model parameters from regressions, and communicates the attribution scores to a marketer client through a network upon a request.

A system serves web pages and/or software application pages with digital ads to client devices by determining 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 then selects digital ads to serve to each field based on the field's viewability score.

A system of multi-modal transmission of packetized data in a voice activated data packet based computer network environment is provided. A natural language processor component can parse an input audio signal to identify a request and a trigger keyword. Based on the input audio signal, a direct action application programming interface can generate a first action data structure, and a content selector component can select a content item based on a count reaches a target number. An interface management component can identify first and second candidate interfaces, and respective resource utilization values. The interface management component can select, based on the resource utilization values, the first candidate interface to present the content item.

Models are built based on existing histories in one identifier space to infer features of entities in a different identifier space. A source model is built using features of an archetypical population in a given identifier space and the standard population. A join panel, i.e., a set of entities operating across both the given identifier space and a second disjoined identifier space, is scored using the source model. Based on the scores and features associated with the entities in the join panel within the second identifier space, a target model specific to the second identifier space is built. An audience of entities within the second identifier space can then be scored using the target model to identify entities that are similar to the archetypical population.

An online system receives a request to present a content item to a viewing user who is associated with a set of user attributes. The online system retrieves a regression model for predicting an expected quality for a particular content item and a particular set of users attributes. The regression model was trained, using machine learning, based on user-assigned quality scores, each corresponding to a content item and provided by a quality-assigning user, and sets of user attributes, each set associated with one of the quality-assigning users. The online system uses the regression model to predict a quality score, indicating the quality of a content item to the viewing user, based on the set of user attributes that is associated with the viewing user. The online system determines to provide the content to the viewing user based on the quality score, and transmits the content item to the viewing user.

Systems and methods for redeeming a reward held by an individual are described. A method for redeeming a reward includes determining threshold criteria for provision of a targeted reward redemption offer, identifying at least one individual based upon the threshold criteria, determining the targeted reward redemption offer, and providing the targeted reward redemption offer to the identified individual. A response to the provided targeted reward redemption offer may be received, and an account of the identified individual may be adjusted in accordance with the targeted reward redemption offer and the received response.