Systems and methods for predicting forward market pricing for renewable energy credit based on human behavioral data are disclosed. An example transaction-enabling system may include a forward market circuit to access a forward energy credit market and a market forecasting circuit to automatically generate a forecast for a forward market price of an energy credit in the forward energy credit market where the forecast is based at least in part on a human behavior information collected from at least one human behavioral data source. The example system may further include wherein the energy credit includes a renewable energy credit associated with a renewable energy system, and a smart contract circuit to perform at least one of selling the renewable energy credit or purchasing the renewable energy credit on the forward energy credit market in response to the forecasted forward market price of the energy credit.

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.

Techniques are disclosed for delivering electronically generated offers to particular users, and more particularly, for obtaining and receiving data of particular types from specified sources to provide output that is generated from a decisioned library. The techniques include retrieving a product score that comprises a probability that a first user will obtain a first product, obtaining a behavioral value, and generating a score of a behavior value. The score of the first product behavioral value is then mathematically combined with a similarly derived second score of a product behavioral value to determine whether to generate and deliver an offer to the first user.

Methods and systems for performing non-linear optimization of reach are described herein. The methods and systems may be used to compute the reach associated with different advertisement campaigns involving different combinations of spots. The method includes computing probabilities that a user will access a certain spot. The method includes retrieving a weight associated with the targeted user demographic. The method includes computing a first reach value using a first non-linear function, and a second reach value using a second non-linear function. The method includes comparing the two reach values to each other and to a predetermined reach threshold. The higher reach value larger than the predetermined reach threshold is selected and information associated with the selected reach value is provided to the user.

A method includes obtaining (i) device data associated with multiple electronic devices and (ii) advertisement data associated with one or more advertisement campaigns. The method also includes identifying first features and second features, where the first features correspond to the device data and the second features correspond to the advertisement data. The method further includes generating a graph relating usage history of the multiple electronic devices and one or more advertisement genres. The method also includes identifying a specified electronic device from among the multiple electronic devices and an advertisement segment from among the one or more advertisement campaigns using the first features, the second features, and the graph. In addition, the method includes providing the advertisement segment to the specified electronic device.

An example apparatus includes processor circuitry to: access first input data from meters, the meters to monitor media devices associated with a plurality of panelists, the first input data including media source data and panel data; reduce a dimensionality of the first input data to generate second input data of reduced dimensionality relative to the first input data, the dimensionality of the first input data to be reduced based on a prior probability of an audience rating associated with the plurality of panelists and an approximation of a dependency of the audience rating on at least one of the media source data and the panel data; and decode the second input data of reduced dimensionality to output a probability model parameter for a multivariate probability model, the multivariate probability model having dimensions corresponding to the first input data, the multivariate probability model to label census data.

A system and methods for an offer placement system for delivering targeted marketing offers to consumers during a session with an online (web-based) Internet portal, such as an online banking portal of a financial institution. A component receives advertising campaign data from an advertiser including a plurality of targeted marketing offers for delivery to consumers in response to a particular condition (e.g. an offer-triggering event), and stores the campaign data in a database. A component determines the occurrence of an offer-triggering event caused by a consumer during an online session with the online portal, for example, viewing a list of banking transactions. A component identifies a predetermined targeted marketing offer stored in the database that corresponds to the offer-triggering event. A component delivers information corresponding to the targeted marketing offer to the consumer during the online session with the portal.

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.

Method and computing device for performing dynamic digital signage campaign optimization. Screen data associated to screens controlled by the computing device and requirements of active campaigns are stored at the computing device. The screen data comprise characteristics of the screens and screen activity data defining the activity the screens for the active campaigns. The computing device receives requirements of a candidate campaign and generates a mathematical model based on the requirements of the candidate campaign, the requirements of the active campaigns, and at least some of the screen data. The mathematical model is transmitted to a mathematical solver and a mathematical solution generated by the mathematical solver is received. The computing devices generates configuration data for the candidate campaign based on the mathematical solution. The configuration data define a configuration for displaying a content of the candidate campaign on selected screens among the screens controlled by the computing device.

An audience proposal creator determines a target cost per thousand (CPM) baseline and a demographics CPM baseline for a deal offering audience spots, determines deal constraints based on a target CPM reduction goal, a demographics CPM cap, and the established parameters, and generates rates by selling title for each selling title-weeks for a duration of pending deal, and for each network of a plurality of networks based on the constraints. Target and demo audience rating estimates are acquired based on the target and demo applicable to the advertiser for the plurality of networks, and a distribution of the audience spots generated across the plurality of selling title-weeks, and networks based on the target audience rating estimates, a budget for the pending deal, the generated rates, and available inventory per selling title-weeks, and a proposal generated based on the distribution. An audience processor schedules audience spots across one or more networks for selling title-weeks based on the distribution.