Systems, methods, and computer media for dynamically updating online advertisements and providing updated online advertisements are provided. An object-oriented online advertisement is provided that has properties inherited from an advertisement class. The inherited properties include metadata fields and corresponding metadata values. The metadata fields and values determine the content, appearance, behavior, and other characteristics of the online advertisement when it is published on a web page. Changes to or addition or deletion of metadata fields and values in an advertisement class or subclass are communicated to the online advertisements, and the metadata of the advertisements are updated to reflect the changes.

A system, method, apparatus and processor readable media are described for real-time prediction of an advertising audience volume through analysis of historical audience data, and tuning of the predicted audience volume. Embodiments enable a user to specify a query for audience volume prediction. Such a query may be a Boolean combination of various audience categories. A time range may be determined that indicates the amount of historical data that is to be analyzed to make the audience volume prediction in real time. Employing the user-specified query, an audience volume prediction may be provided for a future time period, based on an analysis of retrieved historical audience data for the time range. Embodiments may also enable a user to tune the predicted audience volume through modification of the query through one or more iterations.

Systems, devices, and methods are disclosed for determining the quality of traffic received from different web page publishers, and setting a pricing structure for the different traffic based on the determined quality of traffic. Accurately determining the quality of traffic and/or clicks from different publishers allows the network system described herein to offer a fair marketplace with just return on investments (ROI) for advertisers, and offer a robust and accurate traffic quality based pricing model for publishers. Internet based technology, and in particular deep learning techniques available through a neural network, are utilized to determine the pricing structure based on click and/or web page traffic quality measurements generated through the deep learning techniques.

An addressable asset system (100) includes user equipment devices (UEDs) (102), a cloud decisioning system (CDS) (106) and a business data management system (BDMS) (108). The BDMS (108) collects information that is used by the CDS (106) to manage delivery of assets by the UEDs (102). In an exemplary network architecture, the CDS (106) is independent of the network insertion and delivery equipment. In particular, the broadcast content stream is delivered to the UED (102) by a digital content management (DCM) system (110) and certain assets are separately delivered to the UED (102) by the asset download network (AND) (112). This allows the CDS (106) to remain independent of the network of the UED (102) such that the CDS (106) can operate across networks.

A method of operating a computing system capable of allocating an advertisement budget of campaign between a plurality of advertisement entities, the method comprising: obtaining by the computer system, for each of the plurality of advertisement entities, a respective optimal target frontier function representing for each given advertising cost an optimal value of return and configured to follow the law of diminishing return; receiving by the computer system a budget constraint for the advertisement budget; generating, by the computer system, a global target frontier function by summing each of the received optimal target frontier functions; processing, by the computer system, the generated global target frontier function to determine for each of the plurality of advertisement entities an optimal, with respect of at least the received budget constrain, advertising cost value such that a sum of the optimal advertising cost values meets the budget constraint; and reporting the determined values.

A method including receiving, at a first computing system from a second computing system, a first key and encrypted online interaction data, receiving, at the first computing system from a third computing system, a second key and encrypted offline action data encoding data indicating one or more offline actions, receiving, at the first computing system from the third computing system, executable code comprising a third key, and executing, by the first computing system, the executable code. The executable code causing the first computing system to decrypt the encrypted online interaction data and the encrypted offline action data using the first key, the second key, and the third key, correlate one or more of the offline actions in the offline action data to one or more online interactions in the online interaction data, and generate aggregate data indicating a number of offline actions correlated to the online interactions.

[Problem to be solved] An object of the present invention is to provide a method for improving an advertising effect, particularly in a television commercial. [Solution] According to an embodiment of the present invention, the disclosed method is a method for providing an advertisement for a television program, which determines an advertisement-related product related to a product to be advertised, refers supply prediction information of the advertisement-related product, and determines the time for placing the TV program advertisement based on the supply prediction information.

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for increasing the performance of an advertisement network by monitoring requests for advertisements from applications, detecting patterns, and developing and implementing remedial actions to increase system performance.

Systems and methods for the selection of the promotions are provided. A set of possible offers are initially received, each including a set of variables, with each variable having a set of possible values. These form a combination of variable values for each offer. A heuristic is applied to all possible offers to reduce the number of offers being considered. The combination of variable values for these reduced number offers is converted into a vector value, which is then scored, ranked and the top ranked offers are selected for inclusion in a promotional campaign. The remaining offers are then analyzed to select additional offers to include into the promotional campaign which maximizing a determinant for the selected offers using their vectors. All selected offers are administer in a promotional test campaign across many consumer segments. Feedback from the campaign may be collected to generate a “general” offer.

In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system using predictive models derived from tiered feature data.