Abstract: Systems and methods are disclosed for creating a digital consumer profile that may be dynamically and transiently generated, drawing upon attribute data that is available at time of the digital consumer profile creation. The digital consumer profile may further provide for dynamic and transient generations of a plurality of user profiles to fit definitions and use cases not anticipated at the outset of targeting or attribution efforts and attribute data collection. Regulatory conditions, privacy policies, enterprise rules and the like may determine, at least in part, the collection, analysis, and auditing of attribute data, and the merging of such data to form aliases that may be associated with consumers. The digital consumer service may comprise binding expression syntax to dynamically, and transiently, identify profiles to give flexibility and extensibility beyond having a flat match table.

Abstract: In embodiments of the present invention, improved capabilities are described for creating and using Synthetic User Identifiers within an advertising analytic platform for the purpose of targeting the placement of advertising within an available channel based at least in part on Synthetic User Identifier information.

Abstract: In embodiments of the present invention, improved capabilities are described for computing a valuation of a third party dataset based at least in part on a comparison of advertising impression data relating to ad content placed from first and second advertising campaign datasets. The placement of the ad content from the first advertising campaign dataset may be based at least in part on a machine learning algorithm employing the third party dataset to select optimum ad placements. In embodiments, the computation of the valuation and the billing of the advertiser may be automatically performed upon receipt of a request to place content from the advertiser. Further, the computation of the valuation may be the result of comparing the performance of multiple competing valuation algorithms. Moreover, the comparison of the performance of multiple competing valuation algorithms may include the use of valuation algorithms based at least in part on historical data.

Abstract: In embodiments of the present invention, improved capabilities are described for using a plurality of competing economic valuation models to predict an economic valuation for each of a plurality of advertisement placements, advertisements, and advertisement-advertisement placement combinations, in response to receiving a request to place an advertisement. The economic valuation model may be based at least in part on real-time event data, historic event data, user data, third-party commercial data historical advertisement impressions, advertiser data, ad agency data, historical advertising performance data, and machine learning. Further, a computer program product, based on the methods and systems of the present invention, may evaluate each economic valuation produced by each of the plurality of competing economic valuation models to select one as a current valuation of an advertisement placement, advertisement, and/or advertisement-advertisement placement combination.

Abstract: In embodiments of the present invention, improved capabilities are described for applying a plurality of algorithms to predict performance of online advertising placements. The plurality of algorithms to predict performance may include advertiser data, historic event data, user data, real-time event data, contextual data, and third-party commercial data. Further, a computer program product, based on the methods and systems of the present invention, may include a set of instructions for tracking performance of the plurality of algorithms under a variety of market conditions. In embodiments, the computer program product may include a set of instructions for determining performance conditions for an algorithm, and/or include a set of instructions for tracking market conditions. Further, the computer program product may include a set of instructions for selecting an algorithm for predicting performance of advertising placements based on current market conditions.

Abstract: In embodiments of the present invention, improved capabilities are described for refining an economic valuation model, through machine learning, to evaluate information relating to a plurality of available placements, and predicting an economic valuation for each of the plurality of placements in response to receiving a request to place an advertisement for a publisher. In embodiments, the refinement through machine learning may include comparing economic valuation models by retrospectively comparing the extent to which the models reflect actual economic performance of advertisements. In embodiments, the economic valuation model may be used to select at least one of the plurality of available placements and present it to the publisher seeking to place an advertisement.

Abstract: In embodiments of the present invention, improved capabilities are described for dynamically determining an anticipated economic valuation for each of a plurality of potential advertising placements for an advertisement in response to receiving a request to place an advertisement for a publisher, wherein the selection and presentation of an available advertising placement to the publisher is based at least in part on the economic valuation. The economic valuation may be a current valuation, an anticipated valuation, and/or a valuation that is selected from among a set of competing valuations. In embodiments, the request for the placement may be a time limited request. Further, the economic valuation model may evaluate performance information relating to each of the plurality of advertisement placements.