LEARNING SYSTEM FOR ADVERTISING BIDDING AND VALUATION OF THIRD PARTY DATA
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. Furthermore, the computer program product may bill an advertiser a portion of the third-part data valuation to place ad content.
1 . A computer program product embodied in a computer readable medium that, when executing on one or more computers, performs the steps of:
splitting an advertising campaign dataset into a first advertising campaign dataset and a second advertising campaign dataset;
deploying an economic valuation model that is refined through machine learning to evaluate information relating to a plurality of available placements to predict an economic valuation for placement of ad content from the first advertising campaign dataset, wherein the machine learning is based at least in part on a third party dataset;
placing ad content from the first and second advertising campaign datasets within the plurality of available placements, wherein content from the first advertising campaign is placed based at least in part on the predicted economic valuation, and content from the second advertising campaign dataset is placed based on a method that does not rely on the third party dataset;
receiving impression data from a tracking machine relating to the ad content placed from the first and second advertising campaign datasets, wherein the impression data includes data regarding user interactions with the ad content; and
determining a value of the third party dataset based at least in part on a comparison of impression data relating to the ad content placed from the first and second advertising campaign datasets.
2 . The computer program product of claim 1 , wherein the third party dataset includes data relating to users of advertising content.
3 . The computer program product of claim 1 , wherein the data relating to users of advertising content includes demographic data.
4 . The computer program product of claim 1 , wherein the data relating to users of advertising content includes transaction data.
5 . The computer program product of claim 1 , wherein the data relating to users of advertising content includes advertisement conversion data.
6 . The computer program product of claim 1 , wherein the third party dataset includes contextual data relating to the plurality of available placements.
7 . The computer program product of claim 1 , wherein the contextual data derives from a contextualizer service that is associated with the machine learning facility.
8 . The computer program product of claim 1 , wherein the third party dataset includes financial data relating to historical advertisement impressions.
9 . The computer program product of claim 1 , wherein the economic valuation model is based at least in part on real time event data.
10 . The computer program product of claim 1 , wherein the economic valuation model is based at least in part on historic event data.
11 . The computer program product of claim 1 , wherein the economic valuation model is based at least in part on user data.
12 . The computer program product of claim 1 , wherein the economic valuation model is based at least in part on third-party commercial data.
13 . The computer program product of claim 1 , wherein the economic valuation model is based at least in part on advertiser data.
14 . The computer program product of claim 1 , wherein the economic valuation model is based at least in part on advertising agency data relating to creative content of advertising.
15 . A computer program product embodied in a computer readable medium that, when executing on one or more computers, performs the steps of:
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, wherein the placement of the ad content from the first advertising campaign dataset is based at least in part on a machine learning algorithm employing the third party dataset to select optimum ad placements; and
billing an advertiser a portion of the valuation to place an ad content from the first advertising campaign dataset.
16 . The computer program product of claim 15 , wherein the computation of the valuation and the billing of the advertiser is automatically performed upon receipt of a request to place content from the advertiser.
17 . The computer program product of claim 15 , wherein the computation of the valuation is the result of comparing the performance of multiple competing valuation algorithms.
18 . The computer program product of claim 17 , wherein the comparison of the performance of multiple competing valuation algorithms includes the use of valuation algorithms based at least in part on historical data.
19 . A computer program product embodied in a computer readable medium that, when executing on one or more computers, performs the steps of:
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, wherein the placement of the ad content from the first advertising campaign dataset is based at least in part on a machine learning algorithm employing the third party dataset to select optimum ad placements; and
calibrating a bid amount recommendation for a publisher to pay for a placement of an ad content based at least in part on the valuation.
20 . The computer program product of claim 1 , wherein the calibration is adjusted iteratively to account for real-time event data and its effect on the valuation.