Videoamp, Inc.

California Institute of Technology

Debajyoti Ray

Ross Anthony McCray

David Gullo

Jay Prasad

Andreas Krause

Colin Camerer

Daniel Golovin

Ross Mccray

A method may include obtaining an advertising campaign, determining one or more devices associated with a consumer, and associating the one or more devices with a consumer identifier. The method also includes obtaining data associated with the one or more devices associated and corresponding the data with the consumer identifier. The method also includes deduplicating the data related to the consumer identifier across one or more viewing methods. The method also includes displaying the deduplicated data. The method also includes adjusting the advertising campaign based on user input.

Yield optimization of cross-screen advertising placement

The technology relates to sequential or tailored delivery of advertising content across a plurality of media conduits. The invention achieves sequential story telling for advertising campaigns in place of single-series advertising, by delivering over a period of several sessions on a variety of devices. An advertiser can air a campaign on a consumer individual's cell phone device, continue the second portion of the campaign via a desktop browser session, and conclude with the third portion of the campaign on the individual's OTT device. The technology provides advanced controls over targeting and scheduling with high precision.

Sequential delivery of advertising content across media devices

The current invention relates to a computer-generated method for optimizing placement of advertising content across multiple different devices. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer's device graph to predict future consumption behavior. The system includes an interface through which an advertiser can access relevant information about inventory and success of a given placement.

Cross-screen optimization of advertising placement

The current invention relates to a computer-generated method for optimizing placement of advertising content across multiple different devices. The system permits targeting of advertising content to consumers on TV and mobile devices that can be operated from within a multi-channel video programming distributor's environment. The system is able to use hard and soft constraints to come up with a number of possible targets for an advertising campaign and can then provide tools for optimizing those targets. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer's device graph to predict future consumption behavior. The system includes an interface through which a user can adjust various constraints and optimize a distributor's revenue yield from advertising.

The present invention is directed to statistical methods for measuring cross-screen efficacy as it relates to advertising. The improved statistical methods provide the ability for advertisers and advertising agencies to predict the behaviors of consumers based on their aggregated cross-screen behavior. The system creates a new output that includes optimized user segments and classifications. A second part of the output is improved measurement and prediction of future consumer behaviors based on the processed multisided data on cross-screen behavior.

Cross-screen measurement accuracy in advertising performance

Within an advertising buying platform, advertising buyers are able to direct advertising content to TV/OTT/VOD inventory that matches targeted market segments. The advertising buyers select from a list of available market segments, and the system pairs the market segments to an anonymous dataset of media consumers (using a database of hashed online IDs). The dataset of hashed online IDs is then mapped to external OEM IDs (such as from a Smart TV) via the functions of a hashed identity mapping method. This permits the advertiser to target advertising content to a population of TV viewers. Also described herein are methods of bidding on programmatic TV content.

Targeting TV advertising slots based on consumer online behavior

The current invention relates to a computer-generated method for optimizing placement of advertising content to consumers' TV's using a programmatic TV bidding model. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer's device graph to predict future consumption behavior. The system includes an interface through which an advertiser can access relevant information about inventory and success of a given placement.

Programmatic TV advertising placement using cross-screen consumer data

The present invention is directed to statistical methods for measuring cross-screen efficacy as it relates to advertising. The improved statistical methods provide the ability for advertisers and advertising agencies to predict the behaviors of consumers based on their aggregated cross-screen behavior. The system creates a new output that includes optimized user segments and classifications. A second part of the output is improved measurement and prediction of future consumer behaviors based on the processed multi-sided data on cross-screen behavior.

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Marina Del Rey, CA, United States of America

Debajyoti Ray