Adap.tv, Inc.

Lucid Commerce, Inc.

Yahoo Ad Tech LLC

Adapt.tv, Inc.

Brendan Kitts

Dyng Au

Brian M Burdick

Alfred Lee

Liang Wei

Amanda Powter

Tyson Roberts

Jon Borchardt

Sawin Lee

Michael Bardaro

Aaron Eliasib Flores

Junwei Pan

Tian Zhou

Shengjun Pan

Djordje Gligorijevic

Balaji Srinivasa Rao Paladugu

Sih Huseyin Ulger

Bharatbhushan Shetty

Sneha Thomas

Garrett James Badeau

Al Lee

John Sobieski

Yongbo Zeng

Andrew George Potter

Jianlong Zhang

Tingyu Mao

Hao He

San Gultekin

Michael Krishnan

Ishadulta Yadav

Ruofeng Chen

Ethan James Thornburg

Sergey Tolkachov

Davood Shamsi

Sergei Tolkachov

A processing device selects a population of persons and measures sales metrics from the population over a time period and measures an advertising weight over the time period. The processing device determines an effect that the advertising weight has on the sales metrics and additionally calculates values for a degree of targetedness for the advertisement to the population of persons. The processing device determines an effect that the degree of targetedness has on the sales metrics and generates a multi-dimensional model that measures the combined effects of the advertising weight and the degree of targetedness on the sales metrics.

Systems and methods for analyzing data element distribution across a network

Systems and methods are disclosed that measure web activity bursts after ad broadcasts that may be sent to multiple persons. One system uses a cookie-less/cookie-optional, anonymous/personal-identification-not-required, method for web-based conversion tracking that will work on broadcast media systems such as television, and could also be applied to measuring spikes from email, radio, and other forms of advertising where an episodic ad event is broadcast to multiple parties, and where responses occur in a batch after the broadcast.

Systems and methods for web spike attribution

This paper presents a practical method for measuring the impact of multiple marketing events on sales, including marketing events that are not traditionally trackable. The technique infers which of several competing media events are likely to have caused a given conversion. The method is tested using hold-out sets, and also a live media experiment for determining whether the method can accurately predict television-generated web conversions.

System and method for determining effects of multi-channel media sources on multi-channel conversion events

One or more computing devices, systems, and/or methods are provided. Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of minimum bid values to win associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, unshaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using a loss function and/or the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine a shaded bid value for submission based upon one or more first feature parameters, of the feature parameters, associated with the second set of features.

Bid value determination

Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

Bid value determination for a first-price auction

Systems and methods are disclosed for targeting of advertising content for a consumer product, by obtaining consumer demographic data, the consumer demographic data including a plurality of demographic attributes for each person; identifying a plurality of media slots; and obtaining program information for a respective identified program aired in each media slot among the plurality of media slots, the program information including viewing data of a plurality of viewing persons viewing the program and each viewing person being among the plurality of persons. The methods also include enriching the viewing data with the consumer demographic data; identifying a plurality of advertiser industries; enriching the product purchaser data with the consumer demographic data; calculating a relevance of each advertiser industry among the plurality of advertiser industries for each identified program based on demographic attributes of the product purchasers in each advertiser industry and demographic attributes of the viewing persons.

Systems and methods for optimized delivery of targeted media

Television is the largest advertising category in the United States with over 65 billion spent by advertisers per year. A variety of different targeting algorithms are compared, ranging from the traditional age-gender targeting methods employed based on Nielsen ratings, to new approaches that attempt to target high probability buyers using Set Top Box data. The performance of these different algorithms on a real television campaign is shown, and the advantages and limitations of each method are discussed. In contrast to other theoretical work, all methods presented herein are compatible with targeting the existing 115 million Television households in the United States and are implementable on current television delivery systems.

Systems and methods for a television scoring service that learns to reach a target audience

The present disclosure describes a system that attempts to reconcile diverse goals and re-cast the goals into something that is quantifiable and optimizable. One way to reconcile diverse goals is by converting these 'constraints'—with the huge problems of feasibility—into errors that can be minimized. This disclosure also presents solutions for rate constraints which previously have not been dealt with. The resulting system enables advertisers to dynamically adjust their campaign based on the needs of the moment. Such a system can have advantages in terms of controllability, smoothness, as well as avoiding hard stop conditions that plague the constraint-based approach. In order to achieve this result, solutions are presented for problems of pacing, viewability prediction, and most particularly, error minimization.

Ad serving with multiple goals using constraint error minimization

A processing device receives set-top box data, sales data, and advertising data. The processing device associates set-top boxes from the set-top box data with viewers and calculates advertising exposure events for advertisements. The processing device further calculates, for each viewer, an advertising weight as a function of number of exposures of the advertisement as applied to the viewer based on the advertising exposure events. The processing device further calculates, for each viewer, a score representing a degree-of-targetedness between an advertisement campaign and the viewer. The processing device manages the advertisement campaign based on the advertising weight and the degree-of-targetedness.

System and method for tracking advertiser return on investment using set top box data

A method of targeting of advertising content for a consumer product is disclosed. The method comprises obtaining consumer demographic data from a first server over a network, the consumer demographic data including a plurality of demographic attributes for each person among a plurality of persons; obtaining product purchaser data for a plurality of product purchasers of the consumer product from a second server over the network, each product purchaser among the plurality of product purchasers being among the plurality of persons; and enriching the purchaser data with the consumer demographic data. The method further comprises enriching viewing data with consumer demographic data; and selecting viewed media among the aggregated viewed media having the highest similarity to the product purchasers as target media for the advertising content.

Systems and methods for addressable targeting of electronic content

System and method for attributing multi-channel conversion events and subsequent activity to multi-channel media sources

Growing community of inventors

Seattle, WA, United States of America

Brendan Kitts