Amazon Technologies, Inc.

Paula Marie Despins

Evgeniy V Perevodchikov

Sheng Ma

Avishkar Misra

John Michael Nilles

Cyrus Khoshnevisan

Jim Huang

Guthrie Chipman Collin

Gerard Gjonej

Yu Gan

Timothy John Craycroft

Weam Abu Zaki

Kelly Paulson

Fabian Lutz-Frank Wauthier

Richard Everett Edwards, Iii

Jaikannan Ramamoorthy

Shiven Ramji

Kyle Tiffany

Hancheng Ge

Jia Chen

Cody Barnhart

Alman Ahmad Shibli

Teresa Chen

Systems, methods, and computer-readable media are disclosed for modeled advertisement conversion attributions. An example method may include receiving first input data comprising first advertisement impression data and first advertisement conversion data, wherein the first input data includes one or more user identifiers associated with both the advertisement impression data and advertisement conversion data. The example method may also include training one or more machine learning models using the first input data. The example method may also include receiving second input data comprising second advertisement impression data, wherein user identifiers are unavailable for the second input data. The example method may also include determining, using the one or more machine learning models, second predicted conversion data associated with the second input data.

Systems and methods for modeled advertisement conversion attributions

Systems, methods, and computer-readable media are disclosed for predictive user segmentation modeling and browsing interaction analysis for digital advertising. In one embodiment, an example method may include identifying a target consumer segment, generating a predictive user behavior model for the target consumer segment based at least in part on selected user data, and receiving an indication that a user is browsing a website. Example methods may include generating a first customer score for the user using the predictive user behavior model in real-time, and determining that the first customer score meets a consumer segment modification threshold for the target consumer segment.

Predictive user segmentation modeling and browsing interaction analysis for digital advertising

Systems, methods, and computer-readable media are disclosed for automated server-based content delivery. In one embodiment, an example method may include determining campaign information for a content delivery campaign, the campaign information comprising a first allocation value for first content, and a second allocation value for second content, determining first observed data comprising a first user response rate for the first content and a second user response rate for the second content, determining an exponentiated gradient algorithm for the content delivery campaign, and determining a reallocation amount to reallocate a portion of the first allocation value to the second allocation value using the exponentiated gradient algorithm based at least in part on the first observed data, wherein the reallocation amount maximizes an output of the exponentiated gradient algorithm.

Automated server-based content delivery

The systems and methods described herein enable measurement of the exposure of a user to advertising campaigns across different channels (e.g., websites accessed by web browsers of laptops, desktops, or mobile computers, electronic readers or tablets, applications on mobile devices, and the like) and credit the exposure of the user to the advertisement to a specific advertisement in the campaign along with subsequent shopping events generated by the user, such as visiting product detail pages, visiting the read all reviews page for a product, adding a product to a wish list, adding a product to a shopping cart, subscribing to a product through an online subscription service, and/or purchasing a product.

Cross-channel online advertising attribution

Growing community of inventors

Seattle, WA, United States of America

Paula Marie Despins