Automated Bidding and Budget Optimization for Performance Advertising Campaigns

42 Pages Posted: 31 Aug 2021 Last revised: 1 Sep 2021

See all articles by Tong Geng

Tong Geng

JD.com American Technologies Corporation

Fangzhou Sun

JD.com American Technologies Corporation

Di Wu

JD.com American Technologies Corporation

Wei Zhou

LinkedIn

Harikesh Nair

Stanford University - Graduate School of Business

Zhangang Lin

affiliation not provided to SSRN

Date Written: August 22, 2021

Abstract

We present an approach to automate the bidding and budgeting of multi-unit digital advertising campaigns. Such campaigns typically involve groups of ad-units that span multiple user segments, ad-delivery channels and media that together are meant to deliver on the advertiser's goals. Our model handles the simultaneous optimization of a flexible portfolio of such ad-units bought via real-time bidding (RTB). Unlike ``black-box'' automated bidding systems, we solve for optimal bidding and budgeting by linking the automation to a clearly posed optimization problem that produces interpretable and analytic optimality conditions that are verifiable. We also present a set of solution algorithms that have provably fast convergence properties, and reduces the need to estimate complex input functions from the data, thereby reducing estimation error. Further, we show how to integrate this solution with existing multi-touch attribution (MTA) models in a modular way, thereby demonstrating how to leverage the attribution results provided by modern MTA models for campaign bidding and budgeting. The system we present has been fully deployed on the advertising platform of JD.com. We present randomized control trials we implement on the platform that show empirically that usage of the system improves advertiser's campaign goals substantially relative to that under their default choices.

Keywords: Online advertising, performance advertising, automated bidding, real-time bidding, budget allocation, multi touch attribution, e-commerce, randomized controlled trials

JEL Classification: M37, D82, D83, C93

Suggested Citation

Geng, Tong and Sun, Fangzhou and Wu, Di and Zhou, Wei and Nair, Harikesh and Lin, Zhangang, Automated Bidding and Budget Optimization for Performance Advertising Campaigns (August 22, 2021). Available at SSRN: https://ssrn.com/abstract=3913039 or http://dx.doi.org/10.2139/ssrn.3913039

Tong Geng (Contact Author)

JD.com American Technologies Corporation ( email )

United States

Fangzhou Sun

JD.com American Technologies Corporation ( email )

United States

Di Wu

JD.com American Technologies Corporation ( email )

United States

Wei Zhou

LinkedIn ( email )

Mountain View, CA
United States

Harikesh Nair

Stanford University - Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States
650-736-4256 (Phone)

HOME PAGE: http://faculty-gsb.stanford.edu/nair/index.html

Zhangang Lin

affiliation not provided to SSRN

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