research-article

A New Approach to Real-Time Bidding in Online Advertisements: : Auto Pricing Strategy

Authors:

Shalinda Adikari,

Kaushik DuttaAuthors Info & Claims

INFORMS Journal on Computing, Volume 31, Issue 1

Pages 66 - 82

https://doi.org/10.1287/ijoc.2018.0812

Published: 01 February 2019 Publication History

Abstract

Real-time bidding (RTB) for digital advertising is becoming the norm for improving advertisers’ campaigns. Unlike traditional advertising practices, in the process of RTB, the advertisement slots of a mobile application or a website are mapped to a particular advertiser through a real-time auction. The auction is triggered and is held for a few milliseconds after an application is launched. As one of the key components of the RTB ecosystem, the demand-side platform gives the advertisers a full pledge window to bid for available impressions. Because of the fast-growing market of mobile applications and websites, the selection of the most pertinent target audience for a particular advertiser is not a simple human-mediated process. The real-time programmatic approach has become popular instead. To address the complexity and dynamic nature of the RTB process, we propose an auto pricing strategy (APS) approach to determine the applications to bid for and their respective bid prices from the advertising agencies’ perspective. We apply the APS to actual RTB data and demonstrate how it outperforms the existing RTB approaches with a higher conversion rate for a lower target spend.

A video abstract is available at https://doi.org/10.1287/ijoc.2018.0812.

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Cited By

Rhuggenaath JAkcay AZhang YKaymak U(2022)Setting Reserve Prices in Second-Price Auctions with Unobserved BidsINFORMS Journal on Computing10.1287/ijoc.2022.119934:6(2950-2967)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1287/ijoc.2022.1199
Kong CChen BLi SChen JChen YZhang L(2020)An Advanced Q-Learning Model for Multi-agent Negotiation in Real-Time BiddingWeb Information Systems and Applications10.1007/978-3-030-60029-7_44(491-502)Online publication date: 23-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-60029-7_44

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A New Approach to Real-Time Bidding in Online Advertisements: Auto Pricing Strategy

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Published In

cover image INFORMS Journal on Computing

INFORMS Journal on Computing Volume 31, Issue 1

Winter 2019

192 pages

ISSN:1526-5528

DOI:10.1287/ijoc.2019.31.issue-1

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Copyright © 2019, INFORMS.

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INFORMS

Linthicum, MD, United States

Publication History

Published: 01 February 2019

Accepted: 05 February 2018

Received: 18 September 2015

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Cited By

Rhuggenaath JAkcay AZhang YKaymak U(2022)Setting Reserve Prices in Second-Price Auctions with Unobserved BidsINFORMS Journal on Computing10.1287/ijoc.2022.119934:6(2950-2967)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1287/ijoc.2022.1199
Kong CChen BLi SChen JChen YZhang L(2020)An Advanced Q-Learning Model for Multi-agent Negotiation in Real-Time BiddingWeb Information Systems and Applications10.1007/978-3-030-60029-7_44(491-502)Online publication date: 23-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-60029-7_44

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