A Real-World Implementation of Unbiased Lift-based Bidding System

Moriwaki, Daisuke; Hayakawa, Yuta; Matsui, Akira; Saito, Yuta; Munemasa, Isshu; Shibata, Masashi

doi:10.1109/bigdata52589.2021.9671800

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…CPIA also reduces fraud because conversion outcomes involve purchases or taking actions that are costly for bots. Moriwaki et al (2021) implement a incrementality-based bidding algorithm at a Japanese demand side platform, and show that their algorithm obtains a CPIA that is a fraction of that obtained by their baseline, CPA-based bidding algorithm.…”

Section: Incrementality-based Optimizationmentioning

confidence: 99%

Inferno: A guide to field experiments in online display advertising

Johnson

2023

Economics Manag Strategy

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Online display advertising is a hostile medium for field experiments. Display‐ad effects are tiny and necessitate large‐scale experiments. The experimenter has limited control because ad exposure is jointly determined by advertisers, users, algorithms, and market competition. As such, online display ads provide useful lessons for experimenters at the frontier of digital research more generally. Display‐ad experiments place renewed focus on old topics like statistical power and compliance as well as on newer issues like identity fragmentation, experimental spillovers, and incrementality optimization. In this guide, I review these challenges, best practices, and new developments.

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Section: Incrementality-based Optimizationmentioning

confidence: 99%

Inferno: A guide to field experiments in online display advertising

Johnson

2023

Economics Manag Strategy

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“…With this new formulation, the optimal policy allocation search is framed as a multiple treatment allocation problem, and the causal effects (cost and value) of policies are much easier to estimate than that of individual bids. Our approach is not to be understood as in competition with usual bid by bid design approaches (Moriwaki et al 2021) but rather complementary. Indeed, any bid by bid design approach could be included as one of the candidate policies we wish to choose from when allocating policies to users with our methodology.…”

Section: Introductionmentioning

confidence: 99%

Maximizing the Success Probability of Policy Allocations in Online Systems

Betlei,

Vladimirova,

Sebbar

et al. 2024

AAAI

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The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly complex due to various factors such as market competition, user behavior, and the diverse objectives of advertisers. In this paper we consider the problem at the level of user timelines instead of individual bid requests, manipulating full policies (i.e. pre-defined bidding strategies) and not bid values. In order to optimally allocate policies to users, typical multiple treatments allocation methods solve knapsack-like problems which aim at maximizing an expected value under constraints. In the specific context of online advertising, we argue that optimizing for the probability of success is a more suited objective than expected value maximization, and we introduce the SuccessProbaMax algorithm that aims at finding the policy allocation which is the most likely to outperform a fixed reference policy. Finally, we conduct comprehensive experiments both on synthetic and real-world data to evaluate its performance. The results demonstrate that our proposed algorithm outperforms conventional expected-value maximization algorithms in terms of success rate.

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A Survey on Bid Optimization in Real-Time Bidding Display Advertising

Ou,

Chen,

Dai

et al. 2023

ACM Trans. Knowl. Discov. Data

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Real-Time Bidding (RTB) is one of the most important forms of online advertising, where an auction is hosted in real time to sell the individual ad impression. How to design an automated bidding strategy in response to the dynamic auction environment is crucial for improving user experience, protecting the interests of advertisers, and promoting the long-term development of the advertising platform. As an exciting topic in the real-world industry, it has attracted great research interest from several disciplines, most notably data science. There have been abundant studies on bidding strategy design which are based on the large volume of historical ad requests. Despite its popularity and significance, seldom work provides a summary for bid optimization. In this survey, we present the latest overview of the recent works to shed light on the optimization techniques where most of them are validated in practice. We first explore the optimization problem in different works, explaining how these different settings affect the bidding strategy designs. Then, some forms of bidding functions and specific optimization techniques are illustrated. Further, we specifically discuss a new trend about bidding in first-price auctions, which have gradually become popular in recent years. From this survey, both practitioners and researchers can gain insights of the challenges and future prospects of bid optimization in RTB.

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A Real-World Implementation of Unbiased Lift-based Bidding System

Cited by 4 publications

References 38 publications

Inferno: A guide to field experiments in online display advertising

Inferno: A guide to field experiments in online display advertising

Maximizing the Success Probability of Policy Allocations in Online Systems

A Survey on Bid Optimization in Real-Time Bidding Display Advertising

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