Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns

Nuara, Alessandro; Trovò, Francesco; Gatti, Nicola; Restelli, Marcello

doi:10.48550/arxiv.2003.01452

Cited by 2 publications

(4 citation statements)

References 24 publications

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“…This example is typical, as can be seen in figure 4 and in table 1: at n = 10 days, only in 11 experiments the regret is negative, and in most cases the relative regret ranges from 8% to 55%. Fast convergence is especially important if a sliding window strategy is employed to retroactively take time dependence into account, as in [17]. At n = 100 days, the relative regrets are much less spread out and lower on average, and again they are negative only in roughly one in ten cases (13 runs).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Two recent works [16,17] have cast the daily bid/budget optimization as a multi-armed bandit problem. In particular, the goal is to maximise the long term revenue, choosing daily the combination of bids and budgets for the whole portfolio (the collection of campaigns over which the total budget is set).…”

Section: Introductionmentioning

confidence: 99%

“…the number of clicks we would have observed if we did not have budget constraints. The authors of [16] suggest using the time of the day when the daily budget finished and an estimate of the distribution of the number of clicks during the day to perform the extrapolation (see also [17] where the example of constant distribution is given). However, this information is not available, and ad frequency is explicitly reduced throughout the day to make sure the budget lasts until the end of the day [14].…”

Section: Introductionmentioning

confidence: 99%

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Multi-armed Bandits for Performance Marketing

Gigli

Stella

2023

Preprint

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This paper deals with the problem of optimising bids and budgets of a digital advertising portfolio. We improve on the current state of the art by introducing support for multi-ad group marketing campaigns and developing a highly data efficient parametric contextual bandit. The bandit, which exploits domain knowledge to reduce the exploration space, is shown to be effective under the following settings; few clicks and/or small conversion rate, short horizon scenarios, rapidly changing markets and low budget. Furthermore, a Bootstrapped Thompson Sampling algorithm is adapted to fit the parametric bandit. Extensive numerical experiments, performed on synthetic and real world data, show that, on average, the parametric bandit gains more conversions than state of the art bandits. Gains in performance are particularly high when an optimisation algorithm is needed the most, i.e. with tight budget or many ad groups, though gains are present also in the case of a single ad group.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-armed Bandits for Performance Marketing

Gigli

Stella

2023

Preprint

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“…This work provides no guarantees on ROI. More recent works also present pseudo-regret bounds [11] and study subcampaigns interdependencies offline [12]. Thomaidou et al [13] provide a genetic algorithm for budget optimization of advertising campaigns.…”

Section: Related Workmentioning

confidence: 99%

Safe Online Bid Optimization with Return-On-Investment and Budget Constraints subject to Uncertainty

Castiglioni¹,

Nuara²,

Romano³

et al. 2022

Preprint

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In online marketing, the advertisers' goal is usually a tradeoff between achieving high volumes and high profitability. The companies' business units customarily address this tradeoff by maximizing the volumes while guaranteeing a lower bound to the Return On Investment (ROI). Technically speaking, such a task can be naturally modeled as a combinatorial optimization problem subject to ROI and budget constraints to be solved online since the parameter values are uncertain and need to be estimated during the sequential arrival of data. In this picture, the uncertainty over the constraints' parameters plays a crucial role. Indeed, these constraints can be arbitrarily violated during the learning process due to an uncontrolled algorithms' exploration, and such violations represent one of the major obstacles to the adoption of automatic techniques in real-world applications as often considered unacceptable risks by the advertisers. Thus, to make humans trust online learning tools, controlling the algorithms' exploration so as to mitigate the risk and provide safety guarantees during the entire learning process is of paramount importance. In this paper, we study the nature of both the optimization and learning problems. In particular, when focusing on the optimization problem without uncertainty, we show that it is inapproximable within any factor unless P = NP, and we provide a pseudo-polynomial-time

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Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns

Cited by 2 publications

References 24 publications

Multi-armed Bandits for Performance Marketing

Multi-armed Bandits for Performance Marketing

Safe Online Bid Optimization with Return-On-Investment and Budget Constraints subject to Uncertainty

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