Frequency capping in online advertising

We study the following online problem. There are n advertisers. Each advertiser $$a_i$$ a i has a total demand $$d_i$$ d i and a value $$v_i$$ v i for each supply unit. Supply units arrive one by one in an online fashion, and must be allocated to an agent immediately. Each unit is associated with a user, and each advertiser $$a_i$$ a i is willing to accept no more than $$f_i$$ f i units associated with any single user (the value $$f_i$$ f i is called the frequency cap of advertiser $$a_i$$ a i ). The goal is to design an online allocation algorithm maximizing the total value. We first show a deterministic $$3/4$$ 3 / 4 -competitiveness upper bound, which holds even when all frequency caps are $$1$$ 1 , and all advertisers share identical values and demands. A competitive ratio approaching $$1-1/e$$ 1 - 1 / e can be achieved via a reduction to a different model considered by Goel and Mehta (WINE ‘07: Workshop on Internet and Network, Economics: 335-340, 2007). Our main contribution is analyzing two $$3/4$$ 3 / 4 -competitive greedy algorithms for the cases of equal values, and arbitrary valuations with equal integral demand to frequency cap ratios. Finally, we give a primal-dual algorithm which may serve as a good starting point for improving upon the ratio of $$1-1/e$$ 1 - 1 / e

REPP-H: runtime estimation of power and performance on heterogeneous data centers

Modern data centers increasingly demand improved performance with minimal power consumption. Managing the power and performance requirements of the applications is challenging because these data centers, incidentally or intentionally, have to deal with server architecture heterogeneity [19], [22]. One critical challenge that data centers have to face is how to manage system power and performance given the different application behavior across multiple different architectures.This work has been supported by the EU FP7 program (Mont-Blanc 2, ICT-610402), by the Ministerio de Economia (CAP-VII, TIN2015-65316-P), and the Generalitat de Catalunya (MPEXPAR, 2014-SGR-1051). The material herein is based in part upon work supported by the US NSF, grant numbers ACI-1535232 and CNS-1305220.Peer ReviewedPostprint (author's final draft

The Impact of the Transparency Consent Framework on Current Programmatic Advertising Practices

With General Data Protection Regulation (GDPR) introduced, many online advertising practices were affected as data-driven techniques were inhibited by missing user consents. Meanwhile, the IAB Europe introduced the Transparency and Consent Framework to adapt the GDPR requirements into the online advertising ecosystem and provide support in handling consent management for involved actors. In this paper, the impact of the new framework from a programmatic advertising campaign perspective is reflected from a practitioner point of view and implications of missing user consent in five typical techniques which are applied in programmatic campaigns (targeting, retargeting, frequency capping, frequency tracking and cross-device targeting) are addressed and also viewed from an e-commerce perspective. The discussion indicates potential losses in the effectiveness of the applied techniques as well as a potential shift in the market towards walled-garden DSPs such as Google or Facebook. It furthe r provides awareness to raise the potential implications addressed and open future work in this regard

Optimizing the frequency capping: a robust and reliable methodology to define the number of ads to Maximize ROAS

The goal of digital marketing is to connect advertisers with users that are interested in their products. This means serving ads to users, and it could lead to a user receiving hundreds of impressions of the same ad. Consequently, advertisers can define a maximum threshold to the number of impressions a user can receive, referred to as Frequency Cap. However, low frequency caps mean many users are not engaging with the advertiser. By contrast, with high frequency caps, users may receive many ads leading to annoyance and wasting budget. We build a robust and reliable methodology to define the number of ads that should be delivered to different users to maximize the ROAS and reduce the possibility that users get annoyed with the ads" brand. The methodology uses a novel technique to find the optimal frequency capping based on the number of non-clicked impressions rather than the traditional number of received impressions. This methodology is validated using simulations and large-scale datasets obtained from real ad campaigns data. To sum up, our work proves that it is feasible to address the frequency capping optimization as a business problem, and we provide a framework that can be used to configure efficient frequency capping values.The research leading to these results received funding from the European Union’s Horizon 2020 innovation action programme under the grant agreement No 871370 (PIMCITY project); the Ministerio de Economía, Industria y Competitividad, Spain, and the European Social Fund(EU), under the Ramón y Cajal programme (Grant RyC-2015-17732); the Ministerio de Ciencia e Innovación under the project ACHILLES (Grant PID2019-104207RB-I00); the Community of Madrid synergic project EMPATIA-CM (Grant Y2018/TCS-5046); and the Fundación BBVA under the project AERIS

How to increase reach effeciency and effectiveness of MEO's Digital marketing campaings: programmatic buying & targeting techniques

Field lab: Business projectThis paper draws on two new trends in the digital marketing environment: programmatic buying and targeting techniques. These topics arose as the main responses to increase digital marketing campaigns’ efficiency and effectiveness, as requested during a Field Lab carried out at a leading Portuguese telecommunication and media services operator. Once the reader is introduced to the environment in which the project was performed and the reference literature, theoretical recommendations for a strategic implementation of these techniques, and a practical example to increase the targeting efficiency, are provided, passing through the conclusions of four main research techniques that led to these choices