304 research outputs found

    Publicidad Programática: YouTube como ejemplo de la automatización publicitaria

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    Este Trabajo de Fin de Grado se centra en el análisis del ecosistema de la publicidad programática, investigando los agentes involucrados en el proceso de compra-venta de espacios publicitarios en soportes digitales y sus relaciones e interactuaciones. Se ahonda en la transformación de la industria publicitaria con la automatización del sistema. Se estudia cómo los anunciantes pueden segmentar de manera eficiente y precisa a su audiencia objetivo y cómo los editores monetizan sus contenidos. Finalmente, se escoge la plataforma YouTube para explicar gráficamente desde la perspectiva de la demanda cómo sería la inversión publicitaria a través de la herramienta de compra programática de inventario Google Ads.Grado en Publicidad y Relaciones Pública

    Regulating competition in the digital network industry: A proposal for progressive ecosystem regulation

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    The digital sector is a cornerstone of the modern economy, and regulating digital enterprises can be considered the new frontier for regulators and competition authorities. To capture and address the competitive dynamics of digital markets we need to rethink our (competition) laws and regulatory strategies. The thesis develops new approaches to regulating digital markets by viewing them as part of a network industry. By combining insights from our experiences with existing regulation in telecommunications with insights from economics literature and management theory, the thesis concludes by proposing a new regulatory framework called ‘progressive ecosystem regulation’. The thesis is divided in three parts and has three key findings or contributions. The first part explains why digital platforms such as Google’s search engine, Meta’s social media platforms and Amazon’s Marketplace are prone to monopolization. Here, the thesis develops a theory of ‘digital natural monopoly’, which explains why competition in digital platform markets is likely to lead to concentration by its very nature.The second part of the thesis puts forward that competition in digital markets persists, even if there is monopoly in a market. Here, the thesis develops a conceptual framework for competition between digital ecosystems, which consists of group of actors and products. Digital enterprises compete to carve out a part of the digital network industry where they can exert control, and their strong position in a platform market can be used offensively or defensively to steer competition between ecosystems. The thesis then sets out four phases of ecosystem competition, which helps to explain when competition in the digital network industry is healthy and when it is likely to become problematic.The third and final part of the thesis brings together these findings and draws lessons from our experiences of regulating the network industry for telecommunications. Based on the insights developed in the thesis it puts forward a proposal for ‘progressive ecosystem regulation’. The purpose of this regulation is to protect and empower entrants from large digital ecosystems so that they can develop new products and innovate disruptively. This regulatory framework would create three regulatory pools: a heavily regulated, lightly regulated and entrant pool. The layered regulatory framework allows regulators to adjust who receives protection under the regulation and who faces the burdens relatively quickly, so that the regulatory framework reflects the fast pace of innovation and changing nature of digital markets. With this proposal, the thesis challenges and enriches our existing notions on regulation and specifically how we should regulate digital markets

    Regulating competition in the digital network industry: A proposal for progressive ecosystem regulation

    Get PDF
    The digital sector is a cornerstone of the modern economy, and regulating digital enterprises can be considered the new frontier for regulators and competition authorities. To capture and address the competitive dynamics of digital markets we need to rethink our (competition) laws and regulatory strategies. The thesis develops new approaches to regulating digital markets by viewing them as part of a network industry. By combining insights from our experiences with existing regulation in telecommunications with insights from economics literature and management theory, the thesis concludes by proposing a new regulatory framework called ‘progressive ecosystem regulation’. The thesis is divided in three parts and has three key findings or contributions. The first part explains why digital platforms such as Google’s search engine, Meta’s social media platforms and Amazon’s Marketplace are prone to monopolization. Here, the thesis develops a theory of ‘digital natural monopoly’, which explains why competition in digital platform markets is likely to lead to concentration by its very nature.The second part of the thesis puts forward that competition in digital markets persists, even if there is monopoly in a market. Here, the thesis develops a conceptual framework for competition between digital ecosystems, which consists of group of actors and products. Digital enterprises compete to carve out a part of the digital network industry where they can exert control, and their strong position in a platform market can be used offensively or defensively to steer competition between ecosystems. The thesis then sets out four phases of ecosystem competition, which helps to explain when competition in the digital network industry is healthy and when it is likely to become problematic.The third and final part of the thesis brings together these findings and draws lessons from our experiences of regulating the network industry for telecommunications. Based on the insights developed in the thesis it puts forward a proposal for ‘progressive ecosystem regulation’. The purpose of this regulation is to protect and empower entrants from large digital ecosystems so that they can develop new products and innovate disruptively. This regulatory framework would create three regulatory pools: a heavily regulated, lightly regulated and entrant pool. The layered regulatory framework allows regulators to adjust who receives protection under the regulation and who faces the burdens relatively quickly, so that the regulatory framework reflects the fast pace of innovation and changing nature of digital markets. With this proposal, the thesis challenges and enriches our existing notions on regulation and specifically how we should regulate digital markets

    The Inventory is Dark and Full of Misinformation: Understanding the Abuse of Ad Inventory Pooling in the Ad-Tech Supply Chain

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    Ad-tech enables publishers to programmatically sell their ad inventory to millions of demand partners through a complex supply chain. Bogus or low quality publishers can exploit the opaque nature of the ad-tech to deceptively monetize their ad inventory. In this paper, we investigate for the first time how misinformation sites subvert the ad-tech transparency standards and pool their ad inventory with unrelated sites to circumvent brand safety protections. We find that a few major ad exchanges are disproportionately responsible for the dark pools that are exploited by misinformation websites. We further find evidence that dark pooling allows misinformation sites to deceptively sell their ad inventory to reputable brands. We conclude with a discussion of potential countermeasures such as better vetting of ad exchange partners, adoption of new ad-tech transparency standards that enable end-to-end validation of the ad-tech supply chain, as well as widespread deployment of independent audits like ours.Comment: To appear at IEEE Symposium on Security & Privacy (Oakland) 202

    Addax: A fast, private, and accountable ad exchange infrastructure

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    This paper proposes Addax, a fast, verifiable, and private online ad exchange. When a user visits an ad-supported site, Addax runs an auction similar to those of leading exchanges; Addax requests bids, selects the winner, collects payment, and displays the ad to the user. A key distinction is that bids in Addax’s auctions are kept private and the outcome of the auction is publicly verifiable. Addax achieves these properties by adding public verifiability to the affine aggregatable encodings in Prio (NSDI’17) and by building an auction protocol out of them. Our implementation of Addax over WAN with hundreds of bidders can run roughly half the auctions per second as a non-private and non-verifiable exchange, while delivering ads to users in under 600 ms with little additional bandwidth requirements. This efficiency makes Addax the first architecture capable of bringing transparency to this otherwise opaque ecosystem

    Bid Optimization for Offsite Display Ad Campaigns on eCommerce

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    Online retailers often use third-party demand-side-platforms (DSPs) to conduct offsite advertising and reach shoppers across the Internet on behalf of their advertisers. The process involves the retailer participating in instant auctions with real-time bidding for each ad slot of their interest. In this paper, we introduce a bid optimization system that leverages the dimensional bidding function provided by most well-known DSPs for Walmart offsite display ad campaigns. The system starts by automatically searching for the optimal segmentation of the ad requests space based on their characteristics such as geo location, time, ad format, serving website, device type, etc. Then, it assesses the quality of impressions observed from each dimension based on revenue signals driven by the campaign effect. During the campaign, the system iteratively approximates the bid landscape based on the data observed and calculates the bid adjustments for each dimension. Finally, a higher bid adjustment factor is applied to dimensions with potentially higher revenue over ad spend (ROAS), and vice versa. The initial A/B test results of the proposed optimization system has shown its effectiveness of increasing the ROAS and conversion rate while reducing the effective cost per mille for ad serving

    Liquid Welfare guarantees for No-Regret Learning in Sequential Budgeted Auctions

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    We study the liquid welfare in repeated first-price auctions with budget limited buyers. We focus on first-price auctions, which are commonly used in many settings, and consider liquid welfare, a natural and well-studied generalization of social welfare for the case of budget-limited buyers. We use a behavioral model for the buyers, assuming a learning style guarantee: the resulting utility of each buyer is within a γ\gamma factor (where γ1\gamma \ge 1) of the utility achievable by shading her value with the same factor at each iteration. We show a γ+1/2+O(1/γ)\gamma + 1/2 + O(1/\gamma) price of anarchy for liquid welfare assuming buyers have additive valuations. This positive result is in stark contrast to repeated second-price auctions, where even with γ=1\gamma=1, the resulting liquid welfare can be arbitrarily smaller than the maximum liquid welfare. We prove a lower bound of γ\gamma on the liquid welfare loss under the above assumption in first-price auctions, making our bound asymptotically tight. For the case when γ=1\gamma = 1 our theorem implies a price of anarchy upper bound that is about 2.42.4; we show a lower bound of 22 for that case. We also give a learning algorithm that the players can use to achieve the guarantee needed for our liquid welfare result. Our algorithm achieves utility within a γ=O(1)\gamma = O(1) factor of the optimal utility even when a buyer's values and the bids of the other buyers are chosen adversarially, assuming the buyer's budget grows linearly with time. The competitiveness guarantee of the learning algorithm deteriorates somewhat as the budget grows slower than linearly with time. Finally, we extend our liquid welfare results for the case where buyers have submodular valuations over the set of items they win across iterations with a slightly worse price of anarchy bound of γ+1+O(1/γ)\gamma + 1 + O(1/\gamma) compared to the guarantee for the additive case

    5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

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    Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 5th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Martínez Torres, MDR.; Toral Marín, S. (2023). 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2023.2023.1700
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