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Many recent user behavior based click‐through rate models adopt a similar item‐level paradigm: learn the user representation from a list of item representations via a sequence model and/or a pooling mechanism. However, sequence models are usually sensitive to the exact order of the behavior sequence, while item‐level pooling mechanisms simply neglect the chronological information. In this paper, we balance the two approaches by decomposing the long item sequence into a group of extremely short sequences (item pairs) and conducting relational reasoning on them. More specifically, the relational reasoning mechanism consists of two parts, which are designed for capturing various transitional patterns in the behavior sequences. An attentive pooling layer is employed to emphasize those relation‐level signals that are highly related to the target item. Therefore, our approach is essentially a middle ground between the previous two approaches. To verify the effectiveness of our method, we conduct extensive experiments on three public datasets. Experimental results show that our methods achieve better performance than others. Besides, we explore the properties of our model and verify the effectiveness of each component by controlled experiments. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

We analyze the welfare implications of consumer data sharing, and restrictions to that sharing, in the context of online targeted advertising. Targeting technologies offer firms the ability to reach desired audiences through intermediary platforms. The platforms run auctions in real time to display ads on internet sites, leveraging consumers' personal information collected online to personalize the ads. The online advertising industry posits that targeted advertising benefits advertising firms (that is, merchants who want to target ads to the desired consumers), consumers who see ads for preferred products, and the intermediary platforms that match consumers with firms. However, the claims that targeted advertising benefits all players involved have not been fully vetted in the literature. We develop an analytical model to analyze the economic and welfare implications of targeting technologies for those three players under alternative consumer information regimes. The regimes differ in the type and amount of consumer data available to the intermediary and to the advertising firms, and reflect the presence or absence of technological or regulatory restrictions to personal information flows. We find evidence of incentive misalignment among the players, as the intermediary prefers to share only a subset of consumer information with firms, whereas advertising firms prefer having complete information about the consumers. As such, a strategic intermediary with the ability to control which information is shared during the auction can have an incentive to use only the information that maximizes its payoff, overlooking the interests of both advertising firms and consumers. The information regimes that maximize consumer welfare vastly differ depending on consumers' heterogeneity along two dimensions: a horizontal dimension, capturing consumer's heterogeneity in product preferences; and a vertical dimension, capturing consumers' heterogeneity in purchase power. Consumers prefer none of their personal information to be used for targeting only in limited circumstances. Otherwise, consumers are either indifferent or prefer only specific types of information to be used for targeting. [ABSTRACT FROM AUTHOR]

The increasing use of digital media by consumers, companies utilizes digital marketing to outreach their market segments. The purpose of this study is to determine marketing strategies commonly utilized in digital communication and identify the preferred by consumers which influences decision making. Consumers have been identified as a driving force for online shopping. While there have been numerous studies about digital advertising, there has been little academic research focused on type of digital marketing strategies are preferred and influences their consumer’s behavior. A survey of 225 consumes indicated a preference for side panel ads and email ads; they do not like pop – up advertising. If provided a personal benefit like discount or reward they will write an online product review.

On 2 February 2022, the Belgian Data Protection Authority handed down a decision concerning IAB Europe and its Transparency and Consent Framework (TCF), a system designed to facilitate compliance of real-time bidding (RTB), a widespread online advertising approach, with the GDPR. Here, we summarise and analyse this large, complex case. We argue that by characterising IAB Europe as a joint controller with RTB actors, this important decision gives DPAs an agreed-upon blueprint to deal with a structurally difficult enforcement challenge. Furthermore, under the DPA’s simple-looking remedial orders are deep technical and organisational tensions. We analyse these “impossible asks”, concluding that absent a fundamental change to RTB, IAB Europe will be unable to adapt the TCF to bring RTB into compliance with the decision.

The increasing availability of individual-level data has raised the standards for measurability and accountability in digital advertising. Using a massive individual-level data set, our paper captures the effectiveness of display advertising across a wide range of consumer behaviors. Two unique features of our data set that distinguish this paper from prior work are (1) the information on the actual viewability of impressions as on average 55% of the display ads are not rendered viewable and (2) the duration of exposure to the display advertisements, both at the individual-user level. Employing a quasi-experiment enabled by our setting, we use difference-in-differences and corresponding matching methods as well as instrumental variable techniques to control for unobservable and observable confounders. We empirically demonstrate that mere exposure to display advertising increases users’ propensity to search for the brand and the corresponding product; consumers engage both in active search exerting effort to gather information, and in passive search using information sources that arrive exogenously. We also find statistically and economically significant effects of display advertising on increasing consumers’ propensity to make a purchase. Furthermore, our findings reveal that the longer the duration of exposure to display advertising, the more likely the consumers are to engage in direct search behaviors (e.g., direct visits) rather than indirect ones (e.g., search engine inquiries). We also study the effects of various types of display advertising (e.g., prospecting, retargeting, affiliate targeting, video advertising, etc.) and the different goals they achieve. Our framework for evaluating display advertising effectiveness constitutes a stepping stone toward causally addressing the digital attribution problem. [ABSTRACT FROM AUTHOR]

This paper proposes an audience selection framework for online brand advertising based on user activities on social media platforms. It is one of the first studies to our knowledge that develops and analyzes implicit brand–brand networks for online brand advertising. This paper makes several contributions. We first extract and analyze implicit weighted brand–brand networks, representing interactions among users and brands, from a large dataset. We examine network properties and community structures and propose a framework combining text and network analyses to find target audiences. As a part of this framework, we develop a hierarchical community detection algorithm to identify a set of brands that are closely related to a specific brand. This latter brand is referred to as the “focal brand.” We also develop a global ranking algorithm to calculate brand influence and select influential brands from this set of closely related brands. This is then combined with sentiment analysis to identify target users from these selected brands. To process large-scale datasets and networks, we implement several MapReduce-based algorithms. Finally, we design a novel evaluation technique to test the effectiveness of our targeting framework. Experiments conducted with Facebook data show that our framework provides significant performance improvements in identifying target audiences for focal brands. [ABSTRACT FROM AUTHOR]

"Behavioral targeting" is an Internet-based targeting strategy that delivers digital ads to individuals based on their online behavior (e.g., search, shopping). This research explores the unique ways in which consumers respond to ads using this type of targeting (vs. to ads that use more traditional forms of targeting), demonstrating that a behaviorally targeted ad can act as a social label even when it contains no explicit labeling information. Instead, when consumers recognize that the marketer has made an inference about their identity in order to serve them the ad, the ad itself functions as an implied social label. Across four studies, behaviorally targeted ads lead consumers to make adjustments to their self-perceptions to match the implied label; these self-perceptions then impact behavior including purchase intentions for the advertised product and other behaviors related to the implied label. Importantly, these effects only hold when the label is plausibly connected to consumers' prior behavior (i.e., when the targeting is at least moderately accurate). [ABSTRACT FROM AUTHOR]

Eye-tracking studies typically collect enormous amount of data encoding rich information about user behaviours and characteristics on the web. Eye-tracking data has been proved to be useful for usability and accessibility testing and for developing adaptive systems. The main objective of our work is to mine eye-tracking data with machine learning algorithms to automatically detect users' characteristics. In this paper, we focus on exploring different machine learning algorithms to automatically classify whether users are familiar or not with a web page. We present our work with an eye-tracking data of 81 participants on six web pages. Our results show that by using eye-tracking features, we are able to classify whether users are familiar or not with a web page with the best accuracy of approximately 72% for raw data. We also show that with a resampling technique this accuracy can be improved more than 10%. This work paves the way for using eye-tracking data for identifying familiar users that can used for different purposes, for example, it can be used to better locate certain elements on pages such as adverts to meet the users' needs or it can be used to do better profiling of users for usability and accessibility assessment of pages. [ABSTRACT FROM AUTHOR]

As open enrollment public charter school districts have continued to grow in Texas, researchers and policymakers have continued to question how these public charter school districts spend public taxpayer dollars. Although extensive research has addressed how public charter school districts spend money to recruit teachers, complete capital projects, and hire school leaders, no studies have examined how public charter school districts compete with traditional public school districts online to drive traffic and interest in their schools. Using Texas Education Agency and SEMrush data (web metrics, such as organic keywords, organic traffic, and traffic cost) from the 2018–2019 school year, this study compares public charter school district and traditional public school district web metrics to learn more about how charter school districts continue to open and drive enrollment. Results suggest that, when compared to traditional public school districts in Texas, public charter school districts in Texas are less web popular by keyword search (p <.00) and traffic (p <.00), yet these districts spend more on driving Internet traffic to their websites (p <.00) than traditional public school districts. Implications for research, policy, and competition between open enrollment public charter school districts and traditional public school districts are addressed. [ABSTRACT FROM AUTHOR]

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On 2 February 2022, the Belgian Data Protection Authority (DPA) issued a decision concerning IAB Europe and its Transparency and Consent Framework (TCF), a system designed to facilitate compliance of real-time bidding (RTB), a widespread online advertising approach, with the GDPR. In this article, we summarise the context of this decision and analyse the decision itself. We argue that by characterising IAB Europe as a joint controller with RTB actors, the Belgian decision gives DPAs an agreed-upon blueprint to deal with a structurally difficult enforcement challenge. Furthermore, under the DPA's simple-looking remedial orders are deep technical and organisational tensions. We analyse these "impossible asks", concluding that absent a fundamental change to RTB, IAB Europe will be unable to adapt the TCF to bring RTB into compliance with the decision. [ABSTRACT FROM AUTHOR]

In settings where a platform must allocate finite supplies of goods to buyers, balancing overall platform revenues with the fairness of the individual allocations to platform participants is paramount to the well-functioning of the platform. This is made even more difficult by the fact that the supply of goods is in practice stochastic and difficult to forecast, such as in the case of online ad allocation, where the platform manages a supply of impressions that varies over time. In this paper, we design a fair allocation scheme that works in the presence of supply uncertainty. Algorithmically, the scheme repeatedly solves for Fisher market equilibria in a model predictive control fashion and is proved to admit constant factor guarantees versus the offline optimal. In addition, the scheme is tested on a sequence of real ad datasets, showing strong empirical performance. We consider a setting where a platform dynamically allocates a collection of goods that arrive to the platform in an online fashion to budgeted buyers, as exemplified by online advertising systems where platforms decide which impressions to serve to various advertisers. Such dynamic resource allocation problems are challenging for two reasons. (a) The platform must strike a balance between optimizing the advertiser's own revenues and guaranteeing fairness to the advertiser's (repeat) buyers, and (b) the problem is inherently dynamic due to the uncertain, time-varying supply of goods available with the platform. We propose a stochastic approximation scheme akin to a dynamic market equilibrium. Our scheme relies on frequent resolves of an Eisenberg-Gale convex program and does not require the platform to have any knowledge about how the goods' arrival processes evolve over time. The scheme fully extracts buyer budgets (thus maximizing platform revenues) and at the same time provides a 0.64 approximation of the proportionally fair allocation of goods achievable in the offline case, as long as the supply of goods comes from a wide family of (possibly nonstationary) Gaussian processes. We then deal with a multi-objective problem where the platform is concerned with both the proportional fairness and efficiency of the allocation and propose a hybrid algorithm that achieves a 0.3 bicriteria guarantee against fairness and efficiency. Finally, we build a sequence of datasets, one public dataset released by the DSP iPinYou and the second based on real Google AdX data, and use them to test the empirical performance of our schemes. We find that across these datasets there is a surprising relationship between fairness and efficiency that can be used to tune the schemes to nearly optimal performance in practice. [ABSTRACT FROM AUTHOR]

Now a multi-billion-dollar industry, online advertising is what funds free online content. At the core of this industry lies the ability to track users through various technical means, such as cookies, which has sparked privacy concerns, and is thus subject to a growing body of regulation. But the most important rules around tracking seem to come from a handful of large platforms who have assumed the role of a de facto privacy regulator. In this paper we explore in detail Google's decision to phase out support for third-party cookies on Chrome, accompanied by a set of proposals known as the Privacy Sandbox proposals. We query whether this decision raises any antitrust concerns – and if so, how they can be reconciled with the objective of privacy. At a conceptual level, we use this opportunity to reflect on the relationship between competition law and privacy and the trade-offs regulators may have to make. [ABSTRACT FROM AUTHOR]

An experiment was conducted with the goal of determining how mood and arousal influence the way consumers process ad information in a search engine result page (SERP) environment. By focusing on the relationships among mood, arousal, and search ad snippet length, the study shows that mood and arousal states influence information processing (heuristic vs. systematic) on search pages both independently and jointly. Using the heuristic-systematic model, the present research explains how information processing modes triggered by mood and amplified by arousal shape the processing and evaluations of search ads with different snippet lengths. Both theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

How to optimize posted price mechanisms? The sequential posted-price (SPP) mechanism is one of the widely used selling mechanisms in practice. In this mechanism, the seller presents each buyer with a price sequentially and the buyer can either accept or reject the mechanism's offer. Despite the widespread use of the SPP mechanism, the problem of optimizing prices in this mechanism has not been fully addressed. In a paper entitled, "Improved Revenue Bounds for Posted-Price and Second-Price Mechanisms," H. Beyhaghi, N. Golrezaei, R. Paes Leme, M. Pal, and B. Sivan construct SPP mechanisms by considering the best of two simple pricing rules: one that imitates the optimal mechanism and the other that posts a uniform price (same price for every buyer). Their simple pricing rules can be easily generalized to the setting with multiple units and yield the first improvement over long-established approximation factors. We study revenue maximization through sequential posted-price (SPP) mechanisms in single-dimensional settings with n buyers and independent but not necessarily identical value distributions. We construct the SPP mechanisms by considering the best of two simple pricing rules: one that imitates the revenue optimal mechanism, namely, the Myersonian mechanism, via the taxation principle and the other that posts a uniform price. Our pricing rules are rather generalizable and yield the first improvement over long established approximation factors in several settings. We design factor-revealing mathematical programs that crisply capture the approximation factor of our SPP mechanism. In the single-unit setting, our SPP mechanism yields a better approximation factor than the state of the art prior to our work. In the multiunit setting, our SPP mechanism yields the first improved approximation factor over the state of the art after over nine years. Our results on SPP mechanisms immediately imply improved performance guarantees for the equivalent free-order prophet inequality problem. In the position auction setting, our SPP mechanism yields the first higher-than (1 − 1 / e) approximation factor. In eager second-price auctions, our two simple pricing rules lead to the first improved approximation factor that is strictly greater than what is obtained by the SPP mechanism in the single-unit setting. [ABSTRACT FROM AUTHOR]

Since GDPR came into enforcement in 2018, various firms have been found violating or circumventing the ePrivacy Directive known as the Cookie Law which lays out the cookie consent guidelines for websites. To improve the GDPR compliance rate, several conversations are going on between EU commission, Data protection agencies, business & websites owners and ad vendors regarding their cookie policy, obtaining user consent for data collection and its usage. One of the key stakeholders who are the website users, whose privacy is in question seems to be left out from the discussions. The study aimed to understand user perception towards website cookie banners, which are mandatory under GDPR, and the influence of factors like awareness of cookies, user experience, consent banner design, privacy risk, brand trust on user’s willingness for accepting all cookies, to develop recommendations to improve customer’s motivations to give consent. Using a quantitative approach, the primary data was collected from 132 internet users residing in the EU region through an online survey questionnaire shared in social media networks. The results showed that the (i) majority of respondents had more than moderate level of awareness about cookies (ii) they are more likely to accept cookies for quick access or task completion, (iii) acceptance of cookies was varied across different categories of online activity and (iv) given a choice they are more likely to opt-out of 3rd party cookies which are widely used for targeted advertising. Since 3rd party cookies will be phased out in the near future and are likely to be replaced with more advanced customer tracking technologies which are harder to opt-out of, this study proposes a framework for Consent for Advertising Directive (CAD) to go beyond the existing Cookie Law, which will improve user data protection regardless of the tracking technology used, and help brands to improve transparency about their data collection and avoid GDPR violations.

While real-time bidding brings a huge profit for online businesses, it also becomes a potential target for malicious purposes. In real-time bidding, the bid request traffic could be classified into two kinds: intentional and non-intentional. Intentional bid requests come from ordinal web users while non-intentional bid requests come from abnormal web users. From the perspective of a demand-side platform (DSP), the budget of advertisers should be used as effectively as possible by limiting non-intentional traffic. Therefore, it is essential to classify and predict these two kinds of bid request traffic. In this research, we propose a real-time filtering bid requests (RFBR) model to predict whether an incoming bid request is intentional or non-intentional from the DSP’s viewpoint. Our model is built on three stages. In the first stage, we analyzed all potential attributes in the bid request scheme and figured out the relations between abnormal behaviors and their attributes; in the second stage, a classification model was built to classify normal and abnormal audiences by the extracted features and self-defined thresholds; in the third stage, a RFBR model was built to classify intentional and non-intentional bid requests. The experimental result shows that our system can effectively classify incoming bid requests.

We present a formal study of first-look and preferred deals that are a recently introduced generation of contracts for selling online advertisements, which generalize traditional reservation contracts and are suitable for advertisers with advanced targeting capabilities. Under these deals, one or more advertisers gain priority access to an inventory of impressions before others and can choose to purchase in real time. In particular, we propose constant-factor approximation algorithms for maximizing the revenue that can be obtained from these deals when offered to all or a subset of the advertisers, whose valuation distributions can be independent or correlated through a common value component. We evaluate our algorithms using data from Google's ad exchange platform and show they perform better than the approximation guarantees and obtain significantly higher revenue than auctions; in certain cases, the observed revenue is 85%–96% of the optimal revenue achievable. We also prove the NP-hardness of designing deals when advertisers' valuations are arbitrarily correlated and the optimality of menus of deals among a certain class of selling mechanisms in an incomplete distributional information setting. [ABSTRACT FROM AUTHOR]

We present an economic model of competition in the ad tech "stack". We use this model to understand how the complex web of conducts implemented by Google – and under scrutiny by both U.S. and European antitrust enforcers – can be rationalized as a profit-maximizing, exclusionary strategy. We show how vertical integration, linking of services across the stack and the use of complex auction models increases incentive and ability to foreclose rivals and why there might be incentives to limit data and ID sharing so as to undermine rival intermediaries. We show how these conducts can have negative effects on consumer welfare via an increase in the prices paid by online advertisers and a decline in incentives to invest in producing content. We discuss also the level of "take rate" charged by different intermediaries and explain how differences in the ability to price discriminate undermines the relevance of such comparisons. [ABSTRACT FROM AUTHOR]

Generation Z spends much time on mobile Internet and less time accessing Internet services using desktop computers. These traits make them an important niche that can be targeted by retailers. The objective of the present study is to analyze the impact of online advertising, social influence, and usage motivation on the behavioral intention related to m-commerce use among Romanian young adults. The research methodology is based on applying partial least square equation modelling (PLS-SEM), using data collected through a questionnaire. The proposed structural model includes four constructs that are first order reflective. The results reveal that among the influencing factors that determine Generation Z to use m-commerce, the usage motivation factor is the most important one, followed by social influence and online advertising. The research is useful for marketing professionals and retailers in establishing marketing strategies in accordance with the factors that influence the buying decision of young adults, representing Generation Z. [ABSTRACT FROM AUTHOR]

Online advertising is a significant instrument of companies' rivalry, competing for buyers and their attention, shaping brand preferences and affecting buying decisions. A rapid development of information and communication technologies provides firms with a wide range of advertising tools in a digital World. They enable advertising to perform its informative, persuasive as well as competitive functions. The paper aims to identify and empirically evaluate the significance of online advertising in developing corporate competitive strategies in the world of online advertising. A quantitative research study was conducted in January 2020 on a sample of 103 Polish enterprises. The analysis was based on random-quota sampling among the population of all Polish companies. The results of the analysis indicate that companies make a conscious use of online advertising in sectoral market competition; they monitor and analyse competitors' advertising activities, creating the content that has a major impact on consumer behaviour. The intensity of sectoral competition encourages companies to take advantage of their information superiority over consumers and creates a moral hazard in advertising messages and the process of building competitive advantage. [ABSTRACT FROM AUTHOR]

Many of the current online businesses base completely their revenue models in earnings from online advertisement. A problematic fact is that according to recent studies more than half of display ads are not being detected as viewable. The International Advertising Bureau (IAB) has defined a viewable impression as an impression that at least 50% of its pixels are rendered in the viewport during at least one continuous second. Although there is agreement on this definition for measuring viewable impressions in the industry, there is no systematic methodologies on how it should be implemented or the trustworthiness of these methods. In fact, the Media Rating Council (MRC) announced that there are inconsistencies across multiple reports attempting to measure this metric. In order to understand the magnitude of the problem, we conduct an analysis of different methods to track viewable impressions. Then, we test a subset of geometric and strong interaction methods in a webpage registered in the worldwide ad-network ExoClick, which currently serves over 7 billion geo-targeted ads a day to a global network of 65000 web/mobile publisher platforms. We find that the Intersection Observer API is the method that detects more viewable impressions given its robustness towards the technological constraints that face the rest of implementations available. The motivation of this work is to better understand the limitations and advantages of such methods, which can have an impact at a standardisation level in online advertising industry, as well as to provide guidelines for future research based on the lessons learned.

In the online advertising market that has increasingly emerged with the Internet's rapid development, supply side platforms (SSP) use multichannel auctions to sell demand side platform (DSP) advertisements. To explore which channel is dominant, we analyze three channels including direct bidding, bidding with costly information and proxy bidding. We develop an analytical model for studying the SSP's channel decisions and expected profit that will be generated in the context of such multichannel auctions. First, we construct the SSP profit models for these different channels. Then, comparing the profits, we analyze the formulation of strategies for these different situations. Importantly, for the three channels, we study how the probability that bidders will attach a higher value to advertisements influences the multichannel strategy of the SSP. The results show the following: (1) each channel has a market share; (2) it is better for SSPs to use the costly bidding channel if the probability that the bidders are higher value bidders is low or very high; and (3) the SSPs should consider the use of proxy bidding when the reserve price is high and the probability of having higher value bidders is not high.

The bidding strategy plays the most important role to help the Demand Side Platforms (DSPs) making bidding decisions on a large number of bid requests in Real Time Bidding (RTB) to satisfy the different objectives of campaigns under the lifetime and budget constraints. In this paper, we focus on branding campaign whose objective is to obtain as many impressions as possible under the lifetime and budget constraints. To achieve the objectives of branding campaigns, we propose a novel expected win rate-based bidding strategy for branding campaign under the lifetime and budget constraints by utilizing a model-free reinforcement learning model. Specifically, to prevent missing good opportunities resulting from submitting extremely low bid prices, the concept of the base winning price is introduced to determine the lower bound of expected winning price. In addition, to obtain more impressions, the concept of the DSP-specified budget spending plan is proposed to determine the proper winning prices. The base expected win rate is then calculated based on the base winning price and the winning price determined by the DSP-specified budget spending plan. Since RTB is a dynamic environment, we propose a novel expected win rate-based bidding strategy named EWDQN which utilizes Deep Q Network (DQN) to dynamically determine the expected win rate according to the base expected win rate and the current status of the RTB market, and then determines the bid price according to the expected win rate. To the best of our knowledge, this is the first research applying the reinforcement learning technique on the bidding strategies for branding campaign. To measure the performance of EWDQN, several experiments are conducted on two real datasets. Experimental results show that EWDQN outperforms the-state-of-the-art bidding strategies for branding campaign in terms of the number of obtained impressions and CPM (cost per thousand impressions).

In the case of advertising-based revenue model, a company creates a digital media product, attracts interaction and engagement of users and then sells access to this audience to advertisers. This revenue model allows users to receive useful content for free or at a low price, while publishers accelerate the growth and adoption of their digital product. Although overreliance on advertising may result in failure to develop the core product, the advertising-based revenue model has become one of the most popular sources of revenue for many web products. Existing empirical studies on digital media revenue models are conducted from singular viewpoints, focusing on new types of revenue models and lacking the comprehensive research of possibilities of advertising-based revenue model in the modern digital media market. In this study, the focus is on the real case of creating the content website and making it profitable in six months using the pure advertising-based revenue model. It is empirically proven that, in order to stay competitive, a new digital media product should aim at decreasing website costs, increasing revenue and scaling up the product to reach a wider audience. Minimum marketing costs can be achieved through working on the relevance and quality of advertising, reasonable targeting and landing page experience. As for increasing advertising revenue, the main point is constantly experimenting with advertising format optimization, adding new advertising networks and enhancing landing page quality. It is empirically proven that the pure advertising-based revenue model can be profitable for a new digital media product entering a highly competitive market. However, high gross margins can be achieved only by attracting quality target audience, achieving economies of scale and diversifying the revenue model.

In marketing and advertising research, 'zapping' is defined as the action when a viewer stops watching a commercial. Researchers analyze users' behavior in order to prevent zapping which helps advertisers to design effective commercials. Since emotions can be used to engage consumers, in this paper, we leverage automated facial expression analysis to understand consumers' zapping behavior. Firstly, we provide an accurate moment-to-moment smile detection algorithm. Secondly, we formulate a binary classification problem (zapping/non-zapping) based on real-world scenarios, and adopt smile response as the feature to predict zapping. Thirdly, to cope with the lack of a metric in advertising evaluation, we propose a new metric called Zapping Index (ZI). ZI is a moment-to-moment measurement of a user's zapping probability. It gauges not only the reaction of a user, but also the preference of a user to commercials. Finally, extensive experiments are performed to provide insights and we make recommendations that will be useful to both advertisers and advertisement publishers.

This study examines the international activist movement known as Sleeping Giants, a social-media “campaign to make bigotry and sexism less profitable” (Sleeping Giants, n.d.). The campaign originated in the US with an anonymous Twitter account that enlisted followers in encouraging brands to pull their online advertising from Breitbart News. The campaign achieved dramatic success and rapidly spread to regions outside the US, with other anonymously run and loosely allied chapters emerging in 15 different nations (as well as a regional chapter for the EU). Many of these were initially created to take on Breitbart advertisers in their home countries, but in a number of cases they subsequently turned their attention to disrupting financial support for other far-right news media in—or impacting—their home countries. Based on interviews with leaders of eight Sleeping Giants chapters, as well as the related UK-based Stop Funding Hate campaign, this study examines the Sleeping Giants campaign with respect to its continuity with media activism of previous eras, while also seeking to understand its potential as one of the first high-profile activist campaigns to grapple with the impacts of programmatic advertising on the news ecosystem. In particular, we consider how the campaign’s interventions speak to the larger debate around the normative relationship between advertising and the performance of the news ecosystem.

The major technological innovations have led to profound changes in each sector, but also in the behavior of consumers. Thus, the evolution of the Internet over the last two decades has contributed to the blurring of the boundaries between domestic and international affairs, generating the increasing of competition and thus the need to create a sustainable competitive advantage. At the same time, the increase of the number of Internet users has contributed to the development of social media platforms, which offer the possibility to personalize the online presence and to actively participate in the online communication. In this context, the social media platforms have created the premises for developing databases, with which companies can more easily understand and evaluate the way of thinking and the preferences of consumers when they make online purchases. Based on these considerations, the present paper analyzes the results of a qualitative marketing research, in close correlation with the statistical data on the degree of using social media platforms at global, European and national level. The research was based on the method of the semi-structured in-depth interview and focused mainly on identifying the advantages of using the means/tools of online advertising, as well as on assessing their effectiveness. The results of the marketing research showed an increasing trend in the number of social media users, as well as a diversification of the categories of audience targeted by online advertising. At the same time, the managers interviewed expressed their availability to invest more in online advertising, which significantly contributes to attracting new customers and implicitly to increasing their sales.

Promoting products via Instagram is a rapidly growing phenomenon in the world of online advertising. These advertisements’ ability to stand out and obtain desired consumer outcomes has become increasingly challenging due to the enormous amount of advertisements already present on this platform. One strategy that can be useful is celebrity endorsement, which has – because of celebrities’ credibility and attractive characteristics – proven to be an effective way to create favourable attitudes and stronger purchase intentions. The present study investigated the effects of an Instagram advertisement with an (in)genuine, smiling celebrity on consumers’ behaviour. More specifically, this study examined differences between consumers from two countries: the Netherlands and Spain. An online experiment with two conditions was conducted (i.e. ingenuine vs genuine, smiling celebrity). The sample included 174 Dutch and 137 Spanish participants. The results reveal that the type of smile significantly affects consumers’ purchase intentions. Attitudes towards the celebrity also influence the effect of the type of smile on purchase intention. National background was thus found to play a moderating role.

This study investigates the discourse regarding the use of ad blockers. Three themes emerged regarding this technology based on in-depth interviews with consumers who had activated ad blockers: 1) utonomy and control over advertising influence; 2) Internet interactions: asymmetry, paradoxes, and the quest for equity; and 3) Efficiency and convenience. Based on the results, this paper discusses anti-consumption positioning as the study of the “reasons against” consumption. The paper proposes complementary approaches to anti-consumption research, less based on intentional and consumer-conscious aspects and power as an entity disputed by actors and less

Over the last 2 decades, the Internet has transformed our lives, becoming the most frequently used means of communication. Unlike traditional media, the Internet has also become a very important advertising tool thanks to its versatility, interactivity and ability to target relevant consumers. This article analyses consumers’ perception of online advertising, based on by research carried out on a sample of 50 participants. This paper will outline trends in the advertising environment in Romania.

From five-star hotels and Michelin Star restaurants, few industries signal their quality and unique selling points through the use of third-party seals like tourism. However, despite using seals and certifications in advertising being widespread, little academic research has been conducted into their effectiveness. Through the running of campaigns on Facebook’s Ad Manager for Indian airline Jet Airways, this study applies the Third-Party Seal Model to optimise campaign audiences to target the right prospects with the most effective message. Findings and a practical framework for optimal campaign delivery for the airline industry are presented.

This article highlights the platformization of the advertising market as influenced by search engines and social networks. It analyzes how the media try to adjust by mixing data management and brand safety, highlighting coopetition strategies between platforms and media industries. The article examines the strategy of the TF1 group’s advertising department, the first “media” advertising department in France, which is based upon using the advantages of Internet advertising to better promote the television advertising offer. It makes TV spot advertising on large audience channels a key element of any communication campaign, something that the advertising departments of Internet companies cannot offer. But this race to critical size, on both the Internet and television, raises questions concerning the future of small audience media who depend on advertising revenues.

With heightened concerns regarding user privacy, there is a recent movement for empowering consumers with the ability to control how their private data are collected, stored, used and shared. Notably, between 2018 and 2020, the General Data Protection Regulation (GDPR) has been implemented in the European Union (EU), and the California Consumer Privacy Act (CCPA) and the California Privacy Rights Act (CPRA) have been implemented/passed in the state of California in the United States. These regulations address both consumer data security and consumer privacy rights. In this monograph, we provide an overview of some of the key issues that are in play in consumer privacy and in empowering consumers with rights to manage the privacy of their data, viewed primarily in the context of online advertising-related actions of firms. The recent academic work on these topics already provides some important takeaways. Empirical studies, broadly speaking, show that fewer consumers share data with firms post-regulation and this leads to worse personalized marketing, i.e., the firms are at a handicap. Theoretically, a primary insight is that privacy regulations on using data affect the advertising/targeting layer directly and the product/pricing layer directly and/or indirectly; broadly speaking, consumers make data sharing choices by balancing the intrinsic and instrumental values of sharing data, and privacy regulations can generally be expected to benefit consumers at the expense of firms. We also discuss how consumers' understanding of firms' privacy policies and their impact can be enhanced, which is important for regulations to have their intended impact. We briefly discuss the development of privacy-preserving mechanisms for targeted advertising, industry interest in and adoption of which has been recently enhanced due to new regulations. We conclude with a discussion and lay out some directions for future research. [ABSTRACT FROM AUTHOR]

Search advertising, a popular method for online marketing, has been employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and experimentation, which may not be available to health authorities wishing to elicit such changes, especially when dealing with public health crises such as epidemic outbreaks. Here, we develop a framework, comprising two neural network models, that automatically generates ads. The framework first employs a generator model, which creates ads from web pages. These ads are then processed by a translation model, which transcribes ads to improve performance. We trained the networks using 114K health-related ads shown on Microsoft Advertising. We measure ad performance using the click-through rates (CTR). Our experiments show that the generated advertisements received approximately the same CTR as human-authored ads. The marginal contribution of the generator model was, on average, 28% lower than that of human-authored ads, while the translator model received, on average, 32% more clicks than human-authored ads. Our analysis shows that, when compared to human-authored ads, both the translator model and the combined generator + translator framework produce ads reflecting higher values of psychological attributes associated with a user action, including higher valence and arousal, and more calls to action. In contrast, levels of these attributes in ads produced by the generator model alone are similar to those of human-authored ads. Our results demonstrate the ability to automatically generate useful advertisements for the health domain. We believe that our work offers health authorities an improved ability to build effective public health advertising campaigns. [ABSTRACT FROM AUTHOR]

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This paper argues that while the GDPR has arguably delivered positive outcomes by enhancing the protection afforded to data subjects, it has also had adverse effects on competition by strengthening the position of large online platforms in certain markets. In addition, the GDPR has given large platforms a tool to harm rivals by restricting access to the data they need to compete effectively. The present paper focuses on digital advertising and the ad tech industry, where the GDPR appears to have strengthened Google and Facebook. The purpose of this paper is not to call for the weakening of the GDPR, whose positive impact on users cannot be ignored. While from a policy standpoint regulators should thus maintain or even increase the level of protection offered by this legislation, it is vital that they take steps to mitigate its adverse effects on other dimensions of welfare, such as competition. [ABSTRACT FROM AUTHOR]

Managing Advertisers' Budgets in Online Advertising Platforms Most online advertising platforms sell ad placements using repeated auctions. Platforms often provide budget-management strategies that allow advertisers to control their cumulative expenditures: Advertisers declare the maximum daily amount they are willing to pay, and the platform adjusts allocations and payments to guarantee that cumulative expenditures do not exceed budgets. Many strategies can be used to accommodate budget constraints, and each one, when applied to all budget-constrained advertisers simultaneously, drives the system toward a different equilibrium. For example, platforms can modify the advertisers' bids or the auctions' allocation and payment rules. In "Budget-Management Strategies in Repeated Auctions," Balseiro, Kim, Mahdian, and Mirrokni compare the equilibrium outcome of a range of budget-management strategies used in practice. They study the impact of budget-management strategies on the trade-off between the seller's profit and buyers' utility and shed light on the incentive properties of budget-management strategies. In online advertising, advertisers purchase ad placements by participating in a long sequence of repeated auctions. One of the most important features that advertising platforms often provide and advertisers often use is budget management, which allows advertisers to control their cumulative expenditures. Advertisers typically declare the maximum daily amount they are willing to pay, and the platform adjusts allocations and payments to guarantee that cumulative expenditures do not exceed budgets. There are multiple ways to achieve this goal, and each one, when applied to all budget-constrained advertisers simultaneously, drives the system toward a different equilibrium. Our goal is to compare the "system equilibria" of a range of budget-management strategies. In particular, we consider six different budget-management strategies, including probabilistic throttling, thresholding, bid shading, reserve pricing, and two versions of multiplicative boosting. We show that these methods admit a system equilibrium, study their incentive properties, prove dominance relations among them in a simplified setting, and confirm our theoretical findings using real ad auction data from a sponsored search. Our study sheds light on the impact of budget-management strategies on the trade-off between the seller's profit and buyers' utility and may be of practical relevance for advertising platforms. [ABSTRACT FROM AUTHOR]

Pay-for-performance (P4P) pricing schemes such as pay per click and pay per action have increased in popularity in Internet advertising. Meanwhile, pay-per-impression (PPI) schemes persist, and several publishers have begun to offer a hybrid mix of PPI and P4P schemes. Given the proliferation of pricing schemes, this study examines the optimal choices for publishers. The authors highlight two-sided information asymmetries in online advertising markets and the consequent trade-offs faced by a high-quality publisher using P4P schemes. Pay-for-performance schemes enable a high-quality publisher to reveal its superior quality; however, such schemes may incur allocative inefficiencies stemming from inaccurate estimates of advertiser qualities. The authors identify conditions under which a publisher may opt for a PPI, P4P, or hybrid scheme and, in doing so, provide theoretical explanations for the observed variations in the pricing schemes and the increasing popularity of hybrid schemes. Using a new "uncompromised" equilibrium refinement, the authors find that the hybrid scheme can emerge as an equilibrium choice in a variety of conditions. In addition, they provide prescriptive guidelines for firms choosing between different pricing schemes. [ABSTRACT FROM AUTHOR]

This article investigates how Internet users' perceptions of control over their personal information affect how likely they are to click on online advertising on a social networking website. The analysis uses data from a randomized field experiment that examined the effectiveness of personalizing ad text with user-posted personal information relative to generic text. The website gave users more control over their personally identifiable information in the middle of the field test. However, the website did not change how advertisers used data to target and personalize ads. Before the policy change, personalized ads did not perform particularly well. However, after this enhancement of perceived control over privacy, users were nearly twice as likely to click on personalized ads. Ads that targeted but did not use personalized text remained unchanged in effectiveness. The increase in effectiveness was larger for ads that used more unique private information to personalize their message and for target groups that were more likely to use opt-out privacy settings. [ABSTRACT FROM AUTHOR]

Product sales via sponsored keyword advertising on search engines rely on an effective selection of keywords that describe the offerings. In this study, we consider both the direct sales of the advertised products and indirect sales (i.e., cross-selling) of other products, and examine how specific keywords and general keywords influence these two types of sales differently. We also examine how the cross-selling effects may vary across different types of products (main products and accessories). Our results suggest that the use of specific keywords leans toward improving the direct sales of advertised products, while the use of general keywords leans toward improving the indirect sales of other products. The contribution of keywords to indirect sales is influenced by product type. For main products, the use of specific keywords generates a higher marginal contribution to indirect sales than that of general keywords. For accessory products, the use of general keywords generates a higher marginal contribution to indirect sales than that of specific keywords. The key implication of this study is that sellers focusing on different types of sales (direct or indirect sales) or products (main or accessory products) should consider using different types of keywords in search engine advertising to drive sales. [ABSTRACT FROM AUTHOR]