Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising

There are two major ways of selling impressions in display advertising. They are either sold in spot through auction mechanisms or in advance via guaranteed contracts. The former has achieved a significant automation via real-time bidding (RTB); however, the latter is still mainly done over the counter through direct sales. This paper proposes a mathematical model that allocates and prices the future impressions between real-time auctions and guaranteed contracts. Under conventional economic assumptions, our model shows that the two ways can be seamless combined programmatically and the publisher's revenue can be maximized via price discrimination and optimal allocation. We consider advertisers are risk-averse, and they would be willing to purchase guaranteed impressions if the total costs are less than their private values. We also consider that an advertiser's purchase behavior can be affected by both the guaranteed price and the time interval between the purchase time and the impression delivery date. Our solution suggests an optimal percentage of future impressions to sell in advance and provides an explicit formula to calculate at what prices to sell. We find that the optimal guaranteed prices are dynamic and are non-decreasing over time. We evaluate our method with RTB datasets and find that the model adopts different strategies in allocation and pricing according to the level of competition. From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB. In a highly competitive market, advertisers are more willing to purchase the guaranteed contracts and thus higher prices are expected. The revenue gain is largely contributed by the guaranteed selling.Comment: Chen, Bowei and Yuan, Shuai and Wang, Jun (2014) A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising. In: The Eighth International Workshop on Data Mining for Online Advertising, 24 - 27 August 2014, New York Cit

Real-time Bidding for Online Advertising: Measurement and Analysis

The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to automatically buy and sell ads in real-time; It uses per impression context and targets the ads to specific people based on data about them, and hence dramatically increases the effectiveness of display advertising. In this paper, we provide an empirical analysis and measurement of a production ad exchange. Using the data sampled from both demand and supply side, we aim to provide first-hand insights into the emerging new impression selling infrastructure and its bidding behaviours, and help identifying research and design issues in such systems. From our study, we observed that periodic patterns occur in various statistics including impressions, clicks, bids, and conversion rates (both post-view and post-click), which suggest time-dependent models would be appropriate for capturing the repeated patterns in RTB. We also found that despite the claimed second price auction, the first price payment in fact is accounted for 55.4% of total cost due to the arrangement of the soft floor price. As such, we argue that the setting of soft floor price in the current RTB systems puts advertisers in a less favourable position. Furthermore, our analysis on the conversation rates shows that the current bidding strategy is far less optimal, indicating the significant needs for optimisation algorithms incorporating the facts such as the temporal behaviours, the frequency and recency of the ad displays, which have not been well considered in the past.Comment: Accepted by ADKDD '13 worksho

Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

모바일 매체의 수익 최적화를 위한 디스플레이 광고 요소 및 워터폴 입찰 전략 평가

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2020. 8. 윤명환.Advertising revenue has become an important revenue source for mobile publishers, along with in-app purchase. Based on empirical data and academic methodology, this study attempted to solve two key problems that mobile publishers face when trying to maximize advertising revenue. This study analyzed transaction history data of mobile advertising from AD(x) Inc., a company that provides services to optimize ad revenue for mobile publishers by operating multiple ad networks simultaneously, including Google AdMob and Facebook Audience Network. The first problem mobile publishers face when trying to gain revenue through advertising is determining the optimal ad position and ad format for the service UX of mobile publishers. To provide guidelines for the first decision, this study analyzed characteristics of mobile advertising, including native ads and rewarded video ads, which have been relatively recently introduced. As a result, in addition to various ad factors defined by previous research through traditional advertising media, three new ad factors were summarized: ad density, disclosure position, and disclosure method. Moreover, the relationships among the three new derived ad factors, ad revenue, and ad effectiveness were analyzed. First, in relation to ad density, which is the proportion of an advertisements physical area relative to the full-screen area, the higher the ad density, the higher both the ad revenue and advertising effectiveness. On the other hand, among advertisements with similar ad density, there was a difference in ad revenue and advertising effectiveness according to ad format. Among advertisements with low ad density, native banner ads showed higher ad revenue and advertising effectiveness than banner ads. Among advertisements with high ad density, rewarded video ads showed the highest ad revenue, and interstitial ads showed the highest advertising effectiveness. As for the second new ad factor, disclosure position, the effectiveness of advertisements displayed at the top of the screen was higher in the PC web environment, but advertisements displayed at the bottom of the screen in the mobile environment were higher in terms of ad revenue and advertising effectiveness. Lastly, in the analysis of the third new ad factor, disclosure method, advertisements with the same ad format as native ads were classified in three categories, based on their development by mobile publishers: Separated area, List UI, and Pop-up. This study analyzed the relationship between disclosure method, ad revenue, and advertising effectiveness. The results showed that the highest ad revenue and advertising effectiveness were found in the Pop-up disclosure method. The second problem that mobile publishers face after determining ad position and ad format is the optimization of waterfall settings such as the priority and reserve prices of each ad network to maximize ad revenue when mobile advertising is served from multiple ad networks. On the other hand, between ad networks and mobile publishers, there is information asymmetry. Hence, ad networks have more information, so this study proposed a reserve price strategy for the operation of waterfall bidding among multiple ad networks to maximize ad revenue, even under information asymmetry. First, a demand curve-based model was designed to explain the loss of ad revenue when a mobile publisher sells its ad inventory at a non-optimized price using waterfall bidding. In addition, sensitivity analysis was conducted to show that the proposed model performs better than the companys existing bidding strategy. Moreover, this model enabled mobile publishers to have better performance with independent correlation, not a positive correlation of ad networks bid prices. Therefore, mobile publishers can use the key finding that the proposed model is more effective in reducing expected advertising losses under information asymmetry. In addition, it was found that performance improved to a greater extent when ad networks have less bid price similarity. This study provides guidelines that can be utilized not only in an academic sense but also in a real business environment. Standardized knowledge for small- and medium-sized mobile publishers, in particular, which have a relatively high ad network dependency, is suggested to improve their understanding of ad network usage and to establish optimized advertising operation policies.광고 수익은 모바일 매체에게 있어서, 인앱 판매 (in-app purchase) 와 함께 중요한 수익원 중 하나가 되었다. 본 연구에서는 모바일 매체가 광고 수익을 최대화하고자 할 때 마주하게 되는 두 가지 핵심 과제를 실증적인 데이터와 학술적인 방법론을 통해 해결하고자 하였다. 본 연구는, Google AdMob, Facebook Audience Network 를 포함하는 다수의 광고 네트워크를 동시에 운영하여 모바일 매체의 광고 수익을 최적화하는 서비스를 제공하고 있는 기업, 주식회사 애드엑스의 2019년 광고 결과 통계 데이터에서 추출하여 분석과 평가를 진행하였다. 모바일 매체가 광고를 통해 수익을 얻고자 할 때 가장 처음으로 마주하는 과제는, 모바일 매체의 서비스 UX에 최적화된 광고 위치와 광고 포맷을 결정하는 것이다. 이 결정에 가이드라인을 제공하기 위해, 상대적으로 최근 도입된 네이티브 광고, 리워드 비디오 광고를 포함한 모바일 광고가 가지는 특징을 분석하였다. 그 결과, 전통적인 광고 매체에 노출되는 광고를 통해 정의된 다양한 광고 요소 외에, 세 가지 신규 광고 요소; 광고 밀도, 노출 위치, 노출 방법을 정리하였으며, 도출된 신규 광고 요소와 광고 수익, 광고 효과 간의 관계를 분석하였다. 먼저, 서비스 화면 내에 광고가 차지하는 비율인 광고 밀도와 관련하여, 광고 밀도가 높을수록 광고 수익과 광고 효과, 모두 높은 결과를 얻었다. 한편, 유사한 광고 밀도를 가진 광고 간에도 광고 포맷에 따라 광고 수익, 광고 효과가 차이를 보였다. 낮은 광고 밀도를 가진 광고 중에서는 네이티브 배너 광고가 배너 광고 보다 더 높은 광고 수익과 광고 효과를 보였으며, 높은 광고 밀도를 가진 광고 중에서는 리워드 비디오 광고가 가장 높은 광고 수익을 나타냈고, 전면 광고가 가장 높은 광고 효과를 보였다. 두번째 신규 광고 요소인 노출 위치와 관련하여, 기존 PC 또는 웹 환경에서는 화면 상단에 노출된 광고의 광고 효과가 가장 높았으나, 모바일 환경에서는 화면 아래에 노출된 광고가 광고 수익, 광고 효과, 모두 더 높게 나타났다. 마지막으로, 노출 방법 와 관련한 분석에서는, 동일한 네이티브 광고 포맷이지만, 모바일 매체에 의해 개발된 노출 방법에 따라, 분리된 영역, 리스트 UI, Pop-up 로 구분하였고, 다양한 노출 방법에 따른 광고 수익, 광고 효과를 비교 분석해보았다. 그 결과 Pop-up 형태의 노출 방법에서 가장 높은 광고 수익과 광고 효과가 나타났다. 모바일 매체가 광고 위치와 광고 포맷을 결정한 뒤에 직면하는 두번째 핵심 과제는, 다수의 광고 네트워크로부터 광고를 제공받아 노출할 때, 광고 수익이 최대화 될 수 있도록 각 광고 네트워크의 우선순위, 예약 가격 (reserve price) 등 워터폴 세팅을 최적화 하는 것이다. 한편, 광고 네트워크와 모바일 매체 사이에는 광고 네트워크가 더 많은 정보를 가지고 있는 정보 비대칭이 존재하는데, 본 연구는 이런 정보 비대칭 하에서 광고 수익을 최대화 위하여, 최저 가격 (reserve price) 전략을 통한 워터폴 세팅 방법을 제안하였다. 먼저, 모바일 매체의 광고 판매 가격이 최적화 되어 있는지를 설명하기 위하여 수요 곡선 기반 모델을 설계하였다. 그리고, 민감도 분석을 통해 제안된 모델이 기존 운영 전략보다 우수함을 비교해 보였다. 또한, 제안된 모델을 통해, 광고 네트워크 간의 입찰 가격이 상관 관계가 있을 때보다 독립적일 때 더 높은 광고 수익을 얻을 수 있음을 밝혔다. 본 연구를 통해, 학술적인 의미 뿐만 아니라, 실제 경영 환경에서 모바일 매체가 광고 수익을 창출하고 극대화하기 위해서 활용할 수 있는 가이드라인을 제공하였다. 특히 광고 네트워크에 대한 의존도가 높고, 내부 자원의 제약이 있는 중소 개발자들에게 별도의 R&D 없이 최적화된 광고 운영 정책을 수립할 수 있는 방법을 제시하였다.Chapter 1. Introduction 1 Chapter 2. Literature Review 11 2.1 Real-Time Bidding 11 2.2 Ad Format 15 2.2.1 Native Ads 15 2.2.2 Rewarded Video Ads 17 2.3 Advertisement Performance Index 19 Chapter 3. Evaluation of Ad Factor 23 3.1 Introduction 23 3.1.1 Advertisement Factors 26 3.1.2 Environmental Factors 29 3.1.3 Audience Factors 32 3.2 Hypotheses and Dataset 34 3.2.1 Advertisement Density 34 3.2.2 Ad Format with the Same Advertisement Density 35 3.2.3 Disclosure Position with the Same Advertisement Density 36 3.2.4 Disclosure Method of Native Ads 37 3.2.5 Dataset 38 3.3 Results 41 3.3.1 Influence of Advertisement Density on Advertising Revenue and effectiveness 41 3.3.2 Heterogenous Influence with the Same Advertisement Density 43 3.3.3 Heterogenous Influence of Disclosure Position 46 3.3.4 Heterogeneous effect by Disclosure Method 47 3.4 Discussion 49 Chapter 4. Waterfall Strategy Development 57 4.1 Introduction 57 4.1.1 Information Asymmetry 60 4.1.2 Bidding Strategy 61 4.1.3 Price and Demand 63 4.2 Estimation of Ad Networks Demand Curves 65 4.2.1 Dataset 65 4.2.2 Demand Curve Estimation 67 4.3 Waterfall Bidding Strategy 76 4.4 Sensitivity Analysis 82 Chapter 5. Conclusion 91 5.1 Summary of Research Findings 91 5.2 Contribution of this Study 94 5.3 Limitation and Further Studies 96 Bibliography 97 Appendix 109 Abstract (Korean) 121Docto

Suljettujen online-mainosalustojen strategiat — tapaukset Google ja Facebook

This thesis studies closed ad platforms in the modern online advertising industry. The research in the field is still nascent and the concept of a closed ad platform doesn’t exist. The objective of the research was to discover the main factors determining the revenue of online advertising platforms and to understand why some publishers choose to establish their own closed ad platforms instead of selling their inventory for third-party ad platforms. The concept of a closed ad platform is defined leveraging the existing online advertising literature and the platform governance structure theory. Using the case study method, Google and Facebook were chosen as the cases as they have driven most of the innovation in the field and quickly gained significant market share. In total, 47 people were interviewed for this study, most of them working for advanced online advertisers. Based on the interviews, a microeconomic mathematic formula is created for modeling an ad platform’s net advertising revenue. The formula is used to identify the five main drivers of an ad platform’s revenue an each of them are studied in depth. The results suggest that the most important revenue drivers the ad platforms can affect are access to an active user base, the efficiency of ad serving and the comprehensiveness of measurement. Setting up a closed ad platform requires significant investments from a publisher and should be only done if it can improve the advertisers’ results. After it’s been established, a closed platform can leverage its position to collect user data and structured business data to optimize its performance further. The results provide a structured understanding of the main dynamics in the industry that can be used in decision-making and a basis for future research on closed ad platforms.Tämä diplomityö tutkii suljettuja mainosalustoja nykyaikaisella online-mainonta-alalla. Alan tutkimus on vielä aluillaan ja suljetun mainosalustan konseptia ei ole olemassa. Tämän tutkimuksen tavoitteena oli löytää online-mainosalustojen liikevaihdon määrittävät tekijät ja ymmärtää miksi jotkut julkaisijat valitsevat omien suljettujen mainosalustojen perustamisen mainospaikkojen kolmansien osapuolien mainosalustoille myymisen sijaan. Suljetun mainosalustan konsepti määritellään olemassaolevaa online- mainontakirjallisuutta ja alustojen hallintarakenneteoriaa hyödyntäen. Tapaustutkimusmenetelmää käyttäen, Google ja Facebook valittiin tapauksiksi, sillä ne ovat ajaneet eniten innovaatioita alalla ja nopeasti saavuttaneet merkittävän markkinaosuuden. Yhteensä 47 henkilöä haastateltiin tätä tutkimusta varten, useimmat heistä edistyneiden online-mainostajien työntekijöitä. Haastattelujen perusteella luodaan mikrotaloudellinen matemaattinen kaava mainosalustan nettoliikevaihdon mallintamiseksi. Kaavaa käytetään tunnistamaan mainosalustan liikevaihdon viisi pääkomponenttia, ja kuhunkin niistä perehdytään syvällisemmin. Tulokset viittaavat, että tärkeimmät liikevaihdon ajurit, joihin mainosalustat voivat vaikuttaa ovat pääsy aktiiviseen käyttäjäkantaan, mainosten näyttämisen tehokkuus ja mittaamisen kattavuus. Suljetun mainosalustan perustaminen vaatii merkittäviä investointeja julkaisijalta ja tulisi tehdä ainoastaan, jos sillä voidaan parantaa mainostajien tuloksia. Suljetun alustan perustamisen jälkeen sen positiota voidaan hyödyntää käyttäjädatan ja strukturoidun liiketoimintadatan keräämiseksi suorituskyvyn edelleen optimoimiseksi. Tulokset tarjoavat toimialan päädynamiikkojen ymmärryksen, jota voidaan käyttää päätöksenteossa sekä pohjana suljettujen mainosalustojen edelleen tutkimiseksi tulevaisuudessa