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

An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy

Etsy is a global marketplace where people across the world connect to make, buy and sell unique goods. Sellers at Etsy can promote their product listings via advertising campaigns similar to traditional sponsored search ads. Click-Through Rate (CTR) prediction is an integral part of online search advertising systems where it is utilized as an input to auctions which determine the final ranking of promoted listings to a particular user for each query. In this paper, we provide a holistic view of Etsy's promoted listings' CTR prediction system and propose an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings. We obtain representations from texts and images by utilizing state-of-the-art deep learning techniques and employ multimodal learning to combine these different signals. We compare the system to non-trivial baselines on a large-scale real world dataset from Etsy, demonstrating the effectiveness of the model and strong correlations between offline experiments and online performance. The paper is also the first technical overview to this kind of product in e-commerce context

The utilization of artificial intelligence in online advertising and its perceived effectiveness

This study explores the utilization of Artificial Intelligence in online advertising process and the impact of using AI each stage in that process with the overall perceived effectiveness. It also provides a better understanding of the magnitude of using AI in the four stages of advertising online: namely consumer insights, ad creation, media planning and buying, and finally ad evaluation. Process model of AI utilization in online advertising is the conceptual model of the study, which is developed from the previous literature. A triangulation methodology is implemented to enhance the credibility of the research study and leads to a more comprehensive understanding of the topic. Online survey is conducted with digital advertisers worldwide from both agency and client side. Nonrandom sampling (N=60) was implemented to test 5 constructs from the perspective of the respondents. Three in-depth interviews were also conducted before and after the online questionnaire to analyze the findings and results and demonstrate insights on the five proposed research questions. Findings of the study showed beyond doubt that AI is stepping strongly and progressively in the four stages of the data-based online advertising process. Moreover, it significantly showed that there is a relationship between AI utilization in each stage and the following one. Finally, results indicated that using AI in each advertising stage promotes the perceived effectiveness of the overall online ad process

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

THE MAIN APPLICATIONS OF THE INTERNET IN TOURISM MARKETING

The Internet as a marketing media can be of great benefit to virtual all areas of marketing, from marketing research, through market segmentation, targeting and positioning, to the effective use of the marketing mix, and marketing organisation and control. The following discussion does not attempt to provide an exhaustive list of the Net's use in tourism; rather, it simply intends to exemplify its common applications in and main implications for tourism marketing.Internet, tourism, web, marketing

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

Ecommerce using search engine optimization

Ecommerce using Search Engine Optimization (ESEO) is an ecommerce system that focuses on implementing SEO techniques to improve the website‘s ranking in Search Engine Results Page (SERP). When a new web system is available online, it takes time for the site to be available in SERP. For a site to be noticed by consumers, it is shown that the site‘s ranking must be on the top area of the SERP, preferably the first page. This is why SEO is important in improving the ranking of a website. One of the approaches is by modifying various elements in the design of the website to effectively increase the relevance of its content to the query of consumers. In ESEO, item information input by user will be used to customize the content generation of the webpages. The optimization works in the form of auto page generation without requiring users‘ active intervention

The impact of different appeals on the performance of the Facebook advert

The advent of the online social media brought many challenges and opportunities for advertisers. While there are multiple online social networks, only a few sell advertising space. However, this few social media reach millions of consumers. This dissertation focused on Facebook, the largest online social network, to study how to optimize the performance of ads by using different combinations of promotional appeals and product types. Two different types of promotional appeals – hard-sell and soft-sell – were compared based on performance (measured by the CTR, Conversion Rate and/or Like Rate). The performances were compared firstly for the same product types, and secondly for different product types. The results from these comparisons were obtained from the statistical analyses of secondary and primary data. The secondary data originated from Facebook advertising campaigns performed by Revshare in 2014 and 2015. The primary data originated from A/B tests of two Facebook advertising campaigns that combined the advertising appeals with the product types. Independent T-test and regression analyses on secondary data presented that none of the appeals led to a better comparative performance, and that none of the product types affected the performance of each appeal. Independent T-test analyses on primary data wield the same results. The main conclusion taken from both secondary data and primary data is that there are no significant changes in performance on Facebook ads for different types of advertising appeals. This finding remained unaltered when the different advertising appeals are combined with different product types.O advento dos meios de comunicação social em linha trouxe muitos desafios e oportunidades para os anunciantes. Embora existam várias redes sociais online, apenas um número reduzido vende espaço publicitário. No entanto, este número restrito de mídias sociais atinge milhões de consumidores. Esta dissertação focou-se no Facebook, a maior rede social online, para estudar a forma de otimizar o desempenho dos anúncios usando diferentes combinações de apelos promocionais e tipos de produtos. Dois géneros diferentes de apelos promocionais – venda dura e venda suave - foram comparados com base no desempenho (medido pela Taxa de Cliques, Taxa de Conversão e/ou Taxa de Gostos). Os desempenhos foram comparados em primeiro lugar para os mesmos tipos de produtos, e em segundo lugar para diferentes tipos de produtos. Os resultados destas comparações foram obtidos a partir de análises estatísticas de dados secundários e primários. Os dados secundários provieram de campanhas publicitárias no Facebook realizadas pela Revshare em 2014 e 2015. Os dados primários originaram de testes A/B de duas campanhas de publicidade no Facebook que combinaram os apelos publicitários com os tipos de produto. As análises com testes t de Student e com regressão, realizadas em dados secundários, revelaram que nenhum dos apelos originou um melhor desempenho comparativo, e que nenhum dos tipos de produtos afetou o desempenho de cada apelo. Análises com testes t de Student, realizadas em dados primários, apresentaram os mesmos resultados. A principal conclusão, tirada de ambos os dados secundários e primários, é que não há mudanças significativas no desempenho dos anúncios no Facebook para diferentes tipos de apelos publicitários. Esta conclusão permaneceu inalterada quando os diferentes apelos publicitários foram combinados com os diferentes tipos de produtos