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

A survey of the role of viewability within the online advertising ecosystem

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Within the online advertising ecosystem, viewability is defined as the metric that measures if an ad impression had the chance of being viewable by a potential consumer. Although this metric has been presented as a potential game-changer within the ad industry, it has not been fully adopted by the stakeholders, mainly due to disagreement between the different parties on the standards to implement and measure it, and its potential benefits and drawbacks. In this study, we present a survey of the role that viewability can have on the main challenges of the online advertising ecosystem depicting the main applications, benefits and issues. With this objective, we provide an overall picture of how viewability can fit within the ecosystem, which can help the different stakeholders to work on its adoption, integration and establishing a research agenda.This work was supported by the Plan de Doctorados Industriales de la Secretaría de Universidades e Investigación del Departamento de Empresa y Conocimiento de la Generalitat de Catalunya under the Grant 2018-DI-059 and by ExoClick. Furthermore, this work received support from the Fellowship through ‘‘la Caixa’’ Foundation under Grant ID100010434, from the European Union’s Horizon 2020 Research and Innovation Program through Marie Skłodowska-Curie Grant under Agreement 847648, from the Fellowship under Grant CF/BQ/PR20/11770009, from the Spanish Ministry of Economy and Competitiveness through Juan de la Cierva Formación Program under Grant FJCI-2017-34926 and from the Spanish Government under Grant PID2020-113795RB-C31 ‘‘COMPROMISE’’.Peer ReviewedPostprint (published version

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

Personalization in Digital Advertising

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research and CRMThis research studies the impact a well-planned digital advertisement campaign and user-friendly online experience can have on Brands awareness and sales. As the Digital landscape evolves, so has Online Advertisement and Data collection, making it possible for Advertisers to know more about who is navigating online, where they are, and how to approach them. With the gathering of online users’ insights, brands can impact whom they want and how they want to, making their communication more relevant, therefore creating less noise and more conversation. This work aims at proving that these segmented and personalized campaigns lead to more engagement and sales than the ones that just target anybody with no defined criteria. It will also consider the opinions of online users regarding online advertisement and the fact that brands can use their navigating information to plan and implement digital campaigns. With this in mind, it would be possible to detect a relation between advertisement quality and user experience with the increase or decrease of Ad Blockers downloads, especially amongst Generation Z, that is the generation more comfortable with digital technology and will be the consumers of the future. By the end of the study we should be able to understand better the environment of actual digital advertisement and the way it can and should evolve in the future regarding all the insights we are able to collect from this research

Privacy-preserving and fraud-resistant targeted advertising for mobile devices

Online Behavioural Advertising (OBA) enables Ad-Networks to capitalize on the popularity of digital Publishers in order to target users with contextaware promotional materials from Advertisers. OBA has been shown to be very effective at engaging consumers but at the same time presents severe privacy and security threats for both users and Advertisers. Users view OBA as intrusive and are therefore reluctant to share their private data with Ad-Networks. In many cases this results in the adoption of anti-tracking tools and ad-blockers which reduces the system's performance. Advertisers on their part are susceptible to financial fraud due to Ad-Reports that do not correspond to real consumer activity. Consequently, user privacy is further violated as Ad-Networks are provoked into collecting even more data in order to detect fictitious Ad-Reports. Researchers have mostly approached user privacy and fraud prevention as separate issues while ignoring how potential solutions to address one problem will effect the other. As a result, previously proposed privacy-preserving advertising systems are susceptible to fraud or fail to offer fine-grain targeting which makes them undesirable by Advertisers while systems that focus on fraud prevention, require the collection of private data which renders them as a threat for users. The aim of our research is to offer a comprehensive solution which addresses both problems without resulting in a conflict of interest between Advertisers and users. Our work specifically focuses on the preservation of privacy for mobile device users who represent the majority of consumers that are targeted by OBA. To accomplish the set goal, we contribute ADS+R (Advert Distribution System with Reporting) which is an innovative advertising system that supports the delivery of personalized adverts as well as the submission of verifiable Ad-Reports on mobile devices while still maintaining user privacy. Our approach adopts a decentralized architecture which connects mobile users and Advertisers over a hybrid opportunistic network without the need for an Ad-Network to operate as administrative authority. User privacy is preserved through the use of peer-to-peer connections (serving as proxy connections), Anonymous- download technologies and cryptography, while Advertiser fraud is prevented by means of a novel mechanism which we termed Behavioural Verification. Behavioural Verification combines client-side processing with a blockchaininspired construction which enables Advertisers to certify the integrity of Ad-Reports without exposing the identity of the submitting mobile users. In comparison to previously proposed systems, ADS+R provides both (1) user privacy and (2) advert fraud prevention while allowing for (3) a tunable trade-off between resource consumption and security, and (4) the statistical analysis and data mining of consumer behaviours

Supply Side Optimisation in Online Display Advertising

On the Internet there are publishers (the supply side) who provide free contents (e.g., news) and services (e.g., email) to attract users. Publishers get paid by selling ad displaying opportunities (i.e., impressions) to advertisers. Advertisers then sell products to users who are converted by ads. Better supply side revenue allows more free content and services to be created, thus, benefiting the entire online advertising ecosystem. This thesis addresses several optimisation problems for the supply side. When a publisher creates an ad-supported website, he needs to decide the percentage of ads first. The thesis reports a large-scale empirical study of Internet ad density over past seven years, then presents a model that includes many factors, especially the competition among similar publishers, and gives an optimal dynamic ad density that generates the maximum revenue over time. This study also unveils the tragedy of the commons in online advertising where users' attention has been overgrazed which results in a global sub-optimum. After deciding the ad density, the publisher retrieves ads from various sources, including contracts, ad networks, and ad exchanges. This forms an exploration-exploitation problem when ad sources are typically unknown before trail. This problem is modelled using Partially Observable Markov Decision Process (POMDP), and the exploration efficiency is increased by utilising the correlation of ads. The proposed method reports 23.4% better than the best performing baseline in the real-world data based experiments. Since some ad networks allow (or expect) an input of keywords, the thesis also presents an adaptive keyword extraction system using BM25F algorithm and the multi-armed bandits model. This system has been tested by a domain service provider in crowdsourcing based experiments. If the publisher selects a Real-Time Bidding (RTB) ad source, he can use reserve price to manipulate auctions for better payoff. This thesis proposes a simplified game model that considers the competition between seller and buyer to be one-shot instead of repeated and gives heuristics that can be easily implemented. The model has been evaluated in a production environment and reported 12.3% average increase of revenue. The documentation of a prototype system for reserve price optimisation is also presented in the appendix of the thesis