Television advertising is a billion-dollar industry in the United States. Currently, advertisers place their messages in television programs that are estimated to have a high proportion of their target demographic viewers. The advertising spots are usually purchased months in advance at set list prices or at negotiated prices. Technologies that can place advertisements at the cable box level, instead of the program level, will provide advertisers with the ability to target any demographic group directly and in real-time. This thesis explores the new decision-making required by this new technology and how the television advertisement space can be sold more effectively. In particular, it compares a list price system to a number of new auction models. The structure of the auctions for the new targeted television advertising system is unique and has not been previously studied in the literature. This thesis explores new auction models that can capture these unique features and lead to desirable results for the seller of the advertisement space. A simplified analytical model shows how these features impact advertisers’ bidding behavior and how a list price system compares to the auction models in the ability to raise revenue for the seller of the advertising space. These issues are then explored under various market settings with differing numbers of advertisers and value distributions that these advertisers have for the advertising space. Since sequential first price auctions have undesirable consequences such as strong price fluctuations, this work focuses on second price auctions. The Vickrey-Clarke-Groves (VCG) mechanism is customized for this problem by developing an optimization formulation that determines the best set of advertisers for a particular advertisement space. Because execution time may be an issue, other auction models are developed that lead to similar outcomes as the VCG mechanism but require less computational effort. This thesis provides guidance on when a list price system will lead to higher expected revenue than an auction model and vice versa in a targeted television advertising system. It also demonstrates why some of the standard auction models cannot be applied to this problem and what type of new models are required to lead to desirable advertising outcomes.

Television advertising is a billion-dollar industry in the United States. Currently, advertisers place their messages in television programs that are estimated to have a high proportion of their target demographic viewers. The advertising spots are usually purchased months in advance at set list prices or at negotiated prices. Technologies that can place advertisements at the cable box level, instead of the program level, will provide advertisers with the ability to target any demographic group directly and in real-time. This thesis explores the new decision-making required by this new technology and how the television advertisement space can be sold more effectively. In particular, it compares a list price system to a number of new auction models. The structure of the auctions for the new targeted television advertising system is unique and has not been previously studied in the literature. This thesis explores new auction models that can capture these unique features and lead to desirable results for the seller of the advertisement space. A simplified analytical model shows how these features impact advertisers’ bidding behavior and how a list price system compares to the auction models in the ability to raise revenue for the seller of the advertising space. These issues are then explored under various market settings with differing numbers of advertisers and value distributions that these advertisers have for the advertising space. Since sequential first price auctions have undesirable consequences such as strong price fluctuations, this work focuses on second price auctions. The Vickrey-Clarke-Groves (VCG) mechanism is customized for this problem by developing an optimization formulation that determines the best set of advertisers for a particular advertisement space. Because execution time may be an issue, other auction models are developed that lead to similar outcomes as the VCG mechanism but require less computational effort. This thesis provides guidance on when a list price system will lead to higher expected revenue than an auction model and vice versa in a targeted television advertising system. It also demonstrates why some of the standard auction models cannot be applied to this problem and what type of new models are required to lead to desirable advertising outcomes.

Targeting a small portion of customers for advertising has long been recognized by businesses. In this thesis we proposed a novel approach to promoting products with no prior transaction records. This approach starts with discovering the GP-association rules between customer types and product genres that had occurred frequently in transaction records. Customers are characterized by demographic attributes, some of these attributes have concept hierarchies and products can be generalized through some product taxonomy. Based on GP-association rules set, we developed a comprehensive algorithm to locating a short list of prospective customers for a given promotion product. The new approach was evaluated using the patron¡¦s circulation data from OPAC system of our university library. We measured the accuracy of estimated method and the effectiveness of targeted advertising in different parameters. The result shows that our approach achieved higher accuracy and effectiveness than other methods.

Marketers often target women because they represent over half of the population in Canada and Sweden and because they are the purchasing agents for 85% of household items. Advertisers and marketers use targeted marketing on Social Media platforms in the hopes of engaging the audience enough to initiate a purchasing action. For this paper the goal was to understand the relationship between women aged 55+ and their attitudes and perception of targeted advertising, to see whether it was effective in engaging them as consumers. Through interviews and the think-aloud method we found that while the overall perception of online advertising was negative, in practice, the ads on their social media feed were either accepted as relevant ads, simply ignored/not noticed, or not recognized as advertising. Finally we conclude that because social media gives users the impression that they are in control of the content on their feed, the consequences may be that targeted advertising can influence audience’s perception of who they are, more than they think.

The goal of the diploma thesis is to analyze the situation of behavioral targeted advertising in the Czech Internet market, assess its potential in the future and evaluate the effectiveness of behavioral targeting on the Czech market. This thesis is focused first on the Internet advertising market, media planning, explanation of selected targeting approaches and on detailed description of the behaviorally targeted ads including the online privacy issue. In the practical part analysis of the behavioral targeted advertising on the Czech market through expert interviews and evaluation of the effectiveness of behavioral targeting on an example of an advertising campaign of selected company is to be found.

The thesis is firstly focused on television broadcasting. The author briefly describes the development of television broadcasting and the market specifics of TV stations. Then he describes the operation as well as financing issues of TV stations. The commercial broadcasters are dependent on revenues from advertising, so this issue is appropriately emphasized in the thesis. Then the thesis deals with targeted advertising on television from the perspective of advertisers. It describes their approach to targeted marketing, the criteria for selecting appropriate media for advertising message, suitable television channel, programme and type of advertising message. Describes the electronic audience measurement which helps to position the TV ads properly. Author also proposes the use of viewer satisfaction surveys for more effective positioning of TV ads. The last part is devoted to research of targeted advertising on TV Barrandov and observes if it is possible to place the TV spots properly according to the selected target group.

Title: Behavioural advertising on Facebook: The user perspective regarding leisure industry. Authors: Jean-Marc Desfougères and Valentin Bloux Supervisor: Albert Thor Magnusson Level: Bachelor Thesis in Business Administration Marketing Key Words: Behavioural advertising, Facebook, privacy, online consumer behaviour, social network, leisure industry, targeting. Purpose: study how a specific age bracket of Facebook users perceives the leisure industry behavioural advertising on this social networking site. Method: This thesis follows a deductive approach. We are using secondary data from books, articles and studies but also primary data thanks to a questionnaire; which allows us to answer our purpose. Theoretical Framework: First define the online consumer behaviour and its characteristics through existing models and then define behavioural advertising, how is the leisure industry using this marketing tool and what are the drawbacks of such practices. Conclusion: The authors conclude that Facebook users are more and more aware of the use of behavioural advertising. But due to a lack of education about such marketing techniques the 18-30 years old tend to adopt mostly strict privacy settings with the intention to block those advertisements. The privacy issue is important and even if the users seems to be interested in the offers of the leisure industry there is still a long way before obtaining a full acceptance of this practice. Then are presented the contributions given and the further research possible regarding this topic.

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Nous nous proposons, dans le cadre de cette thèse d'examiner des modèles économiques dans lesquels les firmes ont recours à différentes approches de publicité. Nous analysons les effets des investissements et des tarifs publicitaires sur les stratégies des annonceurs et des médias et nous étudions les différentes configurations de marché qui émergent à l'équilibre. Nous nous focalisons dans les Chapitres 2, 3 et 4 sur la publicité informative ciblée. Dans les chapitres suivants nous considérons que la publicité est persuasive et qu'elle peut créer des externalités aussi bien positives que négatives.Ces externalités peuvent être exogènes comme c'est le cas dans le Chapitre 5 ou endogènes (Chapitre 6).Plus précisément, nous examinons, dans le Chapitre 2, dans le cadre d'une différenciation horizontale, la concurrence en prix et en publicité informative ciblée. Dans le Chapitre 3, nous caractérisons l'équilibre d'un média qui a le choix entre éditer un seul journal général publié dans deux localisations géographiques ou bien éditer un journal local dans chaque localisation, donnant ainsi la possibilité aux annonceurs d'acheter des espaces publicitaires séparés dans chaque édition ou un package publicitaire pour les deux éditions. Nous posons dans le Chapitre 4 le problème du signal de la qualité d'une firme qui lance un nouveau produit, dont la qualité n'est pas connue par les consommateurs potentiels. La firme signale sa qualité à travers une publicité informative ciblée en choisissant la taille du marché niche. L'objectif du Chapitre 5 est de mettre en évidence les interactions directes qui existent entre le marché des médias et le marché des producteurs en considérant que le marché est caractérisé par des externalités de publicité aussi bien positives que négatives. Dans le dernier chapitre, nous étudions les incitations de deux firmes, différenciées par la qualité, à investir dans deux types de publicité : une publicité comparative et une publicité non-comparative. / In this thesis, we develop economic models in which firms invest in different types of advertising. We analyze the effects of the advertising investments and prices on the advertisers and media strategies. In addition, we study the various market configurations emerging at equilibrium. On this basis, we focus, in Chapters 2, 3 and 4, on the targeted informative advertising. In the following chapters, we consider persuasive advertising that create negative and positive externalities. These externalities may be exogenous (Chapter 5) or endogenous (Chapter 6).More precisely, we investigate in Chapter 2, through a horizontal differentiation framework, the price competition and targeted advertising investments when firms are able to perfectly target each type of consumer. In Chapter 3, we model a situation in which a printed media has the choice between editing a single national newspaper published in two cities, or editing two local newspapers allowing thus advertisers to buy separated or bundled ads. In Chapter 4, we investigate the best signalling strategy of a monopoly when introducing a new product with unobservable quality. The firm signals its quality to the potential consumers through informative targeted advertising i.e. by choosing the size of the reached consumer's market. The aim of chapter 5 is to highlight the strategic interaction between media and product markets when the market exhibits positive and negative advertising externalities. The last Chapter is interested in the optimal persuasive advertising and pricing decisions of two vertically differentiated firms given that each firm has the choice to advertise in comparative and/or non-comparative advertising.

[pt] O surgimento da World Wide Web representou uma nova oportunidade de publicidade, disponível para qualquer empresa: A possibilidade de exposição global para uma grande audiência a um custo extremamente pequeno. Como conseqüência disso, surgiu toda uma nova indústria oferecendo serviços relacionados à publicidade de busca, na qual uma empresa anunciante paga por uma posição de destaque em listas de anúncios. A fim de manter a credibilidade e a participação de mercado do serviço que os veicula - por exemplo, uma máquina de busca - os anúncios devem ser exibidos apenas para os usuários que se interessem por eles, no que se chama de Publicidade Direcionada. Em virtude disso, surge a necessidade de se utilizar um sistema de recomendação que seja capaz de escolher que anúncios exibir para quais usuários. Nos sistemas de recomendação baseados em filtragem colaborativa, as preferências de outros usuários são utilizadas como atributos para um sistema de aprendizado, pois estas podem ser bastante detalhadas, gerando recomendações não só para os itens mais populares como também para nichos de itens. Neste trabalho, é desenvolvido um sistema de recomendação de anúncios que aplica Filtragem Colaborativa baseada em fatoração de matrizes ao problema de predição do Click- Through Rate, uma métrica em Publicidade Direcionada que expressa a relevância de um anúncio para os usuários que buscam por uma determinada palavra- chave. A fim de validar o método proposto de predição do Click-Through Rate, realizamos vários experimentos em um conjunto de dados sintéticos. Adicionalmente, o trabalho contribui para o projeto do LearnAds, um framework de recomendação de anúncios baseado em Aprendizado de Máquina. / [en] The emergence of the World Wide Web represented a new advertising opportunity available to any company: The possibility of global exposure to a large audience at a very small cost. As a result, a whole new industry has emerged by offering services related to search advertising, in which an advertiser pays for a prominent position in lists of ads. In order to maintain the credibility and market share of the service that conveys them - for example, a search engine - such ads must be displayed only to users who are interested in them, on what is called Targeted Advertising. Therefore, those services need to use a recommendation system that can choose which ads show to which users. Recommendation systems based on collaborative filtering use the preferences of other users as features to a learning system, since such preferences can be quite detailed, generating recommendations not only for the most popular items but also to item niches. In this work, we develop an ads recommendation system that applies Collaborative Filtering based on matrix factorization to the problem of predicting the Click-Through Rate, a Targeted Advertising metric that expresses the relevance of a particular ad for the users searching for a specific keyword. In order to validate the proposed method of Click-Through Rate prediction, we carry out several experiments on a synthetic data set. Additionally, the work contributes to the design of LearnAds, a framework for ads recommendation systems based on Machine Learning.