Open Access

Online marketing, especially Paid Search Advertising, has become one of the most important paid media channels for companies to sell their products and services online. Despite being under intensive examination by a number of researchers for several years, this topic still offers interesting opportunities to contribute to the com- munity, particularly because of its large economic impact and practical relevance as well as the detailed and widely unfiltered view of consumer behavior that such marketing offers. To provide answers to some of the important questions from advertisers in this con- text, I present four papers in my thesis, in which I extend previous works on optimization topics such as click and conversion prediction. I apply and extend methods from other fields of research to specific problems in Paid Search. After a short introduction, I start with a paper in which we illustrate a new method that helps advertisers to predict conversion probabilities in Paid Search using sparse keyword- level data. We address one of the central problems in Paid search advertising, which is optimizing own investments in this channel by placing bids in keyword auctions. In many cases, evaluations and decisions are made with extremely sparse data, al- though anecdotal evidence suggests that online marketing is a typical