Open Access

Tobias-Benedikt Blask

• 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 community, 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 context, the author present four papers in his thesis, in which he extends previous works on optimization topics such as click and conversion prediction. He applies and extends methods from other fields of research to specific problems in Paid Search. After a short introduction, the dissertation starts with a paper in which the authors illustrates a new method that helps advertisers to predict conversion probabilities in Paid Search using sparse keyword-level data. They 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, although anecdotal evidence suggests that online marketing is a typical "Big Data" topic. In the developed algorithm presented in this paper, the authors use information such as the average time that users spend on the advertiser's website and bounce rates for every given keyword. This previously unused data set is shared between all keywords and used as prior knowledge in the proposed model. A modified version of this algorithm is now the core prediction engine in a productive Paid Search Bid Optimization System that calculates and places millions of bids every day for some of the most recognized retailers and service providers in the German market. Next, the author illustrates the development of a non-reactive experimental method for A/B testing of Paid Search Advertising activities. In that paper, the authors provide an answer to the question of whether and under what circumstances it makes economic sense for brand owners to pay for Paid Search ads for their own brand keywords in Google AdWords auctions. Finally, the author presents two consecutive papers with the same theoretical foundation in which he applies Bayesian methods to evaluate the impact of specific text features in Paid Search Advertisements.
• Online-Marketing, insbesondere Suchmaschinenwerbung, ist zu einem der wichtigsten bezahlten Mediakanäle für Unternehmen geworden, um Produkte und Dienstleistungen online zu vertreiben. Insbesondere aufgrund seiner hohen praktischen Relevanz sowie der detaillierten und weitgehend ungefilterten Sichtweise auf Konsumentenverhalten stellt sich dieses Thema als besonders interessantes Forschungsgebiet dar. Um in diesem Zusammenhang Antworten auf wichtige Fragen von Werbetreibenden zu geben, werden in der Arbeit vier Aufsätze vorgestellt. Nach einer kurzen Einführung, wird mit einem Beitrag begonnen, in dem ein neues Verfahren dargestellt wird, mit dessen Hilfe Werbetreibende die Wahrscheinlichkeit von Conversions in der bezahlten Suche auf Keyword-Ebene vorhersagen können. In vielen Fällen werden Bewertungen und Entscheidungen auf Basis von extrem wenigen Daten durchgeführt. Im vorgestellten Algorithmus werden zusätzliche Informationen aus der Gruppierung von Keywords zur Verbesserung von Prognosen verwendet. Im Folgenden wird die Entwicklung eines nicht-reaktiven, experimentellen Verfahrens für A / B-Tests von Paid Search-Werbemaßnahmen beschrieben. Abschließend werden zwei Beiträge präsentiert, in denen Bayes'sche Methoden zur Bewertung der Auswirkungen bestimmter Textinhalte in bezahlten Suchanzeigen angewendet werden.