In this thesis, I describe how search engine advertising works in the sponsored search, the underlying Generalized Second-Price (GSP) Auction, which is the mechanism used by search engines to sell online advertising, and the difficulties to find an optimal bid. For that purpose, I present a model to optimize the bids in an advertiser's campaign and describe the Generalized Method of Moments (GMM) estimator needed to estimate necessary parameters for the bid optimization. Moreover, I analyze whether or not it is possible to improve the performance of a ticket agency's search engine advertising by using the bidding policy and by automizing the bid optimization. To validate the effectiveness of that model, I use a data set from the Google Adwords campaign of that ticket agency, compute the optimal bids, and implement them into their campaign. It appears that for the ticket agency the proposed bidding technique is not as effective as previously imagined. On the contrary, the return on investment of the ticket agency's advertising campaign based on a Difference-in-Differences (DiD) approach decreases by 500%. That result shows that it would be necessary to provide much more information to the model than in this thesis, to be able to improve this ticket agency's Advertising campaign.