Essays on Quantitative Marketing Models and Monte Carlo Integration Methods

The last few decades have led to an enormous increase in the availability of large detailed data sets and in the computing power needed to analyze such data. Furthermore, new models and new computing techniques have been developed to exploit both sources. All of this has allowed for addressing research questions via analyses which were infeasible to carry out previously. This thesis builds on both the modeling and the computing developments. The first part contains three quantitative marketing models. These models can be applied to scanner data to get a better understanding of purchase behavior of households and to infer the effectiveness of promotions on brand performance. The second part of the thesis provides an overview of several Monte Carlo techniques which can be used in Bayesian analyses to get insight into the posterior density of model parameters. Additionally, it describes a new methodology which extends current methods.

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