| Summary: | Across three essays, I explore how modern statistical machine learning approaches can be used to glean novel marketing insights from data and to facilitate data-driven decision support in new domains. In particular, I draw on Bayesian nonparametrics, deep generative modeling, and modern Bayesian computational techniques to develop new methodologies that enhance standard marketing models, address modern challenges in data-driven marketing, and, as I show through applications to real world data, glean new, managerially relevant insights. Substantively, my work addresses issues in customer base analysis, the estimation of consumer preferences, and brand identity and logo design. In my first essay, I address how multi-product firms can understand and predict customer purchasing dynamics in the presence of partial information, by developing a Bayesian nonparametric model for customer purchasing activity. This framework yields an interpretable, model-based dashboard, which can be used to predict future activity, and guide managerial decision making. In my second essay, I explore the flexible modeling of customer brand choice dynamics using a novel form of heterogeneity, which I term dynamic heterogeneity. Specifically, I develop a novel doubly hierarchical Gaussian process framework to flexibly model how the preferences of individual customers evolve relative to one another over time, and illustrate the utility of the framework with an application to purchasing during the Great Recession. Finally, in my third essay, I explore how data and models can inform firms' aesthetic choices, in particular the design of their logos. To that end, I develop image processing algorithms and a deep generative model of brand identity that links visual data with textual descriptions of firms and brand personality perceptions, which can be used for understanding design standards, ideation, and ultimately, data-driven design.
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