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dc.contributor.authorSatyavolu, Saisandeep
dc.date.accessioned2016-07-05T15:30:16Z
dc.date.available2021-05-30T06:00:25Z
dc.date.issued2016-05-29
dc.identifier.otherbibid: 9597260
dc.identifier.urihttps://hdl.handle.net/1813/44387
dc.description.abstractThe proliferation of available data in marketing has placed an emphasis on the applicability of extant marketing models to big data. To tackle this problem, methods from machine learning have been increasingly applied by the marketing community. This line of research is a subset of research in marketing that is becoming interdisciplinary. A number of marketing researchers have successfuly adopted methods from other seemingly unrelated fields in their research. In that vein, this thesis examines the applicability of Bayesian Nonparametric methods (from the field of machine learning) to marketing. The first chapter of this thesis provides a very brief survey of marketing research papers that have enhanced pure marketing models using methods from machine learning. The second chapter describes the Dirichlet Process, a key component of Bayesian Nonparametric analysis and provides two synthetic data applications. Going forward, we study the applicability of Bayesian Nonparametric methods to model Heterogeneity across multiple markets. Bayesian Nonparametric methods have been used in marketing and economics literature to model heterogeneity in discrete choice models, but past applications have only been limited to data from a single market. So as to compare heterogeneity in consumer preferences across multiple markets, we use the Hierarchical Dirichlet Process (HDP) which lets multiple "groups" of data "share statistical strength". Heterogeneity across multiple markets is modeled using the HDP in two different contexts (B2C and B2B) in this thesis. Our work shows that the HDP provides a convenient "middle ground" to other extreme modeling options, which are (1) ignore heterogeneity of preferences across markets and (2) model each market separately. Another aspect of the HDP is the ease with which it can be incorporated into models of discrete choice. The models developed and estimated in this thesis are also helpful for the marketing manager. In the B2C application, the results of the model provide the manager with a practical way of tailoring targeting activities towards consumers with varying preferences. Finally, in the B2B application, we find that based on the Stage of the selling process, some marketing activities play a larger role than others in converting sales leads into clients. These results provide a data driven basis for the manager to appropriately allocate marketing dollars to activities based on the selling process.
dc.language.isoen_US
dc.subjectBayesian Nonparametrics
dc.subjectHeterogeneity
dc.subjectMultiple Markets
dc.titleBayesian Nonparametric Methods In Marketing
dc.typedissertation or thesis
thesis.degree.disciplineManagement
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Management
dc.contributor.chairRao,Vithala R.
dc.contributor.committeeMemberGupta,Sachin
dc.contributor.committeeMemberMolinari,Francesca
dc.contributor.committeeMemberKadiyali,Vrinda
dc.identifier.doihttps://doi.org/10.7298/X4P26W1N


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