The Internet has changed the landscape of marketing, and presented us with new marketing phenomena and challenges. In this dissertation, I focus on two emerging research topics at the frontier of marketing research –capturing consumer purchase patterns from big data and online groupbuy.
Marketers have recognized that the probability of a consumer’s (or household’s) purchase in a particular product category may be influenced by past purchases in the same category, and also by purchases in other, related categories. Past studies of cross-category effects have focused on a limited number of product categories, and have often ignored intertemporal effects in their analyses. The availability of such enormous consumer shopping data sets, and the value of analyzing the complex relationships across categories and over time (for example, for personalized promotions) suggest the need for computationally efficient modeling and estimation methods. We explore the nature of intertemporal cross-product patterns in an enormous consumer purchase data set, using a model that adopts the structure of conditional restricted Boltzmann machines (CRBM). Our empirical results demonstrate that our proposed approach, employing the efficient estimation algorithm embodied in the CRBM, enables us to process very large data sets, and to capture the consumer decision patterns, for both predictive and descriptive purposes, that might not otherwise be apparent.
Online group buying, under which the seller offers discounts based on the size of the pool of buyers, is rapidly growing in popularity in both developed and emerging markets. Our study categorizes the diverse mechanisms across group buying sites into three main types – volume strategy (e.g., Groupon), collective buying (e.g., Mercata), and referral reward (e.g., LivingSocial) – and associated subtypes. Our seller faces a market comprising four segments of consumers who are heterogeneous in their product knowledge or intrinsic valuation or both. Informed consumers may inform and raise the valuation of their less informed peers. Consideration of a broader strategy space combined with a richer consumer behavior model, relative to the extant literature, provides new insights on when and how specific strategies or subtypes are optimal. Collective buying emerges with the largest domain of optimality, followed by referral reward strategy and then the volume strategy. Within collective buying, its subtypes favoring limited penetration are often optimal.
|Advisor:||Chatterjee, Rabikar, Venkatesh, R.|
|School:||University of Pittsburgh|
|School Location:||United States -- Pennsylvania|
|Source:||DAI-A 78/05(E), Dissertation Abstracts International|
|Keywords:||Big data, Consumer, Neural network, Personalized marketing, RBM, Time series|
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