  This dissertation examines three distinct big data analytics problems related
to the social aspects of consumers' choices. The main goal of this line of
research is to help two sided platform firms to target their marketing policies
given the great heterogeneity among their customers. In three essays, I
combined structural modeling and machine learning approaches to first
understand customers' responses to intrinsic and extrinsic factors, using
unique data sets I scraped from the web, and then explore methods to optimize
two sided platforms' firms' reactions accordingly. The first essay examines
"social learning" in the mobile app store context, controlling for intrinsic
value of hedonic and utilitarian mobile apps, price, advertising, and number of
options available. The second essay investigates bidders' anticipated winner
and loser regret in the context of the eBay online auction platform. Using a
large data set from eBay and empirical Bayesian estimation method, I quantify
the bidders' anticipation of regret in various product categories, and
investigate the role of experience in explaining the bidders' regret and
learning behaviors. The third essay investigates the effects of Gamification
incentive mechanisms in an online platform for user generated content. I use an
ensemble method over LDA, mixed normal and k-mean clustering methods to segment
users into competitors, collaborators, achievers, explorers and uninterested
users. These findings help the Gamification platform to target its users. The
simulation counterfactual analysis suggests that a two sided platform can
increase the number of user contributions, by making earning badges more
difficult.
