Social Big Data Analytics of Consumer Choices: A Two Sided Online Platform Perspective

Abstract

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 proposed model extracted a social influence proxy measure from a macro diffusion model using an unscented Kalman filter, and it incorporated this social influence measure in a mixed logit choice model with hierarchical Dirichlet Process prior. Results suggest significant effects of social influence, which underscores the importance of choosing different marketing policies for pervasive goods. The comparison of mobile app adoption parameters suggests that among several classical goods mobile app adoption pattern is very similar to that of music CDs. The simulation counterfactual analysis suggests that early targeted viral marketing policy might be an optimal strategy for the app-store platforms. The second essay investigates bidders’ anticipated winner and loser regret in the context of the eBay online auction platform. I developed a structural model that accounts for bidders’ learning and their anticipation of winner and loser regrets in an auction platform. Winner and loser regrets are defined as regretting for paying too much in case of winning an auction and regretting for not bidding high enough in case of losing it, respectively. 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 counterfactual analyses showed that shutting down the bidder regret via appropriate notification policies can increase eBay’s revenue by 24%.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. Then, I develop a state-dependent choice model that accommodates the effect of number of badges, the rank in the leaderboard, reputation points, inertia, and reciprocity, and allow for heterogeneity by Dirichlet Process prior. The results suggested that estimating the model on small samples generate biased estimates. Furthermore, they suggest that the effects of Gamification elements are heterogeneous, significantly positive or negative for different users. I found sensitivity patterns that explain importance of certain Gamification elements for users with certain nationalities. 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.