Essays on Technology-Driven Marketing

2019-09-04T17:52:04Z (GMT) by Zijun Shi
With the development of technology in business applications, new marketing problems emerge, creating challenges for both practitioners and researchers. In this dissertation, I investigate marketing issues that involve new technology or require research methodologies enabled by new technology. I take an interdisciplinary approach, combining structural modeling, analytical modeling, machine learning, and causal inference, to study problems on pricing, media hype, and branding in three essays. In the first essay, I examine the optimality of the freemium pricing strategy. Despite its immense popularity, the freemium business model remains a complex strategy to master and
often a topic of heated debate. Adopting a generalized version of the screening framework à la Mussa and Rosen (1978), we ask when and why a firm should endogenously offer a zero price on its low-end product when users' product usages generate network externalities on each other.
Our analysis indicates freemium can only emerge if the high- and low-end products provide asymmetric marginal network effects. In other words, the firm would set a zero price for its lowend product only if the high-end product provided larger utility gain from an expansion of the
firm's user base. In contrast to conventional beliefs, a firm pursuing the freemium strategy might increase the baseline quality on its low-end product above the “efficient” level, which seemingly reduces differentiation. In the second essay, I study how hype news from celebrity doctors affects the supply of information for weight-loss products. Consumers’ healthcare choices are heavily influenced by
public information. A distressing trend is desceptive information being propelled to popularity by trusted spokespeople. For example, Dr. Oz, a celebrity doctor, has made medical recommendations directly against scientific evidence. Whether public information from reputable sources could correct misleading health information or not remains unknown. This study fills this research gap. By analyzing textual content using deep learning, I find that
legitimate news outlets responded to The Dr. Oz Show by generating more news articles and carrying higher sentiment, hence amplifying rather than correcting hype news. Research articles reacted too slowly. Consumer reviews provided some correction but were overwhelmed by the opposite voice. Our findings have public policy implications on media content intervention and
consumer protection. In the third essay, I develop a dynamic structural model of fashion choices of brands and
styles to investigate the implication of prohibiting fast fashion copycats, leveraging usergenerated
data from fashion-specific social media and deep learning methods on image analytics. I find that copycats can enhance high-end brands demand, contrary to conventional wisdom, due to several novel mechanisms: first, the affordability of mixing low-end copycats with high-end
brands boosts demand for high-end brands from financially constrained consumers; second, good styles from low-end brands can help a consumer to build up his popularity/likeability, which increases his value for high-end brands and reduces the cost. Substantively, our results shed light on copyright enforcement and have implications on how fashion brands should react to copycats.
Methodically, I developed a framework to analyze consumer choices where visual features are important product attributes and peer feedback hugely affects the decision-making process.