Using TB-Sized Data to Understand Multi-Device Advertising

In this study, we combine the conversion funnel theory with machine learning methods to understand multi-device advertising. We investigate the important question of how the distribution of ads on multiple devices affects the consumer path to purchase. To handle the sheer volume of TB sized impression data, we develop a MapReduce framework to estimate the non-stationary Hidden Markov Model in parallel. To accommodate the iterative nature of the estimation procedure, we leverage the Apache Spark framework and a corporate cloud computing service. We calibrate the model with hundreds of millions of impressions for 100 advertisers. Our preliminary results show increasing the diversity of device for ads delivery can consistently encourage consumers to become more engaged. In addition, advertiser heterogeneity plays an important role in the variety of the conversion process

DESIGNING INFORMATION STRATEGIES FOR DIGITAL PLATFORMS: FINDINGS FROM LARGE-SCALE RANDOMIZED FIELD EXPERIMENTS

The rise of digital platforms has transformed our economy and reshaped consumer behaviors and experiences. While practitioners and researchers have a growing interest in understanding digital platforms, there is still a dearth of research on how platforms can design effective information strategies to mitigate fundamental issues such as information asymmetry and search frictions by leveraging granular data. My dissertation seeks to fill this gap. Specifically, by focusing on significant real-world problems on digital platforms, I aim to examine IT-enabled and analytics-driven information strategies and study the impact of these strategies on the users as well as on the platforms themselves. In collaboration with two different online platforms, I design and conduct three randomized field experiments to investigate the impact of informational interventions and provide actionable suggestions. In Essay 1, I examine incentive strategies for motivating effective mobile app adoptions, by comparing monetary incentives against informational incentives. I find that the usage after app adoption depends on how customers are motivated, and only information induced adoption leads to long-term increase in purchases. In Essay 2, I investigate the role of “verification” when it is made optional, and find that it serves as a very effective signaling device, especially in markets that lack other mechanisms such as reputation systems. I also find that users on the two sides of online platform use the same signal very differently, and that this is attributable to the difference in the credibility of their primary signaling-attribute of each side, viz. income in males and beauty in females. In Essay 3, I examine the effectiveness of three different recommendation systems in two-sided matching platforms with a focus on how the provisioning of potential candidates’ preference information impacts focal user’s decision-making and matching outcomes. I find that compared to “people you might prefer”, users act strategically towards “people who might prefer you” and “people who you might prefer and who might prefer you” by actively reaching out to less desirable candidates, which leads to improved outcomes. In short, the three studies present new empirical evidence of how platforms can leverage information as a tool to design effective incentives, signaling mechanisms and recommender systems to facilitate users’ decision-making, transactions and matching