PhD Dissertation Proposal by HOANG Ai Phuong Emmy | Recovering Consumer Preferences from Large-Scale Data: Applications for Digital Entertainment and Financial Services

Recovering Consumer Preferences from Large-Scale Data: Applications for Digital Entertainment and Financial Services

IT innovations disrupt traditional business models and challenge conventional thinking. Industry incumbents face fierce competition from start-ups with new business models and new ways of engaging customers. Digital entertainment goods and personalized services have become a lucrative market, which has undergone a transformation enabled by seamless Internet connections. Meanwhile, social networks and other online platforms have brought people and business even closer. As consumers’ relationships with firms span geographic boundaries, so does their spending. It is no longer effective nor appropriate to segment consumers based on socioeconomic factors; instead, consumers can be characterized by their direct relationship with the goods and services, or their relationship with technology. Thus, firms need to develop innovative products and services, and adjust their marketing and delivery systems to address new levels of consumer sophistication. My research examines how IT creates new capabilities to inform traditional marketing activities, for products and services in the entertainment industry, as well as in the financial services industry. This thesis poses one fundamental, yet intriguing question: How can firms recover consumer preferences to keep their consumers informed about offerings that are suitable for them? First, we must understand how consumer demand and consumption have changed in the presence of all of these changes. Firms must be informed of consumer preferences and extend their marketing efforts accordingly. Essay 1 examines the impact of content sampling strategies on the marketing of on-demand series dramas. Informed households are willing to pay more for TV shows that fit their viewing preferences. Essay 2 showcases a methodological advance related to a propensity score matching procedure that enables statistical inferences to be made so censored observations can be estimated to support causal analysis in empirical research. This challenge arises in empirical studies that span across different disciplines. Essay 3 aims to recover consumer preferences for financial services from multi-source data-at-scale, and to produce business policy-relevant findings. The theoretical lens of my work spans the IS and Marketing disciplines, with the aim to contribute new insights to the research community as well as industry practitioners.