Han Chen

This is the most recent version of this paper. It is the first project and dissertation essay I began to work on and among those to which I have devoted the most time (so far). Compared to previous marketing promotion literature, research on storewide sales is challenging as storewide price promotion is commonly used when the entire assortment of products across different categories and price ranges is offered on sale. Thus, above and beyond traditional lab studies, we need new methods and new data. Acquiring research data (both primary and secondary) can be difficult and the path to it may be unexpected. We are finally able to supplement the lab studies with a quasi-field experiment via created retail websites (another story of battling Prolific), empirical data, and numerical simulations. There is also a balance between theory and practice. Any comments or questions are welcome and please address them to hanchen@temple.edu.

This paper was initially conceptualized as a method paper, with the goal of solving for a firm's optimal sequential advertising policy to nudge consumer preference expansion and reinforcement consumption behaviors using deep reinforcement learning (DRL) method. The optimal policy (of when, which ad copy, and to whom) prescribes the advertising trajectory for each individual to maximize their total consumption. Despite the initial plan, the empirical findings turn out interesting and should be the focus of the paper before doing method and optimization. The transition takes time but is necessary in terms of first identifying the causal effect of curation source advertising and telling a clear story about consumer preference expansion and reinforcement. (This is thus another example of a research deviating from what it was initially designed to be.) I recognize the importance of being flexible, and I learn from and enjoy the process of reworking and repositioning this paper from a method-driven to a substance-driven research.

This is an ongoing project. The "big" question we try to solve here is that: we know AI and machine learning algorithms are heavily relied on in product recommendations due to their superior capability of predicting individual preferences and thus personalization power, however, how do they affect consumer consumption in a long term? Are algorithms always good for consumers and platforms, especially from the sustainability perspective? If not, how do marketers step in, intervene, and affect consumer behavior to ensure that both consumers and platforms are better off in the long term, by using marketing mix tools such as new product strategies? We are first leveraging observational recommendation feed data to identify the short- vs. long-term effect of personalized (vs. different types of novel recommendations), and then using ML methods to prescribe the optimal recommendation policy (of "what, when, and who").