Han Chen

Han Chen

Doctoral Candidate, Marketing

CV | GitHub | LinkedIn


Biography

I am a Ph.D. candidate in marketing. I employ econometrics and causal inference, lab/field experiments, and machine/deep learning to solve marketing problems in the domains of digital advertising, price promotion, product innovation, and human-algorithm interaction. Broadly, my research examines 1) how firms effectively use marketing mix tools to influence consumer behavior in the era of artificial intelligence and the digital world, and 2) how marketers use machine learning algorithms to boost the efficacy of marketing mix tools. My dissertation and research revolve around (1) the substantive domain of consumers’ behavioral responses to marketing messages/disclosures and recommendations, with a special focus on their sequential, dynamic, and unintended effects, and (2) the optimization of firms' sequential marketing interventions. I will be joining Monash University as an Assistant Professor (Lecturer) of Marketing in July 2023.


Education

PhD Candidate, Marketing (Expected 2023)

    Temple University, Fox School of Business, PA, USA
    Dissertation: Marketing Messages and Recommendations, Sequential Effects, and Machine Learning Optimization Dissertation Committee: Yang Wang (co-chair), Joydeep Srivastava (co-chair), and Subodha Kumar

Master, Management Science and Engineering (ranked 1/31 in class), 2017

    Northeastern University, China
    Thesis: Measurement and Evaluation of User Interactive Experience and Product Design Implications: A Multi-Method Approach Applied to Mobile Shopping Apps

Bachelor, Industrial Engineering (ranked 3/62 in class), 2014

    Northeastern University, China
    Thesis: Optimal Logistics Center Location: Iterative Center-Of-Gravity Method and Application

Research

Dissertation

Flying High, Landing Low? Effect of Tensile Price Promotions on Store Traffic and Sales [Paper] [Simulation Code]

This research examines the effectiveness of three types of tensile discounts in advertising storewide promotions: a maximum discount (i.e., up to Y% off), a range of discounts (i.e., X% off to Y% off), and a minimum discount (i.e., starting at X% off) on consumers’ initial perceptions and expectations, store visits, and purchases. Using multiple methods, including lab studies, a quasi-field study, empirical analysis of data from a large-scale storewide promotion campaign, and numerical simulations, the findings suggest that while a maximum tensile discount is most effective in shaping consumers’ initial perceptions, and thus store visits, it does not necessarily boost purchases in some conditions. Our findings are consistent with the reasoning that because a maximum tensile discount leads to the most favorable discount expectations, the likelihood and the extent of experiencing negative expectation disconfirmation (i.e., realized discount lower than the expected discount) is highest with a maximum tensile discount, thus discouraging purchases and anticipated satisfaction. The lab studies are supplemented with empirical data and numerical simulations to highlight the double-edged nature of using a maximum discount to advertise storewide promotions. There is thus the need to balance raising expectations of discount and the likelihood of consumers experiencing negative expectation disconfirmation.

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.

Han Chen, Joydeep Srivastava

  • Working Paper

First Week Human, Second Week Algorithm: Curation Source Advertising for Breadth-Then-Depth Selling [Paper]

Many platforms like Netflix strategically advertise and highlight their algorithm curation as a core product benefit for customers, while others like HBO Max and Apple News differentiate on the basis of human curation. Using sequential field experiments on a large mobile reading platform, we examine whether and how advertising algorithm versus human curation impacts consumption behaviors, and how they can be optimally sequenced to best improve the advertising effects. Findings suggest that, despite their similar efficacy in lifting purchases when used in isolation, a human-then-algorithm (HA) advertising sequence surpasses the reversed and non-hybrid sequences’ purchases by 7%–11%. We find advertising algorithm (human) curation leads to depth (breadth) selling by increasing purchases in previously (not) consumed genres. In the best performing HA sequence, this complementarity leads to the initial breadth selling amplifying the subsequent depth selling effectiveness. Importantly, we show that the depth (breadth) selling effects are driven by consumer preference shifts towards previously (not) consumed genres, rather than merely shifting attention to source-labeled curated assortments that happen to be deeper (broader). Our findings suggest that carefully-sequenced advertising of product curation source can help cultivate consumers’ ever-renewing interests by stimulating demand across and within product categories.

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.

Han Chen, Yang Wang, Hanbing Xue, Yongjun Li, and Xueming Luo

  • Revise and Resubmit at Journal of Marketing Research

Exploring New, Reinforcing Love: Dynamic Product Curations With Quasi-Experiment and Deep Reinforcement Learning

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").

Han Chen, Yang Wang, Hanbing Xue, and Yongjun Li

  • Work in Progress

Other Research

I'm prioritizing my dissertation while working on other projects that I find very interesting and exciting (e.g., using ML/DL methods, such as CMAB, to automate new product design while considering demand and supply side factors simultaneously). Please feel free to reach out and have a chat/brainstorm!


Contact

Department of Marketing 📧: hanchen@temple.edu
Fox School of Busines, Temple University 🌐: hanchenresearch.github.io
Alter Hall A502e, 1801 Liacouras Walk Philadelphia, PA 19122

Last Updated: 07-21-2022

Han Chen