I provide the first causal evidence that consumers’ reference prices, shaped by past price exposures, affect current demand. In a large-scale two-stage field experiment across over 800 vending machines, I first randomly assign machines to different base price levels for 3 to 6 months, then implement common discounted prices across all machines. Arrivals dip immediately after the price increase, then recover over time, consistent with consumers forming new reference prices. In the second stage, consumers with a 10% higher reference price generate 1 to 3% more net revenue when facing identical current prices. I estimate a structural demand model that separates price sensitivity from reference dependence, enabling counterfactual policy analysis. In my setting, the focal firm earns about 6% higher profits by accounting for reference dependence, while promoting 38 percentage points less frequently than a myopic benchmark. Myopic firms overlook promotions’ long-run cost to pricing power and misattribute reference dependence to price sensitivity. Over-promoting costs firms more than they gain.

We investigate how data ownership structures affect the implementation of personalized pricing in consumer packaged goods (CPG) markets, where retailers typically control customer data through loyalty programs. Using a game-theoretic model calibrated with IRI data from five CPG categories, we analyze the strategic interactions between oligopolistic manufacturers and a monopolistic retailer. Our model incorporates a multi-stage game where the retailer can offer personalized pricing services to manufacturers for a fee. We employ machine learning techniques, specifically Bayesian Bootstrapped Lasso, to estimate heterogeneous consumer preferences and simulate equilibrium outcomes under different pricing regimes. Our findings reveal that full personalized pricing where every brand gets personalized, does not always emerge as an equilibrium outcome, particularly when retailers charge differential access fees. However, the equilibrium outcome almost always increases consumer welfare compared to uniform pricing, with progressive distributional effects benefiting lower-income consumers more. The results suggest that market structure and the misalignment between retailer and manufacturer incentives play crucial roles in determining personalized pricing adoption. These insights have important implications for public policy debates surrounding data privacy and price discrimination.

A sequence of three randomized controlled trials (RCTs) is conducted to support a small online business’ decision of whether and how to implement AI in the creation of email marketing content. Recent developments in frequentist statistical decision theory are used to accommodate small samples available for testing in the small-business setting. The RCTs comprise three test policy cells with email content created by (i) salaried writers (“human”), (ii) a large-language model (“LLM”), and (iii) a “hybrid” combination of a human editing the content created by the LLM, respectively. When a “no email” control policy is included, all three test cells approximately double the gross profits from orders relative to the control cell. The RCTs vary whether the hybrid cell is edited by a salaried writer or the marketing team. The LLM cells vary whether the AI is pre-trained using historic emails or uses a prompt-based generative pre-trained transformer (“GPT”). Decision theory always selects one of the AI cells over the standard human policy on the basis of total annual profit net of related labor and software overhead.

The COVID-19 pandemic caused supply chain disruption and an unprecedented surge in demand due to panic-buying and stockpiling. Both factors have contributed to the skyrocketed grocery prices during and post-COVID in the US, which increased the cost of living across the nation. We utilize nearly 2 decades of scanner data to assess the change in the consumer price index in the grocery industry and examine the differential effects of inflation on households with different income levels. Low-income households could be more sensitive to price and switch to cheaper products more quickly, but there might be limited options they could turn to. Therefore, the differential impact of inflation is ambiguous. To better understand consumers’ price sensitivity, we also examine CPI at the package level, which helps understand consumers’ various response to “shrinkflation.”

We use variation in the growth of broadband to study how the rise of information technology has shaped advertising. From 2010 to 2019, broadband rates increased from 70% to 80%. At the same time, TV advertising remained steady while spending as a proportion of advertising has decreased in other channels. Our results show that a 10 percentage point increase in broadband increases spending in offline advertising in that DMA by approximately 1.2%. Similarly, a 10 percentage point increase in broadband increases spending in TV advertising in that DMA by approximately 1.1%. We show that broadband decreases print advertising in most DMAs.