Submitted at the Journal of Marketing.
Abstract
This paper uses a sequence of three randomized controlled trials (RCTs) to support a small online business’ decision of whether and how to implement AI in the creation of email marketing content. To accommodate the small samples available for testing in the small-business setting, the authors leverage recent developments in frequentist statistical decision theory. 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. Across the RCTs, the hybrid cell varies by whether it is edited by a salaried writer or the marketing team. The LLM cell is varied by whether the AI is pre-trained using historic emails or uses a prompt-based generative pre-trained transformer (“GPT”). Across all three RCTs, an AI policy with automated content generation is always selected over the human cell, based on total annual profit net of related labor and software overhead. Most notably, the prompt-based GPT outperforms the human writers by 8 to 9%.

Abstract
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.

Abstract
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.”