Marketing

Social media platforms have become vital channels for businesses to reach consumers through advertising. But in the United States, the digital advertising market in which these platforms operate is dominated by a few major players, raising concerns for antitrust regulators. In such a concentrated market, the entry or exit of a single platform can reallocate billions in ad spending, affecting businesses and users.

The relationship between social status and green consumption is pivotal in addressing the climate crisis, yet previous research reveals conflicting perspectives. We analyze a nationally representative panel survey of more than 63,000 British respondents, tracking their green consumption, attitudes, and behaviors across multiple years. The findings reveal a previously unidentified S-shaped relationship between social status and green consumption, challenging existing models that propose inverted U-shaped, negative linear, or positive linear associations.

Deciding which customer segment(s) to serve, or targeting, is a cornerstone of marketing strategy. While companies commonly target based on demographic characteristics, recent cultural movements (e.g., #MeToo, Black Lives Matter) have heightened sensitivity to the differential treatment of certain demographic groups. Yet research to date has not examined whether demographic targeting itself seems fair or appropriate.

Large language model (LLM)-based digital twin simulation, where LLMs are used to emulate individual human behavior, holds great promise for research in business, artificial intelligence, social science, and digital experimentation. However, progress in this area has been hindered by the scarcity of real individual-level data sets that are both large and publicly available. To address this gap, we introduce a large-scale public data set designed to capture a rich and holistic view of individual human behavior.

For e-commerce companies to assess how best to invest in improving delivery times, it is important to understand how improving delivery times affects customer demand. In collaboration with a business-to-business (B2B) e-commerce company, we study how the promised delivery time in a quote affects the customer's purchase probability. We use observational and experimental data from our partner, with quote-level variation in promised delivery times.

Personalization has become the heartbeat of modern marketing. The rapid expansion of individual-level data, the proliferation of personalized communication channels, and advancements in experimentation have fundamentally reshaped how firms tailor their marketing strategies. Furthermore, causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic overview of these developments.

Generative Artificial Intelligence (GenAI) tools based on Large Language Models (LLMs) are quickly reshaping how researchers conduct surveys and experiments. From reviewing the literature and designing instruments, to administering studies, coding data, and interpreting results, these tools offer substantial opportunities to improve research productivity and advance methodology. Yet with this potential comes a critical challenge: researchers often use these systems without fully understanding how they work.

From Netflix to Spotify to TikTok, consumers’ entertainment experiences increasingly revolve around personalized content. Does consuming personalized content in a specific entertainment domain influence the likelihood of consumers discussing that domain overall, beyond the specific content they engage with? Across eleven experiments, we investigate the impact of personalized content in the music and short-form video domains (i.e., content that feels tailored to one’s tastes) on the intent to discuss domain-related topics. We find that the impact of consuming personalized content (vs.