with Daniel M. Ringel and Bernd Skiera
[show abstract] Effective online advertising depends on a marketer’s ability to reach a target audience—a specific group of consumers with desired characteristics. Traditionally, marketers have identified these consumers by tracking and analyzing their online behavior. However, growing privacy concerns and new regulations are restricting this practice. In response, this research investigates an alternative strategy for reaching target audiences online: inferring consumer characteristics solely from search queries consumers use when searching online. We empirically test the premise that search queries contain valuable signals about consumer characteristics that allow marketers to identify those queries most indicative of their target audience. Across three contexts—weight loss, online dating, and personal investing—we demonstrate that search queries strongly indicate consumer characteristics such as socio-demographics, category experience, or brand preferences. A subsequent field study further supports the external validity and practical implications of these findings. Using our results, a leading retail bank launched a search advertising campaign targeting a particular high-value audience. This audience-specific campaign converted a higher share of new customers (+21.37%) who generated substantially more revenue (average trading volume per customer: +97.90%), compared to a performance-driven campaign designed by SEA experts.
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with Raymond R. Burke and Alex Leykin
[show abstract] Foundational marketing knowledge, from theories of consumer behavior to segmentation frameworks and pricing models, supports effective marketing decisions, yet managers often struggle to access and apply it in practice. This paper evaluates whether large language models (LLMs) can function as scalable marketing knowledge systems that make such knowledge broadly accessible. Specifically, we assess what LLMs “know” about marketing and how effectively they can reason with and apply that knowledge. We construct a comprehensive dataset of over 30,000 questions drawn from instructor materials of 25 widely used marketing textbooks and assess LLM performance across three dimensions: domain knowledge, reasoning capabilities, and user interaction. Across models and providers, current LLMs achieve high accuracy (84%- 94%), demonstrate near-complete coverage of marketing topics, and show strong performance on recall and conceptual understanding. Performance declines on higher-order reasoning tasks, though newer reasoning models close these gaps substantially. Experimental manipulations of these questions suggest that performance reflects conceptual understanding rather than simple memorization. In a human benchmarking study, LLMs substantially outperform respondents across all training levels, while showing complementary strengths relative to more advanced human reasoning. These findings indicate that LLMs have crossed an important capability threshold: they now provide reliable, on-demand access to much of the discipline’s foundational knowledge, with important implications for how marketing knowledge is disseminated, taught, and applied.
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