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, and the component concepts, frameworks, and principles, are the cornerstone of effective marketing decisions. Yet, much of this knowledge, codified in journals and textbooks, remains difficult for managers to access and apply. This paper evaluates the potential of large language models (LLMs) to bridge this gap by serving as marketing knowledge systems. Specifically, we test what LLMs “know” about marketing and how effectively they can reason with and apply that knowledge. We develop a comprehensive dataset of over 30,000 questions drawn from instructors’ manuals of 25 contemporary marketing textbooks to assess LLM performance. Leveraging within-textbook variation in answer accuracy, we evaluate LLMs on three dimensions: domain knowledge, reasoning capabilities, and user interaction. We find strong domain knowledge across marketing topics, including specialized areas. LLMs demonstrate near-perfect recall and understanding, though accuracy declines on tasks requiring higher-order or numerical reasoning—gaps that are narrowing with newer models. Accuracy remains stable across different phrasings and prompts, and experimental interventions suggest that LLM responses reflect a robust conceptual representation rather than superficial pattern recognition. Our findings indicate that LLMs now offer reliable and scalable access to foundational marketing knowledge, with implications for how such knowledge is disseminated, taught, and applied.
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