As generative AI tools increasingly influence product discovery and decision-making, subtle cues in user language can shape what consumers are shown—and how options are framed. This research examines how implicit and explicit gender signals affect AI-generated product recommendations, revealing systematic differences in categories, brand repetition, descriptive language and price information. The findings raise important questions for advertisers and researchers about bias, brand visibility and the growing cultural role of AI in shaping consumer norms.
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A new ARF Psych of GenAI experiment reveals that large language models apply a rigid, rule-driven logic when evaluating privacy scenarios—even when humans typically shift their reasoning based on framing, emotion and social context. Unlike consumers, who blend intuition, feeling and social perspective into their judgments, GPT-4o relied on a single internal rule across all testing conditions: data use is acceptable only with explicit consent. This consistency offers value for certain analytic tasks but exposes limits for advertising research that depends on emotional nuance and context-sensitive consumer insight.
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The study in this MSI working paper evaluates whether large language models (LLMs) can serve as a reliable source of consumer preference data—potentially transforming how market research is conducted. Using conjoint-style survey questions, the researchers compared LLM-generated choices with human responses to estimate willingness-to-pay (WTP) for a variety of product attributes. They find that LLMs often approximate human preferences surprisingly well, especially when fine-tuned with prior survey data, though important limitations remain. For marketers, the research highlights both the promise and the boundaries of using AI-generated insights to accelerate testing, concept screening and early-stage innovation work
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On September 29-30, the ARF and MSI co-produced the inaugural ANALYTICS & FORECASTING conference, exploring the evolving role of modeling in market research and forecasting, with a particular focus on the opportunities and limitations of synthetic data. Attendees engaged in critical discussions about the opportunities and limitations of modeling in market research applications and heard practical strategies and solutions from leading researchers and practitioners.
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