As generative AI tools increasingly shape how consumers search, shop, compare and evaluate products, understanding how they make recommendations has become critical for marketers. This seventh experiment in our ongoing Psychology of Gen AI series, is the first phase in a study that examines how large language models (LLMs), like ChatGPT and Claude, determine what qualifies as the “best” product—and reveals that their recommendations are far from neutral. Instead, they tend to rely on narrow, repetitive sets of familiar brands and structured response patterns that may reinforce existing market leaders. The findings highlight important implications for brand visibility, competitive dynamics and how marketers should position their products in AI-driven environments.
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Marketing effectiveness analytics is undergoing a profound transformation. As marketers face growing complexity across channels, data sources and consumer journeys, artificial intelligence is accelerating the shift from retrospective measurement toward dynamic, decision-oriented systems. This paper examines a decade of innovation in marketing analytics, highlighting the rise of integrated measurement frameworks, experimentation, machine learning and emerging AI-powered modeling approaches that promise to reshape how organizations understand and optimize marketing performance.
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Generative AI is opening new possibilities for hyper-personalized advertising, including the ability to create AI-generated faces that closely resemble individual consumers. But how similar is too similar? This Journal of Advertising Research study finds that while moderate facial resemblance can improve advertising effectiveness, excessive similarity may backfire. The research introduces a new framework for measuring facial similarity and identifies an optimal personalization threshold that maximizes purchase intentions while avoiding consumer discomfort and resistance.
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This MSI working paper introduces TextBO, a novel AI framework designed to improve marketing decisions more efficiently by minimizing costly evaluation cycles. By combining large language models with Bayesian optimization principles, the approach enables AI systems to iteratively refine outputs—such as ad creatives—while requiring fewer real-world tests. The result: faster learning, better-performing outcomes, and a more scalable path to AI-driven decision-making.
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