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 eighth report in our ongoing Psych of Gen AI series 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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New research reveals that virtual influencers, despite their growing popularity and flexibility, are less effective than human influencers in driving engagement and brand outcomes. The reason lies in consumer psychology: people perceive virtual influencers as less deserving of success, which reduces feelings of envy—an emotion that typically drives social media engagement. However, this disadvantage can be mitigated when virtual influencers are paired with futuristic, technology-focused brands, where their artificial nature feels more congruent.
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Companies run many marketing experiments, but most A/B tests are analyzed independently—limiting what firms can learn about how customers respond to interventions over time. This research introduces a hierarchical Bayesian framework that integrates data from many experiments simultaneously to estimate customer-level responsiveness to marketing. Using large-scale field experiments, the model decomposes treatment effects into customer, campaign and timing components and uses these insights to improve targeting decisions. The results show that most variation in marketing effectiveness comes from persistent differences in customer responsiveness, enabling firms to better identify who to target and when.
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