Advertisers frequently feature both products and human models in print and digital campaigns—but how large each element appears relative to the other can significantly influence consumer responses. This Journal of Advertising Research study shows that the effectiveness of this visual design choice depends on product type. Across a field experiment and multiple online studies, the researchers find that hedonic products perform better when the model is larger than the product, while utilitarian products benefit when the product itself is larger than the model. The reason: these pairings create greater conceptual fluency for consumers, making the advertisement feel more cognitively “right” and leading to stronger product evaluations and purchase intentions.
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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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As generative AI becomes a central tool for producing marketing content, firms increasingly rely on fine-tuning models using engagement data, such as A/B test results. This MSI working paper argues that optimizing only for “what works” risks reward hacking, clickbait and poor generalization. The authors propose a knowledge-guided alignment framework in which large language models (LLMs) generate and validate hypotheses about why content performs well, and then use this knowledge to guide fine-tuning. Using more than 23,000 A/B-tested news headlines, the study shows that knowledge-guided AI produces higher engagement, avoids clickbait and generalizes better—especially in low-data settings.
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