As changes in marketing and research methods and technologies accelerate, keeping up to date is becoming increasingly urgent for researchers. The ARF and New York University (NYU) are providing a solution. Read more »
Member Only AccessAs changes in marketing and research methods and technologies accelerate, keeping up to date is becoming increasingly urgent for researchers. The ARF and New York University (NYU) are providing a solution. Read more »
Member Only AccessAs changes in marketing and research methods and technologies accelerate, keeping up to date is becoming increasingly urgent for researchers. The ARF and New York University (NYU) are providing a solution. Read more »
Member Only AccessAs changes in marketing and research methods and technologies accelerate, keeping up to date is becoming increasingly urgent for researchers. The ARF and New York University (NYU) are providing a solution. Read more »
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Enterprise growth teams often work in silos, resulting in missed opportunities and incomplete insights. A unified Enterprise Growth Model combines all growth levers into one causal framework, revealing hidden relationships and synergies. On June 4, attendees learned how this approach enables better, faster decisions that drive business growth.
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Can facial expressions reliably reveal emotional responses to advertising across cultures? A new study published in the Journal of Advertising Research suggests the answer is yes. Drawing on a massive global database of more than 70,000 advertising studies and nearly 3.8 million frames of viewer facial responses, the authors found remarkably consistent facial expression patterns across 12 world regions. The study provides evidence that certain expressions linked to happiness, disgust, surprise and awe appear universally in response to advertising and entertainment content—supporting the use of automated facial coding as a valuable tool in global advertising research and creative testing.
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As generative AI becomes a key part of how consumers discover and evaluate products, a new question emerges for marketers: how can they ensure their brands show up in AI-driven recommendations? This ARF and MSI experiment, the second phase of the seventh study in the Psychology of Gen AI series, reveals that even small changes in prompt wording can significantly influence which brands appear—helping non-market dominant brands carve out visibility by aligning with specific product attributes rather than competing broadly for “best” status.
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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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