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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As brands increasingly experiment with virtual influencers, new research, published in the Journal of Advertising Research, offers one of the most comprehensive examinations of the virtual influencer landscape to date. Through a systematic review of 117 academic articles, the authors introduce a formal “virtual influencer ecosystem” framework that maps the relationships among creators, brands, consumers, AI technologies and social platforms. The study explores how authenticity, credibility, autonomy, emotional connection and consumer unease shape audience responses to virtual influencers—and what these dynamics mean for marketers navigating the future of AI-driven influence.
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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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