Watching television is often a shared experience, but how does co-viewing affect audience attention? An analysis of nearly 25 billion seconds of TV viewing data reveals that watching with others decreases attention to advertisements while increasing attention to programming. The effects vary by platform, with linear TV outperforming CTV in sustaining attention during co-viewing situations. The study also finds that longer co-viewing sessions strengthen engagement, while longer ads can undermine attention. These findings offer important guidance for advertisers, content creators and media planners seeking to maximize audience engagement in an increasingly fragmented television landscape.
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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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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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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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