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<p>Video ads remain one of the most important ad formats. As multichannel video ad campaigns increasingly span TV, social media, and YouTube, advertisers need insights into specific content features associated with higher recall and attitudes for each channel. Research linking ad content to effectiveness has, however, largely focused on single channels, limiting insight into which granular content features generalize across channels and which are channel-specific. Building on limited-capacity processing and processing fluency, we argue that creative dimensions operate differently across channels because TV, online video, and social video create different attention constraints and fluency demands, leading to different granular content element effects per channel. We analyze these effects empirically using 3495 TV, online, and social media ads. Using hybrid algorithmic and human content coding, we extract 161 common content features and estimate their contributions to recall and attitudes with Bayesian Additive Regression Tree models. The findings identify top elements per channel and reveal substantial differences across channels and objectives, showing that multichannel video creative requires adaptation not only by channel but also by campaign objective. We further show how the approach can support scalable, feature-level creative diagnostics for ad pretesting.</p>

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Creative synergy: leveraging AI and human coding to enhance recall and attitudes in TV, social and online video advertising

  • 期刊论文

  • 发表日期：

    2026年7月4日

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  • Edlira Shehu,
  • Prasad A. Naik,
  • Ziad Elmously,
  • Rick Candelaria

摘要

Video ads remain one of the most important ad formats. As multichannel video ad campaigns increasingly span TV, social media, and YouTube, advertisers need insights into specific content features associated with higher recall and attitudes for each channel. Research linking ad content to effectiveness has, however, largely focused on single channels, limiting insight into which granular content features generalize across channels and which are channel-specific. Building on limited-capacity processing and processing fluency, we argue that creative dimensions operate differently across channels because TV, online video, and social video create different attention constraints and fluency demands, leading to different granular content element effects per channel. We analyze these effects empirically using 3495 TV, online, and social media ads. Using hybrid algorithmic and human content coding, we extract 161 common content features and estimate their contributions to recall and attitudes with Bayesian Additive Regression Tree models. The findings identify top elements per channel and reveal substantial differences across channels and objectives, showing that multichannel video creative requires adaptation not only by channel but also by campaign objective. We further show how the approach can support scalable, feature-level creative diagnostics for ad pretesting.