{"id":9499,"date":"2025-10-20T01:06:10","date_gmt":"2025-10-20T05:06:10","guid":{"rendered":"https:\/\/advertisingweek.com\/aw360\/2025\/10\/20\/multimodal-ai-the-future-of-high-performance-ad-targeting\/"},"modified":"2025-10-20T10:42:58","modified_gmt":"2025-10-20T14:42:58","slug":"multimodal-ai-the-future-of-high-performance-ad-targeting","status":"publish","type":"post","link":"https:\/\/advertisingweek.com\/aw360\/news\/multimodal-ai-the-future-of-high-performance-ad-targeting\/9499\/","title":{"rendered":"Multimodal AI: The Future of High-Performance Ad Targeting"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>By Helen Myers, Advertising Week Correspondent<\/em><\/p>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">In a marketing landscape saturated with data, the challenge isn\u2019t access \u2014 it\u2019s connection.<\/p>\n\n\n\n\n\n\n\n<p class=\"wp-block-paragraph\">When data lives in silos \u2014 behavioral here, contextual there \u2014 performance suffers, and managing it becomes a full-time job.<\/p>\n\n\n\n\n\n\n\n<p class=\"wp-block-paragraph\">At Advertising Week, Melinda Han Williams, Chief Data Scientist at Dstillery, unveiled how <em>multimodal AI<\/em> is reshaping audience targeting. Her core argument: the next wave of performance won\u2019t come from bigger datasets or faster models, but from systems that can learn across data modalities \u2014 unifying behavioral, contextual, and environmental signals to deliver both performance and flexibility.<\/p>\n\n\n\n\n\n\n\n<p class=\"wp-block-paragraph\">The insight was clear: AI\u2019s future isn\u2019t just predictive \u2014 it\u2019s fully connected.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Multimodal Means in Practice<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Just as self-driving cars merge radar, vision, and GPS data to create a coherent view of the road, Dstillery\u2019s multimodal AI integrates information from across the digital ecosystem \u2014 including website journeys, search terms, shopping behavior, CTV viewership, mobile app activity, and even podcasts.<\/p>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">The result is a high-dimensional behavioral map that captures <em>why<\/em> people act, not just <em>what<\/em> they do.<\/p>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">\u201cIn the same way a language model learns what words mean by predicting the next word in a sentence,\u201d Melinda explained, \u201cour model learns what websites mean by predicting the next site in a user\u2019s journey.\u201d<\/p>\n\n\n\n\n\n\n\n<p class=\"wp-block-paragraph\">This embedding \u2014 built across 128 dimensions \u2014 allows Dstillery\u2019s AI to represent the intent behind every digital behavior. The model doesn\u2019t rely on arbitrary category labels; it <em>learns meaning<\/em> directly from how people move through the digital world.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Performance <em>and<\/em> Flexibility<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Because Dstillery\u2019s models are fluent across data types, advertisers can begin with <em>any<\/em> seed behavior \u2014 such as first party data, or a list of search terms or product purchases related to the campaign\u00a0 \u2014 and allow the system to translate that intent seamlessly across channels.<\/p>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">Melinda illustrated this with a performance CTV activation example: \u201cA ski apparel brand could start with first-party purchase data and generate predictive segments to reach the CTV devices most likely to drive outcomes for the campaign. And the same behavioral signal that identifies those likely buyers can also power contextual CTV targeting on specific shows, networks, and genres.\u201d<\/p>\n\n\n\n\n\n\n\n<p class=\"wp-block-paragraph\">That cross-channel consistency produces a level of precision previously out of reach. \u201cWe\u2019re seeing a level of performance on CTV that advertisers aren\u2019t used to expecting \u2014 the ability to apply behavioral predictions not just to audiences, but to <em>individual programs<\/em>,\u201d Melinda said.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\"><strong>From Intelligence to Action: DS-1 and Agentic AI<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">The culmination of this system is DS-1, Dstillery\u2019s <em>agentic AI interface<\/em> \u2014 an intelligent layer that connects directly to all of the company\u2019s multimodal AI audience capabilities.<\/p>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">Unlike typical AI assistants that simply recommend, DS-1 <em>acts<\/em>. It can create deal IDs, activate campaigns, or build custom models through a simple conversational interface \u2014 even inside tools like Slack.<\/p>\n\n\n\n\n\n\n\n<p class=\"wp-block-paragraph\">Marketers can ask, \u201cFind me audiences that will perform for my brand,\u201d and DS-1 instantly shows simulated performance across more than 20,000 audiences to identify top-performing segments before any media spend occurs.<\/p>\n\n\n\n\n\n\n\n<p class=\"wp-block-paragraph\">\u201cThe agent doesn\u2019t replace predictive AI,\u201d Melinda emphasized. \u201cWhen you activate audiences with DS-1, the targeting intelligence comes from multimodal AI predictions, grounded in behavioral data, not generative AI synthesis. DS-1\u2019s role is to make that proven predictive intelligence <em>accessible<\/em>. The accessibility enables faster iteration, richer customization, and ultimately, better results.\u201d<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Multimodality Matters Now<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Early marketing AI focused on automation and content generation \u2014 producing more, faster. But the next competitive edge lies in <em>understanding<\/em>: mapping intent, emotion, and behavior across channels to act with precision and empathy.<\/p>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">Multimodal AI makes that possible. It allows brands to move beyond one-dimensional targeting and see customers as complex, evolving decision-makers. By translating behavioral signals into actionable predictions across every format, it restores a sense of coherence to digital marketing \u2014 and raises expectations for what \u201crelevance\u201d truly means.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The Lesson for Leaders<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">AI\u2019s challenge is no longer capability \u2014 it\u2019s <em>connectivity<\/em>.<\/p>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">Organizations that treat multimodal learning as a foundational skill, pairing it with agentic interfaces like DS-1, will unlock faster workflows, better results, and more human-centered marketing.<\/p>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">The next era of targeting belongs to AI systems that see the whole picture \u2014 and to the leaders who know how to orchestrate them.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The next era of targeting belongs to AI systems that see the whole picture \u2014 and to the leaders who know how to orchestrate them.<\/p>\n","protected":false},"author":7,"featured_media":9500,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","editor_notices":[],"footnotes":""},"categories":[1],"tags":[17,37],"topics":[2],"class_list":["post-9499","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-ai","tag-awnewyork-2025-content","topics-editorial"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.0 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Multimodal AI: The Future of High-Performance Ad Targeting | AW360<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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