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Efficient Brand Growth Through Neuroscience-Trained AI

Efficient Brand Growth Through Neuroscience-Trained AI

Bill Harvey – CEO, RMT-Bill Harvey Consulting

Michael Platt, Ph.D. – Director, Wharton Neuroscience Initiative

Bill Harvey (RMT-Bill Harvey Consulting) and Michael Platt (Wharton Neuroscience Initiative) presented a context-effects framework to help advertisers deliver sales growth more efficiently. Their methodology combines content coding, machine learning and neuroscience. The approach centers on measuring subconscious motivation by analyzing the content people consume. Thousands of psychological descriptors were reduced through machine learning to a smaller set of “Value Signals” that predict choice behavior. These signals are linked to neurological processes, with EEG synchrony identified as a strong predictor of advertising-driven sales outcomes. Resonance Mapping Technology (RMT) analyzes the Value Signals in ads, matches them to media contexts and targets audiences aligned with those signals. Third-party studies provided validation, demonstrating improved prediction of sales and brand lift when context and resonance were applied. Additionally, AI can now provide RMT encoding of ads and programs. Key takeaways:
  • Context is a critical part of the creative effects, and ads should not be measured without considering context and value signals.
  • Content consumption behavior is the key to understanding motivations.
  • All choice behavior in the brain is the same calculation whether the subject is choosing programs or brands.
  • RMT ad-context resonance is the only statistically significant predictor of EEG synchrony, according to Wharton Neuroscience.
  • Synchrony predicts 91% of the sales effect of an ad. The context effect is very strong and EEGsynchrony depends on the content that preceded the ad.
  • Incorporating RMT context significantly improved Wharton Neuroscience AI Digital Twin model’s ability to predict outcomes, such as the impact of ad spend on stock prices.

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Proving Influencer Success with RCTs and Brand Lift Data

Tyler Montague – VP of Client & Research Success, Swayable

Maddie Perkins – Director, Strategic Planning, Influential

Tyler Montague from Swayable and Maddie Perkins of Influential explored whether influencer marketing truly drives business outcomes, positioning it as a measurable, full-funnel performance channel rather than just an awareness tactic. Swayable applies an AI-enabled randomized controlled trial (RCT) framework to pre-test creative, comparing exposed vs. control groups across thousands of respondents to isolate causal impact on key brand metrics (e.g., favorability, purchase intent). This is complemented by Influential’s large-scale campaign data and platform integrations, enabling meta-analyses across campaigns, audiences and verticals to evaluate performance at scale. The approach addresses a core industry challenge: proving effectiveness beyond engagement metrics (likes, clicks) and linking influencer content to meaningful business outcomes.

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Modeling Story Expectations to Understand Engagement: A Generative Framework Using LLMs

Hortense Fong, Ph.D. – Assistant Professor, Columbia Business School

Content engagement with narrative depends not only on the content readers have already consumed but on their expectations concerning how the narrative will unfold, according to a presentation by Hortense Fong (Columbia Business School). Content engagement was analyzed in terms of whether readers continued or stopped reading and whether they shared their opinion of the content on social media. Hortense proposed a generative AI approach to model forward-looking beliefs to understand what influences the engagement of readers. A large language model generated multiple possible continuations from existing story text, extracted features from those continuations and linked those features to engagement outcomes. Dimensions associated with narrative success were emotion related to valence and arousal, psychological themes related to character traits and semantic path related to story pacing and structure. A dataset with 90+ million readers and writers and more than 30,000 book chapters across 1,735 books demonstrated this approach. The method was validated by comparing modeled readers’ belief distribution with the distribution of actual story continuations and by comparing the modeled belief with stated beliefs based on surveys with human respondents.

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The Impression Gap: What Works Harder for Marketers on CTV: Premium Video or YouTube

Yan Liu – CEO & Co-Founder, TVision

Benjamin Vandegrift – SVP, Measurement Strategy and Innovation, VAB

Yan Liu (TVision) and Benjamin Vandegrift (VAB) delve into the “impression gap” in connected TV (CTV) advertising and investigate whether premium video platforms or YouTube deliver more effective impressions for marketers. Using a year-long study of 21 premium platforms compared to YouTube, the researchers argue that traditional metrics like time spent are insufficient, and that attention-based metrics like co-viewing, presence and eyes-on-screen behavior better predict advertising effectiveness. Through TVision’s measurement technology that combines computer vision, device tracking and content recognition, the study evaluated how audiences actually engage with content.

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The Creator Paradox: Why Authentic Content Isn’t Enough

Anastasia Leng – CEO & Founder, CreativeX

Anastasia Leng of CreativeX challenged prevailing assumptions about creator marketing in her presentation, by examining whether creator content is truly effective for brands. While doing so, she introduced what she calls the “creator paradox”—the tension between authenticity and advertising effectiveness. CreativeX analyzed a large-scale dataset of 1.6 million ads representing over $2 billion in media spend across platforms (Meta, TikTok) and 800+ brands, to evaluate how creator ads perform relative to established, digital advertising principles. The analysis tested whether foundational rules of digital effectiveness (e.g., early branding, platform suitability) still apply to creator content, using statistically significant comparisons across creative attributes and performance outcomes (e.g., video completion rates, cost per view).

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Win the Moment: Turn Sports Fans’ Peak Emotions Into Measurable Brand

Jennifer Pelino – SVP Media Growth and Data Partnerships, Genius Sports

Abhijit Shome – VP, Marketing Technology, MLS

Jennifer Pelino (Genius Sports) and Abhijit Shome (MLS) explored how live sports content shapes ad effectiveness. They examined which sports moments directly impact viewers’ emotional state and how unaided recall, purchase intent and downstream actions are shifted. The methodology for this research involved testing memory (via survey), emotion (via facial coding) and engagement (via electrodermal activity). The goal was to advise brands on creating optimal ads to complement game moments. Jennifer also discussed Genius Sports’ use of real-time game data and emotional triggers to activate targeted advertising. Jennifer and Abhijit analyzed how the upcoming FIFA World Cup competition represents a major opportunity for brands to connect with fans.

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Methods for Cross-Platform Reach that are 40% More Accurate than Random Duplication

Brian Morse – Head of Collective, Locality

Prasad Joglegkar – Founder and CEO, Deben Media Corp.

Brian Morse (Locality) and Prasad Joglegkar (Deben Media) describe their data-driven approach to improving cross-platform audience reach measurement by correcting the widely used—but flawed—assumption of random duplication. They analyze commercially available datasets across multiple markets and platforms, including broadcast TV, cable, streaming/OTT and online video, to demonstrate that audience overlap between publishers is systematically undercounted when using random duplication formulas.

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