He explained that by aligning an ad’s emotion to the content’s emotion, brands can improve viewer memorability and campaign engagement which he says, “ultimately drives stronger outcomes and performance. Our own data has shown that advertisers can experience a significant uplift in conversion of 2-3x when they find moments of emotional resonance.”
Charlene Weisler: How does the AI matching component work?
Peter Crofut: Wurl uses GenAI to analyze the emotion of the content in the scene closest to the ad break. That analysis is based on Robert Plutchik’s wheel of emotions which includes eight primary emotions at three different intensities (high, medium, and low) that can be combined to represent the full spectrum of human emotional states. Once the emotional score for the scene is determined, it is then matched to an ad with a similar emotional score. This prevents viewers from having a jarring experience where, say, they’re crying from watching a sad scene and are then disrupted with an overly upbeat ad. When scenes and ads are misaligned that’s where negative attention can be created for a brand.
Weisler: What data is collected – how is the platform measured?
Crofut: To determine the emotional segments of the content for BrandDiscovery, we leverage GenAI to view and analyze content from a variety of different sources including images, sound, and text. From there, BrandDiscovery assigns, at a scene level, the corresponding emotional value as determined by the AI. Notably with GenAI, we offer scene-level targeting through BrandDiscovery as opposed to categorizing an entire program, series or channel under any one contextual category. Equally, BrandDiscovery’s targeting is free – we don’t charge any data fees for emotional segments. In terms of measurement, we work with independent partners like EDO and Kantar to look at things like campaign effectiveness, brand awareness, brand favorability, and purchase intent for BrandDiscovery campaigns.
Weisler: How does scene-level analysis work?
Crofut: BrandDiscovery was born out of a curiosity to consider whether or not ads that were emotionally aligned with content perform better. During initial testing, we discovered that the most significant correlation with ad engagement took place when the emotions depicted in the scene just before the ad break aligned with those portrayed in the ad creative. As a result, Wurl developed its own GenAI models trained against millions of assets to analyze content in real time. As noted earlier, we use Plutchik’s wheel of emotions to determine the emotional score of the scene, and then match it with an ad that scored similarly. This analysis is calculated in real time across FAST (free ad-supported streaming TV) channels just before the ad break for highest relevance.
Weisler: Can you give an example of what a campaign looks like?
Crofut: We recently partnered with Media.Monks, who was tasked with executing a CTV campaign for a financial services client to expand its audience, improve awareness, and ultimately boost purchase intent. Using BrandDiscovery’s emotion-based targeting to run CTV ads across Wurl’s network of premium content publishers, Media.Monks was able to bolster the resonance of their client’s brand messaging and optimize targeting for more impactful brand engagements. The client saw a 7x lift in aided brand awareness and 2x lift in purchase intent compared to five-year industry benchmarks for Lending brand impact studies.
Weisler: What are the opportunities here?
Crofut: CTV offers massive potential for brands to connect with audiences in meaningful ways, especially as the biggest screen in the home. And, while contextual targeting on CTV certainly isn’t a new concept, we’re confident in the idea that emotions-based targeting is truly reshaping how we think about this approach today. By aligning ads based on the emotional context of the scene right before the ad break, brands can meet audiences where they already are and not just increase attention, but increase positive attention among viewers.
Weisler: What are the challenges?
Crofut: Media buyers today face a fragmented CTV ecosystem with multiple walled gardens and no standardized metadata – one movie or TV show could be categorized as multiple different genres depending on where you look. And, while CTV offers a significant step above linear in that it offers the ability to control who sees an ad, it still lacks control over the placement of the ad. All of this has resulted in limited transparency into where ads are displayed and in what contexts, ultimately contributing to the fact that advertising dollars have not kept pace with the shift in viewership from linear to CTV. By aligning ads’ emotions with the emotions of the programming, brands can have more confidence that their ads will show up in the appropriate context and better resonate with audiences.
Weisler: What are your next steps?
Crofut: In advertising, we hear a lot of talk about finding the right user at the right time with the right ad – though, in reality, we don’t really know what “right” is. As with all AI-driven innovation, the technology only gets stronger over time as it learns from more inputs. Right now, we’re in the early stages of seeing brands utilize emotions-based contextual targeting on CTV – as more advertisers adopt this approach, we’ll have even greater knowledge to learn and grow from as an industry and help us define what the “right” context truly is.
This article first appeared in Mediapost.com
Artwork by Charlene Weisler
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