The Study, Conducted by Magna and Zefr Explores The Impact of Machine Learning Combined with Human Supervision on Brand Suitability
Human in the loop” contextual targeting – which uses brand preferences to power machine learning that is overseen by humans – is dramatically more effective than traditional modes of ad targeting, according to “Solving Brand Suitability,” a new study by MAGNA and the IPG Media Lab conducted with Zefr, the Contextual DMP for brands and agencies.
The study aimed to provide a foundational understanding of how brands can better achieve “brand suitability,” defined by advertisers as their unique positive and negative contextual preferences. Advertisers are increasingly focused on how different targeting methods fare in achieving it.
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The study found that just 25% of consumers think brands are doing a good job of advertising on YouTube and only 18% of those who expect relevance between the ads and the video said that the ads are typically aligned with the videos they are watching. The study then explored different methods marketers could employ to improve “Brand Suitability.” Nearly 4,000 consumers were surveyed on their reactions to ads from three brands across verticals – Nationwide (insurance), Ubisoft (gaming) and Scotts Miracle Grow (paper/manufacturing). Ads were delivered to consumers via different forms of targeting: demographic; channel; keyword; and “human in the loop contextual targeting” (where a team consistently reviews videos in order to train machine learning models). The study revealed that ads delivered through “human in the loop” contextual targeting outperformed all other methodologies in a number of key metrics:
- Relevance: 64% of consumers felt ads delivered via “human in the loop” contextual targeting are relevant (48% for channel targeting, 52% for demo targeting and only 44% for keyword targeting).
- In-Market Reach:Â 82% of consumers reached via “human in the loop” contextual targeting were in-market consumers for the product, as ads are naturally reaching a more relevant audience.
- Better Experiences: The same creative is received significantly more positively when delivered via “human in the loop” targeting. Consumers ranked the ads higher quality (83%) as well as more authentic (64%) and innovative (57%) – outperforming all other targeting methodologies.
- Consumers Are More Positive on the Brand: Respondents view brands overall more positively when targeted via “human in the loop,” describing them as more savvy and thoughtful vs. the same ad targeted via the other methodologies.
- …And They’re More Likely to Buy: The same ad generates nearly double the purchase intent for respondents targeted through “human in the loop” (+11%) than the next most powerful methodology – keyword targeting (+6%).
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“Contextual targeting is highly nuanced for each brand, especially in video, and traditional methodologies like static ‘whitelists’ and channel targeting often miss the mark, negatively impacting reach and wasting valuable media dollars,” said Rich Raddon, co-CEO of ZEFR. “This study provides valuable industry insights on how brands can take control with human-in-the-loop contextual targeting and increase the impact on every part of the funnel, from in-market reach to purchase intent.”
A somewhat unexpected insight revealed in the study is the considerable opportunity for advertisers to reach audiences by expanding their definitions of “quality” video. 44% of content machines identify as low-quality is perceived as high-quality by consumers who view it as enjoyable and interesting. The study shows that in video, quality is often in the eye of the beholder, and brands can succeed by tapping into this significant pool of largely uncharted, brand-suitable ad inventory.
UM’s Chief Digital and Innovation Officer, Joshua Lowcock said, “This is a firm reminder context matters as much as the data used to find an audience. The more aligned the ad is with content, the more likely consumers are to view the brand as innovative, savvy, trustworthy and one for which they will pay more. Using human-supervised machine learning to help find suitable content is one way of finding that balance.”
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