Video contextual technology platform for brands and agencies, Zefr, has been selected for the expanded YouTube Measurement Program (YTMP), signalling its commitment to deliver nuanced brand suitable contextual targeting campaigns at scale on YouTube. This announcement coincides with the expansion of Zefr's Context DMP™, which for the first-time ever will allow global brands to expand their human-trained machine learning approach in 20 languages, including Spanish and Japanese.
These announcements come on the heels of the launch of Zefr's Context DMP™ in January, a first of its kind contextual data management platform (DMP). Zefr's Context DMP™ is an identity-less solution that enables brands to build video-level targeting sets based on their unique content preferences. Unlike legacy contextual targeting technologies, which leverage keywords and Natural Language Processing to understand text-based webpages, Zefr's proprietary process combines human cognition with machine learning to capture the nuance of video. In addition to YouTube, Zefr's video-level suitability targeting is also in Beta for Facebook In-Stream video advertising.
"Legacy contextual targeting tools, like keyword blocklists or broad content labelling, come up short when it comes to the nuance of video, leading to inaccurate labels and wasted media spend," said Rich Raddon, Zefr co-CEO. "As a YTMP partner, we're excited that Zefr can deliver nuanced, video-level contextual targeting for our clients on a global scale."
Traditionally, contextual targeting solutions were built for display advertising via keyword analysis to help marketers improve alignment with webpages. But on scaled video platforms like YouTube, keywords alone are insufficient indicators of the nuances of context. Zefr's Context DMP™ leverages proprietary patent-pending Human-in-the-Loop technology to empower brands to align with suitable content, based on their own brand preferences.
As Brand Suitability became a key part of the advertising industry lexicon, MAGNA and the IPG Media Lab released quantitative research on the topic, called "Solving Brand Suitability," in late 2019. The 3,800+ participant study measured different approaches to Brand Suitability, and their effects on consumer perception. Zefr's Human-in-the-Loop, nuanced approach was measured to drive considerable brand lift in key upper-funnel metrics including Purchase Intent, Recommendation Intent, and Relevance when compared to legacy contextual targeting techniques. The full study can be found here.