The key to success in video advertising today? Relevance. It sounds simple enough, but once you layer on challenges such as 3rd party data accuracy, reporting reliability, scalability and cost, it’s no wonder brand advertisers often feel like this –


Like with traditional TV advertising, the best results in digital video advertising are not going to be achieved through audience targeting at the channel level, but rather, content targeting at the video level. But with the vast amount of content on YouTube, that is impossible to do with human review alone. This is where machine learning comes in. Simply put, machine learning is a process that leverages knowledge (in the form of patterns) extracted from human review and feeds it to a machine that applies those patterns to algorithms that allow human-guided review at a massive scale.

In our latest episode of Technically Speaking, I sat down with the Head of Data Science at Zefr, Jon Morra, to discuss how Zefr is using machine learning to scale ad relevance through content targeting at the video level across all of YouTube.

How does it work? Morra explains,

What machine learning is about is extracting patterns. We have a team here at Zefr, who watches a sampling of videos on behalf of brands to identify them as either “relevant” or “irrelevant” for each brand or specific campaign. We look for patterns that identify videos that are good for one brand and bad for another. Once data science has extracted those patterns, the machine can then “watch” every video on YouTube, using the knowledge of the human-identified patterns to deliver results at scale.

Morra acknowledges that understanding videos is at the core of data science at Zefr, but understanding brands is the real differentiator. When you know your clients and you can speak their language, you can deliver the results they are looking for. Morra adds that,

We take our understanding of what is important to brands and we align it with the right content on YouTube at a scale that no one else can do in a brand-specific way.

Watch the full episode to hear more about machine learning and a sneak peek at what’s next for the data scientists at Zefr.