Product & Tech

We have always known at Zefr that whenever a video ad is placed around relevant content the viewer will have a more engaging experience. We have largely delivered on this promise by offering our customers content “packages” which provide an assortment of well-defined online video content targeting solutions around specific themes, trends, or categories beside which our customers can choose to place their digital ads.  

Some clients, however, are less interested in “packages” and simply want to run their campaigns against all YouTube content that is suitable to their brand (or campaign).  This requires a higher level of customization wherein we must identify subtle, yet important, nuances among brand preferences at the video level which means we end up making a unique list of videos for each customer.  The challenge for us then is to make these brand-customized content targeting solutions scalable across campaigns. This is all made possible by data science.

Data Science

We have always relied heavily on data science to generate business growth in a variety of ways. However, as we begin shifting our approach to curation more and more towards these highly customized solutions for every campaign, data science has innervated more of our processes.  Our customers lean on us to provide detailed insights around every online video on YouTube; we use data science to create unique video lists that would be most appropriate for each customer.

To do this, three components must work seamlessly together.  First, we must create campaign-level labeled data to identify videos either as “relevant” or “irrelevant” for each campaign.  Second, we must extract key bits of knowledge about videos to allow machines to “learn” and “understand” what a specific video is all about. This allows us to create context logs.  Finally, we then must leverage machine learning to detect patterns in all this data, helping to more accurately distinguish relevant from irrelevant videos for each customer and every campaign.

Human Review

The most critical step in this process is human review.  Humans must watch every video to determine which videos are “good” or “bad” for any given campaign.  Video-level review is the fuel for the data science engine. At Zefr, our video reviewers are the account executives, account managers, and ad operations experts working on every campaign. They know our customer’s preferences inside and out.  Here’s a great example. Our team has the knowledge to help a major makeup brand ensure their ads are only seen adjacent to videos about “daily makeup application” (and not videos about “Halloween makeup”). This is because, in data science, when humans make decisions holistically, the discoverable patterns in the data are much richer. Combining this with our deep understand of YouTube allows us to make customer- and campaign-level decisions that take all elements of a video into account appropriately.

Information Extraction

The data science team springs into action once the human review of videos is complete. This begins with featurization: the process of taking a complex piece of data, in our case a YouTube video, and breaking it down into its component parts.

No machine learning algorithm that we know of currently exists that can natively “understand” a YouTube video.  Each video consists of numerous pieces of information (i.e. thumbnails, title, description, number of views, publish date, etc.).  During featurization our data scientists extract this information from a video and organize it to be ingested by pattern recognition algorithms.  This is known as multi-modal learning because we are consuming many different kinds of data –  image, audio, text, and numerical. Multi-modal learning allows us to find patterns in the different components of a video. This helps give Zefr a competitive advantage over other solutions that analyze only a single component of the data (i.e.text).

Machine Learning

Machine learning boils down to this: algorithms that find patterns in data.  In Zefr’s case, our objective is to find patterns that distinguish “relevant” videos from “irrelevant” videos for every campaign we run. Human review and featurization are the first steps in this process; machine learning comes next.  

Our data scientists use state-of-the-art algorithms – including random forests, gradient boosting machines, and deep neural networks – to find distinct patterns in our data.  We also tap into some of the best machine learning platforms like Vowpal Wabbit, H2O, MxNet, and scikit-learn to ensure we find every pattern present in our data. Using a variety of machine learning platforms allows us to select the best algorithm to suit both our data and the desired customer outcome.

There are billions videos in the YouTube ecosystem – and we know a lot about them. Because of this, we need both a rapid experimental environment to test new ideas as well as a robust production environment to deploy successful experiments.  We are able to accomplish both using high-quality open source tools as part of our process. In fact, we’ve also contributed to the ecosystem with a paper about our production environment, Aloha.

Conclusion

Our customers get the best outcomes when their ads are aligned with relevant content. Improving our machine learning capabilities is one of the ways we achieve this at the campaign-level.  We start with human review, watching videos on YouTube with our customers’ brand guidelines in mind to create a new data source that is wholly unique to and deeply aligned with our customers’ needs.  Data science allows us to derive actionable insight using the learnings from our own human review of the videos with our customer’s needs in mind. We can then predict, for every video on YouTube, whether or not it will be suitable for every customer.

Video Visionaries interview series with guest Michael Kassan
InterviewsThought LeadershipTrail Blazers

The Importance of Content Adjacency

Regardless of who you speak to, one thing is clear: the concept of relevance is more important now than ever before. Whether it’s relevance of message or relevance of context – or anything in between – the world of digital advertising is now about getting the right message paired up with the right content on the right platform in front of the most relevant and engaged audience. Zefr probably understands this better than anyone else. Content targeting is what we do. It’s all about driving relevance for brands – and their target audiences – through online video content in the most effective, engaging, and brand-safe ways possible.

Video Visionary, Michael Kassan  

But there’s someone else who knows a thing or two about the notion of relevance – and that’s MediaLink Chairman and CEO, Michael Kassan. An industry veteran and an expert on all things digital media, Kassan reiterated the importance of content adjacency as well as why it’s become such a hot topic of conversation among brands, content creators, digital media companies, and tech platforms today – especially at a time when consumers are less trusting of digital advertising, overall. Here’s a sneak peek at our conversation with him.

ZEFR: What is the importance of content adjacency for brands?

Michael Kassan: There’s never been a time when the combination of a strong marketing message paired with great content has mattered so much. In fact, the filter for what’s acceptable versus what’s not acceptable has changed dramatically over the last few years. Brands can no longer risk putting their marketing messages next to content that doesn’t embody the same message or convey the same brand values. This is why the notion of content adjacency has become top of mind for marketers today. Digital advertising has evolved. Our approach to digital advertising must evolve, too. Brands have to be aware and think strategically about where they put their marketing messages now. One slip up could be a disaster.

ZEFR: What are some of the positive developments of online video?

MK: We’re in the “golden age” of content right now. The Internet, from its very beginnings, was a promise of the democratization of content creation. The proliferation of online video content alone proves that to be true. There’s more content out there than we even know what to do with – and this has posed a challenge to the platforms that make it available to consumers today. It’s important for these platforms to understand the responsibility they have when they move from simply being a platform into a bonafide media company. And as they do, content adjacency will become even more important for them.

“Content targeting in online video is more than simple the future of targeting; it’s quickly becoming the best way brands can pair their marketing messages with relevant and engaging content that their target consumers are already viewing. And Zefr is leading the charge here.”

ZEFR: What’s your take on content targeting for online video?

MK: That’s easy – it’s more important today than ever before. Audience targeting is becoming harder and harder to come by given the issues around privacy and data collection affecting the entire industry. Therefore, content targeting in online video is more than simple the future of targeting; it’s quickly becoming the best way brands can pair their marketing messages with relevant and engaging content that their target consumers are already viewing. And Zefr is leading the charge here. The team saw the writing on the wall and realized that a better, more relevant targeting option was critical in order for brands to succeed in their digital advertising efforts in the future. Content targeting is the answer.

ZEFR: How would you define a successful brand partnership?

MK: I like to say that for a deal to be successful, there must be equal parts pleasure and equal parts pain. If you don’t strike the right balance, you won’t have a good deal, when all is said and done, and whatever deal you do have will likely not be a solid long-term solution. Today, we’re seeing brands, publishers, and tech companies (platforms) working together more and more to satisfy each other’s needs. The combination of all three is the recipe for success in today’s rapidly evolving digital media landscape. They all need each other to succeed. The unique role that MediaLink plays in all of this is that we help connect the dots to make these partnerships much more successful. It’s not always easy to navigate. We’re here to help.

ZEFR: How important is a channel like YouTube for marketers today?

MK: There’s no question about it. YouTube is one of the most effective marketing channels today. It’s been proven time and time again. The team there has done an incredible job of creating a better experience for marketers, especially around brand adjacency. They are focused on doing the right thing: creating a safe environment for marketers to communicate their messages linked with content.

Product & Tech

The popularity of YouTube, the world’s largest video platform, continues to soar and with that comes an increased appetite for online video content. This comes at a time when (US) online video viewers are expected to hit 236 million by 2020, up from 213.2 million in 2016. For advertisers, the situation is pretty straightforward: spend more advertising dollars where there are more eyeballs. However, achieving precise content targeting at scale remains a big hurdle.

Zefr harnesses the power of machine learning – with a touch of human curation – to wade through YouTube content and classify it at the individual video level. Why is this important? By doing so, we’re able to provide brands and advertisers with a content targeting solution that is relevant (at the campaign level), brand safe, and highly scalable. Only machine learning can do this – effectively – at the truly massive scale of YouTube. (We’re talking billions of videos!)

Machine Learning

Let’s start with the basics: what is machine learning? Machine learning is a computer’s ability to learn and improve from experience without being explicitly programmed to do so. It is another way of talking about artificial intelligence, which was specifically designed, in its early days, to improve the overall speed and efficiency of data-heavy processes. Today, this allows us to tackle otherwise costly and time-consuming tasks at a manageable scale.

As you would expect, machine learning isn’t a one-size-fits-all concept. There are actually three primary types of machine learning: 1) unsupervised, 2) supervised, and 3) semi-supervised. All three types require data input – either “labeled” (manually done by humans) or “unlabeled.”

  • Unsupervised Machine Learning
    How an “intelligent” system learns through unlabeled data inputs. Because this isn’t human-curated learning, there is no easy way to determine learning accuracy through the output (as it’s hard to identify the relationship between input and output). If the output of unsupervised machine learning eventually delivers against a pre-stated hypothesis, you know that “learning” is taking place and delivering the desired end result(s).
  • Supervised Machine Learning  
    How an “intelligent” system learns through a combination of labeled input and output examples. Because the inputs and outputs have been guided by humans upfront, this is the easiest way to ensure learning objectives and, once achieved, begin applying that learning to new functions.
  • Semi-supervised Machine Learning  
    As you may have guessed, this is a hybrid of the two learning models above. This process is guided primarily by unlabeled data, but influenced by a small amount of labeled data to guide and expedite the learning process to achieve the desired outcomes. Combining the two processes have not only been found to be more effective overall, but more cost-effective as well because it requires less time and attention from a human to curate that learning process.

How it Works

The team at Zefr primarily relies on supervised and semi-supervised machine learning models to make the magic happen. According to Zefr Principal Data Scientist, Ryan Deak:

“We have millions of videos to categorize in some way. If we had an infinite amount of time, we (humans) could go through them one-by-one and say, for example, ‘this video has X property, so we want to group it with other videos that also have X property.’”

Unfortunately, we don’t have an infinite amount of time nor would anyone in their right mind want to dedicate that much budget to tackle a process like this manually. Doing so is simply not scalable in any way. That’s precisely where the supervised machine learning comes into play.

“We can go through the ‘cherry-picked’ process for a select number of cases to label videos based on the properties we find. At that point, however, we pass those labeled videos over to the machine learning algorithm, and it analyzes that input to predict which unlabeled videos should be associated with a particular category. The ultimate goal of machine learning, therefore, is to be able to generalize what it’s learned, based on the labeled data provided, and accurately apply that learning to data it has never analyzed before. This is how Zefr scales a massive amount of online video content to deliver a precise content targeting solution to brands and advertisers.

Precise content targeting for online video content at scale is the challenge that Zefr's technology solves for.

One of our key differentiators are the proprietary tools we use to streamline this machine learning process. We use a framework called Aloha, which makes it easy to extract data (from content) and integrate it into our learning and prediction pipelines. This is huge! All too often, as much as 75 percent of a data engineer’s time is spent on manual tasks – like extracting and cleaning data – instead of focusing on more complex, problem-solving-oriented tasks.

“In the past 5 or 10 years, there has been an explosion of higher quality open source machine learning libraries, like Spark, Vowpal Wabbit, H2O.ai, and MXNet to support our deep learning needs. The fact that we operate on a single framework (Aloha) to integrate all these libraries means both our data scientists and data engineers can do their jobs much more efficiently without being bogged down by the typical troubleshooting that comes with model deployment.”

*More information about these libraries is outlined in “Hidden Technical Debt in Machine Learning Systems” and in our own SysML 2018 conference paper, focused specifically on Aloha.”

Human Review

The massive volume of videos on YouTube means that machine learning is table stakes when it comes to developing and deploying a precise content targeting solution. It is the only efficient way to navigate – and make sense of – the totality of the YouTube online video ecosystem. As Deak points out:

“It’s not like TV where you have maybe 1,000 TV shows at any given time in a year vs. a billion videos on YouTube. It’s both a problem and an opportunity for us as well as for advertisers. The only hitch: it’s an opportunity that’s about a million times the size of TV.”

The truth is, without some human review mixed into this process, it’s difficult to accurately assess the relevant alignment of a particular piece of video content to a particular brand message. That’s all about brand safety and, in spite of how advanced machine learning has become, we’re not ready to let artificially-intelligent algorithms take complete control of the driver’s seat here. There’s a certain ethos involved with human review and curation that rounds out this process – and all with the goal of driving the most value to brands and advertisers at the end of the day. Eliminating human review from the machine learning process raises the chances of “unsafe” content slipping through the cracks or, more generally speaking, content misalignment with brands. At this point in time, that’s not a gamble we’re willing to take.

Our machine learning process starts and ends with human review. We feed our algorithms useful data and then assess and evaluate the output from those algorithms. And it’s thanks in big part to this human-machine collaboration that we’ve been able to create highly accurate models that can blacklist unsafe content, whitelist safe content and deliver more precise content targeting.

The Zefr Touch

The team at Zefr is evolving alongside machine learning. We’re creating new ways of making machine learning more accurate, effective, and efficient vis-à-vis YouTube while simultaneously taking those learnings to make our (human) team more effective as well. The foundation of our system is based on modeling and scoring. This means that our algorithms are built specifically to provide high quality machine learning models that improve as they learn over time. Adding the human layer to this dynamic is our “secret sauce.” We know the ins and outs of the YouTube ecosystem better than anyone else (except maybe for YouTube). For this reason, Zefr is uniquely positioned to provide brands and advertisers with a precisely targeted opportunity to align their promotional messages with contextually relevant online video content – all at scale!