Left-brained readers of Tubular Insights will geek out at the topic that I’m going to tackle today: How artificial intelligence, machine learning, and big data are transmogrifying the online video and internet marketing industries. But our right-brained readers need to read this column, too. Why? Because we’re going to need to use our whole brain to resolve the brand safety brouhaha that not only faces YouTube, but also the 1.3 million YouTube creators or media companies with more than 10,000 lifetime views as well as the 200 leading national advertisers who make this whole ecosystem viable. So, let me begin by defining a few terms:
- Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of “intelligent agents” – any device that perceives its environment and takes actions that maximize its chance of success at some goal.
- Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futurism.
- Machine learning is a type of AI that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.
- Deep learning is a specific machine learning technique. Most deep learning methods involve artificial neural networks, modeling how our brain works. At the moment deep learning forms the basis for most of the incredible advances in machine learning (and in turn AI).
- Big data is a term for extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
Artificial Intelligence (AI)
Now, AI is a term that Intel and others bandy about. And Google has used the term from time to time. Back in November 2011, Think with Google published an article entitled, “Yesterday’s Sci-Fi is Today’s Reality.” It discussed “advances in artificial intelligence” at the time. But, check out the examples that the article mentioned back then of how Google was using AI:
- Google’s photo sharing service Picasa could “recognize faces and suggest tags.” It’s worth noting that Picasa was discontinued in March 2016.
- The Google Goggles mobile app could help you “identify many foreign objects — from landmarks to logos.” It’s worth noting that the Google Goggles feature in Google Mobile for iOS was removed in May 2014 because it was “of no clear use to too many people.”
- Google Instant used AI to guess what a person is searching for as they type keywords into the search box. This feature still exists – saving users two to five seconds per search.
- “Conversation Mode” in Google Translate could translate (and speak) your words into any of 57 languages. The Google Translate app still exists, but “Conversation Mode” only provides two-way instant speech translation in 32 languages.
So, none of us should worry that our jobs will soon become irrelevant because of Google’s advances in artificial intelligence, right? Well, Avinash Kaushik, the Digital Marketing Evangelist at Google, wrote a post on his Occam’s Razor blog last month entitled, “Artificial Intelligence: Implications On Marketing, Analytics, And You.” In it, he said, “My first true moment of worry about my professional future came in March 2016 when AlphaGo beat Lee Sedol, the unassailable Go grandmaster. It was believed, due to the immense complexity of the game of Go, that computers were at least a decade away from beating humans.” But, one did – and at move 37. So, as we make progress in Artificial General Intelligence (AGI), video marketers may discover themselves saying, “Ok Google, open the pod bay doors,” sooner than we expected.
Which brings me to machine learning. Google talks a lot about this term. In fact, Think with Google published an article in December 2016 entitled, “What Machine Learning Means for Search Ads in Australia.” Written by Tris Southey, product manager for DoubleClick Search, the article says, “If you’ve ever finished a YouTube video and then enjoyed watching another (and another) thanks to the related videos that appear at the end of the video or on the sidebar, you’ve already benefited from an enhanced prediction engine. In the same way that YouTube interprets multiple systems and patterns to recommend a video, Smart Bidding can now set the appropriate original bid values and future adjustments for keywords that go far beyond the more obvious head terms into the longer tail.”
And another article in Think with Google provides a link to a form if you are interested in downloading an MIT Technology Review whitepaper about how data-driven organizations are finding new ways to compete and win by collecting data and speeding analysis with machine learning. In fact, data analytics leaders at Progressive and Macy’s are transforming their companies and industries by using data analytics and machine learning to make significant improvements in decision-making and realize measurable productivity and profitability gains. And since deep learning plays such a critical role in all the current AI excitement, where does it fit in? Well, according to Kaushik, one of the problems “to worry about with deep learning is that we might not have massive training datasets for every problem we want to solve (mandatory for deep learning).”
This brings me to another term that’s an even bigger buzzword than artificial intelligence and machine learning: big data. Video marketers know that YouTube Analytics is the ticket to better decisions and stronger results, but many of us still struggle with shoring up that foundation. And we aren’t alone.
In fact, Google Surveys conducted a study of US marketing executives who have analytics or data-driven initiatives in December 2016 and found that 61% of marketing decision makers said they struggled to access or integrate the data they needed last year. And that issue isn’t going away, since the amount of data being created by YouTube as well as Google continues to grow. Solving the problem will take the right talent and support from the top. And this represents both a real threat and a real opportunity for readers of Tubular Insights.
Now, YouTube creators can use Tubular’s free dashboard to avoid the pitfalls, seize the opportunities, and get back home by six o’clock. It enables you to identify other creators who have fans that will also like your content; benchmark your views and engagement against your peers, collaborators, and competition; and use Tubular Creator Profiles to pitch yourself to brands and collaborators.
And media companies can use Tubular’s actionable video intelligence to avoid the pitfalls, seize the opportunities, and get back home by six o’clock, too. It enables you to learn who is watching your content and what else they watch; create the right content and plan a distribution strategy; and grow the monetization of your earned and owned views.
But, mining big data means much, much more than that to YouTube creators and media companies. Let me give you an example. YouTube content creators who get into one of the 13 Google Preferred Lineups in the US are significantly more likely to earn six figures per year than ones that aren’t included in one of the easy-to-buy packages for brand advertisers. So, if you learned that one of the ways that brands plan to get more control over where their ads appear is to sign up in even greater numbers for Google Preferred, which might force Google Preferred to expand from the top 5% of content on YouTube to, say, the top 10%, then wouldn’t you make more of an effort to get included in one of the lineups, which is refreshed on a quarterly basis?
Or, here’s a second example. Philipp Schindler, Google’s Chief Business Officer, recently said, “We’re changing the default settings for ads so that they show on content that meets a higher level of brand safety and excludes potentially objectionable content that advertisers may prefer not to advertise against.” He also said, “We’ll introduce new controls to make it easier for brands to exclude higher risk content and fine-tune where they want their ads to appear.” So, do you think maybe – just maybe – you should think twice about creating potentially objectionable or higher risk content if you wanted to keep this whole ecosystem viable?
Or, here’s a third example. Google Partners need to pass two of the AdWords certification exams to become an AdWords certified professional — the AdWords Fundamentals exam and one of the following: Search Advertising, Display Advertising, Mobile Advertising, Video Advertising, or Shopping Advertising. With 70 percent of YouTube viewing happening on mobile devices, if you learned that Alphabet, Google’s parent company, had announced that performance was led by mobile search and YouTube, would you start lobbying to make Video Advertising a requirement instead of just one of the five options for AdWords certification? In other words, if more Google Partners took the Video Advertising exam, which covers basic and advanced concepts, including best practices for creating, managing, measuring, and optimizing video advertising campaigns across YouTube and the web, then wouldn’t the odds of you earning six figures per year improve?
Marketers Need Essential Insights
Now, don’t get me wrong. YouTube creators and media companies still need to create great content and devise a holistic channel strategy. But, to stand out in today’s evolving digital era, they also need to get essential insights from big data to grow larger, more valuable video audiences. Yes, life isn’t fair. But, that’s a very old story. In fact, it reminds me of a 1958 animated musical short film released by Walt Disney Studios that was entitled, “Paul Bunyan.”
Now, I realize that most video marketers are too young to have seen the original like I did as a kid. But, they can still watch the video version on YouTube. At the 12:20 mark, the video tells the tale of a slick-talking salesman named Joe Muffaw, who encourages the loggers to forget that work and “be modern” by using gas-powered chainsaws and a steam train to transport the timber. Paul Bunyan protests that nothing can replace the heart and soul of himself and Babe, and the two men decide to hold a contest with only one winner.
Well, to cut to the chase, when time was up, the judge measured Paul Bunyan’s pile, which was 240 feet high. Then the judge measured Joe’s pile, which was 240 feet and one quarter inch high. Although that quarter of an inch doesn’t sound like a big deal, it made Joe the winner. The video ends with Paul and Babe despondently walking off into the sunset, never to return. But, in case you want this video with your kids or grandkids, there is a happy ending. It turns out that Paul Bunyan and Babe go up to Alaska, and their playful wrestling is what causes the Aurora Borealis in the night sky.
Nevertheless, the lesson that the short film taught me as an impressionable young Baby Boomer was this: You really have no choice but to “be modern.” To be declared the superior logger, you can’t count of being a folk hero. You just have to start using a gas-powered chainsaw and a steam train to transport the timber.
Yes, in the early days of YouTube, it was all about discovering the best practices and strategies for building your audience. But, today, you also need to discover the best practices and strategies for monetizing your audience. And that means learning more about artificial intelligence, machine learning, and big data.
Hey, I don’t know about you, but I’m not ready to walk off into the sunset. So, if you’re one of the 1.3 million YouTube creators or media companies with more than 10,000 lifetime views who wants some of the 200 leading national advertisers to help you earn six figures per year, then you need to figure out how to meet them halfway. And, if brand safety is now a bigger issue than it was last year, then we all have to transmogrify right along with the rest of the online video and internet marketing industries. And that means brushing up on your math skills – or adding someone to your marketing team who isn’t afraid to tackle artificial intelligence, machine learning, and big data.