You can’t ignore the parallels between our reality and the realities of so many novels that were written decades ago. If you haven’t thought it, you’ve likely seen it: 1984 is flying off the shelves. Stories about how Infinite Jest predicted the smartphone, Netflix, our current president (and more!) abound.
So, as I sit here at my desk, delving into research, getting up to speed on SEO marketing tactics, and reading story after story about chatbots, machine learning, artificial intelligence (AI), social media tools, and automation, I really can’t help but daydream—albeit fitfully—about the future.
Here’s what’s coming: As marketers, we all respect the value of an influencer, and we all rely on social platforms to help our brands (and ourselves) reach influencer status. But, with more and more businesses employing Twitter as a way to tout their own messages, it’s become somewhat lackluster—not for everyone, but certainly for the businesses that haven’t polished their social strategies in a while. Enter AI (not as in Capone; that’s a capital “i”). Soon, the technology will seep into the social sphere, working in chatbot style to craft and post tweets during the times when those enterprises’ audiences are most active on the platform. Before long, social media as we know it will be a series of chatbots that tweet, message, and like comments from other chatbots. You’ll be growing a following without even knowing it! When it trickles down to the individual user level, we’ll all look to those evolving social-bot versions of ourselves for answers. You know how you always wish you could take your own advice? 2020 is coming, and it has solved your problem.
Okay, that’s probably not coming—but it’s interesting to think about. What’s more compelling is the reason that narrative isn’t so hard to belive. In part, it has to do with the fact that most of us are so deeply terrified of losing our humanity, our connection to others and the world around us—existing through layers of plastic, metal, and thick clouds of wireless signal. But it also has to do with the fact that AI is such a volatile technology. Every day, it’s doing something new. Every day, more advancements, more scientific developments, more integration with the marketing world—and, simultaneously, more science fiction. More dystopian fantasies. It’s a lot to parse through. Between that, the stress of the world and our lives within it, and the bright blue light of our cell phones, it’s no wonder none of us can sleep at night.
As we’ve explored, though, there’s another side to machine learning, and it’s much less sinister. This is the side where AI is designed to support, rather than to overtake. I can see great applications for this for the good of humanity—and I’m not alone. Preparing for the Future of Artificial Intelligence, a document prepared by the National Science and Technology Council (NSTC), the Office of Science and Technology Policy, and the Executive Office of the President, specifically calls out potential “applications of AI for the public good”:
One area of great optimism about AI and machine learning is their potential to improve people’s lives by helping to solve some of the world’s greatest challenges and inefficiencies. The promise of AI has been compared to the transformative impacts of advances in mobile computing. Public- and private-sector investments in basic and applied R&D on AI have already begun reaping major benefits for the public in fields as diverse as health care, transportation, the environment, criminal justice, and economic inclusion.
Not only that, but the potential is there to make marketers’ jobs way easier. As Paul Roetzer, CEO of PR/2020, explained, the implementation of AI means that “[marketers will] have more insight and impact for less time, money and effort than ever before. But that won’t change the need to provide relevant, useful, and valuable content to consumers—at the right time, in the right way.” And, traditionally, the way we ensure we’re providing relevant, useful, valuable content to our audiences is through deep audience research and SEO.
Ah, SEO—that most crucial component of almost every digital marketer’s job. We rely on it to get our content, assets, and names out to our specific audiences. We craft living keyword strategies around it. We dedicate time in our days to reading up on the next Google algorithm shift, diving deep into our analytics to see if we are still ranking for certain search terms. We play SEO like a Rubik’s Cube, making the right moves at the right times and knowing that, with the right twists and turns, we’ll succeed.
AI is already changing that. Through RankBrain, for example, Google has already transformed its algorithm; as a result, traditional SEO tactics don’t work like they used to. And with semantic search (more on this later), which was rolled out back in 2013 and has evolved since, even keyword strategy has become more of an art and less of a precise science. That appears to be the trend—and, when you think about the spirit and goals of good content, as well as the aspirations of AI, it makes sense.
When you were a child, say, fourth grade or so, did you learn how to make some kind of basic dessert? For us, it was apple crisp. As a lesson in following directions, we were told to copy down the recipe, follow it precisely (with a teacher’s help for the more treacherous aspects), and, when we were done, we got to enjoy something pretty delicious that we had made.
But now, as an adult, my palate has evolved. I’ve learned a lot about the flavors I like, and, more importantly, the flavor combinations I like. I don’t have to follow a recipe to create the perfect apple dish. I might throw some additional spices in there, like nutmeg, or maybe incorporate some vanilla bean seeds into the mix. I don’t have to do the math there, because I know what works. Now, the accomplishment isn’t so much on my ability to follow directions, but on my ability to appease my very critical audience, which could comprise family, my partner, my friends, or—on an especially hungry day—myself.
To me, that’s sort of where Google is heading. Way back in the day, the algorithm was built on a very functional principle. As Rand Fishkin explained, “In the original system, you needed those people, these individuals here to feed the inputs, to say like, ‘This is what you can consider, system, and the features that we want you to extract from it.'” According to Fishkin, RankBrain has transformed the algorithm into something more like a chef (one more talented than I) whose job it is to envision and design a recipe, uniquely her own, that delights guests for the evening:
You get to the algorithm today, and . . . there are going to be a lot of things in there that are driven by machine learning, if not deep learning yet. So there are derivatives of all of these metrics. There are conglomerations of them. There are extracted pieces like, “Hey, we only want to look and measure anchor text on these types of results when we also see that the anchor text matches up to the search queries that have previously been performed by people who also search for this.” …That’s what the algorithm is designed to do. The. . .system figures out things that humans would never extract, metrics that we would never even create from the inputs that they can see.
Then, over time, the idea is that in the future even the inputs aren’t given by human beings. The machine is getting to figure this stuff out itself.
Another crucial element in Google’s evolution—one driven by deep learning—is semantic search, which was introduced through Google’s Hummingbird update. In an article for Forbes, Jayson DeMers broke this down beautifully. He explained that with semantic search, Google is looking to the intention of a user’s search query, rather than matching for word choice alone: “[For semantic search,] including a keyword or phrase verbatim isn’t a surefire way to optimize for it, and it’s possible to gain rankings for semantically linked words and phrases that you didn’t optimize for directly—and sometimes ones that aren’t even present on the page that’s ranking for them.”
According to DeMers, Hummingbird—as well as their rarity and low competitive scores—has led to the rise in popularity of long-tail keywords.
You already know the strides that Google is taking in 2017—the ones that prove its continued dedication to AI and quality content. And through Fishkin and DeMers’ advice, it’s clear that machine learning is changing the rules of the SEO marketing game. When it comes to your priority list, it sounds like freshness, depth, relevance, and engagement are going to matter more than exact keyword matches and anchor text. And few forms of content can achieve those ends better than compelling stories.
While we can’t predict exactly what’s going to boost our SEO rankings in the face of Google’s AI updates, the one rule that seems to stand is the idea that the answers lie with our audiences. Get to know them, create content that empathizes with them, and, while you’ll still want to maintain your keyword strategies for now, make sure you go beyond them to tell a story you’d want to be told yourself. Be creative and unafraid. By thinking more like a human, and less like an algorithm, you’ll find that your brand gains the relevance, engagement, and reach these AI-driven algorithms prioritize.