Artificial intelligence (AI) is penetrating every department
of every industry, from automating factory work to improving areas previously
thought untouchable by machines (like human resources). But as a veteran in the
online marketing world, I can’t help but let my imagination wander on how AI
and machine learning are going to impact the world of search engine
optimization (SEO)—the strategies organizations use to rank higher in search
engine results pages (SERPs).
Already, we’re seeing the beginnings of a full-scale AI
revolution in SEO, and search marketers are scrambling to keep pace with the
changes. But what will the next few years bring? What about the next decade?
The Big Picture:
We say “search engines,” but most of the time, we’re talking
about Google. Bing, Yahoo!, DuckDuckGo, and other engines only share a fraction
of the search user base, and most of their systems are modeled after Google’s
in the first place. So our big question is, how is Google going to incorporate
AI in the future to change how search works for the average user?
Historically, Google
has updated its algorithms with two primary goals in mind:
- ·
Improve
user experience. Google wants users to find the answers they’re looking
for, and receive accurate, valuable content. This is an important category, and
a complicated one; to achieve this, Google not only has to perfect how its
search engine functions, but also how it finds, organizes, and evaluates the
quality of content on the web.
- ·
Keep
users on Google. Google makes money when people use it, and stay on the
platform as long as possible. We’ll see why that’s important in a future
section.
Google is already making use of machine learning in a few
different ways, and it’s only a matter of time before it advances.
RankBrain and Machine Learning:
First, let’s consider RankBrain, a machine learning-based
upgrade to Google’s Hummingbird algorithm, which launched in 2015. The
Hummingbird update, from 2013, originally rolled out “semantic search”
capabilities. It was designed to evaluate the context of user queries, rather
than the exact contents; rather than prioritizing exact match keywords,
Hummingbird allowed Google to consider synonyms, related phrases, and more.
This was a step in the right direction, because it meant users could find
better results, and search optimizers could no longer get away with keyword
stuffing.
RankBrain was a modification that allowed Google to study
massive quantities of user search data and automatically improve its
interpretation of user phrases. It was primarily focused on long, convoluted,
or hard-to-understand phrases, ultimately reducing them down to a length and
simplicity level the algorithm could more easily handle. It’s been
self-updating and improving ever since.
This is an important indication of how search will evolve in
the future; I’m guessing that rather than seeing manual update after manual
update, we’ll see more algorithm changes designed to self-update based on
machine learning insights. This is much faster and less costly than having
humans doing all the work.
Content Quality and Link Quality:
I suspect we’ll also see major AI advancements applied to
better understand the quality of the content and links produced by search
optimizers.
Links and content are the focal points of most
SEOstrategies. Google studies links to calculate domain- and page-level authority
(or trustworthiness); generally, the more links a site has pointed to it, and
the better those links are, the higher it’s going to rank. Similarly,
better-written, more relevant content tends to rise in SERP rankings—and appeal
to web users. Better content and better links mean you’ll end up with a higher
return on investment (ROI) for your SEO strategy.
Over the years, Google has gotten better at analyzing the
quality of content and links from websites; search marketers have evolved from
trying to trick Google’s algorithm to simply trying to produce their best
possible work.
Right now, Google’s methods for evaluating the subjective “quality”
of content and links are good—but they could always be better. It would be
easier for an AI agent to gradually learn what makes good content “good,” than
to rely on a manual agent coding those parameters into a system. I believe
Google will make more efforts to automate quality evaluation in the near
future.
Individualization:
Google has also taken great efforts to individualize its
search results. If you search for the same phrase in Phoenix, Arizona and
Cleveland, Ohio, you’re probably going to get radically different results. You
may also get different results based on your search history, and even the
demographic information Google “knows” about you.
Right now, these individualization efforts are impressive,
but limited. We’re not surprised that Google knows where we are, or the last
few things we’ve searched for. But in the near future, Google may be capable of
using AI to make more intensive predictions. Based on your historical searches
and search data from millions of other users like you, Google may be able to
recommend searches or search results before you even know you need them.
For search marketers, this is both an opportunity and a
threat. If you can capitalize on predictive searches, you can get a massive
edge on the competition—but then again, if Google’s algorithmic methods are
opaque, you may have a hard time understanding how and when your results appear
for users.
Smart Results:
Over the past few years, Google has stepped up its efforts
to keep users on the SERPs, rather than clicking links to visit other websites.
The Knowledge Graph and rich snippets now appear to provide immediate answers
to user queries, preventing the need to click any further. As Google gets
better at dissecting user queries with RankBrain and Hummingbird, and becomes
better at parsing the web with smart algorithms, I suspect we’ll see even more
of these user-attention-grabbing entries.
For search marketers, this is again both an opportunity and
a threat. If you can game the system and get your content to appear in the
SERPs above your competitors’, you’ll get a major boost to your brand reputation.
But at the same time, if users stay in the SERPs, and never visit, you’ll miss
out on a ton of organic traffic.
Real Time Changes and Adaptability:
AI is remarkably good at analyzing vast amounts of data, and
far faster than even an experienced human team. Historically, Google has made
periodic updates to its algorithm with major, game-changing algorithm changes
dropped every few months. But recently, those algorithm updates have tapered
off in favor of much smaller, much more frequent updates.
This trend will likely develop further in the future as
Google’s AI systems optimize toward real-time analytics. It will “learn”
constantly, with every new search query, and possibly roll new updates to its
live algorithm on a constant basis, making it difficult to keep up with its
iterative evolution.
Content Production and Onsite
Optimization:
It’s also worth noting that AI won’t just be harnessed by
Google and other search engines. We’ll also see the development and utility of
AI on behalf of search marketers. AI-based content generators are becoming more
advanced and more common; eventually, search marketers may be able to use them
to produce and distribute content good enough to “fool” Google’s algorithms.
From there, this will likely turn into an arms race between search marketers
and search algorithms—not too unlike what we already have.
Furthermore, smart onsite optimization engines could greatly
simplify the technical efforts that search marketers currently have to make.
Current plugins and onsite SEO tools are helpful, but incomplete; in the near
future, AI and machine learning could make these substantially more capable.
Overall, it’s unlikely that we’ll see such a radical
transformation that SERPs become unrecognizable, or that SEO disappears as an
online marketing strategy. However, search marketers and users will both have
to make some serious adjustments if they’re going to stay relevant as AI
infiltrates this space.
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