When you think about it, few things in the digital landscape have remained unchanged over the last couple of years.

Online advertising is hardly what it used to be, isn’t it?

Social media took brand communications to a whole new level.

And even SERPs, once the foundation of the online buying experience, have moved so far away from the original ten blue links.

What’s more, the rapid advancements in technology continuously push the envelope further and further.

And in this post, I want to discuss another new aspect of today’s digital marketing - machine learning and show you what impact it will have on SEO.

So, to begin at the beginning.

What Exactly is Machine Learning?

The definition provided by Tom M. Mitchell, a computer scientist, and professor, explains machine learning this way:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."

And although it’s accepted as the “official” or “original” definition, for the purpose of this article, we’re probably better off putting it in less scientific terms.

So, in short, machine learning refers to building computer systems that can learn from, and also make predictions based on the available data.

We can build computers that can act or made data-based decisions, as opposite to carrying out specific tasks (like the computer or a smartphone you might be reading this article on does). They can analyze complex sets of predefined data, and uncover general rules behind it (this is called supervised learning). More advanced machine learning systems can also find patterns and relationships in the unstructured data provided (and we refer to this as unsupervised learning).

All in all, machine learning allows us to create machines that can make decisions based on the data sets provided, and then, learn from them, to further improve their analytical skills.

How is Machine Learning Affecting SEO?

The effect of various machine learning systems on our industry has already been widely discussed in the SEO industry.

But I want to offer a unique take on this topic. I’ll show you not only how machine learning will change your job, but also how to use machine learning systems to improve your work continuously.

1. Think Topics, Not Keywords

It will force us to shift from thinking in keywords to targeting topics to rank. Up until now, keywords provided us with the information and guidance about buyer needs and the intent behind those.

However, RankBrain, the Google’s AI uses machine learning to analyze new search queries and provide more relevant results to users. This forces us to shift our approach from keywords to topics and answer questions from our audience.

And there is data to prove it too. For example, here’s what MarketingProfs discovered (note, the emphasis in bold is mine):

“The correlation between keywords and high search rankings has decreased across the board. More and more high-ranking sites are not using the corresponding target keyword in the body, description, or links, the analysis found. Sites are also using keywords less in URLs themselves, with only 6% doing so in the 2015 study.”

This data clearly suggests that keyword relevance has evolved beyond just a simple "string match" - RankBrain can easily identify relevant content, even if it doesn’t contain keywords from a person’s search query, .

Enter topics. Instead of focusing solely on keywords, we need to shift to targeting specific topics or user questions to provide the most satisfying information.

(Note: I wrote in depth about the need for this change in my earlier article - If you’ve missed it, check it out here.)

But how do you identify targets instead of keywords?

Use tools like seoClarity’s Content Ideas that show you the exact questions your potential audience asks via various online channels.

2. Understand Customer Intent

Next, we’ll have to understand our customers' intent better.  I love this quote from Gary Vaynerchuk (note, I’m paraphrasing it from memory):

“Nike hasn’t made me buy their shoes with their latest ad. They’ve done it by building a relationship with me through a series of small interactions.”

And the secret phrase here is “a series of small interactions.” I’m sure you’ll agree - to achieve similar results you need to have an in depth knowledge of your customers’ intent.

A buyer intent is still a relatively gray area in SEO. We all know it exists, and some segment keywords based on intent. But on the whole, many SEOs still seem to fail to understand correctly to then target buyers based on that intent.

And yet, knowing exactly what questions a person at different stages of the buying cycle would be asking online, gives us an unprecedented marketing opportunity:

  • We can position our brands at the exact touch points, and with the exact message they’re looking for at that.
  • And we can direct the person to the next stage, ensuring they continue the journey with our brand, rather than switching to someone else.

For example, when a customer searches for Nike, the customer intent is navigational. But, if a user searches for UGG Boots, it’s transactional.

By understanding the difference you can:

  • Match copy on a page to the relevant intent
  • Place relevant calls to action that seamlessly move a person from one stage to the other.

3. Use Data to Analyze Challenges

Finally, we will have to get used to fewer updates to the algorithm and use other data to analyze potential challenges and changes. 

Machine learning allows search engines to create algorithms that learn, and adapt to new data. Going forward, this may mean that there will be less need for major algorithm updates (since it’s going to adapt on its own through machine learning). As a result, ranking shifts may happen more organically over time. Instead of noticing a major change, your rankings might gradually evolve.

And the only way to spot any potential challenges will be by using real-time data that allows accessing the most current information right away.

Using deep, machine-learning on top of billions of data points, our platform enables SEOs to quickly turn complex data sets into actionable insights, and offers a deeper understanding of the most viable opportunities for increasing online visibility.

You can find out more about it here.