As we head into a future with more machine learning, there are certain areas you need to consider for your online business. Machine learning is one of the most transformative technologies in recent memory and a crucial part of any marketing plan. 

You need to learn how you can use machine learning to build links, content, and advance your SEO goals.

Many SEO professionals may feel anxiety about the potential impact of machine learning on their jobs. Will machines be able to do everything better than humans? And what will the implications be for SEO if they do?

Marketers, for example, who rely on understanding and serving the target users instead of the engine may be breathing a sigh of relief more than the SEO professionals who have historically worked on an understanding of the signals at play and their fluctuations.

There was a time when the “build great content and they will come” approach was deemed ideal. Today, those who subscribe to that SEO approach are likely less worried about machine learning and its impact on SEO. They actually should be. 

Before we get into all the details, let’s first treat the question:

What is Machine Learning and How Does It Relate to SEO?

Don’t worry, we won’t get into all the details about machine learning here. The important thing to know is that it will impact our SEO strategy in the future.

From a high level view, all you really need to know is that it improves Google’s capabilities and gives it an incredible speed of:

  • Reaction
  • Data accumulation
  • Interpretation

If you want to learn more about machine learning, you can take Google’s free crash course. It doesn’t cost anything.

The Influence of Machine Learning on Links & Link Building

Among the greatest examples of machine learning, one of it is how it boosts Google’s capabilities through its link creation.

Machine learning is one of the core technologies in the development of an intelligent spam filter. A minor example is how machine learning can significantly aid in one of the major aspects of evaluating links i.e filtering spam.

Machine learning is the future of Gmail, and Google utilizes it to deliver high-quality results with an already reaching 99.9 percent accuracy and yielding false positives only 0.05 percent of the time.

Integrate machine learning into link evaluations and you’ll achieve a very successful and efficient model.

In the past, Google engineers would have to go through a long process and:

  • Program certain features of a bad link based on past examples they had documented.
  • Create an inventory of sites with poor quality and restrict their flow of link equity manually.
  • Set up devaluation functions into link calculations and hope that it didn’t include too many false positives.

With machine learning, breakthroughs can be made more rapidly, allowing for exciting innovation in diverse fields.

Yes, there are pre-populated lists of domains that serve as starting points. Technically an inventory of confirmed bad domains and another that is made for presumed links and signals that are bad.

However, these are merely training materials used by a machine learning system to:

  • Learn and identify how to use these signals to treat other links they encounter.
  • Produce their own signals for spams that they seem to encounter or even good links as a matter of fact. This also allows the system to learn from context and make more accurate suggestions in the future.

The machines rather than depending on certain requirements or criteria have the potential to learn on their own and evolve themselves just by observing patterns.

Analyzing websites with presumably bad signals that are either linking-in or linking-out, helps train the machine and create a profile.

As soon as there is confirmation that the signal is bad, the machine then starts reverse-engineering the patterns, enabling it to detect similar signals faster in the future.

  • The kind of sites these spam sites link to.
  • What are the types of links these spam receive?
  • Is there a specific or certain link growth pattern?
  • Do pages that engage in the sale of paid links also try to link to other specific sites (they do) and if that is the case, which sites do they link to?

The system can then include the values of those parameters in the metrics it uses.

This is a highly skilled and technical process that requires a level of detail and a scientific approach that only machines can do. It is also a tip of an iceberg when considering the capabilities of machines and how they are able to simulate human behavior and then amplify it.

While most of the major search engines are taking steps to reduce spam, Google has a different strategy. They have begun downgrading sites that contain numerous link spam on a wholesale level rather than manually removing them.

This is made possible by making use of new developments in AI technology and machines capable of learning as well as applying these devaluations at an amazing speed with minimal error margin.

In addition to this, machines are able to understand the quality of content or the relevancy of a page and then input that understanding into the algorithm both individually and in bulk.

A machine is able to process queries like why the weight of a link on an individual site is so high and then poses another query on the possibilities and high likelihood that such link might pose a problem or is a paid link. It will then proceed to analyze the context of data and other links that exist on that page and domain.

These are some of the advanced examples of how the use of machine learning can be applied to links.

Spam patterns and low-quality links will be detected and penalized by the machine at a highly successful rate, while quality links will rise in their rankings and be rewarded at a higher rate.

This increases the focus on relevancy, quality, and legitimacy unless you have by some chance devised a way to bypass the system and deceive Google faster than their machine can notice.

How The Use of Machine Learning Affects Content SEO

While the examples above are for links, there are other areas and aspects of SEO that will also be greatly influenced by the use of machine learning that content.

To emphasize this, we need to cite Google’s work in translation as an example.

For about 10 years they solved the problem by using a phrase-based machine translation. This simply meant that the system was matching known phrases and producing results that were not so accurate.

Although this method got the job done, it was a very primitive process.

However, by September 2016, Google had transitioned to a machine-learning system (Google Neural Machine Translation) and within 24 hours of its implementation, the system had improved language translation by a decade’s worth.

More efficient than humans, machine learning can interpret and understand language more effectively and efficiently than a human could in 3,650 times the time, even with the use of machine assistance.

How does this development affect SEO professionals?

The ultimate goal of content marketing is on its way. A time when we’ll no longer need to chase metrics but instead create content that satisfies more user intents and preferences than everyone else. And once this has been achieved, there is a high probability that Google’s system will understand this as well.

We’re not saying that machines will replace SEO professionals or squeeze them out of a job. After all, we cannot excuse the fact that machines are flawed.

In fact, I believe we might have an even greater role to play in the future of Google search. But it won’t be in keyword usage, it will be in formulating how users can attain satisfaction from services offered.

Wil Reynolds described unique summaries of future possibilities with machine learning and things we should expect. He proposed that we as humans asked ourselves the question:

What would happen if Google evolved and always showed the best answer?

These are one of the possibilities we need to explore. What made the question he posed more interesting was that “best” which he used in that context was subjective.

This is because whenever possible, people get linked to video tutorials that boil all the steps into easy-to-follow instructions. If there’s no video, there’s a list of steps with captions for images. While some may hate video instructions, not everyone shares the same hatred. Some love it, while some might also prefer PDF downloads that they can easily print out.

In addition to this, there are some tasks that video instructions are not suitable for. A perfect example will be a hands-on task like an oil change. Not many people will want their mobile device under the car with them while performing an oil change, which makes a PDF printout the suitable option.

So, what one might consider “best” in terms of a result Google provides might depend on a wide range of factors based on the user’s preferences, their location, the kind of tasks they aim to accomplish, and the kind of device they currently use.

This kind of programming cannot be achieved by a human. Yes, there were some trials in the past with trying to achieve personalized results, but its possibility was greatly limited, until the age of machine learning.

Humans cannot create a tailored experience specific to your needs. They don’t know what you want, don’t like, and how you react to things.

While people can’t dedicate the resources to understand what you specifically like at specific times, based on the device you’re using or your current location, machines can.

A machine is able to keep track of all this information and learn with time not just the kind of results you like, but also the kind of results that meets your intent and will ultimately provide the best result for you from your current preference.

It’s been crucial in the past for us to emphasize our services that reflect global best practices. Now, it’s more about fulfilling the intents of the target audience.

After all, if we want to rank for more generic terms like ‘laptops’ and not just ‘buy HP laptops’ in eCommerce sites, then we need to meet the intents of the users who aren’t just interested in purchasing one.

Also, maybe we should also strive to provide that info in various formats in order to achieve impeccable results regardless of the device being used by the target audience.

Before machine learning, you had to rely on human ingenuity and insight to understand what your bounce rates and time on site meant, as data provided on Google’s part were crude.

Now, with the existence of machine learning and its assistance in understanding language context as well as what user-generated signals might mean, understanding and utilizing user data is not just possible but is now easily deployed.

For instance, when you create a new video, you would typically want that great new video to address a basic question or questions that your demographic audience requires answers to.

Now if your target audience is seeking answers to these questions via Google Home or other voice-first methods, then the content you will be providing will not have to be made available in every possible format as these users may not require them. However, you need to be certain just in case.

How The Use of Machine Learning Influences Technical SEO

Pay attention to Cindy Krum. When it comes to technical SEO, she’s on the right track. Cindy Krum is a master of technical SEO and the right person to follow if you’re interested in improving your technical SEO.

In a discussion about mobile-first indexing, she formulated the term “portable-first” to replace “mobile-first” which she asserted to be incorrect. Her new term describes the future of SEO and how we should be thinking about things.

She explained that your content should be easily separated from your technical structure as well as design so that it can be accessed from anywhere and at any time (i.e., portability). She is not wrong.

As we head into a world of machine learning which is constantly evolving, the goal is to provide the target audience with content that fulfills their intent.

Our goal is to build content that is easily understandable and interpretable. We do this by examining both the content itself, and every construct it exists in, to determine its suitability for its intended purpose either through XML feeds, markup, or simply structuring the content on a page in a way that is clear and interpretable.

So What Comes Next?

How do you utilize this information? The domain of machine learning is growing and it is the key to our ongoing enhancement as a species. We need professionals who can harness the power to influence every industry. With machine learning, Google has recorded rapid growth in its capacity to understand the world around us and our personal preferences or needs. It is this power that defines the next steps we all need to take.

While it would be irresponsible to tell anyone to entirely discard their SEO efforts based on the tried-and-true metrics that currently work well, the truth is that those metrics are evolving and fading rapidly. There is also a possibility that they won’t survive over the next two or three years.

This is what you need to do with machine learning to excel as an SEO professional:

Make sure your content can reach the target audience through their PC, mobile, and tablet. Use your tone of voice to make your content more personal and friendly that it speaks to them directly.

If you have multiple audiences with different needs, it is advisable to provide content that appeals to all of them or create a separate piece of content for each audience that fits their needs. This way people who are interested in one particular subject can find that information without having to click off the page.

As an SEO professional who wishes to utilize the power of machine learning, you should think about keywords being more than a mere instrument to apply on a page and in the anchor text, but should rather view them for the questions and intent they hint or suggest.

Machines are tools that are designed to help us understand our visitors, so we can figure out ways to provide exactly what they want.

Why?

Because at the end of the day, the machine only seeks to serve a user or audience that will gain satisfaction in whatever service that is being offered. They’ll have all the metrics and info needed to figure out whether you or any of your competitors are doing a great job in delivering a satisfactory service that will retain users.

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