The world of SEO is standing on the constantly shifting plates. All the theories and technical SEO you were following last year are obsolete today. It is because of the rapid intervention of machine learning.

The number of online media consumers is growing rapidly, so does the variations in online tasks. It takes only a few minutes to look at the recent developments with Google’s RankBrain and understand the changes developed in recent online markets.

If you are still not sure why and how machine learning has a strong impact on search engine optimization, it is time to pay attention and di deep.

What is Machine Learning

A technical definition of machine learning is – machine learning is a part of artificial intelligence, which is concerned with the algorithms that help computers to learn.

Machine learning could be of 3 types. Supervised learning, unsupervised learning, reinforcement learning.

Supervised learning is task-driven, where labeled data is used to predict outcomes.  

Unsupervised learning is data-driven where clustering face recognition, image recognition, and data visualization are included.

On the other hand, Reinforcement learning is what algorithms can adapt, it’s mainly learning of actions, learning tasks, learning from mistakes, and real-time decision making.

The next level of machine learning is deep learning technologies – based on neural networks. Here pattern recognition capabilities and massive data sets are combined to recognise patterns and empower to make decisions.

Why things are changing

Today’s consumers believe in speed and accuracy. They expect information and results are delivered in a few seconds.

This is the main reason that pushed Google, Amazon, Facebook, Amazon to fall for machine learning so that they can serve their clients efficiently and accurately.

As SEO professionals, we are entitled to do the same.

It is important for SEO professionals to keep up with the rapid updates in SERPs along with making sense of the enormous data sets.

We also require updating our knowledge regarding data sets and search insights.

When it comes to working with data, we have to admit that a machine can hold trillions of gigabytes, and thus human brains need the significant help of machine brains.

Ai and machine learning in SEO – how influencing is it?

While launching Google RankBrain in 2015, Google took a bigger step in data mining and implementing machine learning to understand customer behaviour. Google is continually learning with updated algorithms how the consumers are reacting and how to dig deeper.

It helps to improve search result quality and satisfy users’ intent. New responsibilities of the SEO personnel start with studying rapidly updating Google Algorithms based on machine learning.  

SEO personnel also need to build content, which is more relevant and useful on various platforms. If your content fails to match the users’ expectations, then it will be difficult for you to reach the top 10 list of the SERPs.

From the SEO specialists, Google wants one and only thing, which is quality content, that matches user interests as well as preferences.

Machine learning can hugely help SEO in understanding personalisation, queries, and voice search. Machine learning is also helpful in terms of generating useful content, social listening, and coming up with a complete and useful content strategy.

Search Ranking

Deployment of Google’s RankBrain can be marked as the first massive investment in machine learning in order to improve search results. However, there are other search algorithm update too, such as PageRank, BERT that along with RankBrain helps to interpret user queries just like human intelligence.

In 2016, Google officially acknowledged that there are two most critical ranking factors – content and links. On the other hand, BERT is the latest neural network-based addition to the Google algorithm family.  This algorithm uses NLP (Natural Language Processing) to interpret user search queries.

SEO specialists now have to learn more about the Google algorithm updates. In TechnoKrats, we believe in data assessing by Ai-powered SEO analysis in order to defining the content niche and narrow the competition funnel to upraise client ranking.

Voice Search

Voice search is a new concern to the SEO specialist since 2016 when Sundar Pichai announced that 20% of all users search on Android and the Google mobile App were voice search.  

Not only mobile phones searches, with the rise of worldwide web traffic information served on mobile as well as sprouting sell of US households with smart speakers, but it also becomes a necessity to the SEO specialists to digest the voice search data with little help of machine learning.

Google’s deep learning algorithms with machine learning is capable in understanding the phrases used by the users in queries. Advance SEO technique is henceforth required to find out more relevant and long tail keywords to rank higher in the voice search.

Content

As discussed above, the impact of infusion of machine learning SEO has changed the concept of “content” usually developed by SEO specialists. Now AI can help you to generate unique content as good as drafted by any human mind.

Clients who are focused on content marketing can be benefited from the help of AI language modeling, which is a worthwhile addition to the existing content development tools. AI-based natural language generators (paid versions) are already developing scalable content for various industries. SEO specialists need to consider these language generators significantly. 

Structured Data

Because of the advancement of machine learning, SEO specialists are more into cooperating with Google and making it easier for Google to crawl and index website pages as much as possible.

Schema is one of the most important ways that can drive more organic traffic. With schema markup, you can easily catch Google’s attention. Having a high CTR (click-through rate) will show you which pages are getting more engagements and are popular.

Structured data is mainly helpful to clients with ecommerce sites. Schema markup can help in enhancing the search results by displaying product information, reviews, Prices, and other requirements.

Link Quality

Link building is still important as much as machine learning. However, as always, it’s all about quality rather than link numbers. The more quality links placed from related domains, the higher chances of ranking higher in SERPs.

With high-quality links, having a backup of good content, you can rank better. A link audit is preeminent to distinguish the toxic links and remove those who are actually hurting the search visibility.

Not to mention that losing good links with higher domain authority could have a negative impact on your search engine presence and traffic.

Is Machine Learning for SEO Going to Replace Technical SEO?

The last thing we need to do is question whether technical SEO will remain relevant or not. Well, the answer is, technical SEO will always be admissible.

However, with time it will take less time to do repetitive tasks like link building. It is assumed that Google Search Console (GSC) will eventually be taken over by machine learning technology and will require minimum or no human intelligence to run it.

Most of the CMSs are already built with SEO best practices keeping in mind. Most of the applications are now also SEO-friendly and require minimal human interaction.

Although human intelligence is required, machine learning technology is there to help ease the task. For now, with the right machine learning technology, you can save time for many tasks related to technical SEO, such as – Keyword research, Technical audit of websites, Content optimization, interlinking, and content distribution.

Conclusion

With the advancement of machine learning, SEO in India is rapidly changing, so does digital marketing India. With machine learning, we can understand which contents are more relevant and the quality of the content. It helps us in digesting massive data and sort out most of the tedious and laborious work for us. It’s time to learn the use of machine learning and use it to improve the content quality, meet the intent of the users and offer them a positive content experience.