How Machine Learning and AI Will Change SEO?

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It’s no secret that many people are afraid that AI and machine learning technologies will take over many jobs soon. Does the same threat apply to digital marketers and SEO specialists?

On the one hand, various SEO tools incorporate machine learning and AI-based technologies allowing marketers an easier time to analyze various metrics and implement SEO optimizations. On the other hand, there’s also the looming threat that these AI technologies might replace human SEO specialists in the future. 

At the moment, however, these AI-based solutions are not yet at the level to replace SEO specialists and aren’t so effective in performing technical tasks. Here, we will discuss our predictions on how machine learning and AI can change SEO strategy in 2020 and onwards

1. Google and the Search Engines Incorporating AI

Obviously when we discuss “search engines”, our main concern is always Google. So, the main questions are how Google is currently integrating AI in its search algorithm, and how are they going to incorporate AI in the future. 

Since 2015, Google has incorporated an algorithm update called Google Rankbrain, which has utilized AI technologies especially to gather and analyze data related to user experience metrics like bounce rate, dwell time, and click-through rate. 

Rankbrain allows Google to analyze (in real-time) how users react to the search results of the SERP and might adjust SERP ranking based on the result. For example, if a web page has a #1 spot in the SERP but a lot of people exit the page after clicking on the search result, it might be demoted. 

Then, in October 2019, Google launched another update dubbed Google BERT, which is designed to better understand the context of a page. 

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In the future, we can expect further implementations of AI and machine learning technologies in Google’s algorithm to provide the most relevant and reliable information and also to keep users on Google’s platforms (Google itself, YouTube, etc.) as long as possible. 

2. Helping Marketers Optimize Content Marketing and Improve Productivity

AI and machine learning technologies have helped Google to better parse and understand visual content (images and videos). So, image and video optimizations are now very important in content optimization strategy for 2020 and onwards.

On the other hand, SEO practitioners and digital marketers must develop a more integrated content marketing strategy that is properly aligned with visual content, and also with voice search (more on this later).

This will also drive the evolution of analytics tools. With how fast the search engine algorithms have evolved, marketers must also utilize machine learning to better analyze behavior in social media and how the target audience interacts with different platforms/websites. 

Doing this would allow marketers to create a more comprehensive and accurate buyer persona, and at the same time, marketers can use the insights to create a better content strategy. 

Now, marketers can no longer rely on textual content (blog posts) alone but must diversify their presence in various mediums from podcasts to videos to social media activities to leverage the AI’s abilities to grasp the context of various types of content. 

3. Changes in SERP With AI

In addition to the several major algorithm updates discussed above, Google has also incorporated several major changes in its SERP by utilizing AI and machine learning technologies. 

The most important change in SERP is Google’s Rich Snippets, famously called zero-click results since these snippets won’t drive users into the websites providing the content. In the past, rich snippets are only given to sites that have properly implemented structured data markups. 

Yet, today Google can properly understand the context of a site and may reward a site with rich snippets even without any structured data markup implemented. 

Here are some examples: 

Star-rating (reviews) markups

Google can now automatically pick up star ratings on your site even when there isn’t any aggregate rating or reviews schema markup

Video content

Google now rewards the thumbnail SERP feature for videos to help drive higher CTR, even when the video content isn’t yet marked up.

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Breadcrumbs

There are cases where a page is given the breadcrumb snippet without the breadcrumb markup existing on the page. However, without any breadcrumb markup, the resulting breadcrumb navigation might not be too accurate. 

4. Assistance in Content Creation and Curation 

AI also has the potential to help marketers in curating and creating content, especially regarding content optimization and keyword/topic research. Buzzsumo, for example, is a topic research tool that has been around for a while, utilizing AI and machine learning to perform its task. 

So, to stay ahead of the competition, marketers should now take advantage of the AI-based tools to help optimize their content strategy. 

Various text analytics (text scraper/text mining) solutions are also available today that can help us analyze text-based content to reveal potential SEO optimizations and known issues. These tools can also discover keyword gaps and even suggest new topics based on the gathered data. 

Even Google Analytics now includes a new feature called Analytics Intelligence, which utilizes machine learning to answer users’ questions about their data like comparing between your data and your competitors’, analyzing charts, and so on. 

With these, SEO practitioners and content marketers can take advantage of these tools to deal with data analysis and reporting, and focus on what they should do best: making informed decisions based on the insights. 

5. Further Personalization of Search Results

In the past, Google’s SERP was personalized based on location: different regions have different Google data centers that will provide different results. So, if you search for something in New York, your result might be different compared to if you search for the same query in LA. 

However, with AI technologies, Google can now further personalize the SERPs for different individual searchers based on past search histories, demographic data, and even the user’s specific actions. 

In the future, we can predict that Google will further use the advancements in AI technologies to make extra specific personalization results for each user, and will be even better at predicting what the user will search. Google might be able to suggest searches or even provide the search results right away before you search for it (or even wanting to search for it).

For marketers, this can be both an opportunity and a risk. On the one hand, if you can capitalize on these predictive searches, you can stay ahead of the competition. On the other hand, SEO will be even more complicated than it is today, potentially with new ranking factors and SERPs. 

End Words

AI and machine learning won’t only affect SEO, but also the whole digital marketing industry. However, some of these impacts will be positive, and AI will allow massive growth opportunities by automating and enhancing your SEO and marketing efforts. AI can simplify the complex SEO process by helping marketers gather and analyze insights related to consumer behavior, content curation/creation, keyword searching, and even lead generation.

On the other hand, we can expect AI and machine learning to also affect Google’s search algorithm and SERP. Google will get even better in delivering relevant and reliable information for its users, but at the same time, it will create new challenges and complexities in SEO.

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