Decoding The Impact Of AI & Machine Learning On SEO: Present & Future

Summary: In this article, we explained the way AI and Machine Learning are shaping the present and future of SEO and discussed the following: 

• How search engine results evolved over time 
• The machine learning algorithms for search engine 
• Key SEO areas impacted by AI and Machine Learning 

Artificial Intelligence (AI) and Machine Learning (ML) technologies continue to penetrate digital spaces resulting in intelligent automation and insight-driven user experience. Content creation and search engine optimization (SEO) also have been impacted by these two technologies in recent years. 

While major search engines like Google continue to have their focus fixed on creating more value for the web users by listing the most relevant and high-quality content, the role of AI and Machine learning in SEO is influencing search algorithms continues to grow in importance. Naturally, the SEO strategy of companies also needs to adopt AI and ML-based algorithms for powerful and search-friendly content creation and optimization. 

Over the years search engine optimization (SEO) strategies evolved through a whole array of changes ranging from meticulous keyword research, semantic use of keywords, voice search-friendly keywords, dynamic keywords focusing on user intent, focusing readability metrics, and deep linking strategy, etc. While these factors and strategic aspects remain the same, AI and ML technologies are going to tune them to user demands and search engine trends with more accuracy and precision.  

Here in this blog post, we are going to explain the key ways AI and ML technologies impact search engine results.  

The Evolving Science Of Search Result Delivery 

Traditionally, to get your required information through search engine results you need to correctly frame a perfect query pointing to what the user wants. By this logic, you need to search for “French eateries in Birmingham” to find several French eateries in the city of Birmingham and the results will be different if you just search for “French eateries” while you sit in Birmingham. This traditional approach was changed due to location-aware search algorithms and a user located in Birmingham just by typing “French Eateries” can find results showcasing French eateries in the same city. 

Location awareness is a small but significant step towards making search engine algorithms more intelligent and customer-specific. Now, search engine results across major platforms such as Google, Facebook, Amazon and streaming platforms continue to be context-aware. Being aware of user context corresponding to the particular location and needs of the hour is an advanced dimension that search engine algorithms are trying to adopt. 

Machine learning is a key technology to help roll out smart recommendations for creating more value for users while saving great resources. The use of machine learning for personalised content recommendations has helped Netflix save $1 Billion of resources in 2017 alone. Machine Learning technology helps the algorithms over time to understand user preferences and specific contexts. 

Natural Language Processing (NLP) is another key intelligent capability that search engines are increasingly being equipped with. The NLP technology allows the search engine algorithms to understand the user intent despite different use of terminologies and variations of text queries. Understanding user intents despite linguistic differences is a key capability of Machine Learning (ML) technology. 

Stats of an entire article written by AI tool

Ultimate SEO Push Through Machine Learning Algorithms 

Machine learning technology plays a key role now in shaping the intelligent capabilities of search engine algorithms. The basics of machine learning are all about the ability of the computer or mobile devices and operating systems to learn from the user inputs, user behaviour and user preferences over time. By processing, this information machine learning algorithms can perform certain tasks without any prior programming. The more relevant and quality data the computer is fed with, the better it can process the information and deliver more accurate results. 

Now just think about the powerful impact this automatic learning and computing capabilities of Machine Learning (ML) algorithms can have on Search Engine Optimisation (SEO). Machine Learning (ML) can be of two types such as unsupervised and supervised. 

While supervised learning is mostly about feeding a set of data for automating certain tasks with predictable results, unsupervised learning is more about learning from new information and handling a variety of tasks with no predictable outcome. The first is mostly about automating simple computing tasks, the latter is equipping the machine with reasoning capabilities to take certain decisions to carry out tasks. Advanced machine learning algorithms rely on unsupervised learning models. 

As of now, AlphaGo remains to be the most powerful and widely acclaimed algorithm equipped with unsupervised machine learning. The algorithm was trained to play an ancient Chinese game called Go so that it can play the game like a pro and become an undisputed champion defeating human intelligence. The algorithm was only fed with game rules and some elementary instructions and it was allowed to play the game on its own by learning the gaming skills and tricks over time through match experience. Ultimately, in 2016 AlphaGo achieved the feat of defeating world champion Go player and could win four matches in a series of five matches.

Key SEO Areas Impacted By Machine Learning And AI Technologies 

AI and Machine Learning technologies are widely used now for a whole array of SEO applications including all major search engine algorithms. The impact is not just visible through the way search engine crawlers discover relevant content based upon audience intent and keyword search, but it has also made its impact visible through powerful content creation. 

Let’s have a quick look at the key areas of SEO impacted by the AI and ML technologies in a never-before manner. 

People-Centric Focus Instead Of Algorithm-Centric 

Earlier, SEO efforts used to focus mostly on a semi-transparent understanding of how the Google search algorithms work. Multiple factors such as keyword use, appropriate link building, content quality, page structure, loading speed etc are used to influence how search algorithms work. With the introduction of machine learning in search algorithms, there is now an enhanced focus on page structure, content relevance regarding search intent and overall user experience. So, SEO is no longer about tick marking a set of requirements, it is more about user experience and content quality that can be improved with SEO data science. 

This is why using exact keywords and search terms has taken a backseat and search engines now can detect similar keywords and search terms by using semantics. Keywords are still important for SEO, but they need to be more natural and organic with the express intention to cater quality content to user needs. By compromising on quality parameters, optimising with keywords no longer helps your SEO efforts to derive expected results. 

Voice Search And Natural Language Search 

For some years, voice search has become tremendously popular across all platforms and search engines. Young audiences, particularly millennials, prefer voice search and voice commands more than text-based search. AI and Machine Learning coupled up with Natural Language Processing (NLP) technology has made search engine algorithms more equipped to understand user queries spoken and written in everyday language with differences in tone and modulations. 

The continuous shift to and from voice search and text queries continues to make search engine algorithms learn better about the search intent of the users with increased accuracy. Decoding the spoken and written search terms with modulations and variations has become simpler because of the way search engines continue to learn from user inputs, queries and their contexts. 

More Precision In Content & Keyword Research  

When it comes to the selection of content topics, businesses always try to adapt to new trends, the most opportune keywords for ranking and the evolving preferences of users for content types. On the other hand, every website, despite catering to a consistent user category, always finds it difficult to meet the demands of all users with one content strategy. Content marketers despite their niche focus need to prepare content for different segments of users. 

This increasingly segmented, individually focused approach requires high-precision content and keyword research. No wonder, the most advanced tools have now embraced AI and ML technologies for content and AI based keyword research to focus on different customer segments and to address the evolving market trends just at the right time. AI-powered SEO tools by analysing massive amounts of search data can provide the most trending topics and opportune keywords for each customer segment.  

Quality Content Creation 

Quality content creation is another area where AI and ML-based tools are increasingly playing an era-defining role. Since content plays the most influential role in making people discover products and services they need, business brands in the years to come will rely more on AI-based insights for customer-centric content creation. 

The biggest advantage offered by AI-based content creation is the personalised focus to cater to specific audience demands and preferences. This ensures better and faster content delivery while addressing user demands and needs. On the other hand, AI and ML technologies are helping to make a better analysis of the competition to address the shortcomings and provide better quality. 

Enhanced User Experience

The outcome of Machine Learning technology on the web is a satisfactory user experience. AI and ML-based website audit and content audit tools can provide the most relevant insights on the ways to increase loading speed, ease of search and better visibility. 

Machine Learning technology by offering real-time data-driven insights into the user behaviour and intent can help make recommendations that users are likely to engage with. Recommendation engines across all major platforms now rely on Machine Learning technology to know about user intent and address them with appropriate suggestions of products, services and content. 

Parting Words 

The future of SEO is now inseparable from intelligent algorithm technologies such as artificial intelligence and machine learning. How well and fast your content and SEO strategy cope with these new technologies, will be a decisive factor for your success. The major shift of SEO approaches has already stepped in and become an irreversible reality.