Machine learning within SEO

SEO changes so regularly, that half the battle of best practice(https://www.talkdigital.com.au/category/search-engine-optimisation/) is staying ahead of the curb. What works one day, may not be as effective, the next. This is why the SEO community makes it part of their professional practice to read and educate about developing trends. One of these developments, is Google RankBrain. There have been many shake ups in the industry, but possibly one of the most significant introductions, has to be Machine Learning.

Following Google’s revolutionary concept of PageRank, whereby links are used to determine a user’s

intended search, AI may prove to be the most significant evolution to date.

Way back in 2015 Google acknowledged the active use of a machine learning algorithm. It’s purpose

is to interpret user intent within Googles search engine.

User intent, or search intent, is what Google focuses on to ensure that they maintain their excellent service delivery. There are some basic SEO principles (https://www.talkdigital.com.au/search-engine-optimisation/seo-statistics-everyone-know/) which are extremely important in understanding how this works. There is a formula to Googles 92.05 percent market share! Even though this concept stretches across many different companies and applications, Google generally sets the standard. Historically they have been ahead of the curb and have spearhead many revolutionary technologies.

What is machine learning?

Think about when you use Google Maps to look for the closest bar. Search Engine Evaluators are partly to thank for the results that you are seeing. Within per-determined metrics, they monitor and assess the search result you are seeing. When you search for ‘Paris’, you might be referring to the city, or the restaurant down the road. Ask yourself who interprets your search and makes sure that they are relevant? Can this be done through an algorithm? To a certain extent, absolutely. In certain circumstances, it is difficult for a program to interpret what you are telling it. Hence, the need for Search Engine Evaluators.

These evaluations take place on a massive scale and are time consuming and expensive. Companies are contracted to recruit people en-masse, to deliver these evaluations of search results. Would it not be more effective to allow an algorithm, that can adapt itself, to evaluate user intent on a search engines behalf?

This is the exact problem machine learning aims to resolve.

Now what does that entail?

 Within data science, machine learning is a scientific approach to make meaningful extractions from raw data autonomously. In the case of user intent, it takes massive amounts of raw input values, and tries to make sense of it through extremely advanced mathematical algorithms. The kicker is, it does this, but it can also adapt to external input values as time goes by and collects more data. This means it can ‘learn to understand’ what a user means and change its comprehension of user intent as time goes by.

RankBrain teaches itself based on the data it receives and the parameters given to it by Google. When you search for ‘Formula 1 Champion’, would you be happy to receive ‘Ayrton Senna’ as your top search result? Or would ‘Louis Hamilton’ be closer to what you intended? This is what an AI aims to learn, and it can mold your results based on who submitted the query.

Impressive right? Your interests, age, nationality, location and culture all play a part in trying to determine the ultimate question: ‘What search result do you want to see?’. Many developers will tell you that good SEO optimization (https://www.talkdigital.com.au/category/search-engine-optimisation/) is an art form.

As impressive as this is, where does this fit into SEO?

The general consensus is, that Machine Learning will make a significant difference in in what type of SEO will be successful. Gone are the days of trying to find loopholes and attempting to trick Google into ranking your page. Going forward, the science of SEO will rely on best practice.

A good understanding of Machine Learning will help you understand the future of SEO campaigns, regardless of your means to an end. AI is exceedingly sophisticated, but it also gives us another arrow in our quiver of SEO, without any extra effort.

We have established that RankBrain aims to provide the best search result to the user. It does this by collecting user input data, and interpreting the statistical likelihood of a search result being relevant.

 A horse can have three different meanings, depending on whether you speak to a equestrian, woodworker or gymnast, as was so eloquently put by Bill Slawski.

We need to know who we are dealing with, who we are trying to appeal to and what we are trying to achieve. Regardless excellent SEO (https://www.talkdigital.com.au/seo-services-brisbane) aims to do just this.

When we create a campaign, we are trying to show the user and the search engine that the content being presented, is in-fact what is being looked for. If we have a website that offers boating shoes for sale, and a sailor successfully reaches the website because he constantly slips on deck of his ship, it was an effective campaign. This person may not have known what the solution is to the problem, or was even interested in buying shoes, but through effective SEO we have identified his user intent.

An effective and well-constructed SEO campaign should not clash with a machine algorithm, instead we should take care in understanding what user intent is. They should complement one another, as both are trying to do the same thing. Both are attempting to get relevant search results, to the people that have queried them. This is why a good foundational understanding of SEO is so incredibly important. The future of SEO will favor those which utilize responsible and well-defined campaigns. The days of quick tricks and simple fixes have been gone for a while, and we are simply moving further away from it.

If this has reached you, we are hopeful to have met your user intent!