June 2, 2017
Recently we’ve heard more and more about machine learning. The futuristic sounding buzz words of machine learning and artificial intelligence (AI) draw both intimidation and curiosity, especially within the world of search. Many people have heard about machine learning being used for search, but not everyone has followed its development or knows how it’s actually being used. To address some of those concerns and shed light on how machine learning came to be used for search, here’s an overview of everything you need to know about it.
Why We Have Machine Learning in Search
As I previously touched on in this article, machine learning and artificial intelligence developed to meet the needs of the constantly changing search landscape. Over time, search engines have become increasingly sophisticated to better suit the demands of users. The factors that search engines use to determine search results and understand user intent has expanded significantly, so search algorithms have had to start looking at everything more comprehensively. These algorithms account for and measured all the different factors and tons of data points, naturally pushing search engines to find the most effective and efficient ways to develop.
That push towards efficiency is where machine learning and AI systems come to play. Using AI in search helps process more algorithm and search results findings much faster and more efficiently. The AI is trained to look at everything you’d expect to have a high value for both users and search engines, including content, links, user behavior, trust, citations, patterns, hacks, intent, and more.
How AI Data is used
Based on what information and data is collected by machine learning from the algorithms, search engines then develop new sets of ranking factors and insights that will determine future updates. This is a big deal for search because this AI system is designed to continuously evolve and adapt-just like search.
What This Means for Users & Marketers
Because this AI is not a black and white formula but rather a sophisticated system, there isn’t a way to game it. In the past, search algorithms have been formulas with pretty clearly defined edges that people could skirt around. Now that machine learning is being used to drive future algorithms and understand present search patterns, gaming the system or short-cutting through SEO will be a whole lot more difficult.
The inclusion of machine learning in search has the potential to drastically change SEO. In light of that, there will be an even greater emphasis placed on authenticity and quality than ever before. On the user side of things, the search experience will continue to improve and become more refined, producing better quality results and weeding out unhelpful results. For marketers, there’s some speculation that SEO practices will change or die out as a result of machine learning in search. Whether or not that’s true remains to be seen, but what is clear is that AI is actively helping to keep quality results and user experience at the forefront of the ever-changing search landscape.
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