SEO & Machine Learning: Adapting To Google's New Realities
Machine learning has been on the horizon for a couple of years, but only a few SEO professionals have been paying close attention. Most SEOs opt for tried-and-true on-page and off-page optimization methods, as machine learning is still in its infancy and has not yet fundamentally changed the SEO landscape. As time goes on, however, SEOs who choose to turn a blind eye to machine learning will definitely be left behind. It is beyond doubt that the impact of machine learning algorithms on a site’s visibility in search will only increase.
In the not-too-distant future, you may notice your clients’ sites experiencing massive ups and downs in Google ranking. Your first guess might be that the search giant has released a new algorithm update. And while that may be the case, erratic drops in SERPs and traffic could possibly be related to Google’s efforts to implement machine learning, in order to rank sites more efficiently. It is time for SEO professionals to admit that their role and goals will have to evolve over the next couple of years. We had best be prepared for the upcoming revolution, and figure out what we can do to adapt to the new realities of search. But first let’s clarify what machine learning actually is.
According to Wikipedia, “Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.” Basically, being a subfield of artificial intelligence, machine learning teaches machines to perform activities that require human intelligence. Though machine learning has been around as a distinct field of computer science since the 1990s, its heyday only began in the 2010s, when corporations such as Google, Facebook, Amazon, and Apple started collecting dozens of terabytes of data about their customers.
Big data is key to the massive advances that machine learning as a field has experienced since the 2010s. The more data fed to AI algorithms, the more they can be taught to complete specific actions. More data means more variety for AI.
