By Chris Pitt
There are plenty of articles covering machine learning right now but most of them talk about how it will impact search engines and the way we optimise for them. Which is fine, except there is another side of the story: what machine learning will do for marketers.
It’s time to stop obsessing about Google’s algorithm and realise that machine learning is much bigger than Google. This technology is going to provide us with marketing tools like we’ve never had before – and knowing how to make the most of machine learning will be one of the most important marketing skills you can have.
The impact of machine learning on search engines
The goal with machine learning for Google and search engines in general is to better understand the the meaning behind each individual query (15% of which Google has never come across before).
RankBrain brought machine learning to Google’s core algorithm in 2015, essentially making it less reliant on a rigid formula of ranking factors – like links and keywords – for every query. RankBrain is capable of deciding which ranking factors are most important for individual queries and it’s going to get better at doing this by its own accord.
So, essentially, Google’s algorithm will be different for every query. Or, more accurately, it will adapt each time, based on the context of individual searches, and the ranking factors that determine your place in the SERPs will vary.
“Google now looks at hundreds of ranking factors. RankBrain uses machine learning to combine many factors into one, which means factors are weighted differently for each query. That means it’s very likely that even Google’s engineers don’t know the exact composition of their highly complex algorithm.” – Marcus Tober, founder of Searchmetrics, quoted in Search Engine Land
So what does this mean in terms of optimising for search in the age of machine learning? Well, essentially, there are two main areas we need to think about: technical SEO and content.
Technical SEO in the age of machine learning
For years, technical SEO meant optimizing images, title tags, and other HTML/XML-level tasks. The problem with this approach is you’re fixing mistakes that were made during the initial development stage of a website. As Google puts less emphasis on these factors and more on the contextual elements of each search, technical SEO needs to move out of HTML and into understanding how machine learning operates.
HTML-level optimization should be taken care of in the development stage of a site and maintained by developers – that’s their job.
Technical SEOs, on the other hand, will be using machine learning to run more advanced diagnostic tests to spot severe issues like duplicate content and flag up causes of slow loading times, for example. By crunching multiple data sets, machine learning will enable SEOs to gain insights that would take years to acquire manually.
The key for SEOs/marketers will be collecting the right kind of data – ie: verified, targeted data on a large scale – to make their machine Go to the full article.