Over the past few years, Google has rolled out its RankBrain to use machine learning to improve search results. When someone fires up Google Chrome and slaps a few words into the search bar, RankBrain tries to figure out what people are really looking for, delivering results catered to those needs. RankBrain grows and learns over time to improve its feedback process.
If you’re still trying to drive traffic through individual, hyper-specific keywords, you’re running SEO like its 2015 and RankBrain doesn’t exist yet.
With machine learning and big data, Google has been able to more effectively figure out what a searcher really wants and how to give it to them.
With machine learning’s power, searches will become concept-driven instead of keyword-driven. Misspellings, misidentifications, and garbled combinations of words typed by people who still aren’t sure how the internet works will be turned alchemy-like into search engine gold by Google’s predictive algorithms.
If you drive high rankings with high bounce rates by fooling unsuspecting searchers, those rankings will fall off the bottom of people’s computer screens.
So, let’s dive in to the nitty gritty of machine learning and how to optimize it in your favor.
What is Machine Learning?
“Machine Learning” is a method of data analysis that allows a machine to not only analyze your data, but also learn from the analysis to improve future methods and objectives of analysis. In traditional programming, computer scientists need to manually enter code and program analytics.
Machine learning is a kind of AI that uses statistics for programming in a way that allows machines to “learn” without someone manually banging away at a keyboard. It is a quicker, more efficient way of programming.
When Google launched machine learning for Google Translate, Neural Machine Translation (NMT), the system learned about as much in a day as it had progressed in the previous decade.
How Google’s Machine Learning Affects SEO
Concept-based HTML Tags
Titles, Meta, headings, and URLS are more important than ever. Because machine learning allows for significantly higher levels of conceptual understanding for Google’s systems, your tags and meta data will help Google decide what kind of concepts your pages deliver, and whether or not those concepts are relevant to a given search.
Keyword stuffing has been ineffective for many years, but keywords themselves will actually become less important in the traditional sense. Google’s conceptually driven algorithm means that a page that contains 0 instances of a search keyword could rank higher than a page with many instances of that same search keyword. That’s if Google learns that the second page isn’t satisfying the searcher’s true intent.
Links, Click-through, and Bounce Rates
Back in the old days of Google, programmers in dark rooms with carbonated sodas had to manually piece together the spam links and non-authoritative follows.
But now, Google’s algorithms can use that data to make predictions about the legitimacy and helpfulness of other links, significantly improving its ability to understand and interpret the quality of links with greater efficiency, increasing the importance of authoritative backlinks.
The bottom line is this: with machine learning, Google’s search system is far more dynamic than ever before, and requires good content and consistent website scaling to keep up with competitive search space.
Width of Content
Google has more data than you. Their search algorithms will update faster than you can update your content or SEO strategies.
Remember that you aren’t trying to beat Google, you’re trying to win with Google. If Google is trying to satisfy searcher queries faster and more efficiently than ever before, then the future of marketing is also satisfying searcher queries faster and more efficiently than ever before.
Ranking for “new phones” might require a website that delivers news updates, how-to videos, and a marketplace to buy phones, all with an authoritative and recognizable brand that users trust.
Width of content also means creating web spaces that have all types of content that a searcher might have wanted–text, images, graphics, videos, products, lists, links elsewhere–in a way that feels genuine and organic.
How You Can Use Machine Learning to Improve SEO
Machine learning is a great buzzword, and you may be itching to unleash some of its power into your marketing strategy. Remember that machine learning will only be as good as your data, and your data will only be as good as your goal.
The types of machine learning that you’ll have access to will probably not go toe-to-toe with Google RankBrain. That’s why the bulk of this article is about optimizing content to work with Rank Brain instead of using machine learning to make adjustments to your SEO strategy.
Getting Good Data
Machine learning is only ever as good as the data that you’ve collected. Pull up Google Search Console and plug your domain and individual URLs to see some relevant data.
But, to really maximize data collection, you should grab an easy plugin for Excel or Sheets that can collect the data into a workable table. This way you can see the changes over time and how machine learning is affecting your rankings.
Analyzing the Data
SEO Tools for Excel offers a plugin for excel that will directly insert data from Google Search Console into Excel spreadsheets, and Search Analytics for Sheets offers the same service for Google Sheets.
Once you’ve gathered your data, Excel actually offers some easy machine learning tools to get started. You can even begin advanced forecasting models with machine learning, all without ever leaving Excel. Of course, if you know some Python or SQL, the possibilities open up from there.
The Machine Learning Feedback Loop
Machine learning can help you breakdown your data and figure out what you should focus on in your next SEO overhaul.
You might see that the length of titles and meta descriptions correlates to ranking or CTR, or you might find that a high bounce rate is related to pages that contain a certain keyword.
If you can pull more data from your site past Google Search Console, like data from a CRM system or data from ads and marketplaces, you can fine tune the machine learning algorithms further.
Machine learning is being used by Google to improve its search algorithms, and it should be an area of data science used by you to improve your SEO.
When your marketing team switches from manual data analysis to machine-based solutions, there’s a paradigm shift from running SEO as an analyst to running SEO as a predictor. Machine learning, as you start to utilize the advanced data metrics, allows your data to help you predict realistic SEO improvements for your site.
Machine learning may seem daunting and ominous for the uninitiated, especially if you follow those links above and start plugging website data into Excel and get overwhelmed by the numbers.
Know that at first machine learning is a less efficient way of running data analysis, because there’s setup work on the backend, but eventually you reach a tipping point where machine learning becomes leaps and bounds more efficient.
Push through the initial technical difficulties. Play the long game.