How Machine Learning Is Changing the SEO Rules
More than 40 updates in four years — that's how often Google updates its search engine algorithms. And while most of these updates only caused ripples, others made waves that left digital marketers scrambling for solid ground.
What if search engine algorithms evolved seamlessly without updates?
Thanks to machine learning, the days of potentially jarring updates could someday be behind us. Machine learning occurs when programs can make predictions or determinations based on a wide range of signals or parameters. Uber, Auto Trader and Expedia are among the many large companies that employ machine learning; the technology is also proving useful in the fields of fraud detection, data security and financial trading. And yes, machine learning is already commonplace within Google and Microsoft, two of the world's largest search and technology giants.
Don't expect Google's programmers to bow down to artificial intelligence anytime soon. However, there's no denying that machine learning will play a big role in SEO.
Machine Learning's Place in Google
You don't need to travel far back in history to find Google casting doubt on the quality of machine learning.
Back in 2008, Google officials still believed their human programmers were more capable and less error-prone than the artificial intelligence available at the time, according to the marketing analysis blog Datawocky. In a 2011 discussion on Quora, a poster who claimed to work at Google from 2008 through 2010 said the company's search team preferred a rule-based system over a machine-learning system because it could implement faster and more definitive algorithm changes.
However, machine learning was a core component of Google AdWords by 2012. The platform's machine learning system - referred to as SmartASS — could determine whether users would be interested in ads enough to click them. One year later, Google officials were speaking publicly about working machine learning into their search engine algorithms.
Today, Google uses machine learning with its search algorithms mostly for "coming up with new signals and signal aggregations," Gary Illyes of Google told Search Engine Land in October. He explained how Google's search team uses machine learning to predict which algorithm adjustments are most worthwhile.
Illyes also talked about RankBrain, a machine-learning system implemented by Google in 2015.
RankBrain plays a vital role in Google's ability to interpret long-tail search terms - like those often spoken into smartphones -- and return relevant search results. In a Bloomberg article published in October 2015, Google senior research scientist Greg Corrado said the machine-learning system had become the third-most important page-ranking factor out of roughly 200 signals that impact the search algorithm. RankBrain was rolled out after a year of programming and testing, and it's regularly fed loads of new data to improve its capabilities, Corrado said.
So, we know Google uses machine-learning to test and shape its algorithms. We also know Google is much more open now to embracing this technology. That begs the question: What's next?
What Machine Learning Means for SEO
The more machine learning plays a role in search engine algorithms, the more digital markers will need to be proactive about maximizing the user experiences of their websites and landing pages. Machine-learning systems will result in more fluid search algorithms that make real-time determinations based on positive and negative reactions to content.
With that in mind, SEO experts can prepare for the machine-learning revolution by focusing on the following questions.
- Is your landing page relevant?
Visitors who arrive at your site on the most appropriate landing pages are much less likely to bounce back to the search engine results page (SERP), and high bounce rates are easily detectible red flags of a poor user experience.
- Could my landing pages be more engaging?
You're halfway there if your visitors are arriving on the right pages. Now, think of new ways to capture their attention. Can you add videos, guides or additional products that add value for your visitors and make each visit more compelling?
Phil is Founder and COO of Main Street ROI. Phil leads the company’s operations and is primary creator of Main Street ROI’s marketing training programs. He is an expert in search engine marketing, website analytics, and sales funnel optimization. Phil’s marketing thought leadership has been published on Forbes.com, Inc.com, MSN.com, and many other major business media outlets.
Phil earned his Master of Engineering Management degree from Thayer School of Engineering and Tuck School of Business and his Bachelor of Arts and Bachelor of Engineering degrees from Dartmouth College. While attending Dartmouth, Phil started every game on the varsity football team as the defensive safety.