Machine learning & neural networks: The real future of SEO
TensorFlow & SEO
So what do machine learning and TensorFlow have to do with SEO, algorithms and reaching that coveted No. 1 spot in the SERPs?
As Google’s RankBrain gets smarter in understanding users and their intent, it’s also learning to better understand content, information and if that content will provide the right answer, not only to the query, but also to the individual user. With the algorithm now truly understanding the query intent on a linguistic level, it can deliver new kinds of results that are correlated and weighted in a way that a human brain can’t even begin to predict. This dramatically changes two aspects of SEO: technical SEO and content SEO.
As many have said before, the role of technical SEO in the context of fixing links, optimizing title tags and ensuring correct markup is no longer a valid SEO role, meaning there should be no brands out there hiring a practitioner just for that purpose. This nuts-and-bolts work should be done from the beginning of the website build and audited by the web dev team on an ongoing basis.
Instead, the true technical SEOs of the future need to understand more than just HTML and XML; they need to understand how machine learning works, how TensorFlow handles data and weighs inputs, and how to understand and train models. There will always be crawling and discovery, but the main focus is now more analytical — truly data-driven, with the SEO practitioner more a mathematician and software developer than web designer.
The last few years have seen a convergence of SEO content and content marketing. We know we must now create contextually relevant content that is authoritative, not just keyword-stuffed. Now it’s time to look at more than minimum/maximum character counts and keyword density. We have to start using machine learning models and linguistic analysis to weigh and score our content to ensure it truly answers the consumer question, instead of just telling a brand story.