AI SEO is crucial for responding to market activity. An AI-powered system can very quickly report how the behavior of the target audience has changed during each market disruption (as with, say, the past year’s pandemic-induced economic downturn). How an online business interprets and responds to such changes is still very much the domain of marketing and SEO professionals. Machine learning can help you recognize patterns in visitor behavior that point to opportunities and areas that are in need of improvement. But a purely quantitative assessment of online activity is woefully insufficient. To accurately grasp online behavior, it is now necessary to ascertain user INTENT.
These days, it doesn’t matter how many links or how much authority a page or domain has; unless you’re creating content that is optimized for intent, not going to achieve the rankings you want. After all, we have to assume there’s a reason Google prefers certain intent types for specific keywords.
There are many ways to assay intent. But AI tools are the best. When a ranking position changes–whether it’s a competitor moving above you in the SERPs or your site suddenly outranking a competitor, it is important to trace such developments to specific changes (e.g. a page becoming visible for a new set of keywords or a competitor launching a set of new pages). This could be as a result of building / earning new (back)links or adjusting metadata, altering page structure, or simply having better SEO technology–which is often able to attribute ranking movements to a specific set of changes. After all, there are certain clues that help us understand ranking serve as a window into the types of pages that Google likes to rank for certain queries. Using deep learning to assay intent can help you diagnose why a site might be having trouble gaining visibility (viz. salient keywords). The trick is to scale up the process of investigating such clues–specifically how Google interprets INTENT for any given set of keywords.
AI SEO can help you discover new topics, identify content gaps, and optimize for specific types of queries / results. Meanwhile, automation saves valuable time on tasks that are time-consuming, mundane, and repetitive. Such technology can even help online businesses determine the best next steps to take in response to these insights.
This is where NLP (natural language processing) comes into play; as it is required to truly understand queries as they are normally entered. Deep learning is necessary to carry out this task. In order to train a predictive model to classify queries, you need to encode the text into word vectors and word embeddings. These are very important concepts that all SEOs must understand. For when people refer to the same thing in many different ways, the system must be able to take this into account.
Even the most advanced AI needs a universal way to refer to things that are context independent. Traditional word embedding approaches assign the equivalent of a GPS coordinate to each word. A system that assigns the same coordinates regardless of context won’t be very precise. BERT works by encoding different word embeddings for each word usage, and considers the surrounding words to accomplish this. (It reads the context words bi-directionally–that is: both from left to right and from right to left.)
Word vectors represent words as numbers. The idea is to take all the unique words in the source text and build a glossary where each word gets a number. So when you provide the training text to the AI system, it encodes the words into vectors/embeddings in a way that makes it easy to compute their distance or similarity. In practice, embeddings tend to be precomputed and stored in lookup tables. Such tables expedite the training process.
Machine learning makes use of “transfer learning” (the basis for “deep learning”), which makes training a model possible even when you have very small training datasets to work with. (Traditionally, transfer learning was used in machine vision, so that the system could figure out what it was seeing.)
Machine learning goes beyond intent classification; it enables you to build a model that can parse text and extract actions (or any information needed to complete the actions). This NLU (natural language understanding) capacity is crucial, and is enabled by the NLP made possible by AI. The system can use such parsed datasets that are specific to the industry at hand. As SEO experts learn to build custom training datasets, they can use machine learning to provide vital insights into what types of content needed to rank higher on the SERPs.