Let’s look at some of the best ways to achieve better visibility via machine-learning-enabled SEO tools.  Competitive SEO entails learning to work with AI-powered tech; so let’s look at how and why this has become imperative in recent years.  The idea is to effectively optimize content using less manual labor, so staff can devote their time and energy to other things that machines can’t do, like things that involve imagination (read: innovation).  Embracing AI-based automation means building synergy with human creativity.

An AI-powered system is capable of uncovering SEO insights and noticing patterns even the most astute marketing teams would never have recognized.  AI tools can make decisions and implement strategic planning across digital channels.  The trick is to ascertain which SEO tasks to automate–retaining the ability to do so at scale.

A point to remember is that machine learning is not entirely replacing SEO professionals.  While most ML-related tasks are automated, the expertise on what sort of data (input into the model) translates to good predictions will remain a valuable skill to have for years to come.

The machine learning process is carried out by a protocol known as extract, transform, load.  The idea is to move useful data from one database to another.  But with machine learning, you rarely have the source training data in the format expected by the models. So it is necessary to prep the dataset for training.

In order to build predictive models, you need ways to categorize relevant data.  Hence a need for the “smart” automation of content classification.  The power of machine learning is its ability to convert massive amounts of data into actionable insights and automate actions that marketers can use.

If a page isn’t matched to intent (from an engagement or conversion perspective), it is unlikely that traffic will perform well after arriving on the site.  If many people abandon a site (due to having been led there by their intent being mis-interpreted), the site will be docked on the SERPs.  Thematic content clusters may be established as answers to search queries.  This may be done using various analytic techniques to identify and improve the cluster that best matches the query’s intent; but AI is best equipped to perform this vital task.

There are generally two intent types: informational and transactional.  Add the words you want that act as a signifier for a certain intent classification.  Informational intent is indicated by words like: how, when, what, where, who[se], best / worst, top, and most popular.  Transactional intent is indicated by words like: get, lease, buy, sell, rent, and find.  The presence of a date in a meta-description means the page listed is probably an article, which indicates informational intent.  NLP (Natural Language Processing; such as textual analysis) is crucial to being able to make such judgements accurately.  Only AI is capable of performing such a function automatically, expediently, and at scale.

This is a reminder of how machine learning can be used to understand the content of a website, and, consequently, suggest ways to ensure that content (including purpose) is picked up by the search engine’s algorithm.  (Textual analysis and similar content topics are taken into account.)  Rather than classify each URL individually, thematic content clusters of content are created and refined.

Google’s algorithm likely classifies pages into clusters, then determines whether to publish those pages as results for any given search categories.  It is necessary to adapt to unique paths taken by each potential customer; a feat that can only be accomplished using machine learning.

Crawl behavior on a website serves purposes beyond the discovery of web pages.  The cyclical nature of crawls required to determine the category of a page means that a page that is never revisited may continue to not be considered by Google.  This means that companies must avail themselves of the latest technology in order to stay abreast of this dynamic online ecosystem.