How Google’s Ranking Process Works
Google’s ranking process involves three main components: traditional systems, AI systems, and quality rater scores. The traditional systems are used for initial ranking, while AI systems such as RankBrain, DeepRank, and RankEmbed BERT re-rank the top 20-30 documents. These systems are fine-tuned by quality rater scores and results from live user tests.
The Importance of User Data
The DOJ vs. Google lawsuit highlighted the significance of user data in Google’s ranking process. Google uses user data in two main ways: in a system called Glue, which incorporates Navboost and looks at what users click on and engage with, and in the RankEmbed model. RankEmbed embeds the user’s query into a vector space, where content that is likely to be relevant to that query will be found nearby.
How RankEmbed Works
RankEmbed is fine-tuned by two things: ratings from quality raters and real-world live experiments. Quality raters are given two sets of results: "Frozen" Google results and "Retrained" results. Their scores help Google’s systems understand whether the retrained algorithms are producing higher-quality search results. Real-world live experiments involve showing a small percentage of real searchers results from the old vs. retrained algorithms, and their clicks and actions help fine-tune the system.
The Goal of Google’s Ranking Process
The ultimate goal of these systems is to continually improve on producing rankings that satisfy the searcher. Google’s live user tests aren’t just about gathering data on specific pages, but about training the system to recognize patterns. Google isn’t necessarily tracking every single user interaction to rank that one specific URL, but instead, it is using that data to teach its AI what "helpful" looks like.
What This Means for SEO
If you’re ranking in the top few pages of search, you have convinced the traditional ranking systems to put you in the ranking auction. Once there, a multitude of AI systems work to predict which of the top results truly is the best for the searcher. This is even more important now that Google is starting to use "Personal Intelligence" in Gemini and AI Mode.
Optimizing for Vector Search
While it can be tempting to work to reverse engineer Google’s AI systems, it’s essential to remember that the systems are fine-tuned to continually improve upon producing results that are the most satisfying for the searcher. Looking good to AI is nowhere near as important as truly being the result that is the most helpful. Optimizing for vector search can do more harm than good unless you truly do have the type of content that users go on to find more helpful than the other options they have.
My Advice
My advice is to optimize loosely for vector search. Understand what it is your audience wants and be sure that your pages meet the specific needs they have. Use headings to help your readers understand that the things they are looking for are on your page. Look at the pages that Google is ranking for queries that should lead to your page, and truly ask yourself what it is about these pages that searchers are finding helpful.
Improving the User Experience
Instead of obsessing over keywords, work to improve the actual user experience. Make your page more engaging, focusing more on metrics like scrolls and session duration, and rankings should naturally improve. Obsess over helpfulness, and consider having an external party look at your content and share why it may or may not be helpful.
Conclusion
Google’s ranking process is designed to continually learn and improve upon showing searchers pages they are likely to find helpful. As content creators, our goal should be the same. By focusing on creating original, insightful, and valuable content, we can increase our chances of ranking well in search results. Remember, the type of content that people tend to find helpful is content that provides substantial value when compared to other pages in the search results. By keeping this in mind, we can create content that not only ranks well but also resonates with our audience.