Let me see if I can convince you!
I’ve shared a bunch in this video and summarized my thoughts in the article below. Also, this is the second blog post I’ve written on this topic in the last week. There is much more information on user data and how Google uses it in my previous blog post.
Ranking Has 3 Components
We learned in the DOJ vs Google trial that Google’s ranking process involves three main components:
- Traditional systems are used for initial ranking.
- AI Systems (such as RankBrain, DeepRank, and RankEmbed BERT) re-rank the top 20-30 documents.
- Those systems are fine-tuned by Quality Rater scores, and more importantly IMO, results from live user tests.
The DOJ vs. Google lawsuit talked extensively about how Google’s massive advantage stems from the large amounts of user data it uses. In its appeal, Google said that it does not want to comply with the judge’s mandate to hand over user data to competitors. It listed two ways it uses user data – in a system called Glue, a system which incorporates Navboost that looks at what users click on and engage with, and also in the RankEmbed model.
RankEmbed is fascinating. It embeds the user’s query into a vector space. Content that is likely to be relevant to that query will be found nearby. RankEmbed is fine-tuned by two things:
1. Ratings from the Quality Raters. They are given two sets of results – “Frozen” Google results and “Retrained” results – or, in other words, the results of the newly trained and refined AI-driven search algorithms. Their scores help Google’s systems understand whether the retrained algorithms are producing higher-quality search results.
2. Real-world live experiments where a small percentage of real searchers are shown results from the old vs. retrained algorithms. Their clicks and actions help fine-tune the system.
The ultimate goal of these systems is to continually improve on producing rankings that satisfy the searcher.
More Thinking On Live Tests – Users Tell Google The Types Of Pages That Are Helpful, Not The Actual Pages
I’ve realized that Google’s live user tests aren’t just about gathering data on specific pages. They are about training the system to recognize patterns. Google isn’t necessarily tracking every single user interaction to rank that one specific URL. Instead, it is using that data to teach its AI what “helpful” looks like. The system learns to identify the types of content that satisfy user intent, then predicts whether your site fits that successful mold.
It will continue to evolve its process in predicting which content is likely to be helpful. It definitely extends far beyond simple vector search. Google is continually finding new ways to understand user intent and how to meet it.
What This Means For SEO
If you’re ranking in the top few pages of search, you have…
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