User Data Is Important In Google Search, Per Liz Reid’s DOJ Filing
Author: Super Admin
Published On: 23 January, 2026
I found some interesting things in the latest document in the DOJ vs. Google trial. Google has appealed the ruling that says they need to give proprietary information to competitors.

OK, let’s get into the interesting stuff!
This really isn’t a surprise. I did find it interesting that freshness signals are at the heart of Google’s proprietary secrets.

Again, here’s more on the importance of Google’s proprietary freshness signals:

Every page in Google’s index is marked up with annotations to help it understand the page. These include signals to identify spam and duplicate pages. I’ve written before about how every page in the index has a spam score.

Google doesn’t want to share information with its competitors on these scores.

If the spam scores get out, it could lead to more spamming and more difficulty for Google in fighting spam.

The pages that Google has added page understanding annotations on are organized based on how frequently Google expects the content will need to be accessed and how fresh the content needs to be.

Google argues that giving competitors a list of indexed URLs will enable them to “forgo crawling and analyzing the larger web, and to instead focus their efforts on crawling only the fraction of pages Google has included in its index.” Building this index costs Google extensive time and money. They don’t want to give that away for free.

This is the most interesting part. I feel that we do not pay enough attention to Google’s use of user data. (Stay tuned to my YouTube channel as I’m soon about to release a very interesting video with my thoughts on how user-side data is so important – likely the MOST important factor in Google’s ranking systems.)
Google Glue is a huge table of user activity. It collects the text of the queries searched, the user’s language, location and device type, and information on what appeared on the SERP, what the user clicked on or hovered over, how long they stayed on a SERP, and more.
RankEmbed BERT is even more interesting. RankEmbed BERT is one of the deep learning systems that underpins Search. In the Pandu Nayak testimony, we learned that RankEmbed BERT is used in reranking the results returned by traditional ranking systems. RankEmbed BERT is trained on click and query data from actual users.
The AI systems behind search are continually learning to improve upon presenting searchers with satisfying results. Google looks at what they are clicking on and whether they return to the SERPs or not. Google also runs live experiments that look at what searchers choose to click on and stay on. Those actions help train RankEmbed BERT. It is further fine-tuned by ratings from the quality raters. I will be publishing more on this soon. The take-home point I want to hammer on is that user satisfaction is by far the most important thing we should be optimizing for!
From the Liz Reid document we are analyzing today, we can see that user data is used to train, build, and operate RankEmbed models.

Once again, we learn that the user data that is used to train these models includes query, location, time of search, and how the user interacted with what was displayed to them.

This is talking about the actions that users take from within the Google Search results. What I really want to know is how much of a role Chrome data uses. Does Google look at whether people are engaging with your pages, filling out your forms, making your recipes, and more? I think they do. The judgment summary of this trial hints that Chrome data is used in the ranking systems, but not a lot of detail is shared.

This user data is the key to Google’s success.

It’s worthwhile reading the whole declaration from Liz Reid.
More Resources:
This post was originally published on Marie Haynes Consulting.
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