Google is not the leader in web search for nothing. For decades now, it has consistently offered the best results—often by far—compared to its competitors. This dominance is underpinned by advanced algorithms and precise analysis of data from web searches.
However, this hegemony has landed Google in an antitrust lawsuit in the United States. Documents revealed during this trial in 2024 have shed light on the relevance of its results and how Google leverages user interactions to enhance them.
Professional web SEOs had many hunches about how the search engine operates, sometimes supported by academic articles or experiments. Yet, much of it remained unexplained.
Thanks to this trial, the world has been privy to enlightening documents about how Google has evolved over the past decade, bringing much-needed clarity. One cornerstone of the search engine, known as Navboost, stands out among all the others.
Navboost enabled Google to move beyond purely algorithmic, well-crafted search mechanisms, which were often too easy to manipulate.
Navboost accounts for what users value. It considers user behavior.
A webpage is good if the people visiting it find it good. This may sound obvious, but let’s dive into Navboost.
User Behavior Integration at Google
In reality, integrating user behavior into Google’s systems is not particularly new. For at least a decade, algorithms like learning to rank or painstaking feedback from quality raters have been part of web search. These data sources have long served as the basis for adjusting the rankings of the content displayed in results.
The overarching goal remains simple: to continuously improve the relevance and quality of the results displayed in the SERP. Google knows that to stay competitive, it must provide a search experience aligned with user expectations. Trial documents clearly show that quality alone was no longer enough. The algorithm needed to evolve to incorporate more complex behavioral interactions and data.
Thus, as revealed in striking slides from the antitrust trial, Google one day decided to change the search engine paradigm. Initially, the aim was to enhance SERP quality. But they realized this approach had limitations, as defining quality is an extremely complex and often subjective task (especially before high-performing LLMs emerged). So, they shifted their focus. The goal was no longer to offer the most qualitative content but the content most liked by users.
Google was well aware of the implications: clickbait and deceptive content would inevitably surface more easily. Meanwhile, high-quality but challenging content might disappear.
Nonetheless, they chose not to worry. The traditional algorithmic layers (QBST, Mustang, etc.) would handle the initial rankings, while “twiddlers” (mini-algorithms) would act as police to weed out the outliers. Navboost would then reorder the best remaining pages so the final ranking aligns with user preferences.
In 2005, Navboost officially emerged, although initially, it is likely that optimization was not based on clicks, or at least its effect was not as strong as what has been observed since 2018-2021. It monitors user interactions with the SERP, subtly adjusting rankings to consistently present pages predicted to generate the most user engagement.
How Does Navboost Work?
Navboost is a prediction algorithm. For instance, it aims to maximize the chances that when it reorders a SERP, the first result is the one most likely to be clicked by a human user. It applies the same logic to the second result, the third, and so on.
In essence, it predicts the CTR (Click-Through Rate) to determine which results will be clicked. A good prediction ensures that users are happy because the SERP appears in the order they would naturally prefer to click on.
This is made possible by Google’s phenomenal capacity for large-scale machine learning. To build the prediction model, they reportedly collect one billion user behaviors daily and analyze 100 billion clicks to identify relevant patterns. In short, Google monitors a massive portion of user traffic, storing behavior data for up to 13 months to enable Navboost’s predictions.
The behavioral signals exploited include click rates on SERP pages, dwell time (how long a user stays on a page after clicking it from the SERP), and navigation patterns (such as back-and-forth actions between SERPs and sites, scrolling on the SERP, hovering, etc.). Virtually anything that can be monitored and found useful through testing is likely to be actively tracked.
However, Google doesn’t rely solely on data from the SERP. Chrome, its browser, also plays a major role in capturing user behavior. Distinguishing between what Navboost specifically uses and what other algorithms like Mustang exploit is challenging. What is certain is that Navboost heavily relies on behavioral data from the SERP. In contrast, Mustang appears to integrate additional information gathered via Chrome, such as interactions within a site or more detailed navigation behaviors.
The trial documents do not fully clarify the data sources used by each algorithm, illustrating how interconnected and opaque Google’s algorithmic ecosystem remains.
Contrary to what one might think, Navboost doesn’t aim to predict a generalized average behavior. Google segments data based on criteria like location, language, device, or operating system. This segmentation, known as “slicing,” personalizes results for specific user groups, making search more relevant. For instance, a user in Paris using Chrome might see a different SERP than someone in Nantes using Safari.
Using a 13-month historical dataset allows for observing many behaviors and making accurate predictions in numerous cases. However, in some instances, a faster version of Navboost, based on only 24 hours of data, takes precedence to provide results better aligned with current events or rapidly shifting trends.
Is Navboost Google Search’s Swiss Army Knife?
The trial documents reveal that within Google, Navboost is viewed as a flagship algorithm. Leveraging refined search results from other algorithms, it learns how to optimize a SERP but simultaneously claims credit for previous efforts. There appears to be some internal jealousy toward Navboost.
It’s akin to an LLM-based AI (like ChatGPT and others) becoming highly effective by assimilating content sourced from everywhere. One can understand why that might cause frustration.
Nonetheless, since Navboost delivers excellent results, Google employs it for tasks beyond adjusting page rankings. For example, snippets and sitelinks are optimized to maximize click-through rates.
Leaked Google documents also reference Navboost’s involvement with named entities, suggesting that SERPs and the Knowledge Graph could be tailored—yet again—with its help.
Isn’t Navboost Overly Complex?
It’s remarkable that such a critical component as Navboost flew under the radar for years. While SEOs had some intuition and made educated guesses, Google’s communication strategy made understanding difficult.
For years, Google publicly claimed that CTR was not a ranking signal, a message it emphatically repeated. This created a gap: while SEO experts speculated among themselves, broader and less specialized audiences dismissed the idea. Yet, it turns out the SEOs were right. CTR does play a role in Google’s rankings, as internal documents confirm—not just speculation.
Beyond communication and trust issues (companies are entitled to maintain secrecy about their technology and algorithms, but outright dishonesty is another matter), Navboost presents other significant challenges.
Data collection is not always straightforward. For instance, scrolling behavior on a phone likely conveys different information than scrolling on a 12-inch or 32-inch screen. As interfaces become more complex, capturing subtle nuances grows increasingly challenging.
The segmentation of data helps address this issue to some extent: similar contexts are grouped together, reducing the influence of undetectable yet present differences.
As previously mentioned, Chrome seems to play a central role in capturing user data. As Google’s browser, it sends a wealth of information back to the company—whether from the SERP (which could theoretically be done via JavaScript on any browser) or from site visits. For example, tracking internal clicks within a site could help identify key pages, enabling better sitelinks.
In my view, Chrome is at the core of a significant portion of data collection. Solely relying on SERP data seems insufficient to fully understand a site’s or page’s appeal. Observing user navigation within a site provides much deeper insight into what users like and why.
Navboost’s Bias and Limitations
Navboost is not without its flaws. Data slicing could inadvertently isolate behaviors tied to cultural or political preferences, potentially reinforcing biases.
The most significant drawback of Navboost, already mentioned but worth reiterating, is its reliance on clicks, which limits result diversity. Over time, the same pages consistently highlighted as favorites remain dominant, leaving little room for newcomers to achieve high rankings. Without visibility in top positions, there’s limited interaction data to collect, making it difficult for new pages to break through.
How to Leverage Navboost in SEO
To conclude, let’s discuss how to adapt to Navboost and take advantage of it.
First and foremost, it’s crucial to convince users to click on your site when it appears in a Google SERP. Titles, meta descriptions, and every customizable snippet element must be optimized to encourage clicks. Clicks, clicks, clicks! This is vital.
Next, ensure your page content is engaging. This doesn’t necessarily mean expert-level content (though it often helps) but rather content that resonates with users. Google’s advice to “think about the user” isn’t entirely wrong.
Monitoring site metrics like CTR in Google Search Console and retention time can indicate success. Additionally, if your content performs well on non-organic platforms like social media, it’s likely appealing to Navboost.
Remember, Navboost reorders SERPs generated by traditional Google ranking algorithms. To benefit, you must first rank among the top candidates in the SERP.
If your site has never appeared in the top 10, Navboost likely won’t influence it. Work on classic SEO fundamentals (popularity, semantics) or leverage tactics to make Chrome users engage with your site positively, regardless of the initial traffic source.
If you consistently rank well but drop quickly, it’s likely Navboost deeming your content unappealing.
Finally, if your metrics match a competitor’s but you’re still ranked lower, Navboost might be the key to changing that. Even slightly higher click-through rates can tip the balance in your favor.
Navboost: Google’s Re-Ranking Engine
Navboost is the bane of SEO professionals. We suspected something like it existed but couldn’t fully grasp how it worked.
With what we now know, it’s clear SEO practices must evolve. Relying solely on technical prowess or content quality is no longer enough. Traffic engagement has become paramount.
For a high-performing SEO site, Navboost teaches us that broader visibility and strong user reception are essential.
Promoting your site outside of SEO efforts is now indispensable. The more non-SEO traffic and tailored content you generate, the easier it becomes to climb the rankings—and stay there.
Technical SEO isn’t dead, as Navboost doesn’t govern the entire ranking process. The foundational practices of 2020–2024 remain crucial. But we need to push further.
The three traditional pillars—technical, popularity, and content—are not obsolete. However, popularity now translates to authority: the site must gain recognition from users. Content, meanwhile, must be attractive, not just excellent.
Navboost is reshaping SEO. Let’s adapt by focusing more than ever on our users, readers, and future visitors. Let’s nurture them and trust them to recognize and engage with pages designed to inform, entertain, and help them.
