In the labyrinthine world of SEO, understanding Google's inner workings is akin to holding a treasure map. The U.S. vs. Google antitrust trial, particularly Pandu Nayak's testimony in October, was a watershed moment for SEO enthusiasts and professionals alike.
In this post, we wanted to highlight important takeaways for SEO professionals that they can learn from all this and implement to increase their organic visibility and rankings.
Let’s begin.
Key insights from Nayak’s Testimony
Here are a few key insights we found from Pandu Nayak’s testimony:
The evolution of Google's indexing
The Scale of the Index
Nayak brought to light the colossal scale of Google's indexing efforts. As of 2020, the index encompassed an estimated 400 billion documents. This number not only reflects the vastness of the web but also Google's relentless efforts to mirror its exponential growth.
Quality Over Quantity
More intriguing, however, was Nayak's emphasis on the quality of this index. He articulated a significant shift in Google's approach: prioritizing the elimination of redundant or low-quality content to enhance the index's overall quality. This shift underscores a crucial understanding for SEOs – that Google values the relevance and quality of content over sheer volume.
The retrieval and ranking mechanism
Core retrieval mechanism
Nayak shed light on the 'inverted index' system, a fundamental component in Google's document retrieval process. This system, likened to an index in a book, forms the backbone of Google's ability to match queries with relevant documents efficiently.
Ranking complexity
Nayak's revelation about the use of numerous algorithms and machine learning models for ranking was a striking contradiction to the oversimplified perception of Google's ranking factors. This complexity signifies that the SEO landscape is more intricate than mere keyword optimization.
Understanding Google's core algorithms
Navboost and Glue systems
Delving deeper, Nayak detailed the Navboost and Glue systems, which are instrumental in refining search results based on historical user data. These systems underscore the significance of user interaction patterns in determining search result relevance.
According to Nayak, Navboost “is one of the important signals” that Google has.
“Navboost is looking at a lot of documents and figuring out things about it. So it’s the thing that culls from a lot of documents to fewer documents,” Nayak said. He also confirmed that Navboost isn’t the only core algorithm that Google uses to retrieve results.
Nayak explains Glue as “another name for Navboost that includes all of the other features on the page.”
“Glue aggregates diverse types of user interactions–such as clicks, hovers, scrolls, and swipes–and creates a common metric to compare web results and search features. This process determines both whether a search feature is triggered and where it triggers on the page.”
The role of deep learning
Nayak also highlighted Google's advanced use of deep learning models, such as RankBrain and DeepRank, in interpreting and responding to user queries. These models represent a sophisticated understanding of language and user intent, going beyond traditional keyword matching.
One of the important things Nayak mentioned about RankBrain is that “RankBrain understands long-tail user needs as it trains.”
The impact of click data
The use and misuse of click data
The testimony shed light on the nuanced use of click data in ranking. Nayak delineated the difference between simply memorizing user clicks and genuinely understanding user intent, a distinction that is pivotal for SEOs to grasp.
The significance of user feedback
The role of user interaction
Nayak's insights into the importance of user behavior, like clicks and interaction patterns, in refining search results, are a clarion call for SEOs to focus on user engagement and satisfaction as key metrics.
Nayak’s testimony offers a few key insights about user feedback (i.e., click data)
First, when Google talks about collecting user feedback data for a period, that includes “all the queries and the clicks that occurred over that period of time from all users.” A US-only model would only include a subset of US users. But for a global model, it’d encompass every data point from all users.
Second, sometimes, fresher users and clicks may not be as valuable as older data.
“It depends on the query … there are situations where the older data is actually more valuable. So I think these are all sort[s] of empirical questions to say, well, what exactly is happening. There are clearly situations where fresh data is better, but there are also cases where the older data is more valuable,” Nayak said.
The evolution of Google’s ranking signals
From 200 to 100+
Perhaps one of the most striking revelations was the reduction in the number of ranking signals. Nayak suggested that the number has decreased to "maybe over a hundred," a significant shift from the previously touted 200 search ranking signals. This reduction indicates a more focused approach by Google, emphasizing the impact of signals like page quality, reliability, and localization.
Additional context from antitrust trial exhibits
Ranking documents and Deep Learning
Nayak's testimony, supplemented by other trial exhibits, provided a glimpse into the interplay between traditional ranking methods and the increasing reliance on deep learning systems. This insight is crucial for SEOs to understand the evolving landscape of search engine algorithms.
Challenges with human raters
The limitations of human raters in evaluating the relevance and quality of search results were also highlighted. This revelation underscores the complexity of creating algorithms that can accurately mimic human judgment.
According to the documents from 2018 and 2021, the 3 big problems with human raters were identified as:
- Raters may not understand technical queries
- Raters may not accurately judge the popularity of anything
- In IS Ratings, human raters don’t always pay enough attention to the freshness aspect of relevance or lack the time context for the query, thus undervaluing fresh results for fresh-seeking queries.
The Priors algorithm and its implications
Understanding the Priors algorithm
In an intriguing development, Nayak touched upon the 'Priors' algorithm during his testimony. This algorithm, not to be confused with an algorithm update, plays a pivotal role in ranking choices based on popularity.
It underscores Google's approach of scoring and ranking choices based on how frequently they are selected, a principle that resonates deeply with the core of SEO.
You can learn more about the Priors algorithm here.
Personalization in search results
Nayak also highlighted Google's personalized approach, where they measure the similarity of new users to those behind each door, based on past actions.
This nuanced approach to personalization indicates that Google's algorithm is constantly evolving to better understand and cater to individual user behaviors and preferences.
The role of Deep Learning in Google search
The intricacies of DeepRank
DeepRank, identified as BERT when used for ranking, plays a crucial role in understanding language and context. Nayak's testimony revealed the depth of DeepRank's capabilities in language understanding and world knowledge, two critical components in the effective ranking of search results.
RankEmbed BERT's evolution
RankEmbed BERT, an enhancement of the initial RankEmbed algorithm, was augmented with BERT to better comprehend language nuances. This evolution signifies Google's commitment to refining its understanding of user queries, emphasizing the importance of context and semantics in SEO strategies.
7 actionable takeaways for SEO professionals
As SEO professionals, understanding the implications of Nayak's testimony is critical for refining strategies and improving site rankings. Here are some actionable takeaways:
- Focus on Quality Over Quantity: With Google prioritizing high-quality content in its index, SEO efforts should be directed towards creating valuable, relevant, and unique content rather than simply increasing the quantity of content.
- Leverage User Interaction Data: Given the importance of user interaction patterns in Google's ranking, it's crucial to engage users effectively on your site. This includes optimizing for a better user experience, encouraging clicks, and enhancing site navigation.
- Understand the Role of Machine Learning: As Google increasingly relies on machine learning models like DeepRank and RankBrain, SEO strategies must adapt to focus on context, user intent, and semantic relevance in content.
- Personalization is Key: Tailor your content and SEO strategy to cater to the behaviors and preferences of your target audience. Understanding user intent and delivering personalized content can significantly improve your site's relevance and ranking.
- Embrace the Importance of Click Data: Recognize that Google uses click data to understand user preferences. Ensure that your titles and meta descriptions are enticing and relevant to encourage clicks and engagement.
- Optimize for the Priors Algorithm: Understand that popularity and user preference play a significant role in Google's ranking. Focus on creating content that resonates with your audience, encouraging repeat visits and interactions.
- Stay Updated with Algorithm Changes: Continuous learning and adaptation are crucial in SEO. Stay informed about the latest developments in Google's algorithms, especially regarding deep learning and AI advancements.
Conclusion
Pandu Nayak's testimony at the U.S. vs. Google antitrust trial offers an unprecedented peek behind the curtain of Google Search's complex mechanisms.
For SEO professionals, this knowledge is not just theoretical; it's a roadmap to refining strategies and achieving better rankings.
By understanding and adapting to Google's evolving algorithms, focusing on quality content, and leveraging user data, SEO professionals can significantly enhance their effectiveness in the ever-changing landscape of search engine optimization.
