Learn How Google Ranks Results for Best SEO Practices

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  • November 8, 2023
  • Anthony Scott
  • 0 Comment

Learn How Google Ranks Results for Best SEO Practices

Google is undoubtedly the most popular search engine in the world, serving billions of search queries every day. Have you ever wondered how Google determines the order of search results? The truth is, that Google keeps its ranking algorithm shrouded in secrecy, but there have been some insights shared by experts in the field to understand how Google ranks results for best SEO practices.  In a 2022 episode of the “Search Off the Record Podcast” Gary Illyes, a Webmaster Trends Analyst at Google, confirmed seven key ranking factors. Interestingly, these factors are consistent with what he shared with Jason in 2019.  Let’s take a closer look at these factors and delve into the complex world of search engine rankings:-


Google’s ranking algorithm places a significant emphasis on the relevance of a web page’s content to the user’s query. Pages that are highly topical to a particular search query are more likely to rank higher in the search results, Read more..


Quality is a multifaceted factor that encompasses various elements, including backlinks (PageRank), content, user experience, and more. Google aims to deliver high-quality, informative, and trustworthy content to its users. Read more..

Page Speed

Website loading speed is crucial for user experience. Google rewards websites that load quickly, as slow-loading pages can frustrate users and deter them from engaging with the content. Read more..


 RankBrain is a machine learning algorithm used by Google to help understand and interpret user search queries. It assists in delivering more relevant search results by learning from user behavior. Read more..


In the context of search, entities refer to specific people, places, things, or concepts. Google’s algorithm takes into account the presence and relevance of entities within a webpage to improve search accuracy. Read more..

Structured Data

This factor involves the use of structured data markup, such as Schema.org, to provide search engines with additional context about a webpage’s content. Structured data helps search engines better understand and display information in search results, such as rich snippets. Read more..


Google values fresh and up-to-date content. Pages that are regularly updated with new information or trends are more likely to rank well, especially for queries related to current events or rapidly changing topics. Read more..

While these seven ranking factors provide a general framework for understanding Google’s approach to ranking webpages, each factor contains multiple sub-factors, making the ranking algorithm exceedingly complex. It’s crucial to recognize that search engines like Google and Bing operate in a competitive environment where the principles of Darwinism come into play. The Search Engine Results Page (SERP) is essentially a product, and search engines earn revenue through various channels: 

Search users contribute valuable behavioral data, which can be monetized by search engines to enhance their algorithms and advertising strategies.

– Advertisers pay for clicks on the numerous ad elements displayed in the SERP, which constitutes a significant portion of search engine revenue.

– Search engine optimization (SEO) experts, businesses, and content creators provide free content that populates the SERP. This content fuels the search engines’ proprietary Large Language Models (LLM) and Knowledge Graphs, which are essential for generating search results.

A recent and significant development in the world of search is the integration of generative AI results into the SERP by both Google and Bing. These generative AI models, powered by massive LLMs, have learned how to predict word patterns and relationships between entities by accessing extensive knowledge graphs, research papers, books, and publicly available internet data. 

Knowledge Graphs, which are like vast repositories of facts, are not unique to Google or Bing but are used worldwide. LLMs are now being integrated with these Knowledge Graphs, allowing the AI models to learn from the factual information stored within them. This integration results in robust and highly accurate generative AI results. Notable examples of generative AI results include Google’s SGE, Bing Chat, and Bard. These AI models differ from the traditional “bidding” process of Rich Elements, as they are displayed when search engines are confident in their understanding of a topic, question, or business. 

In conclusion, Google’s ranking algorithm remains a well-guarded secret, but insights from experts like Gary Illyes shed light on its key factors. The world of search engines is influenced by a form of Darwinism, where user data, advertisers, and content creators all contribute to the search engine’s success. The integration of generative AI results marks a significant advancement in delivering more accurate and relevant search results to users, further enhancing the search experience. 

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