Google’s BERT (Bidirectional Encoder Representations from Transformers) is a natural language processing (NLP) model that helps Google understand the context and intent of user queries better. BERT was introduced in 2019 and has been applied to various search features, such as featured snippets, people also ask, and top stories. BERT can also affect the ranking of web pages, especially for longer and more conversational queries.

To optimize your content for BERT, you need to create informative and relevant content that directly addresses the user’s query. This involves using natural language, avoiding keyword stuffing, and using long-tail keywords and conversational language. In this blog, we will explain how BERT works, how it affects SEO, and how you can use BERT to improve your content quality and relevance.

What is BERT and how does it work?

BERT is a deep learning model that can analyze large amounts of text and learn from the relationships between words and sentences. BERT is bidirectional, which means it can look at the words before and after a given word to understand its meaning and context. This allows BERT to capture the nuances and subtleties of natural language, such as prepositions, pronouns, and synonyms.

For example, consider the query “how to catch a cow fishing”. A traditional NLP model might interpret this query as related to cows or farming, and return irrelevant results. However, BERT can understand that the word “cow” in this query is actually a type of fish, and return more relevant results.

BERT can also help Google understand the intent behind a query, and match it with the most suitable content. For example, consider the query “parking on a hill with no curb”. A traditional NLP model might focus on the keywords “parking” and “hill”, and return results about parking lots or hills. However, BERT can understand that the user is looking for a specific instruction, and return results that provide the answer.

How does BERT affect SEO?
BERT can also change the ranking of web pages, depending on how well they match the user’s query and intent. BERT can reward web pages that provide high-quality, informative, and relevant content, and penalize web pages that use keyword stuffing, low-quality content, or irrelevant content. BERT can also affect the ranking of web pages for different queries, depending on the context and meaning of the words.

For example, consider the query “2019 brazil traveler to usa need a visa”. A traditional NLP model might rank web pages that contain the keywords “brazil”, “traveler”, “usa”, and “visa”, regardless of the order or context. However, BERT can understand that the word “to” is important in this query, and rank web pages that provide information about Brazilian travelers going to the USA, rather than the other way around.

How to use BERT to improve your content quality and relevance?
To use BERT to improve your content quality and relevance, you need to follow some best practices and tips, such as:

Focus on the user’s query and intent: BERT can help Google understand what the user is looking for, and match it with the most suitable content. Therefore, you need to create content that directly answers the user’s query, and provides useful and relevant information. You can use tools like Google Search Console, Google Trends, or AnswerThePublic to find out what users are searching for, and create content around those topics.

Use natural language and conversational tone: BERT can help Google understand the context and meaning of natural language, and rank content that uses natural language and conversational tone higher. Therefore, you need to write content that sounds human and natural, and avoid using unnatural or forced keywords. You can use tools like Hemingway or Grammarly to check your content for readability and grammar.

Use long-tail keywords and semantic variations: BERT can help Google understand the nuances and subtleties of natural language, and rank content that uses long-tail keywords and semantic variations higher. Therefore, you need to use long-tail keywords and semantic variations that match the user’s query and intent, and avoid using generic or short keywords. You can use tools like Ubersuggest or Moz Keyword Explorer to find long-tail keywords and semantic variations for your content.

Provide clear and structured content: BERT can help Google understand the structure and organization of your content, and rank content that provides clear and structured content higher. Therefore, you need to provide clear and structured content that follows a logical flow and hierarchy, and uses headings, subheadings, bullet points, lists, and other formatting elements. You can use tools like Yoast SEO or Rank Math to check your content for structure and SEO.