BERT (2019)

What Is the Google BERT Algorithm Update (2019)?

The Google BERT Algorithm Update, released in October 2019, marked a monumental leap in natural language processing (NLP) for Google Search. BERT stands for Bidirectional Encoder Representations from Transformers, a deep learning model that enables Google to better understand the meaning and context of words in a sentence.

Before BERT, Google’s search engine primarily relied on keyword matching to determine search results. This often led to inaccurate results when queries contained ambiguous words, prepositions, or complex phrases. BERT transformed Google’s search ability by understanding search intent and delivering more relevant and contextual results.

Why Was the Google BERT Algorithm Introduced?

1. Handling Complex, Conversational, and Contextual Queries:

Before BERT, Google struggled to interpret longer, conversational search queries. For example:

• Query before BERT: “2019 brazil traveler to USA need a visa”

• Google’s previous interpretation: It might return results for U.S. travelers visiting Brazil instead of Brazilian travelers visiting the U.S.

BERT resolved such issues by analyzing the full context of a query rather than treating words as isolated entities.

2. Improving Search Results for Voice and Natural Language Queries:

With voice search growing in popularity, users started using more natural phrasing in their searches. For example:

• Before BERT: Users searched for “best laptop students 2022”.

• After BERT: Users began asking “What’s the best laptop for college students in 2022?”.

BERT helped Google interpret prepositions and connectors in queries to improve the accuracy of results.

3. Addressing Ambiguity in Search Queries:

Words often have multiple meanings. For example:

• “Bank” could mean a financial institution or the side of a river.

BERT allowed Google to analyze the context in which the word was used, ensuring more accurate results.

How Did the BERT Update Change SEO?

BERT introduced deep learning-powered language understanding, which led to major changes in search rankings and SEO strategies.

1. Improved Understanding of Search Intent and Context:

Before BERT, Google mainly focused on keyword matching. For instance:

• Before BERT: The query “Can you get medicine for a cold at a pharmacy?” might return results about how pharmacies operate.

• After BERT: Google understood the intent behind the query and returned results for cold medicine.

2. Increased Focus on Natural Language Processing (NLP):

BERT allowed Google to:

• Analyze full sentences rather than isolated keywords.

• Identify relationships between words to determine search intent accurately.

For example, BERT helped Google recognize:

• “What are the best running shoes for flat feet?”

• “Which running shoes provide the best arch support?”

3. Long-Tail and Conversational Search Queries Took Center Stage:

BERT made long-tail queries more important because it improved Google’s ability to interpret detailed, question-based searches.

• Before BERT: The query “cheap hotels NYC” would return generic results with those keywords.

• After BERT: A search like “Where can I find budget-friendly hotels in New York City with free WiFi?” would return more relevant, intent-driven results.

4. Reduced Emphasis on Exact-Match Keywords:

Before BERT, SEO relied heavily on exact-match keywords. With BERT, Google began rewarding content that provided contextually relevant, well-structured answers instead of relying on exact phrases.

For example:

• Before BERT: A keyword-centric page might focus on “best smartphones 2025”.

• After BERT: Google rewarded content that answered broader queries, such as “What features should I consider when buying a smartphone in 2025?”

Key Features of the BERT Update

1. Bidirectional Understanding of Text:

BERT reads text in both directions simultaneously. This allows Google to:

• Understand word relationships more accurately.

• Interpret the meaning of words based on context.

For example:

• Before BERT: A phrase like “Can you park a car on a hill without a brake?” would misinterpret the focus on “brake”.

• After BERT: Google would correctly identify the meaning of “without a brake” and return results about driving safety tips.

2. Improved Contextual Matching for Long-Tail Queries:

BERT was particularly useful for long-tail, question-based queries, ensuring Google returned results that matched specific user intent.

For example:

• Before BERT: The query “best running shoes” might return broad results.

• After BERT: A query like “What are the best running shoes for flat feet?” would lead to specific, relevant content.

3. Focus on Natural Language Queries and Voice Search:

BERT improved Google’s ability to process natural, voice-based queries, like:

• Before BERT: “cheap flights NYC LA”

• After BERT: “What are the cheapest flights from New York to Los Angeles next weekend?”

How Can Websites Optimize for the BERT Algorithm?

To optimize for BERT, websites should focus on:

1. Writing Content That Directly Answers User Questions:

• Create in-depth and well-researched content that directly answers queries.
• Use FAQ sections and structured headings for easy navigation.

2. Using Natural, Conversational Language:

• Write naturally, avoiding keyword stuffing.
• Optimize for voice search and long-tail queries.

3. Focusing on Semantic SEO and Related Keywords:

• Use synonyms and related terms.
• Make content flow naturally, answering user intent.

4. Optimizing for Featured Snippets:

• Structure content with clear, concise answers.
• Use lists, tables, and bullet points to increase chances of appearing in featured snippets.

Current Status of Google BERT

Since its launch, BERT has remained a key part of Google’s core algorithm. It has been refined by subsequent updates like RankBrain (2015) and BERT’s machine learning model. While BERT laid the foundation for semantic search, modern algorithms like RankBrain and BERT help refine its ability to understand natural language.

Final Thoughts on BERT (2019)

The Google BERT Algorithm Update was a game-changer in search by enhancing Google’s ability to understand contextual meaning, search intent, and natural language. By focusing on user intent and high-quality content, businesses can stay ahead in the world of SEO.

Would you like additional tips on optimizing for BERT or guidance on the latest SEO trends?