Google BERT Update: Content Strategy for Google BERT

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Google BERT Update

Google BERT update is one of the biggest leaps in the history of search in how Google understands search queries.

Google processes billions of searches every day, and around 15% of those search queries are new to Google.

That’s why Google has built a system that returns the most useful and relevant results for queries it can’t anticipate.

Understanding language is one of the core functions of a search engine.

When people search for something online, the search engine needs to bring helpful information on the web, no matter how they have spelled or combined the search query.

Although Google’s language understanding capabilities have improved over the years, sometimes it may not get what the searcher intended to find, especially the complex and conversational searches.

That’s why people use a string of words they think Google understands.

Machine learning has helped Google in the science of Language understanding. It’s making significant improvements in how Google understands search queries to represent the biggest leap forward in the past five years of Search.

Applying BERT Model in Search – Google BERT Explained

In 2018, Google introduced and open-sourced a neutral-network-based technique for NLP called Bidirectional Encoders Representation from Transformers, or in short, BERT. (Source: Google AI Blog)

BERT models process words in relation to all other words in a particular sentence instead of in one-by-one or unidirectional order.

By applying BERT, Google can interpret the search query and user intent behind it by considering the full context of a particular word by looking at words before and after it.

It helps Google better understand the longer, more conversational, and complex search queries where prepositions like “to” and “for” contribute much to the meaning.

Since some BERT models can be complex to handle by traditional hardware, Google is using the latest, custom-developed Cloud Tensor Processing Units (TPUs) to serve the more relevant search results to users. (Source: Google Cloud)

Google Cloud TPU, Tensor Processing Unit
Google Cloud TPU (Source:

Google BERT Examples

Google has conducted rigorous testing to ensure that the changes in the search results are more helpful.

Here are a couple of examples that demonstrate how BERT can help to understand the search intent better.

Google BERT Example 1

Google BERT Example
Google BERT Example 1

Here is a search term “2019 brazil traveler to usa need a visa.”

The word “to” in this search query and its relationship to the other words are particularly important to understand the meaning.

It’s about “a Brazilian traveler who needs a visa to the U.S. in 2019”, and not the other way around.

Earlier, Google returned different results by focusing on words.

With BERT, Google Search can know the small word “to” matter a lot in this query. This helps Google bring more relevant results for a particular query by understanding the full context of each word.

Google BERT Example 2

Google BERT Example
Google BERT Example 2

Take a look at another search query: “do estheticians stand a lot at work.”

Previously, Google Search was taking an approach of matching keywords by matching the word “stand” in the query with the term “stand-alone” in the result.

However, that’s not the correct interpretation of the word “stand” in context.

On the other hand, BERT understands that the word “stand” is related to the concept of the physical activity or physical demands of a job and thus displays a more useful result.

What Does Google BERT Mean for SEO?

At its core, the Google BERT update is focused on better interpreting the search intent using the NLP technique.

Google’s BERT algorithm update is much similar to RankBrain, which uses machine learning and AI to bring the most relevant search results to a user.

By applying BERT, Google is providing more useful results in ranking and featured snippets.

This algorithm has impacted around 10% of the English search queries in the US.

Google will bring this algorithmic change to more languages and regions over time.

Please take a look at how it works.

Google BERT uses machine learning at its core, allowing it to take learnings from one language and apply them to others.

For example,

BERT Models can learn from improvements in English, where the majority of web content currently exists, and apply them to the other languages that Google Search offered.

By using this method, Google can display relevant results in many languages where Google Search is available.

Google is also using BERT models for the featured snippets and seeing significant improvements in languages like Hindi, Korean, and Portuguese.

Content Strategy for Google BERT

Google BERT update was released to understand the search query and user intent better, not to penalize anyone.

The best way to win this update is to create content that satisfies user intent.

Developing comprehensive guides that answer the common as well as uncommon questions of your target audience will improve your search visibility.

Here are a few things that you should incorporate into your content strategy.

1. Technical Optimization

Whenever you start optimizing a website, make sure it’s secure, mobile-friendly, loads faster, and does not have crawling issues, too many redirects, or 404 errors.

2. Optimize Your Existing Content

If you want to win a Google BERT update, then optimize existing content on your site for humans and Googlebot.

Perform Content Gap Analysis to know the difference between the top-ranking pages and your web page.

Here are a few things to start with:

  • Usability and Relevance with the search query
  • Content Freshness and Accuracy
  • Content Depth (Comprehensiveness)
  • Traffic Analysis

SEO tools like Ahrefs and Semrush are handy for spying on competitors.

Once you get all insights, you can use Google NLP API or third-party tools like Semrush Writing Assistant to improve your copy.

After BERT, Google will to better understand the context of the search query, especially when prepositions like” to” and “for” contribute a lot to meaning.

Previously, these words were considered stop words and omitted from optimization.

However, you should use these words naturally in your copy to get better rankings in organic and voice searches.

Optimizing existing content is beneficial if your site traffic is declining or not getting results as you expected.

3. Create High-Quality Content

People love to read content that feels natural instead of keyword-stuffed copy.

Google also asks to write content primarily for people, not search engines. (Source: Quality Guidelines)

Knowing your potential users will help you craft great content.

Hiring an expert who knows the ins and out of that particular niche is the best way to write high-quality content.

If you want to learn copywriting secrets, you can get help from some popular books.


People use Google Search in a way that they feel is natural to them, and even after BERT, Google doesn’t always get your query right.

Language understanding is an ongoing challenge for Google, especially when newer services like Voice Search are getting traction.

However, Google is getting better every day so that it can return the most helpful information for each search query.

Google BERT is just the beginning of a new era.

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One Comment

  1. Valuable info. You’ve explained this topic that anybody can understand. I got a huge traffic drop after Google BERT launched but it’s recovering now.
    I’ll use your content strategy and let you know how it works.

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