Are your pages still ranking for keywords but missing the mark on user intent? If so, you're likely missing the key to modern SEO success.
I've witnessed countless algorithm updates, but few have been as revolutionary as Google BERT. This natural language processing breakthrough has fundamentally changed how search engines understand user queries and content, making traditional keyword-focused SEO tactics increasingly obsolete.
In this comprehensive guide, I'll walk you through what BERT is, why it matters for your website's visibility, and exactly how to adapt your SEO strategy to thrive in this new era of semantic search. Whether you're an SEO novice or veteran, understanding BERT's impact is crucial for maintaining and improving your search rankings.
BERT (Bidirectional Encoder Representations from Transformers) is a neural network-based technique for natural language processing (NLP) that Google officially integrated into its search algorithm in late 2019. Unlike previous algorithms, BERT helps Google understand the nuance and context of words in searches, allowing the search engine to better interpret user intent.
Before BERT, search engines analyzed queries in a linear fashion - one word after another. BERT revolutionized this by examining words in relation to all other words in a sentence, rather than one-by-one in order. This bidirectional understanding allows Google to grasp the full context of a search query.
For example, in the phrase "can you get medicine for someone at a pharmacy," the word "for" and its relationship to "someone" is crucial. Pre-BERT, Google might have ignored this connection, returning results about getting your own prescriptions. With BERT, Google understands you're asking about picking up medication on someone else's behalf.
According to Google, BERT impacts 10% of all English search queries - a massive portion when you consider the billions of searches performed daily. Its introduction marks one of the most significant advances in search technology of the past decade, comparable only to the RankBrain algorithm update in 2015.
While you don't need to be a machine learning expert to leverage BERT for SEO, understanding its basic mechanics will help you grasp why certain content optimization strategies work better than others.
At its core, BERT uses a transformer architecture that processes words in relation to all other words in a sentence, rather than analyzing them one by one in sequence. This is what makes it "bidirectional" - it looks at words that come both before and after each word to determine meaning.
Here's a simplified explanation of BERT's processing approach:
| Traditional NLP | BERT NLP |
|---|---|
| Processes words sequentially (left to right) | Processes all words simultaneously, understanding relationships between them |
| Struggles with context and nuance | Excels at understanding contextual nuance |
| Might miss prepositions and connecting words | Considers importance of every word, including prepositions |
| Requires exact keyword matching | Understands semantic relationships and synonyms |
BERT was trained on an enormous corpus of text from Wikipedia - 2,500 million words to be exact. This extensive training enables it to understand the complex ways humans express themselves through language.
The transformer models that power BERT use something called "attention mechanisms" to weigh the importance of different words in understanding the overall meaning of a sentence. This helps Google deliver more relevant results even for complex, conversational queries that might have stumped previous algorithms.
Google's implementation of BERT builds on open-source research originally published by Google AI researchers in 2018. You can read the original paper here if you're technically inclined.
BERT has fundamentally changed the SEO landscape in several critical ways:
With BERT, Google can better discern the intent behind searches, making keyword density and exact-match phrases less important than ever. Content that thoroughly addresses the user's underlying question or need now outperforms content that merely contains the target keywords.
voice search and conversational queries are increasing, and BERT excels at interpreting these natural language patterns. Websites that answer questions in a conversational, natural tone tend to perform better post-BERT.
BERT has dramatically improved Google's ability to interpret long-tail keywords and complex queries. This opens up opportunities to target highly specific search intent that may have less competition.
Superficial content stuffed with keywords performs worse post-BERT. The algorithm rewards content that demonstrates expertise, answers questions comprehensively, and provides genuine value to readers.
BERT doesn't necessarily change your fundamental SEO strategy. Rather, it reinforces the importance of quality content that genuinely addresses user needs. The sites that have suffered most from BERT are those relying on outdated tactics like keyword stuffing and thin content.
To illustrate BERT's impact, let's examine some before-and-after examples of how search results have changed:
| Query | Pre-BERT Results | Post-BERT Results |
|---|---|---|
| "how to park on a hill with no curb" | General articles about parking on hills, many focusing on curb-related instructions | Specific content addressing the exact scenario of parking on hills without curbs |
| "do estheticians stand a lot at work" | General career information about estheticians | Content specifically addressing the physical demands and standing requirements of esthetician work |
| "can you get medicine for someone pharmacy" | Information about filling your own prescriptions | Content explaining how to pick up prescriptions for another person |
These examples demonstrate how BERT now better understands the nuances of prepositions like "for" and "with" that can completely change the meaning of a query.
For website owners, these changes mean that pages ranking well for keywords might not be addressing the actual intent behind those searches. This explains why some sites saw dramatic ranking changes post-BERT despite not changing their content.
To succeed in the BERT era, your content strategy needs to evolve. Here are practical steps to optimize for this algorithm:
Create content that directly answers questions your audience is asking. Use question-based headings and provide clear, concise answers. Tools like AnswerThePublic and AlsoAsked can help identify common questions in your niche.
BERT rewards natural language. Write as you would speak, using conversational language rather than awkward keyword-stuffed sentences. Read your content aloud - if it sounds unnatural, it likely needs revision.
Rather than creating multiple thin pages targeting slight keyword variations, develop comprehensive resources that cover all aspects of a topic. This helps Google understand your content as an authoritative source.
Build a robust vocabulary of related terms, concepts, and entities around your core topics. Use tools like IBM Watson's Natural Language Understanding to identify related concepts to include in your content.
Use clear headings, subheadings, and a logical content structure to help both users and search engines understand your information hierarchy. This improves both readability and BERT's understanding of your content.
BERT has improved Google's ability to pull featured snippets from relevant content. Format answers to common questions concisely, using appropriate HTML like paragraphs, lists, and tables to increase your chances of earning these positions.
Let's examine real-world examples of websites that have thrived after the BERT update:
A medical information website saw a 37% increase in organic traffic after BERT's implementation. Their success factors included:
Before BERT, their content focused heavily on keyword optimization. By shifting to an intent-focused approach, they captured traffic for complex medical queries that previous algorithm versions couldn't properly interpret.
A home improvement tutorial site experienced a 22% increase in traffic and a 15% improvement in time-on-page metrics following these BERT-friendly changes:
Their most successful pages directly addressed situational variations (like "how to install flooring on uneven concrete" rather than just "flooring installation").
A financial advice blog saw featured snippet acquisition increase by 42% after BERT by:
Their traffic increased most dramatically for long-tail queries where users were asking about specific financial situations rather than general concepts.
As SEO professionals adapt to BERT, several misconceptions and mistakes have emerged:
Google has explicitly stated you cannot optimize for BERT directly. The algorithm is designed to understand natural language better - trying to "game" it with technical tricks is counterproductive. Focus instead on creating genuinely helpful content.
Some SEOs still fixate on keyword density percentages or exact keyword repetition. BERT understands synonyms and related concepts, making this approach outdated. Focus on topical coverage instead.
Not recognizing that the same keyword can have different intents in different contexts. BERT excels at distinguishing these nuances, so your content should address them as well.
Some mistakenly believe that using technical or complex language will make content appear more authoritative to BERT. In reality, clear, precise language that effectively communicates concepts performs better.
BERT is particularly important for mobile searches, where queries tend to be more conversational. Content that isn't mobile-friendly or doesn't address voice search patterns misses opportunities BERT creates.
BERT represents a significant milestone in search evolution, but it's just one step in Google's AI journey. Here's what to watch for:
Google's Multitask Unified Model (MUM) is 1,000 times more powerful than BERT and can understand information across text, images, and eventually video. It can simultaneously understand 75 different languages and transfer knowledge between them.
Search is evolving beyond text to incorporate images, voice, and video content. Future algorithms will increasingly understand content across these different formats simultaneously.
As voice assistants become more sophisticated, search will continue evolving to handle complex, multi-turn conversations where context carries across multiple queries.
The best way to prepare for these changes is to follow the same principles that work for BERT - create genuinely valuable content that addresses real user needs in natural language. Additionally:
Here's a practical 7-step plan to ensure your content thrives in the age of BERT:
| Step | Action | Implementation |
|---|---|---|
| 1 | Audit Current Rankings | Identify pages that have experienced significant ranking changes since BERT's implementation. Look for patterns in content that gained or lost visibility. |
| 2 | Analyze Search Intent | For each target keyword, examine the top 10 results to understand what intent Google believes users have. Categorize queries as informational, navigational, commercial, or transactional. |
| 3 | Question Mapping | Identify all questions related to your topic and map them to stages of the user journey. Tools like AnswerThePublic, AlsoAsked, and People Also Ask results can help. |
| 4 | Content Restructuring | Reorganize content to directly address specific questions and user needs. Use clear headings that match natural language queries. |
| 5 | Expand Semantic Vocabulary | Use NLP tools to identify related concepts, entities, and terms to incorporate into your content for comprehensive coverage. |
| 6 | Readability Enhancement | Simplify complex sentences, use active voice, and ensure content flows naturally. Read content aloud to identify awkward phrasing. |
| 7 | Performance Tracking | Monitor changes in rankings, traffic, and engagement metrics. Pay special attention to featured snippet acquisition and long-tail keyword performance. |
Google BERT represents a fundamental shift in how search engines understand language and user intent. While it may require adjustments to your SEO approach, the core principles remain aligned with what has always made for great content: addressing real user needs with clear, helpful information.
The sites that will thrive in the BERT era are those that genuinely seek to understand their audience and create content that serves their specific questions and needs. By focusing on natural language, comprehensive coverage, and clear communication, your content will not only perform well with current algorithms but be well-positioned for future advancements in search technology.
Remember, the goal is not to optimize for algorithms but to optimize for the humans using those algorithms to find information. BERT simply helps Google better understand what those humans are really asking for.
As you implement these strategies, I'd love to hear about your results. What changes have you seen since adapting your content for the BERT era? Share your experiences in the comments below!
This article was written by Gaz Hall, a UK based SEO Consultant on 3rd November 2019. Gaz has over 25 years experience working on SEO projects large and small, locally and globally across a range of sectors. If you need any SEO advice or would like me to look at your next project then get in touch to arrange a free consultation.
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