Story Based Question
You run a blog that provides in-depth tech tutorials, and you’ve been seeing an uptick in traffic from voice search. One day, you receive a report showing a drop in some of your keyword rankings, and you wonder if something has changed. After reading about Google’s BERT update, you start thinking: “How does Google’s BERT algorithm influence voice search queries, and what can I do about it?”
Exact Answer
Google’s BERT algorithm impacts voice search by helping it understand the context and nuances of natural language. It improves the accuracy of voice search results by interpreting complex queries and conversational speech better.
Explanation
BERT, which stands for Bidirectional Encoder Representations from Transformers, is an algorithm Google uses to understand language more like a human would. Before BERT, Google focused on keywords and matching them with search queries. With BERT, Google can interpret full sentences, including the meaning behind the words, which is especially useful for voice search. Here’s how BERT influences voice search:
- Improved Understanding of Natural Language
- Voice search queries are often more conversational. People don’t always use the same structure as they would when typing. With BERT, Google is better at understanding these conversational phrases and the intent behind them. For example, instead of just searching for “best laptops,” a voice search might ask, “What are the best laptops for students under $500?” BERT helps Google accurately interpret this complex, conversational query.
- Contextual Relevance
- BERT helps Google understand the context of a sentence. For example, a query like “How to fix my kitchen sink” could have different meanings based on the situation. BERT helps Google understand that the user is asking for a tutorial, not for the purchase of a new sink. This helps voice search provide more relevant answers.
- Long-Tail Queries and User Intent
- Voice search typically involves long-tail keywords—more specific, natural phrases. BERT excels at understanding these complex, longer queries. If someone asks, “Where can I find a local coffee shop that serves vegan pastries?” BERT helps Google understand the entire request and pull up the most relevant results.
- Better Matching of Synonyms and Variations
- BERT also enables Google to understand synonyms and variations of words. So instead of matching exact words in the query, it focuses on the overall meaning. For example, if someone asks, “What’s the best way to repair a leaky faucet?” Google understands that “repair” and “fix” mean the same thing in this context, and will return the most relevant results.
Example
Imagine you’ve written a detailed blog post about setting up a home network. Your post is well-optimized for traditional keywords, but you want to target voice search queries. Here’s how BERT affects the ranking of your content:
- Step 1: Improve Natural Language Phrasing
You notice that voice search users often ask questions like, “How can I set up a Wi-Fi network at home?” or “What’s the best router for a large house?” With BERT, Google is now able to better understand these natural, full-sentence queries. - Step 2: Focus on User Intent
Google’s BERT can now understand if someone is asking for a tutorial versus searching for product recommendations. So, you optimize your post to match different types of queries, ensuring you provide content for both informational and transactional intent. - Step 3: Include Long-Tail Keywords
Instead of just using “home network setup” as your main keyword, you start targeting phrases like “how to set up Wi-Fi at home with multiple devices.” These longer, more specific queries are more likely to come up in voice search, and BERT helps Google match your content with these types of questions.
One day, a voice assistant responds to a query like, “What is the best way to set up Wi-Fi in my home?” with your tutorial post, because BERT understands that the user is looking for a step-by-step guide, not just a product list.
Google’s BERT algorithm makes voice search more accurate by understanding the full context of natural language queries. It helps voice search understand conversational speech, long-tail questions, and user intent. By optimizing for natural phrasing, long-tail keywords, and specific user needs, you can improve your chances of ranking in voice search results.