Definition
BERT, or Bidirectional Encoder Representations from Transformers, is a natural language processing (NLP) algorithm developed by Google. It enables Google’s search engine to better understand the contextual meaning of words within a search query by analyzing the entire phrase—not just individual keywords. Unlike previous models, BERT processes language bidirectionally, allowing it to interpret the nuances and intent behind a searcher’s query. In the context of SEO, BERT plays a crucial role in aligning content with user intent, thereby impacting how webpages are ranked in search engine results pages (SERPs).
Is It Still Relevant?
Yes, BERT remains highly relevant in today’s SEO landscape. Since its integration into Google Search in late 2019, BERT has become foundational to how Google interprets language. It directly affects “natural language” queries—long-tail keywords and voice searches—by focusing on context rather than simply matching specific words.
With the rise of conversational search and AI-driven search assistants, BERT’s importance continues to grow. Google’s more recent advancements, like MUM (Multitask Unified Model), build upon BERT’s architecture, further solidifying its role in improving search understanding. While BERT itself isn’t something marketers can “optimize for” in the traditional sense, its presence encourages the creation of well-written, context-rich, and user-focused content.
Real-world Context
In practice, BERT affects SEO by changing how search engines evaluate content relevancy. For example, if someone searches for “can you get medicine for someone at the pharmacy,” traditional algorithms might focus on keywords like “medicine” or “pharmacy.” BERT, however, understands that the query is about picking up a prescription on someone else’s behalf.
For digital marketers, this means that overly keyword-optimized content—that ignores natural language use—may underperform. Businesses investing in helpful, informative content that speaks directly to user questions and intent see better results. A blog answering “How to renew a driver’s license in Texas” in plain, user-friendly language will likely outperform one stuffed with generic keywords like “driver’s license renewal” repeated unnaturally.
Background
BERT was first introduced by Google in October 2018 as an open-source research project and was incorporated into Google Search in October 2019. It represented a significant evolution in how Google interprets language by enabling models to handle the intricacies of language far more effectively than previous one-directional models.
Before BERT, search algorithms often misinterpreted prepositions and word relationships—leading to irrelevant results. BERT’s bidirectional architecture marked a shift from syntactic search toward semantic, intent-based search. This made it a game-changer for content strategists aiming to rank not just for keywords, but for meaningful, query-matched content.
What to Focus on Today
Today, optimizing for BERT means aligning your content with natural language processing principles rather than trying to manipulate search engines through outdated tactics like keyword stuffing. Key focus areas include:
– Crafting content that answers user questions clearly and comprehensively.
– Writing in a conversational, user-centric tone that mimics the way people speak or search.
– Using schema markup where appropriate to help contextualize your content for search engines.
– Ensuring your content structure—like headers, transitional phrases, and clarity—is easy to understand.
– Investing in content research tools like Google Search Console, SEMrush, or Ahrefs to identify the actual queries users are searching.
Voice search, mobile-first indexing, and AI-based search behavior are increasingly dominant. To stay competitive, marketers should embrace a BERT-friendly SEO strategy that prioritizes topic authority, search intent, and content clarity over keyword density.
BERT has reaffirmed a longstanding mantra in SEO: optimize for users first, search engines second. Content that is helpful, natural, and contextually rich won’t just survive algorithm changes—it will thrive because of them.