What Is BERT?
BERT stands for Bidirectional Encoder Representations from Transformers. It is a neural network-based natural language processing model developed by Google researchers and published in 2018. Google began applying BERT to Google Search in October 2019, describing it as the most significant change to search in the past five years. At launch, it affected approximately one in ten English-language queries.
The key innovation in BERT is bidirectionality. Earlier language models processed text in one direction, reading left to right or right to left. BERT reads in both directions simultaneously, which allows it to understand the context of each word based on all the other words in a sentence at once. This distinction is critical for understanding the role of prepositions and small relational words like "for," "to," "by," and "without" that fundamentally change the meaning of a query.
A classic example: the query "parking on a hill with no curb" previously caused Google to focus on "parking on a hill" and return results about steep parking. BERT's contextual understanding of "with no curb" changed the interpretation entirely. This precision in understanding nuance makes BERT particularly important for queries that are phrased naturally rather than in the abbreviated keyword-style that users once had to use to get useful results.
For South African businesses investing in SEO, BERT reinforces the same message as every major semantic update: write naturally for human readers. BERT processes content as it would naturally be written, so the best response is well-crafted, clear writing that directly addresses a topic. BERT also powers Google's evaluation of whether a passage in an article precisely answers a specific query, which directly influences featured snippets and the People Also Ask box.
BERT In Practice
A South African medical practice publishes a FAQ page covering common questions about its services. Previously, pages were structured around keywords like "GP Sandton" and "medical aid consultation." After BERT, pages that answered specific natural language questions, such as "do I need a referral to see a specialist on Discovery Health?", began appearing in featured snippets and PAA results.
The practice rewrote its FAQ using complete sentences that matched how patients actually phrased questions. Each answer was concise, factually accurate, and conversational. The structured FAQ format combined with natural phrasing made it easy for BERT to evaluate whether each passage precisely answered the corresponding question.
Traffic from long-tail, conversational queries increased substantially over the following months. The lesson extends to any South African business: structured, question-answer content written in plain language is one of the most practical ways to align with BERT's evaluation of passage relevance. Combining this with schema markup for FAQ pages further increases the chance of being selected for enhanced search features.
FAQ
How should South African content writers respond to BERT?
Write naturally and accurately. BERT is designed to understand content written for humans, so clear, grammatically correct prose that precisely answers the reader's question performs well. Over-optimising for keywords can actually hurt rankings by making text less natural to BERT's evaluation.
Does BERT affect featured snippets and People Also Ask results?
Yes. Google uses BERT to evaluate which passages are best suited for featured snippets and direct answers. Content that directly and precisely answers a specific question, using natural language rather than keyword-stuffed phrasing, is more likely to be selected for these prominent SERP positions.