How Deep is the BERT Update’s Influence on Organic Search Strategy?

BERT Update’s Influence on Organic Search Strategy

While search engine innovations have always been happening since Google became a household name, causing website design & development to keep reinventing itself, there are some revolutionary changes that redefine the whole concept. The BERT update Google brought in has the potential of bringing about such a rethink.

Neural Networking for Understanding User Search Intent

The BERT update was launched with the idea of using neural networking techniques to better understand the intent of the user behind the search queries they enter. That means there would be more relevant results. That does raise the importance of having relevant content in your web pages. BERT particularly works for long tail keywords and question words. And that’s how people mostly search these days, particularly those who are closer to making a sale or signing up for a service. The content of your web pages must be focused on the way your readers would search with questions or long tail phrases.

How Google Understands What the User Intends

For example, the website of a physician clinic would have to optimize its content for queries like “how to check blood pressure”.

Google Understands What the User Intends

So when someone types that in the search box, BERT helps Google realize that the individual is looking for a step-by-step procedure to check blood pressure, which is why the People Also Ask section has specific queries with results involving step-by-step procedures.

Google Understands What the User Intends

That’s artificial intelligence at work in the search engine’s algorithms. So, the content of your web pages must contain bullet points that include step-by-step procedures for checking blood pressure. So Google’s algorithms would hopefully find your content to be concise, easily understandable and yet comprehensive enough to figure in the People Also Ask section or perhaps right at the top, as a featured snippet. With Google recently launching the featured snippet deduplication update it becomes a position 1 listing. And the BERT update enables the right content to get up there.

BERT Would Demand a Content Overhaul

As a result of the BERT release, many website or business owners have been thinking of overhauling their content rather than incorporating small changes as they would with the usual algorithmic updates. When conversions are what businesses are looking for, they need to pull all the stops when Google has reportedly become more intelligent with machine learning and artificial intelligence (AI).

BERT is the acronym for Bidirectional Encoder Representations from Transformers. It basically refers to a neural network-powered system that enables Google to understand the context of a user’s search better through natural language processing (NLP).

Pattern Recognition in Search Queries

What algorithm neural networks do is help in pattern recognition. Neural networks can recognize handwriting and categorize images. This recognition comes from their study of data sets for recognizing patterns. That’s why, in the real world, neural networks are also used for predicting market trends in finance. Google has explained that BERT’s neural networks were pre-trained with Wikipedia’s plain text corpus.

Natural language processing is basically the application of artificial intelligence that enables computers to figure out the manner in which human beings communicate. NLP powers chatbots and even the word suggestions that come up on your smartphone. While NLP is already part of the search engine mechanism, what BERT does is advance NLP further by introducing bidirectional training.

BERT’s Bidirectional Training Method to Better Understand Context

BERT’s Bidirectional Training Method

The bidirectional training method of BERT can train language models on the basis of word sets in a search query instead of the conventional training on the basis of an ordered word sequence as it was before the BERT update. Bidirectional means that the language model can figure out the word context on the basis of words surrounding that main keyword both before it and after it, rather than just on the basis of words either preceding or following the keyword. Thus the search engine is able to return results that are closer to what the intention of the user is. That’s because the words before and after the main search term are studied.

The screenshot above shows that, after the BERT update, the search engine is able to figure out the importance of the word “no” as indicating that the user wants to know how to park a car in a slope with no curbing at the side of the road. Before BERT Google didn’t realize that, and provided the answer for how to park on a road having curbing at the side. The relevance of the word “no” wasn’t grasped by the search engine.

So, as we mentioned in the example above, BERT’s context understanding qualities can only be understood with long-tail keywords. And now, Search Engine Journal reports that BERT is causing content creators to work in collaboration with search engine optimizers for figuring out user intention and creating content accordingly. While, as with most algorithm updates, BERT focuses on quality content, it is able to understand the context of long search queries better now. So there might need to be a change in the conventional strategy.

Long-tail Keywords Keep Getting More and More Important

For one, long-tail keywords and search phrases have become more important. The reason why you need to focus on long-tail keywords is that they are normally used by users who are closer to making a sale or signing up for a service. So you want to be able to get those audiences. And BERT has been conceived for just those kinds of audiences who are serious about what they’re looking for, so serious that they type exactly what they want through a long, detailed search phrase. Focusing on these long-tail keywords could potentially give you some conversions. The BERT update is claimed to affect 10% of searches, and that 10% is probably accounted for by long-tail searches.

Relevant Content and Readability, not Infusing Keywords

Secondly, you can’t do as much keyword stuffing as before. Well, keyword stuffing was something always condemned by Google, and it was targeted by many algorithmic updates. But now it’s taken even further because what BERT prioritizes are sentences that are structured well. Google is viewing any manipulation by adding keywords wherever possible quite seriously. Many webmasters still employ this strategy just because of what they conceive as a greater ranking potential that can be tapped into if a high-volume keyword or search term is added. There are still websites out there where you find readability being compromised by keywords being added without essentially contributing to the structure or semantic richness of the content.

Google’s Pygmalion has developed the advanced computational linguistics that power BERT. Pygmalion is also behind Google Assistant. So now Google doesn’t just select words in isolation to figure out what the user is searching for. It’s the comprehensive understanding.

The Future of Search Is Here

These changes are likely to define the future of search. So content creation needs an overhaul, and SEOs would need to coordinate with content writers to bring out the best and most relevant content. The focus on filling in keywords has to make way for a more data-based approach that manages to specifically target users searching with long tail keywords and search phrases, optimizing for phrases conveying the search intent of the user. The featured snippet deduplication update further adds to the equation, since the featured snippet is being made a position 1 result in the search page. You need content that satisfies the urgent and pressing need of users, who could be your customers and source of conversions and earnings.

That’s the transformation search engine optimization strategies will have to go through to brace themselves for a future of intelligent search.