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Article Summary: Google search has undergone substantial changes since the rollout of Bert, the latest technology developed by Google’s AI team. For digital marketers, BERT is a real challenge because, even though we reached some conclusions upon certain important factors to pay attention when optimising websites, there is still a lot to discover and test.

From RankBrain to BERT: All You Need to Know About the Update

If you are a digital marketer, I am sure you are aware of the significant changes brought to Google Search by the introduction of BERT, Google Search’s most significant change since the introduction of RankBrain. The BERT update started rolling out on October 24th, 2019 and has impacted rankings for many English language queries.

For SEO specialists, BERT is a real challenge because, even though we reached some conclusions upon certain essential factors to pay attention to when optimising websites, there is still a lot to discover and test.

We know that, for a busy marketer, staying in touch with all the news in the field may be difficult. This is why, since we have already read a ton of material on Bert and NLP, we would like to share our most exciting findings.

What Is Bert Update, and what is it used for?

We start by explaining what BERT stands for and how it functions. Bidirectional Encoder Representations from Transformers is Google’s neural network-based technique for natural language processing (NLP) pre-training. The AI-powered Google update was created and published in 2018 by Jacob Devlin and his colleagues from Google and is used to help machines better understand the nuance and context of words in search queries and better match the queries with valuable results. In short, if Google used to process words one-by-one in order, thanks to the new technology, Google processes texts as a whole, understanding context and the searcher’s intent behind search queries.

How Does BERT function?

According to Search algorithm patent expert Bill Slawski (@bill_slawski of @GoFishDigital), this is how BERT functions:
“Bert is a natural language processing pre-training approach that can be used on a large body of the text. It handles tasks such as named entity recognition, part of speech tagging, and question-answering, among other natural language processes. Bert helps Google understand natural language text from the Web.”

Bert vs Rank Brain

When RankBrain was introduced by Google in 2015, it had the same purpose as Bert: to improve users’ search intent and deliver more valuable results. RankBrain analysed web content and users’ queries to understand the relationship between words and the context. The Bert update complements the RankBrain algorithm and doesn’t replace it entirely. Google may decide how to interpret a specific query and apply one technique or the other, or sometimes even a combination of the two.
Here are some examples of query results before and after the algorithm change offered by Neil Patel:

We can clearly notice that the second result better matches the searcher’s intent. The result offered by Bert is more relevant in every example.

Why Is Bert Useful?

By applying BERT models to rankings and featured snippets in searches, Google can offer its users more helpful information. When it comes to ranking results, BERT will help machines better understand one in 10 English language searches in the U.S. The algorithm will be extended to more languages and locales over time.

What is NLP (natural language processing)?

Natural language processing (NLP) is an AI-powered technology that helps computers understand human natural communication. The objective of NLP is to read, understand, and make sense of human languages in a valuable way. Most NLP techniques rely on machine learning to derive meaning from human languages. NLP is not a new feature offered by search engines, but Bert represents a breakthrough in NLP due to bidirectional training (instead of analysing and training ordered sequences of words, Bert analyses and trains language models based on an entire set of words in a sentence or query).

To sum up, NLP is the process of analysing a text, understanding the meaning of the words and establishing relationships between them, in order to interpret them more naturally.

Major components of NLP

A simple, clear sentence structure is the key to well-optimised content for NLP. To do a good SEO & content marketing job, you need to have a basic understanding of NLP’s core components:
Sentiment can be defined as the score of the sentiment (view or attitude) about entities in an article.

Named Entity Extraction: entities are generally people, places, and things (nouns). Entities can also include product names – generally, the words that trigger a Knowledge Graph.

Tokenisation is the process of breaking a sentence into separate terms, while Parts-of-Speech Tagging classifies words by parts of speech.

Through grammar, Google can determine if words have different forms, and word dependency creates relationships between the words based on the grammar rules.

With the help of Subject Categorization, NLP classifies text into subject categories, like Arts & Entertainment, Adult, beauty, Law& Government, etc.

How to Optimize Content After Bert
In October 2019, the famous Google search engine liaison Danny Sullivan claimed in a Tweet post that there is nothing to be done to better otimiseize content for Bert.

According to Neil Patel, Bert “mainly impacts top-of-the-funnel keywords, which are informational”. Many other SEOs noticed that longtail expressions and conversational queries are impacted mainly by the update.

Therefore, to maintain good rankings and beat the competition, you should try to get very specific with your content. And, while many SEOs may advise clients to write super long content, algorithms mainly focus on content quality, not quantity. Also, keyword density is not necessary anymore because Google considers the context in which the keywords are used rather than the number of repetitions.

Using different tools like Buzzfeed, Buzzsumo, Quora or Reddit to find new topics for website/blog content is a great idea, but content should be super specific to be able to rank. For example, suppose you write an article on “The best way to lose weight without diet or exercise”. In that case, you should really focus on many alternative weight loss methods, without writing about any diet or exercise type.

Actually, the best way to optimise for Bert is to write good quality content for your targeted audience. Increasing your website’s relevance increases its chances of getting better rankings. In its press release from October 24th 2019, Google claimed this significant change “represents the biggest leap forward in the past five years and one of the biggest leaps forward in the history of Search.”

You don’t have to create a 5000-word blog post dealing with 100 topics to rank better than the competition for a specific keyword. It’s more beneficial to create a unique, detailed piece of content that answers a searcher’s question and provides more value than the competition.

Another good search optimisation idea is trying to deal with secondary topics in your blog articles. You can treat many secondary topics if the initial target keyword is a broad header. This way, you’ll naturally use additional expressions that improve salience. But if you optimise for a specific longtail keyphrase, you should only stick to the main topic.
The structure and formatting of the content are essential for ranking. To help the algorithm understand your content and consider it straightforward and applicable to the users, make sure you use the following: headings, subtopics, HTML tags, ordered lists, and inverted pyramid structure.

Targeting featured snippets, paying attention to content readability, focusing on thorough topic research, and using checklists and Q&As may prove successful content marketing techniques in the Bert era.

Even if you lost some traffic due to the latest update, this is not necessarily bad. Maybe the content you produced attracted fewer visits, but if the bounce rate decreased and the time on the page increased, this probably means that the users who checked your piece of content found precisely what they were looking for.

To sum up, in order to consider the new algorithm a threat, we should all focus on the opportunities that Bert offers us to create better, more complete, super-specific content to answer our targeted public’s questions.

In the following video, Search Engine Journal specialists thoroughly explain how Bert functions and what are the implications for marketers :

Also, find below an infographic explaining and summarising all of BERT’s main characteristics.