Google’s BERT Algorithm Update & Impact on Home Services
5 Nov 2020 · 4 min
Google's BERT algorithm uses AI to better understand what people actually mean when they search, not just the words they use. For home services, this means searches about furnace location or SEER ratings will find more relevant results. The update affects featured snippets and local rankings, so focus on writing for humans, not keywords.
Questions this episode answers
BiDirectional Encoder Representations from Transformers.
U.S. English queries (at the time of the podcast).
Chapters
You're listening to a Rocket Media blogcast prepare for launch as we share another great episode. Google's Bert BERT Algorithm Update and Impact on Home Services this week, marketers saw some large fluctuations in organic rankings while Google quietly rolled out one of the largest updates in their algorithm in years. Google is calling this latest algorithm bert, and like many marketers were, we want to better understand what this update means for our clients. So what is bert? BERT stands for Bi Directional Encoder Representations from Transformers. This is a natural language processing NLP algorithm. How does BERT operate? BERT operates by helping Google to better understand the context of words and make better connections between the context of entities and actions. Imagine for instance that you write write a conversational query that has a select elements that would be interpreted as vague by Google. For example, where can I find my furnace? What the user actually wants to know is where should my furnace be located in my home? We see this kind of vague search all the time when we use voice search to execute something that we say it or them when referring to an exact entity. Now imagine if Google could better understand the relationship between that pronoun and something or someone else using Google's API from Google Natural Language Processing Machine. Let's test this with a query like what is a good SEER rating for my home? Google previously may have made a relationship between SEER and home, but what may have been missing is the understanding that SEER ratings are directly tied to H VAC systems. By making this connection, BERT recognizes the relationships between entities, making the appearance of a relevant result much higher. Another simple query we can test here is average alarm response time for a security business, this is a big one. Imagine if Google could differentiate between a user who was searching for this in regards to a commercial alarm or a residential alarm. Google knows this is related to security, but what if Google also knew that that this question should be answered by stating from the time the alarm sounds to call that the average security company not stated in search should be able to respond within x amount of time. How will it impact local search? Number one, BERT only applies to US English queries at this time of post. Number two, BERT will impact featured snippets across multiple languages. Thirdly, BERT is similar to Google's Rank Brain as algorithm and that it is machine learning based. It will continue to evolve so the first results of the implementation most likely won't mirror what is there today. Number four, BERT is naturally network based, so optimizing for it isn't necessarily possible. Instead, think about this as we are optimized for humans. And finally, number five, this update will allow Google to better understand the context of words within searches and make better connections between the context of entities and actions. What Rocket expects in the coming months. This advancement places a greater emphasis on brands and their ability to produce relevant and valuable content. Our approach has always been to produce timely and informative content that's relevant to our users, so our approach won't really change based on this update. If you are worried about how this update has impacted your strategy, don't hesitate to contact us. For more resources on bert, check out these trusted publications Search Engine Land Chat Meter.