Machine learning is becoming increasingly prevalent in the SEO landscape, anticipated to be a $209.91 billion industry by 2029. And with search engines like Google utilizing machine learning and AI technology to improve the online search experience, digital marketers will need to pay attention to how search engine functionality changes in order to stay visible. Learn more about the impact of machine learning on search and how to adjust your SEO strategy below.
- How Does Google Use Machine Learning?
- How Do Advances in Machine Learning Impact Search?
- How to Adapt Your SEO Strategy Around Machine Learning
How Does Google Use Machine Learning?
Google has pioneered machine learning for years, with its most recent contributions being two natural language processing (NLP) tools: Multitask Unified Model (MUM) and Language Model for Dialogue Applications (LaMDA). NLPs are a subfield of AI and function to make machine learning-based communication more usable for humans, which is one of Google’s primary goals with both MUM and LaMDA. But how do each of these NLPs function, and what is their impact on search?
What Is MUM?
MUM is an NLP developed by Google to succeed its Bidirectional Encoder Representations from Transformers tool—more commonly known as BERT. Like BERT, MUM understands and generates language and is capable of comprehending and answering complex search engine queries with multiple modes of information from images to text and beyond. That said, MUM is 1,000 times more powerful than BERT and has an additional emphasis on improved user experience. Theoretically, this means a searcher could input a photo of a certain type of fabric, and MUM could respond with information like the name of the fabric, its characteristics, what it’s commonly used for, where to buy it, and more.
What Is LaMDA?
Developed to help Google function more intuitively, LaMDA is an NLP that reimagines searches as conversations. Unlike a traditional chatbot programmed with set responses, LaMDA understands user intent and can deliver information accordingly. It utilizes deep learning to communicate what it has learned from search engine results in a more human-like way. To accomplish this, it combs through a massive number of sources and presents synthesized, relevant information to the searcher. Although it’s not yet in use, LaMDA represents SEO’s trajectory toward AI.
How Does Machine Learning Impact Search?
While many advances in machine learning are not yet in use for the general public—as is the case with both MUM and LaMDA—their effect on search engine functionality will change how we search online, as well as how we think about search engine optimization strategies and tactics. Here are some of the changes we can expect to see with Google search over the next few years.
A Shift in User Search Behavior
Google’s search engine results pages (SERPs) change all the time, and as machine learning technology continues to prioritize intuitive, human-like search experiences, user search behavior will likely adapt in response.
“The main thing that’s going to change [as a result of machine learning] is what’s being offered on screen,” says our SEO Director Ross Allen. Soon, people may be able to submit Google searches as chat messages using AI technology like LaMDA, or use an image or piece of audio to complete a search with MUM. One change that’s already taking place is an increase in voice search, with 72% of American consumers using voice search via digital assistants like Siri and Alexa.
More Intuitive & Complex Search Results
NLPs like MUM aim to help Google shorten the searcher’s user journey, while also delivering better search results. One way they do so is by pulling and indexing relevant information directly from a source when answering a search query, like with Google Passages Ranking. This helps searchers find more specific results, potentially without even needing to click into a website.
“Machine learning is leading [SEO] toward zero-click,” says Allen. From the perspective of a search engine, a zero-click search often means that the information delivered on the results page itself was complex and comprehensive enough to satisfy the searchers intent right away.
Reprioritized Google Ranking Factors
Although SEO basics like having authoritative content, good user experience, and quality link-building will remain important ranking factors for Google, more complex machine learning will likely bring about changes to Google’s ranking priorities.
For one, we may see higher rankings for on-page features with visual components, as machine learning makes it easier for Google to crawl and index non-text-based elements. This trend has already been observed in recent years, with web content that includes images or videos receiving 94% more views than content without. The inclusion of more on-page assets will therefore make technical SEO elements like page speed and website architecture even more valuable as Google ranking factors.
Additionally, as NLPs make Google more adept at understanding context, the search engine giant may place greater emphasis on SERP features like People Also Ask, Knowledge Graph, and featured snippets, where its users can find zero-click answers.
Reduced Language Barriers
One major way machine learning will impact SEO is an eventual breakdown of language barriers. With NLPs like MUM, which has been trained in up to 75 languages, searchers gain access to a much greater volume of information. And NLPs go far beyond simply translating content—MUM can pull and synthesize content from sources in French, Japanese, Spanish, and dozens of other languages, and then present it to a searcher in their own native tongue.
This not only massively broadens the horizons of what information searchers can access, but also delivers more accurate search results. For example, a recipe for a traditional Brazilian dish that’s been pulled and translated from a Portuguese website is probably more accurate and useful to the searcher than information from a travel blog written in English.
Multimodal Search Options
To better facilitate to complex queries, MUM can utilize and understand multimodal information, including text, audio, images, videos, and more. This opens up a new world of search potential.
Inputting an image as a search query, for instance, is already possible, but machine learning will bring about even more complex ways to search the web by offering multimodal search. In other words, you may be able to search with an image and text simultaneously by uploading a picture of running shoes along with a written query about whether they’re suitable for a certain hiking trail.
How to Adapt Your SEO Strategy for Machine Learning
With so much changing in the SEO industry as a result of machine learning, how should marketers respond? According to Allen, “[Machine learning] makes good SEO even more important than before.” With that in mind, there are a few things SEO professionals can do to plan ahead.
Prioritize User Experience
Google’s key focus with machine learning is to make the search experience better for users, which means continued prioritization of good UX will make a big difference for your SEO results. In addition to meeting the recommended Core Web Vitals metrics, ensure your website is also ADA compliant, mobile-friendly, and has a sound user interface. These and other technical SEO best practices can boost your website’s overall user experience and give it a competitive edge in SERPs.
Optimize Content for Voice Search
LaMDA will allow users to search via verbal conversations, taking voice search to a new level. To prepare for this upcoming search change, SEO marketers should make sure their websites are optimized for voice search. One simple tactic to employ now is including FAQs in website copy and utilizing FAQ schema markup. This kind of question-answer formatted content may very well be what LaMDA pulls first to answer user queries in the future.
Create Quality, Authoritative Content
While keyword optimization will continue to be an important part of SEO content marketing, machine learning tools will also look for information with human syntax, context, and behavior in mind. This means entity-based SEO—which relies on the semantic connection of ideas—will become increasingly important. “You have to write for the user—not the search engines,” says Allen. Remember that MUM, LaMDA, and other AI technology seek to deliver users the most accurate, authoritative information possible, which means your website content needs to demonstrate experience, expertise, authoritativeness, and trustworthiness (E-E-A-T).
Want to make sure your SEO stays competitive as the industry changes? Hurrdat offers search engine optimization services to help you get found online. Contact us today to learn more!
