Machine learning is making rapid advancements in many sectors for efficient decision-making and performance, and search engine optimization has no exceptions. Search engines use machine learning in many ways to decide search ranking and performance for a better user experience. Apart from learning search engine algorithms and how they work, machine learning is also a crucial area that you need to understand in the world of SEO.
But how do machine learning and actually impact search results and SEO performance? To understand this, we have to explore how machine learning in search engines works. And, before getting into this, let’s define what we mean by machine learning.
According to the definition provided by Stanford University:
“Machine learning is the science of getting computers to act without being explicitly programmed.”
It means machine learning is the capability of computers to come to conclusions based on data and information available without being specifically programmed for it. In simple words, it is a way to solve problems and make independent decisions by analyzing complex and unstructured data.
Today, we already see machine learning advancements in search engine algorithms. Back in 2009, Google has already shown its intention by rolling out the BERT search query algorithm, which is applied to both rankings and featured snippets to get more accurate results for users. The search engine giant is already making steady progress in implementing machine learning in its processes to connect users to the content they’re looking for in the following ways:
Pattern Detection
Pattern detection is the process of identifying spam or duplicate content in search engines using machine learning. Low-quality content has some similarities such as
- The presence of many outbound links to irrelevant pages.
- Too many stop words and synonyms.
- Spammy keywords
Machine learning flags contents by recognizing these patterns and utilizing data from user interactions. It detects spam structures and methods being used in content creation to flag low-quality content and encourage a better user experience in search results.
Identify New Ranking Signals
Google has developed RankBrain, a machine learning algorithm, to not only identify patterns in queries but also possible new ranking signals. Before RankBrain, the entire coding is done by hand for Google’s algorithm. It requires a team of engineers to analyze search queries, improve their quality, and release updates by implementing necessary changes. Even though Google still uses human engineers, it uses RankBrain to run background tests to identify how changes affect user interaction and what could be possible new ranking signals for better search result optimization.
It’s weighted as a small impact on SERPs
However, even machine learning is making steady progress in search engines to find and rank websites, it doesn’t mean it is a key player to impact search results. While it helps engineers better understand various issues in search queries and results. Google’s John Mueller explains that:
“Machine learning isn’t just this one black box that does everything for you where you feed the internet in on one side and the other side comes out search results. It may also adjust the weight of various ranking signals, but you will still need real humans to manually check and adjust those values.”
The end goal of using machine learning in search engines is to provide a better user experience. Google doesn’t want to automate the entire indexing and ranking process to maintain the integrity of the process with human input. Machine learning is just a small portion of search engine algorithms to implement things in a better and easier way.
Custom Ranking Signals
Google clearly mentions in its privacy policy how the search engine uses a user’s interaction and behavior to create personalized search results. According to Google:
“Personalized search generates different search results to different users of the search engine based on their interests and past behavior.”
It means two different users find different search results for a string of queries based on what they’ve searched in the past. You may also find different search results in normal browsing mode and in incognito mode for a similar string of queries. It is because machine learning advancements create custom ranking signals based on user’s behavior for a better search experience.
Natural Language Processing
Machine learning also enables search engines to recognize different pieces of texts and phrases to understand what a user is searching for more accurately. It helps the search engine show deeper results based on specific pieces of text and user behavior. People don’t use similar search queries even though they are searching for the same product, it is where natural language processing is very helpful in describing things differently from a user’s point of view. As people are searching for more and more new phrases each day on the internet, machine learning is finding better ways to show more accurate and deeper results for those queries.
BERT (Bidirectional Encoder Representations from Transformers) is Google’s natural learning processing framework to understand the context of a search query in a more accurate and better way. BERT is designed to learn how users interact to match search terms with more relevant results.
Image Search
Millions of photos are being uploaded and shared every day on various social networks and digital platforms. It is not an easy task for a human to analyze and categorize each submission accurately and appropriately for relevant search results. Machine learning helps in analyzing the color, shapes, and patterns of all the images uploaded and categorize them based on existing schema data about the photograph. It helps a search engine better understand what an image actually is before displaying it in search results. It is how Google powers its image search and reverse image search to show the most relevant and accurate search results.
Synonyms Identification
Google uses RankBrain to identify synonyms in the keywords to display quality content for a better user experience. Sometimes, Google also highlights results based on synonyms to provide more accurate search results with help of machine learning advancements in search engines.
Improve Ad Quality and Targeting
Machine Learning also helps in providing the most relevant ad experience to individual users based on their interaction and behavior in the past. According to Google, machine learning can influence the AdRank, and hence improve Ad quality and targeting for each search query.
Conclusion
We all will have the same opinion that a machine learning system cannot be perfect. It can improve over time with more human interaction to become smarter and accurate. It is designed for a better experience with technology to improve the accessibility and quality of life. And with machine learning in search engines, we are able to find more accurate and relevant information easily, when we need it through search results.
