Over recent times, there has been a lot of talk on Artificial Intelligence and its incredible expanse in helping better peoples’ lives. So does Rank Brain. And while Google’s AI tries to simplify people’s search for answers over the internet, Rank Brain can also improve your website SEO quality. So what if you cannot specifically optimize for Rank Brain, it is the third most important ranking factor for your website. With Rank Brain you concentrate more on improving the search experience of target customers. And while doing so, it’s also good for improving a website’s SEO practice. Let’s just try to understand what Rank Brain is and how is it important for a website to understand how they can utilize Rank Brain to improve their search engine rank for a better position on the SERP. About Rank Brain Rank Brain is an algorithm learning artificial intelligence system that uses machine learning format to process unfathomable amounts of qualitative data into quantitative data. Like converting written contents into mathematical entities that are comprehensible by the computers. So basically when someone searches for a query, Google’s Rank Brain will try to interpret that. It’s going to look up the words that are present in that query like the location or the personalization or maybe some other things. When done with interpretation, it’s going to determine the intent behind such a query. Then it is going to select the ranking signals from its database that will turn out to be appropriate to the actual query. SEO expert Rand Fishkin of Moz, uses a good example to explain how Rank Brain works when you search a query on Google. You can visit the page to get a clear picture as to how Rank Brain does it. But despite understanding Google’s AI workflow and how it is going to help both Google and the searcher, some of you might wonder whether Rank Brain could actually help when Google does not have any relevant data collection in store. Almost 15 percent of the queries made on the internet are actually new. In that case, Rank Brain uses its previously processed vectors and shards to make an intelligent guesswork based on similar queries that have similar meaning. For this, I might have to refer to Rand Fishkin’s example again. If you haven’t visited the Moz page yet, here’s a similar looking example that can give you a good picture of how Google’s Rank Brain will treat the search query. Suppose you want to search for the best Netflix shows in Google. You can type down your query in a number of different ways. For example – ‘Best Netflix shows’, ‘Best shows on Netflix’, ‘What to watch on Netflix’, ‘What are good Netflix shows’, ‘Good Netflix shows’. You can type down your query in a number of different ways, with Rank Brain, Google is going to try interpreting these queries. You would notice that each of these five sentences have been constructed differently. So no two sentences follow the same structure. The only thing that is similar between all five of them are – ‘Netflix’ and ‘Shows’. Google’s Rank Brain will interpret that and will determine what kind of signals are going to be appropriate to respond to such queries. That’s what going to take us to the next step – understanding the relationship between machine learning and signals. What is Machine Learning? According to Expert System, Machine Learning is an application of Artificial Intelligence that enables systems to automatically learn from experience without being explicitly programmed. The process begins with the observation of data and closely inspecting the search behavior patterns, based on which appropriate results are then determined. That when we understand the importance of signals. Signals Google uses Signals to follow patterns. For a query like Netflix, here are some of the signals that Google can use to determine its user intent – Relevant Keywords Link Diversity Anchor Text Freshness Domain Authority Engagement Of all these signals, Freshness is very important when we are talking about Netflix shows. An old result on Netflix shows will not have the same relevance as a fresh content. Users will search for the upcoming stories or news when it comes to watching Netflix shows. Apart from Freshness, engagement is another signal that you cannot miss out. This may vary since there are two sets of people who search the net. One – Low engagement audience and Two – high engagement audience. Low engagement audience are people who will make a quick search without expecting anything too big and lengthy. These are people who might want to know things like at what time does a particular Netflix show is aired. High engagement audience on the other hand are people who would want to learn more in detail about a particular Netflix slow. Like for example – ‘What should I watch on Netflix’. Such kind of queries are generally associated with further queries. Maybe they are going to watch the trailer, read the plot and try learning about the casts, etc. And that’s where we introduce Association Rule Learning. What is Association Rule Learning? It is a method of machine learning that is used to discover the relationship between the variables in large databases. ARL has been previously used in supermarkets for gathering data, determining customer buying behavior, produce loyalty cards, etc. Now it is used by Artificial Intelligence like Rank Brain to find out the association between a search query and the result present in a database. It is also very helpful when a particular phrase can have multiple meanings. For example, the phrase ‘Dench’ can have multiple meanings. It is a slang A line of clothing Actress Judi Dench In such cases, Google will use search quality evaluators to show as many variations in result inorder to satisfy users’ search intent. Association Rule Learning users for factors four evaluating a search query. These are –Support, Confidence, Lift and Conviction. In case of Rank Brain, we would focus on Support and Confidence. #1. Support In ARL, support means the number of times a query appears in the database. You must notice that it is different from a keyword since the type of keywords may appear different. #2. Confidence This is measure how often the rule has appeared to be true. For example, if a person searches for the word ‘ POTUS’, it could be that they might also end up searching for satisfactory results on Barack Obama, Abraham Lincoln, George Bush, etc. Rank Brain will use ARL for satisfying the user specified minimum support and the user specified minimum confidence. Both support and confidence follow individual processes. That is – Minimum Support Threshold is used to establish and apply to all database items Minimum Confidence Threshold is applied to frequent items to form a set of rules Using these rules, Google Rank Brain will try prioritizing which signals are more relevant to the user search and how to weight them accordingly. Conclusion SEO optimization therefore will not work when it comes to Rank Brain However, you may need to change your strategic focus and concentrate more on improving the quality and nature of your marketing. Does that mean going back to the roots of organic content marketing and SEO marketing? Yes it is. Stay tuned for our next article on how to begin your content marketing from grass root level.