Artificial intelligence is not science fiction anymore. AI is here, and it’s probably part of your daily life. In fact, when you search the web at any time from any device, whether you’re typing into a browser or talking to Siri, you are interacting with AI. Search engines use AI to deliver the best results based on five factors: intent, prediction, buyer cycle, niche and real-time data.

To understand how AI affects these factors and how you can use AI to improve your SEO, you first need to understand RankBrain, Google’s machine-learning AI system. RankBrain, introduced in 2015, has quickly become the third most important factor used in Google’s search algorithm (content and links are the other two). Ignoring the impact of RankBrain on SEO is not an option if you want to maintain or make gains in your page ranking. RankBrain makes it almost impossible to game the system with black hat SEO tactics, so it’s better to work with RankBrain instead of trying to outsmart it.

When optimizing your website for search, you already know content is king, and it’s no different when artificial intelligence comes into play. RankBrain, and other search engine AI, is trying to think like humans, so it makes sense that your content should be written for humans. That means speaking to your target audiences in a conversational manner and giving them timely, relevant information as you lead them through the customer journey.

Let’s look a little closer at how AI works in SEO.

AI and Intent

Intent is what you meant to search for – even if the exact phrase you used doesn’t appear on any web page. Using AI, search engines can still deliver relevant results because they use “natural language processing” to better understand and interpret human language.

For instance, if you do a voice search by saying, “How do I bake an apple pie?” your results likely will be recipes for apple pie. Even though you didn’t say “I want a recipe for apple pie,” AI takes your ambiguous query and calculates your intent.

Intent can also be gauged by the device a searcher uses. For example, your friend mentioned seeing a great new movie and now you want to learn more about it. If you search for the movie on your desktop computer, the search engines likely will show results such as a website, a trailer and reviews. If you search for the movie on your phone, the results are more likely to show the nearest theaters and movie times.

AI and Prediction

Staying with the movie example, let’s say your child loves the Toy Story movies and is eager to learn more about the characters. If you type “Toy Story characters” into a search engine, you will get information about the leading characters as well as the supporting characters. You may also get videos, cast lists and other links with more in-depth information. The search engine is trying to predict what actions you want to take after seeing the results, so it is showing you a variety of secondary information.

This secondary information is being pulled in by an AI component called Schema. Schema is code on a website that helps search engines better understand the content on that website. When search engines understand the content, they are more likely to see it as relevant and more likely to return it as a search result.

Sometimes, however, your search doesn’t yield the results you expected, and you need to refine it. Let’s say that during your “Toy Story characters” query, you didn’t immediately see information about Jessie, the yodeling cowgirl. Once you refine your search to “Toy Story characters Jessie,” you get the results you want. Now, the next time you search for Toy Story, Google may use RankBrain (AI) to suggest a few topics that include Jessie, because it has learned that Jessie is a topic related to Toy Story and is predicting that you may want to see similar results again.

When you research a new topic, you may have noticed that Google suggests related topics that are commonly searched by others. As Google learns what you want to know about a certain subject, it will suggest other associated subjects. The search engine, through AI, is trying to make predictions based on current interactions.

AI and the Buyer Cycle

A buyer cycle, also known as the sales cycle, is the process consumers follow when they are making a decision about a purchase. The first step is curiosity, the second is interest and the third is decision. When it comes to content, it’s wise to have content that’s crafted for each part of cycle. Let’s look at how this would work for a pet store that sells exotic animals.

Curiosity

A woman at the airport, let’s call her Franny, overhears a conversation about fennec foxes from a family that has just returned from a safari. She pulls out her phone and searches “what is a fennec fox” on Google. She clicks on the first site in the results and lands on the ExoticPets4You website. Because she finds interesting information on the site about the size, weight, diet and distinctive traits of the fox, Franny spends a few minutes reading and looking at the cute pictures.

Interest

A few weeks later, Franny happens upon a documentary about fennec foxes. Even more intrigued, she goes back to Google and searches “is a fennec fox a good pet,” and the ExoticPets4You site appears again in the results, this time displaying a page detailing how to care for a fennec fox at home. Google displays this site because Franny spent quite a bit of time on it during her first search.

Decision

Once Franny sees that she can care and provide for a fennec fox, she wants to buy one. After payday, Franny searches Google for “where can I buy a fennec fox,” and one more time ExoticPets4You pops up. Franny scans the available animals, reading a description of each fox’s personality and temperament. She finally decides that Freddie is the fennec fox for her. She notices that the store has easy online checkout, free shipping, free returns (not that Franny would ever return Freddie) and a free brochure on how to train a fox. Throughout the customer journey, ExoticPets4You provided relevant information to Franny and made it easy for her to take the next step.

AI and Niche

Brands that present themselves as subject matter experts or thought leaders are rewarded by search engines. If a brand becomes the expert on a topic, RankBrain will notice that the brand’s website is getting lots of traffic from certain searches and is filling a need in a niche market. Take gripper slippers, for instance. If your company sells gripper slippers, your site should specifically say that you sell gripper slippers. (In fact, your target keyword may be “gripper slippers.”) But many potential customers will want additional information about gripper slippers, such as “how do you clean gripper slippers,” “how long do gripper slippers last,” and “are gripper slippers better than gripper socks.” By creating content around these broader concepts, search engines can use AI to send you even more targeted traffic.

AI and Real-time Data

AI helps search engines deliver timely information by using live data, including location, hours, services and more.

A common example is the data that Google displays for restaurants. When you search for your favorite eatery, the results will show popular days and times, wait times and visit duration for the restaurant. This is information is based on data from users who have opted into Google’s Location History. If you are the restaurant owner, you may use this real-time data to adjust your marketing efforts. For instance, if Tuesday at lunchtime is slow, you might want to offer a special promotion to diners who visit between noon and 2 p.m.

Considering all of these factors, it’s easy to see how AI is a major part of search engine optimization. If your business wants to rank well and drive organic traffic to its website, you’re going to need to embrace AI in your SEO strategy. We can help with that.

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