Artificial intelligence, machine learning and neural networks are major buzzwords in the SEO community today. Marketers have highlighted these technologies’ ability to automate time-consuming tasks at scale, which can lead to more successful campaigns. Yet many professionals often have trouble distinguishing between these concepts.
“Artificial intelligence is essentially the term that defines the whole space,” said Eric Enge, president of Pilot Holding and former principal at Perficient, in his presentation at SMX Next. “Machine learning is a subset of that [AI] set around specific algorithms.”
Natural language processing (NLP) is another system that’s been used for SEO tasks in recent years. It’s primarily focused on understanding the meanings behind human speech.
“NLP is about helping computers better understand language the way a human does, including the contextual nuances,” he said.
With so many developing technologies available, marketers would be wise to learn how they can be applied to their campaigns. Here are three ways AI and its branches can automate SEO tasks at scale.
AI can address customers’ long-tail needs
Enge pointed to a customer search engagement study from Bloomreach that found that 82% of B2C shoppers’ experience is spent searching and browsing. This leaves room for plenty of long-tail searches, which are more niche in nature and, consequently, often overlooked by marketers.
Bloomreach’s own AI tool focuses primarily on extracting insights from this phase of discovery, Enge explained. It can identify site content that’s both underutilized and matches customer long-tail searches.
“AI improves pages by presenting more related pages that currently aren’t being linked to,” he said, “Or even potentially create new pages to fill the holes of those long-tail needs to create a better customer experience.”
Marketers can use AI systems to generate more relevant pages based on these long-tail interests. But, there are some caveats to be aware of.
“Just be careful not to create too many new pages,” Enge said. “There are certainly cases where too many pages can be a bad thing. But deployed properly, this can be very effective.”
AI can enable automated content creation
Enge shared some information about GPT-3, a popular AI language model, to demonstrate AI’s content creation capabilities. While impressive, he noted how a system like this can get out of control if there aren’t proper constraints.
“They [AI systems] currently don’t have any model of the real world,” he said. “They only have the data that they were trained on. They don’t have any perspective or context for anything, so they can make really bad mistakes, and when they write, they’re prone to bias.”
“The wonderful thing about the web is that it has all the world’s information on it — the terrible thing about the web is all the world’s disinformation is on it, too,” he added.
Despite these weaknesses, AI systems have a lot of promise. Continuous improvements in these technologies can help marketers scale content efforts to meet customer expectations.
GPT-3, in particular, has the ability to generate content in a variety of formats, allowing SEOs to focus more on optimization efforts.
“You can use it [GPT-3] to create new content,” Enge said. “You’re going to have to put in a lot of effort and bring a lot of expertise to the table to do it. It might be more cost-effective than writing from scratch, or it may not, depending on how good you are.”
AI can leverage deep learning to help establish topical authority
Having topical authority means your site is a perceived expert on a given subject. This is one of the factors many SEOs believe is vital for improving rankings, which is why so many have leveraged AI’s capabilities.
Enge pointed to seoClarity, which uses an AI tool called Content Fusion designed to help brands write with more authority, to highlight these deep learning capabilities: “The approach is to leverage deep learning to identify entities and words that help you establish authority in a topic,” Enge said. “It extracts intent, entities, terms and potentially related topics. Then they apply their machine learning models that are specific to your market space.”
The deep learning capabilities offer marketers a clearer view of their brand’s area of expertise, which can then be used to further develop their web properties. Establishing an automated deep learning system can provide them with fresh data to help demonstrate E-A-T (Expertise, Authoritativeness, Trustworthiness).
Every AI integration will look different, but each one has the potential to streamline your SEO efforts through automation and machine learning.
“There’s an incredible amount of stuff happening out there with AI,” Enge said. “Some of it you can take into your own hands if you’re willing to do the programming; in other cases, you can use tools. It’s up to you.”
Watch the full SMX Next presentation here (free registration required).
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About The Author
Corey Patterson is an Editor for MarTech and Search Engine Land. With a background in SEO, content marketing, and journalism, he covers SEO and PPC industry news to help marketers improve their campaigns.