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.