Like your weird uncle, nobody knows exactly how AI engines choose the sources they cite. But experiments are starting to point to ways you can get on their radar.<\/p>\n
And as consumers increasingly turn to AI search for product and service recommendations, you really want to be on their radar. (Ironically, unlike your weird uncle, who you try to avoid.)<\/p>\n
Today, I\u2019ve got one such experiment that contributed to a 642% increase in citations by AI tools<\/strong> like ChatGPT.<\/p>\n And to the delight of you word nerds, it\u2019s all about semantics. But first, everyone\u2019s favorite part: The disclaimer!<\/p>\n Before you go any further, it\u2019s important to know that this tactic is just one piece of a wider playbook our Growth team lovingly calls the \u201ceverything bagel strategy.\u201d<\/p>\n \u201cOur experimentation hasn\u2019t [shown that] this one <\/em>tactic is the key to better AI visibility,\u201d says Amanda Sellers, HubSpot\u2019s head of EN blog strategy. \u201cWhat we\u2019ve found is that <\/strong>the sum of the parts <\/em><\/strong>is what\u2019s good for AI visibility.<\/strong>\u201d<\/p>\n But if I covered all of those parts at once, this would be a novel, not a newsletter \u2014 so think of this more like part 1.<\/p>\n \u201cA human might be able to tell you what the sentence \u2018Paris is cool\u2019 means,\u201d Sellers says. \u201cBut an AI engine without [immediate] context wouldn\u2019t know if we\u2019re talking about Paris, France, <\/em>or Paris Hilton.<\/em>\u201d<\/p>\n AI tools can sound very human, but the way they understand language is very different from us.<\/p>\n Keeping with Sellers\u2019 example about Paris, before reading, you would know from the start whether an article you clicked on was about travel tips or one about celebrity gossip. That context would be all you needed to understand the word \u201cParis.\u201d AI models need a little more handholding.<\/p>\n One way to coddle their cold, metallic hands is with a framework called \u201csemantic triples.\u201d<\/p>\n As simply as I can explain it: Semantic triples are a writing pattern that creates context using the sequence subject \u2013 predicate \u2013 object. <\/strong><\/p>\n If you also pushed third-grade English out of your brain to make room for Lord of the Rings trivia, here\u2019s a very quick recap of what those mean:<\/p>\n Subject: <\/strong>Who or what a sentence is about. <\/p>\n Predicate: <\/strong>Information about (or the action of) the subject. <\/p>\n Object: <\/strong>The noun or pronoun that receives that action. <\/p>\n A real-world marketing example might look like: \u201cHubSpot (subject) can automate (predicate) email marketing (object).\u201d<\/p>\n With only one sentence, I\u2019m able to quickly guide a bot to connect HubSpot with email automation. Why does that matter?<\/p>\n \u201cWe want HubSpot to be associated with \u2018marketing automation,\u2019 so that when someone asks ChatGPT, \u2018What\u2019s the best marketing automation platform?\u2019 we\u2019re mentioned in that conversation.\u201d<\/p>\n During the experiment, Sellers\u2019 team took key information on pages that they wanted AI models to understand, and rewrote it from paragraph format into a bulleted list of semantic triples.<\/p>\n Below is a snapshot from Sellers\u2019 recent INBOUND presentation<\/a> that highlights what that content looked like before and after the changes.<\/p>\nThe sum vs. the parts<\/h2>\n
A little why behind the AI<\/h2>\n
Semantics in action<\/h2>\n