<\/p>
Adding FAQ sections to a set of test pages lifted AI citations. Removing them dropped citations back down.<\/p>
That reversion is the difference between correlation and causation, and almost no team measuring AI search today can produce it.<\/p>
That standard of proof anchored the latest SEJ webinar with seoClarity\u2019s Mark Traphagen, VP of Product Marketing & Training, Mihir Naik, Senior Product Manager, AI, and Suraj Lalchandani, Sr. IT Project Manager. Their core argument: \u201cVisibility scores tell you if you showed up. Page-level performance and split testing tell you if what you did actually mattered.\u201d<\/p>
The session walked through the split testing methodology seoClarity\u2019s enterprise clients run across ChatGPT, Claude, Perplexity, Gemini, and Google\u2019s AI surfaces: how to build a funnel-spanning golden set of prompts, how to construct a control group when LLMs will not let you A\/B test, and where Google\u2019s new first-party Search Console AI data fits.<\/p>
They also shared results from three real client tests, including the one change that moved citations and two results nobody in the room predicted.<\/p>
Watch the full webinar on demand to get the complete testing methodology.<\/p>
For a subset of sites, yes. On June 3, Google launched dedicated Search Console reports for AI Overviews and AI Mode, showing page by page how often each URL appears inside Google\u2019s AI search features.<\/p>
Lalchandani called it the biggest measurement upgrade AI search testing has received. \u201cThis has been the hardest thing to measure in AI search. Everyone was sampling. Everyone was inferring. But now Google is just giving it to you.\u201d<\/p>
First-party data straight from the source carries a different level of trust than any third-party tool. But the team was direct about the limitations: the new reports cover only part of what an AI search testing program needs, and ChatGPT, Claude, and Perplexity still require structured third-party tracking.<\/p>
In the session, the team maps exactly which gaps the new reports close, which they leave open, and the platform-by-platform reference for what each AI engine can crawl and render.<\/p>
Action item: Check Search Console for the new AI reports, then see where first-party data fits your testing program before you build around it.<\/p>
The ones where you are almost winning. The team builds a golden set of prompts spanning the full AI search funnel, awareness through retention, with every prompt tagged by stage, then sorts each prompt into tiers by where the brand currently stands in the AI\u2019s response.<\/p>
Tier 1 prompts are the easy wins. As Lalchandani put it, \u201cYou\u2019re relevant, but AI just hasn\u2019t been given a URL worth linking to.\u201d<\/p>
Tier 2 is the heavier lift, and one bucket of prompts gets dropped from testing entirely, a move that surprised many attendees.<\/p>
The sequencing is deliberate: early wins buy the political capital to run harder tests later. The session covers how to build and tag the golden prompt set, how the tiers are defined, and the tracking unit that pairs each prompt with the exact page you want cited.<\/p>
You cannot split live traffic 50-50, so you build a control group instead: a set of correlated pages that acts as your noise filter against model updates and algorithmic shifts.<\/p>
\u201cWithout a control group, every result would be guesswork,\u201d Lalchandani said. \u201cWith one, you can tell a real win from the background noise.\u201d<\/p>
Timing is the discipline most teams skip. The methodology sets a specific baseline period before any change goes live and a minimum test window after, because AI search does not respond overnight the way traditional SEO sometimes does. Cut the window short and, in Lalchandani\u2019s words, \u201cyou could be reading noise.\u201d<\/p>
Every test lands in one of three outcomes, and each one tells you something about your hypothesis. The full session walks through how to construct the correlated control group, the exact baseline and test windows, and how to read all three outcomes.<\/p>
Watch the full webinar on demand to get the complete test setup.<\/p>
seoClarity ran the same methodology for three clients and got three very different outcomes, which is exactly the point.<\/p>
The FAQ test was the clear win. With roughly 1,000 prompts under measurement, adding FAQ sections to test pages pushed citations up versus control, and they stayed elevated as long as the change was live. Then the team reverted the change. \u201cThe citations fell back down. That\u2019s the second half of proof. Not that citations just went up when we added FAQs, but that they went back down when we took them away. That\u2019s causation, not correlation.\u201d<\/p>
The other two tests, one on meta descriptions and one on listicle formatting, ended very differently, and the reasons why hold lessons for anyone about to invest in either tactic. See how both tests played out in the full session.<\/p>
Naik\u2019s framing: every result is a win, because you have evidence instead of guesses. That is more than most teams in AI search have today.<\/p>
The session also lays out the schema and markdown test blueprints, two of the most argued questions in AEO right now, plus a set of fast structural tests for high-value templates you can run in a few weeks.<\/p>
\u201cAI authority is basically how much the model trusts you as a source for this topic. I don\u2019t think there\u2019s a clean number for it or a single number for it, but there\u2019s a couple of signals that you can stack to give you kind of a working picture.\u201d<\/p>
Lalchandani named four stackable signals, starting with citation share on your top prompts and cross-engine consistency, because \u201cconsistency across engines just means that you become the authoritative source in your category for specific kinds of questions.\u201d He walks through all four, and how to track them, in the full session.<\/p>
\u201cCollapsible can mean many different things. It\u2019s how you are having it collapsible.\u201d<\/p>
It depends entirely on implementation: one common setup keeps collapsed FAQs fully readable to AI search engines and Google, and another makes the content invisible to both, because \u201ceven Google will not click around on your site.\u201d Lalchandani explains which is which in the recording, with his standing advice attached: \u201cIf you\u2019re unsure of something, just test it out. It takes effort, but it\u2019ll give you a sure answer.\u201d<\/p>
\u201cYou want to be cited because you are controlling the answer that is actually going to be showing up.\u201d<\/p>
Even without a click, Naik explained, your cited page shapes the narrative inside the answer, especially in comparison queries where citations do the heavy work of positioning both brands. The question shifts from traffic to representation: are your USPs highlighted correctly, is the comparison set right, are inaccuracies surfacing. Lalchandani added a cautionary example from a real restaurant client that shows exactly what happens when AI cannot reach your content, told in full in the recording.<\/p>
\u201cAbsolutely. It is foundational. It is the foundation.\u201d<\/p>
Traphagan noted that seoClarity\u2019s longest-standing clients, the ones with well-optimized content and technically healthy sites, are also performing best in AI search, with AI optimization as the extra layer on top. Lalchandani added: \u201cWhen we run tests with our clients, we\u2019ve rarely, if ever, found a situation where something works for SEO and does not work for AI search.\u201d<\/p>
The on-demand recording contains everything the recap holds back: the golden prompt set build, the tier definitions, the control group construction with exact baseline and test windows, the platform-by-platform crawler reference, the meta description and listicle results, and the schema and markdown test blueprints. Register to watch the full session on demand.<\/p>