We've been deep in the world of Answer Engine Optimization, running the CiteMET playbook across our best content. The initial results were solid - we saw users engaging with our AI Share Buttons, and we knew we were successfully seeding our content into AI platforms. But with our team's background in AI, we saw this as just the first step.
We understood that for a Large Language Model (LLM), a single, quick interaction is a whisper. A true signal of authority, one that builds lasting memory, comes from a deeper conversation. We saw an opportunity to transform that initial whisper into a meaningful dialogue.
You shipped CiteMET. Now how do you tell if it worked?
You've done the work: AI Share Buttons live, llms.txt shipped, technical basics tidy, and you're thinking in CiteMET terms instead of running old keyword rituals. The site is ready for answer engines, and the obvious next question is whether any of this is moving the needle.
Page views won't tell you. A traditional traffic dashboard right now feels like checking the fuel gauge on an electric bike. The win you care about is an answer engine treating you as a trusted source, and if you aren't measuring that, you're guessing.
The T in CiteMET stands for Trackable. No proof of impact, no budget defence. Here's how to see whether this is working in 2025.
Shift the lens, then build a scorecard
Old SEO was simple: more visitors, more shots at conversion. AEO works differently — you're trying to earn a spot inside the model's answer space, which means presence plus perceived authority. When an answer engine picks your page, that's a vote, and the vote is the asset. Your scorecard has to reflect it.
Skip vanity spikes for a few weeks and start logging five data points. A lightweight sheet or a Notion database is fine until you have reason to automate.
1. AI citations. This is the North Star. A citation means the answer engine showed or linked your URL as a source. A sample log row looks like 2025-10-22 | perplexity.ai | topic: what is CiteMET | source: /content/what-is-citemet. Track count, topic, page. Pay attention to which formats keep getting reused (clear definitions, step lists, concise tables). Flat numbers usually mean you need to rework structure, not prose.
2. Brand mentions. When a model names you or your product without linking. Still useful, but only as a weak signal of topical association. Log them separately so they don't muddy your citation numbers. Classic example: ChatGPT names your brand in a tooling roundup but links three competitors. That's a nudge to write a sharper comparative page.
3. Mention vs citation gap. If mentions climb and citations stall, you have awareness without trust. Usually the cause is thin facts, vague headings, or walls of narrative with nothing extractable. Fix it with scannable H2/H3 hierarchy, explicit definitions, sourceable stats with provenance, and schema where it makes sense. Goal is to shrink the gap quarter over quarter.
4. Share of voice in AI. Pick 10–20 core question patterns (real user phrasing, not internal jargon) and sample daily or weekly. For each question, tally who gets cited or mentioned. Your share is (your citations + mentions) / (all tracked citations + mentions). A scrappy brand can grow this number well before raw traffic notices. Plot a simple line chart. If it dips, audit what changed: maybe a competitor published a glossary, or you removed a canonical explainer.
5. Sentiment and context. Not every mention is good. Note whether the answer uses you for a positive example, a neutral definition, or a cautionary tale. Even a manual tag helps before you have tooling. One misleading negative summary can propagate fast. When you catch one, write a clarifying resource the model can prefer.
The tools that actually surface this
Google Analytics alone won't surface any of this. You need visibility into AI surfaces, and as of late 2025 most teams reach for one of the newer AEO/GEO tools.
Goodie AI gives you broad monitoring in a single pane. Semrush AI Toolkit is the easy answer if you already live in Semrush dashboards. For regulated sectors that need deeper controls, Profound is the one to look at. Writesonic GEO suits content-heavy teams who want a tight feedback loop between drafting and visibility. And if your job is to convince a CFO, Conductor pipes AEO signals toward business KPIs in language leadership recognises.
Pick one to start with, automate citation capture, then layer mention tracking, then SOV dashboards. Don't buy everything at once.
Closing the loop: show ROI not just counts
Leadership cares about revenue, not the fact that you got 42 citations last week. Tie it together: track citations by page and topic, build a referral segment for the obvious sources (chat.openai.com, perplexity.ai, and so on), and compare conversion and engagement against your baseline organic search. When a cited page sees a spike in high-intent visits after appearing in multiple answers, flag it as attributable uplift.
Early data we've seen, plus what peers are reporting, suggests AI-referred visitors often convert far higher than generic search clicks. Sometimes by an order of magnitude. They show up mid-funnel already scoped, which is the whole point. Even a small sample is enough ammunition to argue for deeper investment.
A weekly rhythm and the usual mistakes
A simple cadence you can run starting Monday:
Monday: pull new citations, tag topics.
Midweek: spot-check 5 priority questions for SOV shifts.
Friday: update the gap metric (mentions vs citations) and note one fix action for next sprint.
Monthly: run conversion comparison and sentiment audit.
Watch for the obvious traps. Pumping out fluffy list posts rarely earns citations; tight, factual, structured pieces do. Slow pages or messy canonicals reduce reuse, so don't skip technical basics. Treat llms.txt as a living file and revisit it whenever a new cornerstone page launches. And don't over-weight generic AI answers. Focus on questions aligned to the product journey.
Small wins compound
You don't need a massive overhaul to start. One page tuned for clarity can get picked up repeatedly, and that momentum builds internal trust and budget faster than you'd expect. Track early even if it's manual. Patterns show up sooner than you'd think.
Measure what models reflect back about you, then shape it. That's Trackable. Once you can show lift with real numbers, the rest of the framework sells itself.
