How to Connect Earned Media to AI Visibility

To connect earned media to AI visibility, collect every placement URL from a campaign into one tracked list, score each one on a 0-100 scale for AI accessibility and citation quality, then check which URLs ChatGPT, Gemini, and Perplexity are actually citing in live answers. Attach the list to a project that runs nightly, and report the campaign's influence rate: the share of an AI answer's sources that came from your placements, not just the coverage you landed.

PR teams already know how to prove a campaign worked in traditional terms: impressions, reach, tone, share of voice. None of that tells you whether ChatGPT, Gemini, or Perplexity ever read the story. A study of the top 51 U.S. news outlets found 61% block AI crawlers entirely, so a Tier 1 placement that looks great in a coverage deck can be completely invisible to AI, while a smaller outlet that's fully open to crawlers quietly does more work in AI answers. The only way to know which is which is to check, placement by placement, and roll the results up into one number a client understands: a 40-placement campaign might land 8 to 12 citations once the AI-accessible outlets are separated from the blocked ones.

This guide walks through that process end to end. It's written to work with any AI visibility setup; the callouts show how each step runs in Spyglasses.

Step	What you're doing	What tells you it's working
1. Build a coverage list	Collecting every placement URL into one tracked group	All URLs imported, syndicated copies included
2. Score for AI value	Checking accessibility and positioning, not just tone	Every placement has a quality score
3. Check what AI cites	Comparing cited placements against the full list	You know which URLs AI is using and why
4. Track influence over time	Attaching the group to a nightly-run project	One influence rate, updated automatically
5. Report the result	Translating the data for a client or executive	A document that answers "did this work?"

Build a coverage group from the campaign's placements

Start by pulling every URL the campaign generated: the original story, plus every syndicated copy. If a piece ran on a local news site and got picked up by two aggregators, all three URLs matter, because AI platforms cite syndicated copies often and each one only matches itself exactly.

Treat this as one tracked list, not a spreadsheet of individual placements you'll check by hand later. That distinction matters once a campaign runs past a handful of hits; a client-facing PR effort can easily produce dozens to hundreds of placements a month, and scoring them one at a time doesn't scale.

Group placements the way you'll want to report on them. A single campaign group shows total AI influence for that push. Monthly groups make month-over-month comparisons easy for a retainer client. Pick whichever matches your reporting cadence, and don't mix the two inside one group.

Score each placement for AI value

A placement report answers "did the story run." AI citation scoring answers a different question: is this specific mention positioned so an AI system can find it and judge it worth quoting. That comes down to a few concrete factors, not gut feel.

Factor	What it checks	Why it moves the score
Position in the article	Where the mention sits in the document	A lead mention is easier for an AI to extract than one buried in paragraph 30
Surrounding context	Whether the chunk of text around the mention stands on its own	AI reads content in chunks; a self-contained chunk is more citable
Placement type	Dedicated article vs. roundup mention vs. passing reference	A dedicated article carries more weight than a one-line mention in a listicle
Link attribute	Dofollow vs. nofollow, and whether the link points at the brand	Some AI retrieval signals weight link attribution the way search ranking does
Sentiment	How favorably the surrounding text reads	A strongly negative mention gets discounted even if every other factor scores well

None of this replaces the traditional read on a placement. It adds a second lens: not "is this a good story" but "can AI actually use it." A placement can score well on both, well on one and poorly on the other, or poorly on both, and each combination changes what you do next.

Check which placements AI actually cites

Scoring tells you which placements are well-positioned to be cited. It doesn't tell you which ones actually were. For that, compare the full coverage list against the citations showing up in your tracked AI answers, and look at the gap between the two groups.

Most campaigns show a funnel: every URL was imported, a smaller set was actually fetched or considered by an AI assistant while composing an answer, and a smaller set still was referenced directly in the answer text. Each stage of that funnel points at a different fix. A placement AI never touches at all is usually an accessibility problem: a blocked crawler, a slow page, thin content. A placement AI considers but doesn't cite is usually a content problem: the chunk around the mention isn't self-contained enough to quote.

Once you have real citation data, compare the cited group against the rest. If cited placements skew toward higher scores, longer articles, or more recent publish dates, that's a specific brief for the next round of pitching, not a vague note to "get better coverage."

Track the campaign's influence on AI visibility

A single "11 out of 42 cited" snapshot is useful, but campaigns run for weeks and citations keep arriving after you've already sent the first report. Checking each placement by hand every week doesn't scale, and it's easy to miss a citation that landed after your last manual pass.

Attach the coverage group to a project that runs your tracked prompts nightly instead. Every new citation gets matched against the group automatically, and the project reports one number: what share of the AI answers' sources, across every tracked prompt and platform, came from this campaign's placements. That's the number that answers the question a client actually asks, which isn't "how many placements did we get" but "is our earned media showing up when someone asks AI about us."

Package the results in a client-ready report

The data only does its job once someone outside the team reads it. Translate the placement list, the citation funnel, and the influence rate into a document built for a client or an executive, not a dashboard screenshot with no context.

A useful package usually has three parts: the headline influence number with a plain-language explanation, the funnel showing how many placements AI can see versus cite, and a short list of what to do differently next round based on what the cited placements had in common. Keep it to a page or two. The goal is a document that survives being forwarded to a board member without a follow-up call to explain it.

The report changes once you can see AI reading it

Earned media has always needed proof it worked. AI visibility just adds a new, checkable layer to that proof: not just did the story run, but can the systems shaping how people find your brand actually see it. Build the coverage list, score it for AI access instead of assuming reach equals visibility, check what's actually getting cited, and track the influence rate instead of re-counting placements by hand every week.

The same coverage-group approach works outside of proving a single campaign, too. It's the same mechanism a comms team uses to track whether corrective coverage is displacing a negative story, and the accessibility check matters most before you pitch: 61% of major outlets block AI crawlers outright, which is worth knowing before you build a media list, not after the placements run. If you're drafting the pitch itself, structuring it the way AI actually reads content is the other half of this problem. And if you haven't checked how AI describes the brand at all yet, a free AI Visibility Report is the fastest way to see where you stand before the next campaign starts.