Count Who Gets Recommended, Not Who Gets Cited

For a local business, the AEO number that matters is recommendation share: how often an AI answer to a buying question actually recommends the business, out of repeated runs, from its own area. Being cited somewhere in an answer is different. As of September 2026 most citations in local answers go to platforms such as Google's help pages, YouTube and Reddit, not to the businesses themselves. So a citation count mostly measures which pages the system trusts, not who it sends customers to. On 4 August 2026 a practitioner on r/bigseo complained that AEO reporting "collapse[s] the whole problem into 'was the brand cited or not'", and proposed six better measures. The thread drew 14 replies.

  SIX MEASURES FROM THE THREAD, READ FOR A LOCAL CLIENT
  ┌──────────────────────────┬────────────────────────────────────────────┐
  │ measure                  │ local version                              │
  ├──────────────────────────┼────────────────────────────────────────────┤
  │ prompt intent coverage   │ info vs "best X near me" vs "X open now"   │
  │ recommendation share     │ named as a pick, % of runs, from the town  │
  │ framing                  │ cheap? trusted? slow? — words attached     │
  │ source dependency        │ which pages the answer leans on            │
  │ stability                │ same answer across runs, or noise          │
  │ gap mapping              │ missing because of proof, pages, or entity │
  └──────────────────────────┴────────────────────────────────────────────┘

Citations Mostly Go to Platforms

We counted sources in our cached archive of 524 recorded Google AI Overview answers to local SEO and Business Profile questions (US, August 2025 to September 2026). Google's help pages were cited in 85.5% of answers, YouTube in 57.8%, business.google.com in 38.9% and Reddit in 16.6%. The most-cited SEO vendor pages appeared in under 10% each.

These are mostly how-to questions, so they don't show how a business gets picked for "best dentist near me". They do show where citations concentrate. An agency counting "citations of our client" is counting the rare cases where a small business page gets into a list dominated by platforms.

Recommendation Share Is the Local Equivalent of Map Pack Coverage

The thread's second measure, "who is actually recommended when the prompt has commercial intent, not just who gets mentioned", maps directly onto local work. For a buying question, the useful record is which businesses the answer recommends, run after run:

  "best physio near me" · client's suburb · 10 runs
  client named as a pick        4 / 10
  competitor A                  7 / 10
  competitor B                  5 / 10
  illustrative — the format, not a result

That's a number a client can follow month to month, like points in the top 3 on a grid. It also names the competitors to study.

Intent Coverage Keeps the Report Honest

The first measure, separating prompts by intent, stops a report from looking better than it is. A local business can do well on informational questions such as "how long does a root canal take?" and never be recommended for "best dentist near me". Mixing the two into one visibility score hides the part that brings patients. Keep buying questions in their own line, and weight the report towards them.

Stability Decides Whether Anything Is Worth Optimising

The fifth measure is the one most reports skip. If the same question gives a different set of businesses on every run, a single check is noise, and a change between two single checks is noise too. Recording stability, meaning how often the same businesses come back, tells you whether the answer is settled enough to work on or still random. A separate r/bigseo thread in August 2026 described the same problem from the testing side: the outputs change from run to run, with no systematic way to log them.

Running manual prompts is fine for a quick check, but the outputs change and I have no systematic way to log what the model retrieved versus what it ignored.

r/bigseo, August 2026

Framing and Source Dependency Point to the Fix

Framing records the words an answer attaches to a business: cheap, trusted, slow, family-run. Source dependency records which pages it leans on. For a local business those two usually point to reviews and directory text. That's consistent with the agency case where an AI Overview summarised two one-star reviews on a branded search. The fix sat in review responses, not in the business's own site.

A Local AEO Report in Five Lines

1. Pick 5–10 buying questions in customers' words, plus a few informational ones.
2. Run each several times from the client's town. Record every business recommended.
3. Report recommendation share and stability, not citation counts. See an AEO add-on is only useful if it asks customer questions.
4. Note framing words and the main sources behind each answer.
5. Put it next to the map pack grid for the same service. See AI answers and the map pack pick businesses differently, and for the starting point, local businesses invisible to ChatGPT.