Cited is not chosen. Measure both.
Three examples from the corpus.
Snowflake, in data warehousing. Appears in 71.7% of AI answers. Pick share (sole plus primary recommendation) 3.7%. Sixty-eight point gap. Snowflake is the incumbent citation and rarely the recommended answer. Nobody looking at a citation dashboard would spot this.
Mailchimp, in email marketing platforms. Appears in 44.4% of answers. Pick share 1.1%. The gap is 43 points. Mailchimp shows up as an example. It does not show up as an answer.
Zendesk, in customer support software. Appears in 64.3% of answers. Pick share 8.2%. 56-point gap. Zendesk is the reference brand for the category. The category recommendation is going elsewhere.
Why the gap exists.
AI engines are trained on the entire web and pull from live search results. Incumbent brands accumulate mentions because they are cited in news, reviews, blog posts, courses, and forum threads. Some of those mentions frame the incumbent as the answer. Many frame it as the thing being compared against, the thing being replaced, the thing being warned about, or the thing being cited for context. All of them push the brand higher in the appearance count.
Only recommendation-framed mentions push the brand higher in the pick count. Those are much rarer. And the ratio of one to the other tells you what is happening.
The six-code classifier.
The Hint Co. audit classifies every confirmed brand mention into one of six treatments:
- SOLE. Only vendor named as the answer.
- PRIMARY. Named first or explicitly as the best pick.
- ALTERNATIVE. Named in a short recommended set (two to four), no clear top.
- CONDITIONAL. Recommended only under a stated narrow case ("if you specifically need X").
- NEUTRAL. Named without endorsement, or cited as a source only.
- UNFAVORABLE. Named with a negative or dismissive frame.
Pick share is the percentage of total responses where the brand is SOLE or PRIMARY. Recommendation-when-mentioned is the percentage of appearances that land as SOLE, PRIMARY, or ALTERNATIVE. Neutral-only is the "cited but never chosen" trap. Unfavorable count is the active anti-sales conversation.
What the classifier surfaces that appearance counts miss.
- Clay appears 21 times in B2B SaaS growth tools. 90.5% recommendation-when-mentioned. Four sole picks. Four primary picks. Zero neutral. Zero unfavorable. Clay's problem is not recommendation quality. It is retrieval. Different fix than the "publish more content" advice everyone gives.
- Marketo appears 48 times. Fifteen of those are unfavorable ("Marketo is dated," "Marketo has fallen behind"). That is 31% of mentions actively negative. That is a specific sales conversation with a specific answer.
- Salesforce appears 77 times. 34 neutral, 9 unfavorable. 43 of 77 mentions do nothing for pipeline. Salesforce needs a positioning fix, not a content-velocity fix.
What to do with the number.
The gap between appearance share and pick share is a diagnostic input for what to fix. Large appearance, low pick, high neutral: you are the reference brand for the category. Fix the positioning so mentions land as recommendations. Small appearance, high recommendation-when-mentioned: you are chosen when named. Fix the retrieval so you are named more. Any appearance, high unfavorable: you have a sentiment problem. Fix the story being told about you, in the sources the engines quote from.
Different diagnoses, different work. The read is where a Hint Co. audit becomes a fix list sequenced by which of these applies to you.
Want this measured for your category?
Every 30-day pilot at The Hint Co. starts with a full AI visibility audit. Or buy the read on its own if the pilot is not the shape you need. Written by one operator, no agency team, no handoffs.
Apply