Presence Share in the Answer (QPR)
The core GEO metric: the percentage of relevant decision queries in which a company appears inside generative-system answers.
Presence Share in the Answer, or QPR, measures the percentage of relevant decision queries in which a company appears inside generative-system answers.
It captures exactly what traffic and rankings do not capture: visibility at the moment in which alternatives are formed before the buyer starts navigating websites.
Formula
QPR = (number of queries in which the company appears / total tested queries) × 100
A QPR of 40% means the company appears in four out of ten relevant decision answers. A QPR of 0% means total absence from the tested AI answers, regardless of how strong SEO visibility may look.
How to build the query set
- Decision-oriented — the query must reflect how a buyer compares alternatives, not a generic search keyword.
- Real — the wording should come from actual sales conversations and technical evaluations.
- Comparable over time — the same set should be reused in monitoring cycles so change is measurable.
How to interpret it
A QPR of 0% means total absence from the tested AI answers. A QPR of 40% means the company appears in four out of ten relevant decision answers. Improvement can usually be observed in weeks if the intervention is structural.
Why the query set matters
A bad query set produces a bad measurement. If the queries are too generic, too informational, or different at every cycle, QPR becomes impossible to compare and operationally useless.
Strategic role
QPR is the main outcome metric of GEO because it measures whether the company is actually present when generative systems shape the shortlist.
Check your presence
Does your company appear in AI answers when buyers search for suppliers in your sector?
Enter your sector and product. The tool generates the real decision queries your buyers use on ChatGPT and Perplexity and shows where you appear - and where you do not.
Test your Presence Share in 5 minutesThe full method to work on structural citability is explained in Dentro la Risposta.
Learn moreFurther reading
CRM and AI citability: why technical knowledge does not reach answers
Many manufacturing SMEs use the CRM as an implicit repository of commercial knowledge: product suitability, configurations and application cases. But the CRM is designed to manage contacts and opportunities, not to expose queryable technical criteria. The knowledge that generative systems need to build a shortlist remains isolated in notes, emails and PDFs: the result is not only an internal efficiency issue, but a direct gap in external citability.
Strong SEO, weak AI citability in industrial supplier shortlists
A manufacturing company can hold strong positions on Google while remaining weak in the AI responses that B2B buyers use to identify and compare suppliers. This is not a contradiction: SEO and structural citability operate at different stages of the decision process. The pattern is common in hydraulic components and across industrial sectors where technical information remains descriptive rather than parameterized.
Generalist vs Vertical AI: what actually changes in business operations
Your company already has the information it needs—but can’t use it when it matters. Every request becomes a process of searching, waiting, and verifying, creating hidden costs, slower responses, and lost opportunities. The issue isn’t content or tools. It’s that company information is not queryable.