§01What “cited” means
A citation is counted when an AI model’s response to a query explicitly names your brand or domain in a positive or neutral recommendation context. We don’t count:
- Mentions in a negative comparison (“unlike X, avoid Y”)
- Generic category mentions without a specific brand name
- Responses that name your brand only as context for recommending a competitor
Each query is run three times per model to account for stochasticity. A citation is counted as “consistent” if it appears in at least 2 of 3 runs. This filters out one-off hallucinations and reflects what a real user would reliably see.
§02The score calculation
Your overall citation score is a weighted average across all queries and models:
score = (citations / total_query_model_pairs) × 100
# example: 12 queries × 4 models = 48 pairs
# if you were cited in 18 of 48 pairs:
score = (18 / 48) × 100 = 37.5%
The score is intentionally simple — it’s a percentage. The complex part is the query set. We run your domain against buyer-intent queries in your category, not vanity queries like “what is [your brand name].” Getting cited on branded queries is easy and doesn’t tell you much.
A 40% score on hard buyer-intent queries beats an 80% score on easy branded queries. The queries matter as much as the score.
§03Per-model breakdown
Beyond the overall score, we show you your citation rate broken down by model. This matters because the models have meaningfully different citation behaviors (see Understanding the 4 AI models).
A brand that scores 80% on Perplexity but 15% on Claude should interpret that very differently than a brand that scores 40% across all four. Perplexity cites broadly; Claude cites selectively. Being cited by Claude at all puts you in a small group.
We also show which specific queries you were and weren’t cited on. The uncited queries are your highest-leverage improvement targets — they represent buyer-intent moments where you’re invisible.
§04What to optimize for
Don’t optimize for your overall score in the abstract. Optimize for specific uncited queries first.
If you’re not cited on “best [category] tool for [use case]” — that’s a content gap. The fix is usually building the page that most directly answers that query: a comparison page, a use-case landing page, or a structured explanation of your positioning in the category.
The overall score is a lagging indicator. The query-level breakdown is where you find the levers.
§05Score benchmarks
Median score across the B2B SaaS brands we’ve checked: 31%. Most brands are visible but inconsistent. The gap between them and category leaders is almost always content clarity, not brand authority.