**AI engines prefer to cite specific, verifiable facts over vague claims.** This check looks at how data-rich your content is — whether it backs assertions with concrete numbers, percentages, dates and measurements rather than generic statements. Research shows content rich in statistics earns substantially more visibility in AI answers, because a precise figure is exactly the kind of quotable fact an engine reaches for.
It measures how much of your content is grounded in concrete data versus vague assertion. It looks for:
- Specific figures — numbers, percentages, dates, prices and measurements rather than "many", "recently", "a lot".
- Verifiability — facts precise enough to be checked and, ideally, attributed to a source.
- Density — whether data appears throughout the content or it's mostly generic prose.
Content rich in specific, verifiable figures passes; some data amid mostly generic prose is a warning; no specific figures is a fail.
GEObubbly counts paragraphs that carry concrete numbers, percentages, dates or measurements against those making only vague claims. It's a core GEO / LLM Readiness check worth 3 points, because specificity is one of the clearest predictors of whether a passage is quotable enough for an engine to cite.
AI engines prefer to cite specific, verifiable facts over vague claims, because a precise statement is exactly what an answer can be built on. "Conversions rose 30% in Q1 2026" is quotable; "conversions improved significantly" is not. Studies of AI citations have found that content rich in statistics, data and concrete figures earns substantially higher visibility in generative answers. The practical move is to audit your content for vague assertions that could be made specific: replace "many users" with a number, "recently" with a date, "a lot faster" with a measurement, and cite the source of the figure where you can — that adds the credibility engines look for. This isn't about stuffing numbers in for their own sake; it's about making your genuine claims precise and verifiable, which is exactly what makes a passage worth quoting. It works alongside sourced claims and self-contained passages across GEO / LLM Readiness.
AI answer engines build responses from facts they can quote, and a specific figure is the most quotable kind of fact — it's precise, verifiable and self-contained. "Page speed improved by 1.2 seconds" gives an engine something concrete to cite; "page speed improved" does not. Analyses of AI citations have found that content rich in statistics and concrete data earns meaningfully higher visibility in generative answers, because precise claims are easier to trust and to lift than vague ones. Specificity, in short, makes your content the better source.
Start by auditing for vague claims you can make specific: replace "many users" with an actual count, "recently" with a date, "much faster" with a measurement, and "a large share" with a percentage. Pull figures from your own data, original research, or credible third-party sources, and cite where each number comes from to add credibility. The goal isn't to cram in numbers for their own sake — it's to ground your existing claims in concrete, verifiable detail, which is exactly what makes them worth quoting.
Both work, and the best content uses a mix. Original data — your own metrics, surveys or experiments — is the most distinctive and gives engines a reason to cite you specifically. But citing credible third-party statistics, with clear attribution, is also valuable and signals that your content is well-researched. What matters is that the figures are specific and verifiable, and that you attribute external numbers to their source rather than stating them unsupported. Either way, the precision is what makes the passage citable.
Yes — data should support your points, not bury them. If every sentence is a number with no narrative connecting them, the content becomes hard to read and the meaning gets lost, which hurts both human comprehension and the quotability of any single passage. The aim is balance: use specific figures to ground your key claims and make them verifiable, while keeping enough explanation that each statistic has context. A well-placed, well-attributed statistic in a clear sentence is far more citable than a dense block of disconnected numbers.
The most useful figures are specific, recent and relevant to the question your page answers: percentages and growth rates, dated facts (so freshness is clear), measurements and benchmarks, prices, and counts. Attributed third-party statistics and your own original data both carry weight. Pair each figure with enough context that it reads as a self-contained, verifiable claim — for example a number, a unit, a timeframe and a source. Those are exactly the passages AI engines reach for when assembling an answer, so they directly improve your citability.