B
Evidence Grade B
Graded
accuracy verified by the review judge
Safe to Cite
Recent
40%
40% -- llm citation lift for content with specific quantitative data and inline sources (2025)
Content with specific quantitative data and inline sources receives 40% more citations by LLMs than content using generic statements.
Attribution:
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Lighthouse Research Team
Last verified:
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Source verified
Evidence graded
Value
40%
Unit
Percentage
Data Vintage
2025
Entity
Evidence Quality
B
Strong, reliable evidence
Sourced from a well-regarded research firm or industry leader with transparent methodology and a reasonably large sample.
Single source - not yet corroborated
Score breakdown
Extraction
75
Source Trust
60
Citation Chain
60
Independence
30
Methodology
45
Freshness
95
Source
Princeton / arXiv (GEO-16 framework)
Primary
Tier B
https://www.fortop.it/en/blog/market-insights/angle-not-topic/ ↗
Source Article
Angle, not topic: the content LLMs actually cite | Fortop
Angle, not topic
Why your content feeds your competitors in the era of LLMs
65% of B2B content is never consumed by buyers. A figure that has been circulating for years and surprises in the same way every time, as if the cause were still a mystery. It isn’t.
Content that doesn’t work usually doesn’t…
Original Research
Princeton / arXiv (GEO-16 framework)
· Original study ↗
Methodology
GEO-16 framework, Princeton/arXiv 2025
Methodology documented
Segmentation
Industrymarketing
Cite this stat
Plain Text
content with specific quantitative data and inline sources: 40% llm citation lift. Source: Princeton / arXiv (GEO-16 framework) (2025). Via Lighthouse Intelligence -- https://lighthousedata.io/data/llm-citations-quantitative-data-2025
HTML Embed
<blockquote cite="https://lighthousedata.io/data/llm-citations-quantitative-data-2025" style="border-left:3px solid #2563eb;padding:12px 16px;margin:16px 0;font-family:system-ui,sans-serif;background:#f0f4ff;"><strong>40%</strong> -- llm citation lift for content with specific quantitative data and inline sources<br><small>Source: Princeton / arXiv (GEO-16 framework) (2025) · <a href="https://lighthousedata.io/data/llm-citations-quantitative-data-2025" target="_blank" rel="noopener">Lighthouse Intelligence</a></small></blockquote>
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