Share of LLM is the new B2B visibility metric.
In 2026, your buyer asks ChatGPT before they ask Google. The brands that get cited compound. The brands that don't disappear from the consideration set. Here's how to measure it.
What Share of LLM actually measures
Share of LLM (SoL, sometimes SoLLM) is the percentage of times a brand is cited, recommended, or mentioned by AI language models, ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, when a B2B buyer asks a vendor-selection or category-research question.
It replaces Domain Authority because Domain Authority measured what mattered when Google was the only buyer interface. In 2026, when 60% of B2B research is mediated by AI agents, the relevant question is no longer "do I rank?", it is "do I get cited?"
Citation rates are not a vanity metric. LLM-referred traffic converts at 14.2% versus 2.8% for organic, a five-times difference that compounds across the funnel.
How to measure Share of LLM (manual method, 2-3 hours)
SoL measurement is not yet automated by mainstream analytics tools. The practical approach takes 2-3 hours per quarter and runs across five AI surfaces.
Why the citation moat closes fast
AI engines re-cite sources they have already learned to trust. ChatGPT in particular shows strong recursive authority, sources cited by other cited sources rank disproportionately. This means early citation wins compound, and late entrants face an asymmetric uphill climb.
The Princeton GEO study found that adding expert quotes boosts visibility by roughly 41%, statistical density boosts citation by 30%, and embedded citations boost recommendation by 30%. These optimizations stack. They are not interchangeable with backlink quantity.
The brands that take AEO seriously in the next 24 months will compound a citation advantage that is hard to dislodge. The brands that wait will spend the rest of the decade trying to climb back.
What raises your Share of LLM
- Answer-first formatting: 40-60 word direct answer at the top of every key page. AEO weights this at 19% of the citation signal.
- FAQ schema quality: 20% of citation signal. Pages with FAQPage schema are dramatically more likely to be cited in AI Overviews.
- Statistical density: 16% of citation signal. Quote real numbers from real sources. The Princeton GEO finding: stats boost citation by 30%.
- Expert quotes: 41% visibility lift in Princeton testing. Quote your own named operators, link to outside experts.
- Entity clarity: consistent brand definition across all pages so the LLM's knowledge graph has a stable node to cite.
- llms.txt at root: tells AI crawlers how to use your content. Most B2B sites still don't have one.
- Freshness: updated date stamps within the last year. The large majority of AI citations come from recently-updated pages.
The five sub-metrics that make up Share of LLM
A single number for SoL hides too much. The teams who win in citation graphs track five sub-metrics together and react to the one that's drifting.
Common SoL measurement mistakes
SoL discipline is new enough that most teams measure it badly. The errors below are the ones we see most often when we audit a competitor's program before quoting an engagement.
- One-surface bias. Measuring only ChatGPT because it's the loudest. ChatGPT serves the most volume but Claude and Perplexity dominate research-mode queries where comparison decisions get made.
- Vanity queries. Running queries the buyer would never ask. Test against the queries your sales team actually hears, not the ones your marketing team would prefer.
- Snapshot thinking. Running SoL once and treating it as a permanent number. Citation share moves week to week as models retrain and as competitors push new content. Quarterly minimum, monthly for active programs.
- Ignoring negative citations. A model citing you with a caveat ("X is solid but lacks Y") is signal. It tells you exactly what the next piece of content needs to address.
- No control set. Tracking your citation share without tracking a comparable competitor's. The relative number is what matters when you walk into a board meeting.
How SoL fits alongside traditional SEO
SoL doesn't replace organic SEO. It sits on top of it. The two metrics correlate but the playbooks diverge in important ways.
Organic SEO optimizes for click-through from a results page. SoL optimizes for inclusion in a synthesized answer. Both reward authoritative content with structured data, but SoL adds new requirements: answer-first formatting, statistical density, FAQPage schema, llms.txt, and explicit attribution-ready phrasing.
The brands winning in SoL run both playbooks in parallel. We measure organic rankings quarterly and citation share quarterly, and we let the leading indicator be SoL because it moves first when the underlying authority compounds. If SoL is rising and organic is flat, the moat is forming. If SoL is flat and organic is falling, the moat is eroding faster than the dashboard suggests.
A six-week SoL launch plan
If a B2B company is starting from zero on Share of LLM, here's the engagement we run with portfolio companies. Six weeks, three deliverables, one named operator.
Frequently asked questions
What is Share of LLM?
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Measure your SoL before the moat closes.
IG runs the SoL audit + the optimization stack as part of the SEO and content engine workstream. You get a baseline by next Friday, a 90-day improvement plan, and weekly tracking against ChatGPT, Claude, Perplexity, Gemini, and AI Overviews.
Book a SoL audit →