· 6 min read
GEO metrics that actually drive revenue (not just mentions)
Most GEO reporting counts mentions across a wide prompt list and calls it progress. That number can go up while revenue stays flat, because it mixes prompts nobody buys from with the few that actually precede a purchase. The fix is not more prompts — it is fewer, better ones, measured the same way every week and matched against what actually closes.
This is a narrower, harder standard than "were we mentioned." It is also the only one that tells you whether GEO work is paying for itself.
Build a short list of money prompts, not a long one
A broad prompt list — every keyword variant, every informational question — dilutes the signal. Most of those prompts are asked by people nowhere near a purchase decision. The prompts worth tracking are the ones a buyer types in the days before they pick a vendor: comparison prompts, "is X good for Y" prompts, pricing and fit questions, and category shortlists.
Ten to twenty well-chosen money prompts, re-checked on a fixed schedule, tell you more than two hundred generic ones. The goal is a number you can defend to a revenue owner, not a vanity count.
A mention is not a result — a deal is
Citation rate on money prompts is a leading indicator, not the finish line. The only way to know if it means anything is to compare it against what actually happens in the pipeline: are deals that come in after a spike in citation share closing faster, or not at all? Without that link, citation-rate improvements are an assumption, not a measured outcome.
This is why GEO reporting works best sitting next to CRM data, even informally — a monthly note of "citation share on our top 15 prompts" next to "deals sourced or influenced by organic/AI discovery" is enough to start seeing whether the two move together.
What content actually earns the citation
- Answer the real question, trade-offs included. Content that states when you are *not* the right fit reads as more trustworthy to a model summarizing multiple sources — and to the buyer reading the summary.
- Publish data nobody else has. A model cannot cite you for a number it can already get from five other sources. Original data — a survey, a benchmark, an internal dataset — gives it a reason to point at you specifically.
- Name a real author. Bylines with a name and a bio outperform anonymous or "admin"-authored pages when models weigh source trust.
- Kill duplicate content ruthlessly. If a competitor could put their logo on your page and nothing about the substance would need to change, it is not distinctive enough to be the one source a model picks.
- Treat llms.txt and schema.org for what they are. Schema is hygiene, not a lever — it helps engines parse a page correctly but does not make it more likely to be chosen. llms.txt is unproven; Google has stated directly it does not use it for retrieval.
Frequently asked questions
- How many prompts should a money-prompt list have?
- Ten to twenty is usually enough. More than that and the list starts including prompts that are informational rather than purchase-adjacent, which dilutes the signal you are trying to read.
- Does llms.txt help GEO?
- Its impact is unproven and Google has said directly it does not use it for retrieval. Treat it as low priority next to answer-shaped content and entity consistency.
- How do I connect citation rate to revenue without a big analytics project?
- Start informally: log citation share on your top money prompts monthly, and put it next to a simple pipeline metric — deals sourced or influenced by organic/AI discovery. Even a rough correlation over a few months tells you more than mention count alone.
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