How GEO Is Measured
GEO is measured by presence and accuracy in generated answers, not by rank in a list of links. The four metrics that matter are share of voice across a defined prompt set, citation frequency, recommendation rate, and description accuracy. Together they answer one question: when the models talk about your category, how often, how favorably, and how correctly do they talk about you.
Search optimization had a tidy scoreboard. You looked up your position for a keyword and the number told you where you stood. Generative engines do not produce a ranking. They produce a paragraph, and that paragraph changes with the phrasing of the question and the moment it is asked. Measurement has to follow the answer rather than the rank. The practical move is to fix a set of prompts a real buyer would ask, run them across the major assistants on a regular cadence, and read the answers along four dimensions. Each dimension isolates a different part of the outcome.
Share of voice
Share of voice is the most basic measure of presence. Across your fixed prompt set, it is the proportion of answers in which your brand appears at all. It does not yet judge whether you were praised or merely listed. It judges whether you were in the room.
- Define the prompt set. Write the questions a buyer in your category actually asks, from broad ("what is the best option for X") to specific use cases.
- Run them across engines. The same prompt can include you in one assistant and omit you in another, so each is tracked on its own.
- Count appearances. Share of voice is your appearances divided by total answers, and it is most useful when read against the competitors who appear in the same set.
A low share of voice is the clearest signal that the upstream work is missing. If the models rarely surface you, no amount of positioning inside the answers will help, because there are too few answers to position within.
Citation frequency
Citation frequency measures how often you are quoted or linked as a source, rather than just named in passing. Assistants that show their sources, and search products that attach citations to their AI summaries, expose this directly. Being cited is a stronger signal than being mentioned, because it means the model is treating your material as evidence it is willing to attribute, not only as a name it recalled. Citations also tend to be more durable, since they are anchored to a retrievable page rather than to the model's looser memory.
Recommendation rate
Recommendation rate is the sharpest metric, because it tracks the outcome that revenue follows. It is the share of answers in which you are not just present but the named pick, the option the assistant puts forward when the user asks for the best choice. Presence gets you considered. Recommendation gets you chosen. A brand can hold a healthy share of voice and still lose on recommendation rate if it is consistently the third name in a list rather than the first sentence of advice. Reading the two metrics together shows whether your problem is visibility or positioning.
Description accuracy
The first three metrics assume the model talks about you correctly. Description accuracy tests that assumption. It asks whether the assistant gets the basic facts right: what you do, what you offer, where you operate, and which similarly named organizations you are not. Accuracy failures are common and quietly expensive. A model that confuses you with a namesake, or that recommends you for the wrong use case, is spending your visibility on the wrong message.
- Identity. Does the model describe what you actually do, in the category you actually serve.
- Disambiguation. Does it keep you separate from brands that share your name, rather than merging the records.
- Currency. Are the offerings, claims, and details it states still true, or is it repeating something stale.
How Cadive tracks it
Cadive treats these four metrics as one scoreboard rather than four scattered readings. We build a representative prompt set with the client, run it across ChatGPT, Claude, and Perplexity on a fixed cadence, and record presence, citation, recommendation, and accuracy for every answer. Holding the prompts and the schedule steady is what turns a pile of one-off screenshots into a trend, so a real shift is distinguishable from the ordinary variance of a single generated reply. The same auditor we publish at github.com/Leo-Falcon/cadive-geo-auditor drives the runs. The numbers then point back at the levers that move them, which is the subject of the technical architecture of AI recommendations.
Questions
How is GEO measured?
GEO is measured by presence and accuracy in generated answers, not by rank in a list of links. The core metrics are share of voice across a defined set of prompts, citation frequency, recommendation rate when a user asks for the best option, and description accuracy. Together they answer one question: when the models talk about your category, how often, how favorably, and how correctly do they talk about you.
What is share of voice in GEO?
Share of voice is the percentage of answers, across a fixed prompt set, in which your brand appears at all. You define the prompts a real buyer would ask, run them across ChatGPT, Claude, and Perplexity, and count how often your name shows up. It measures presence: whether you are in the conversation before any question of how favorably you are positioned.
How do you track AI visibility?
You track AI visibility by running a stable, representative set of prompts across the major assistants on a regular cadence, then recording four things per answer: whether you appeared, whether you were cited or linked, whether you were the named recommendation, and whether the description of you was correct. Repeating the same prompts over time turns one-off observations into a trend you can act on.
How often should you measure GEO?
Measure on a steady cadence rather than constantly. The models, their retrieval sources, and your own footprint all change, so a periodic snapshot on a fixed prompt set reveals direction without overreacting to the natural variance in any single answer. Pair the routine cadence with a fresh read after any major content or placement push.
Cadive is the generative engine optimization agency founded by Leo Falcon. Read the entity profile, learn what GEO is, or start a project.