{"id":18355,"date":"2026-10-05T13:33:47","date_gmt":"2026-10-05T11:33:47","guid":{"rendered":"https:\/\/trilogi.com\/blog\/does-your-company-appear-in-chatgpt-how-to-measure-geo-visibility\/"},"modified":"2026-10-23T18:16:24","modified_gmt":"2026-10-23T16:16:24","slug":"does-your-company-appear-in-chatgpt-how-to-measure-geo-visibility","status":"publish","type":"post","link":"https:\/\/trilogi.com\/blog\/en\/does-your-company-appear-in-chatgpt-how-to-measure-geo-visibility\/","title":{"rendered":"Does your company appear in ChatGPT? How to measure GEO visibility"},"content":{"rendered":"\n
In SEO we\u2019re used to talking about rankings. A keyword, a SERP and a website that appears in a certain position. We can monitor it over time, compare it with competitors and analyze whether it goes up or down.<\/p>\n\n\n\n
When we try to apply the same logic to ChatGPT and other AI-based search systems, we run into a problem: an AI-generated answer doesn\u2019t work like a traditional SERP<\/strong>.<\/p>\n\n\n\n This directly affects how we should measure a brand\u2019s visibility in GEO<\/a> (Generative Engine Optimization).<\/p>\n\n\n\n Perhaps the question should no longer be \u201cwhat position does my company appear in?\u201d, but another one:<\/p>\n\n\n\n how often does my brand appear when a user asks questions relevant to my business?<\/strong><\/p>\n\n\n\n Google results aren\u2019t completely fixed. They can vary by location, device, time of the query and other factors. Even so, traditional SEO offers a relatively observable environment: we can monitor keywords, URLs and rankings and analyze how they evolve.<\/p>\n\n\n\n With generative systems, that stability is lower.<\/p>\n\n\n\n Repeating the exact same prompt doesn\u2019t guarantee the same answer every time. The brands mentioned, the recommendations, the structure of the answer and the sources used can all change.<\/p>\n\n\n\n In addition, factors such as these can affect the result:<\/p>\n\n\n\n So two seemingly identical runs won\u2019t necessarily produce the same result.<\/p>\n\n\n\n This variability is one of the main differences to keep in mind when we move from measuring SEO to measuring GEO.<\/p>\n\n\n\n Variability doesn\u2019t only affect the content of the answers. It also affects the sources AI systems choose to cite.<\/p>\n\n\n\n This phenomenon is commonly known as citation drift<\/strong>, or citation volatility.<\/p>\n\n\n\n In May this year SISTRIX published research based on 82,619 prompts, more than 1.5 million observations and 17 weeks of tracking across different platforms and countries.<\/p>\n\n\n\n The results show high turnover of cited sources.<\/p>\n\n\n\n For ChatGPT Search, SISTRIX observed weekly domain turnover of up to 74%<\/strong>, although the research itself shows significant differences depending on the country analyzed.<\/p>\n\n\n\n In other words: a domain being cited today doesn\u2019t necessarily mean it will still be one of the sources used the following week.<\/p>\n\n\n\n At the URL level, according to the same research, variability is even greater.<\/p>\n\n\n\n This introduces an important difference from SEO: getting a one-off citation isn\u2019t necessarily the same as achieving stable visibility<\/strong>.<\/p>\n\n\n\n Here\u2019s one of the common problems when analyzing a brand\u2019s presence in AI.<\/p>\n\n\n\n We ask ChatGPT a question.<\/p>\n\n\n\n The brand appears.<\/p>\n\n\n\n We take a screenshot.<\/p>\n\n\n\n And we conclude we have visibility.<\/p>\n\n\n\n But that answer is just one observation of a variable system<\/strong>.<\/p>\n\n\n\n The opposite can also happen: we run a prompt once, our company doesn\u2019t appear and we conclude we have no GEO visibility.<\/p>\n\n\n\n Both conclusions may be premature.<\/p>\n\n\n\n If we know the result can vary between runs, a single query isn\u2019t enough to draw solid conclusions. To get a more representative picture, you need to work with multiple observations and analyze the results in aggregate.<\/p>\n\n\n\n One possible approach is to repeat the same prompt enough times to identify patterns of appearance, mentions and citations. The sample size will depend on the goal of the analysis, the resources available and the level of variability we want to observe.<\/p>\n\n\n\n For example, in a test with a large number of runs we could analyze:<\/p>\n\n\n\n The goal isn\u2019t to review each answer in isolation, but to turn a set of runs into data that reveals trends.<\/p>\n\n\n\n If, for example, a company appears in 68 out of 100 runs carried out under the same test conditions, we could speak of a 68% appearance frequency within that sample.<\/p>\n\n\n\n It wouldn\u2019t make much sense to interpret that result as a \u201cGEO position\u201d. What we\u2019re measuring is something different: how consistently a brand appears within a set of answers.<\/p>\n\n\n\n That\u2019s why, in this context, it can be more useful to talk about visibility frequency<\/strong> than position.<\/p>\n\n\n\n There\u2019s a second dimension to the problem.<\/p>\n\n\n\n Real users don\u2019t all ask exactly the same question.<\/p>\n\n\n\n One person might ask:<\/p>\n\n\n\n What\u2019s the best X company?<\/strong><\/p>\n\n\n\n Another:<\/p>\n\n\n\n Which X companies do you recommend?<\/strong><\/p>\n\n\n\n And a third:<\/p>\n\n\n\n I\u2019m looking for X for a small business with a budget of Y. What are my options?<\/strong><\/p>\n\n\n\n The intent may be very similar, but the prompts are different.<\/p>\n\n\n\n That\u2019s why a GEO monitoring strategy shouldn\u2019t rely solely on repeating one question many times. It should also work with clusters of prompts representing different user intents<\/strong>.<\/p>\n\n\n\n SE Ranking, for example, suggests starting by monitoring 20 to 40 prompts, tracking them across two or three models and gathering at least 30 days of data before drawing conclusions.<\/p>\n\n\n\n The two approaches actually answer different questions.<\/p>\n\n\n\n Repeating a prompt helps us understand the variability of a single query<\/strong>.<\/p>\n\n\n\n Monitoring different prompts over time helps us understand the brand\u2019s visibility within a topic or search intent<\/strong>.<\/p>\n\n\n\n Combining both dimensions can give us a much more representative picture.<\/p>\n\n\n\n As interest in visibility on ChatGPT and other AI systems grows, it\u2019s tempting to look for a direct equivalent of SEO rankings.<\/p>\n\n\n\n Traditional SEO has taught us to think in rankings. In GEO it may be more useful to think in terms of probabilities, recurrence and distribution of visibility<\/strong>.<\/p>\n\n\n\n So, faced with the question:<\/p>\n\n\n\n \u201cDoes my company appear in ChatGPT?\u201d<\/strong><\/p>\n\n\n\n perhaps we should start answering:<\/p>\n\n\n\n \u201cHow often does it appear, for what types of questions, against which competitors, in which models and with which sources?\u201d<\/strong><\/p>\n\n\n\n Because in a variable environment, a single answer isn\u2019t enough: visibility is better understood by observing appearance patterns over time.<\/p>\n\n\n\n GEO stands for Generative Engine Optimization and refers to optimizing a brand\u2019s presence in the answers of generative AI systems. Measuring its visibility means analyzing how often it appears, for which questions, against which competitors and with which sources.<\/p> <\/div> Select relevant questions your potential customers might ask and observe the answers across several runs and points in time. Record mentions of your brand, citations of your domain and the presence of competitors. A single isolated query can\u2019t tell you whether visibility is stable.<\/p> <\/div> Answers can vary between runs. The prompt wording, conversation context, language, location and model used can also play a role. That\u2019s why it\u2019s worth recording the conditions of each test and comparing results obtained under similar conditions.<\/p> <\/div> Divide the number of answers in which the brand appears by the total number of answers analyzed and multiply by 100. If it appears in 68 out of 100 runs, its appearance frequency is 68% in that sample and under those test conditions.<\/p> <\/div> No. A mention happens when the answer names the company. A citation happens when it uses its domain or a specific URL as a source. It\u2019s best to measure them separately, since the brand can appear without its website being cited.<\/p> <\/div> There\u2019s no universal number for every business. As a starting point, SE Ranking suggests 20 to 40 prompts, across two or three models, for at least 30 days. Sample size and repetitions should be adapted to the goal, the resources and the variability observed.<\/p> <\/div> Citation drift is the variation over time in the sources cited by AI systems. A domain or URL may be cited in one answer and disappear in another. That\u2019s why a one-off citation should be interpreted within ongoing tracking.<\/p> <\/div> <\/div>\n\n\n\n Sources and recommended reading<\/strong><\/p>\n\n\n\n SISTRIX (2026). AI Citation Drift: How stable are sources in AI search results?<\/a><\/p>\n\n\n\nWhy visibility in ChatGPT is different from SEO rankings<\/h2>\n\n\n\n
Factors that influence AI-generated answers<\/h3>\n\n\n\n
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Citation drift and the volatility of sources cited by AI<\/h2>\n\n\n\n
Why a single ChatGPT query doesn\u2019t prove GEO visibility<\/h2>\n\n\n\n
How to measure a company\u2019s GEO visibility in ChatGPT<\/h2>\n\n\n\n
Mention and citation metrics to measure GEO visibility<\/h3>\n\n\n\n
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How to calculate a brand\u2019s appearance frequency<\/h3>\n\n\n\n
How to choose prompts to monitor GEO visibility<\/h2>\n\n\n\n
Examples of prompts with similar search intent<\/h3>\n\n\n\n
Monitoring prompts across different models over time<\/h3>\n\n\n\n
GEO visibility is measured through appearance patterns over time<\/h2>\n\n\n\n
Frequently asked questions about ChatGPT visibility and GEO<\/h2>\n\n\n\n