Someone types "what is the best project management tool for a small agency" into ChatGPT. It names three products. Yours is either one of them or it is not. You cannot see that in Google Analytics. You cannot see it in Search Console. If you want to track brand mentions in ChatGPT, you have to ask ChatGPT, and you have to do it in a way that produces numbers you can trust. This guide walks through how. Start by hand. Then decide when hand-work stops being worth it. Why ChatGPT answers are hard to track A search ranking is stable enough to check once a day. A ChatGPT answer is not. Three things get in the way. Answers vary. Ask the same question twice and you may get a different list of brands, in a different order. One run tells you almost nothing. You need repeated runs to see how often you appear. Prompts vary. Your buyers do not all phrase the question the same way. "Best CRM for startups" and "which CRM should a five-person startup use" can return different brands. Context varies. Whether ChatGPT searches the web for an answer changes what it can cite. Answers built from search can link to sources. Answers built from the model alone may only name brands. So the job is not "check if we show up." The job is "measure how often we show up across a fair set of prompts, over time." Step 1: Build a prompt set Write down the questions your buyers actually ask. Aim for 20 to 50 to start. Cover four types: Category prompts. "Best [category] tools." These decide who gets shortlisted. Use-case prompts. "[Category] for [audience or job]." These are narrower and often easier to win. Comparison prompts. "[Your brand] vs [competitor]." These show how you are framed. Problem prompts. "How do I [solve the problem you solve]?" These catch buyers before they know your category name. Pull the wording from sales calls, support tickets, search queries and community threads. Real phrasing beats invented phrasing. If you are short on ideas, the prompt fan-out generator takes one seed prompt and expands it into the related sub-questions AI platforms tend to run behind the scenes. It is a fast way to widen a prompt set beyond the obvious ten. Step 2: Run each prompt more than once Open a fresh chat for every run. Old conversation context bleeds into later answers, and you want each test clean. Run each prompt at least five times. Ten is better for the prompts that matter most. Log every run. You are not looking for one answer. You are looking for a rate. Two practical notes: Test with and without web search when the option is available to you. They are different behaviours and may produce different brands. Log the date. Answers change as models and sources change, so a result from March says little about September. Step 3: Record the right fields For every run, capture: Field What it tells you Prompt Which question was asked Brand mentioned (yes/no) Whether you are in the answer at all Position First, second, third, or later in the list Sentiment How the answer describes you Competitors named Who you are sharing the answer with Sources cited Which pages the answer linked to A spreadsheet is enough at this stage. One row per run. The sources column is the one people skip and the one that pays off. If ChatGPT keeps citing the same review site or the same competitor comparison page, that page is shaping your category. It is where your work should go. Step 4: Turn rows into metrics Raw rows are noise. Turn them into three numbers. Visibility rate. Runs where your brand appeared, divided by total runs. If you appeared in 12 of 40 runs, your visibility rate is 30%. Share of voice. Your mentions divided by all brand mentions across the same runs. This tells you how you compare with competitors, not just how you do alone. Average position. Where you land when you do appear. Being named first is worth more than being named fifth. Track each by prompt group, not only overall. A strong overall number can hide a category prompt where you never appear. Step 5: Read the sources, not just the mentions A mention is the outcome. A citation is the cause. When ChatGPT answers with web search, it often links the pages it used. Collect those links. Sort them into types: your own site, competitor sites, review platforms, forums, news, listicles. Then ask two questions. Which sources show up for prompts where we win? Protect and extend those. Which sources show up where we lose? These are your targets. Get listed, get reviewed, or publish something better. This is where tracking turns into a plan. You are no longer guessing why you are missing. You are looking at the pages the answer was built from. Step 6: Set a cadence Run the full prompt set on a fixed schedule. Weekly works for most teams. Monthly is the floor. Anything less and you will miss a change until it has already cost you. Keep the prompt set stable between runs. If you change the prompts every time, you cannot tell whether the answers moved or you did. Add new prompts, but keep a core set fixed for trend lines. Where manual tracking breaks down Manual tracking works. It also gets expensive fast. Do the arithmetic. 40 prompts, 5 runs each, is 200 chats per pass. Weekly, that is 800 a month, for one platform. Add a second platform and you have doubled it. Add logging, sorting sources and building charts, and it becomes somebody's job. It also stays stuck in ChatGPT. Your buyers use several assistants. Gemini, Perplexity, Claude, Google AI Overviews, AI Mode, Grok and Copilot each pull from their own mix of sources, and a brand that wins on one can be invisible on another. Tracking one engine gives you a partial picture. A rule of thumb: do it by hand until you have a prompt set you believe in and you know which metrics you care about. Then automate it. What to do with the results Tracking is only useful if it changes what you do. A few common moves: Fix missing basics. If you never appear for a use case, check that you have a page that answers it plainly. Get into the cited sources. If a comparison article dominates citations, work out how to be included in it. Correct wrong descriptions. If ChatGPT gets your pricing or features wrong, find the page it learned that from and fix or outrank it. Watch competitors. Sudden jumps in a rival's visibility usually trace back to a specific piece of content or coverage. Retest after each change. That is the loop: measure, change one thing, measure again. Track it automatically If you would rather not run 800 chats a month yourself, Pineprompt does it for you. It runs your prompts on a schedule across ChatGPT and seven other AI engines, then reports visibility, share of voice, position, sentiment and the sources behind each answer. There is a page on ChatGPT visibility tracking that shows what that looks like. Plans start at $99 a month with every engine included, and the details are on the pricing page. Either way, start this week. Write 20 prompts, run each five times, and see where you stand. You will learn more from that one spreadsheet than from any amount of guessing. Originally published on pineprompt.com. Read the original.
