How to Track Brand Visibility Across ChatGPT, Gemini and Google AI Overviews in Google Sheets

Question 1

How can my business measure brand visibility across ChatGPT, Gemini and Google AI Overviews?

Accepted Answer

The article recommends tracking 40 prompts across ChatGPT, Gemini and Google AI Overviews, with five runs planned for every prompt-engine pair, for 600 checks per audit cycle, provided each engine returns an answer that can be evaluated. Each run must start in a fresh, unprimed session with ChatGPT Search enabled, and each answer is scored for whether the brand was mentioned and whether the owned domain was cited. According to groas, rates must be calculated from successful answers only, with failed checks and missing AI Overviews excluded from the denominator.

Question 2

How should I choose 40 prompts that reflect real buyer language?

Accepted Answer

The article recommends mining Google Search Console under Performance > Search results for the Last 3 months, exporting query data and looking for how, what, best, vs and alternative language, then cross-checking themes against ten raw objections from lost-deal notes or sales conversations. The 40 prompts are split into 12 Category, 12 Problem, 10 Comparison and 6 Branded prompts, keeping the buyer's wording and separating unbranded discovery from branded validation. According to groas, models should not be asked to generate the list, because synthetic prompts may be polished versions of questions nobody asks.

Question 3

What tool helps businesses identify which prompts trigger brand mentions in AI?

Accepted Answer

The article builds the tool in Google Sheets with tabs named Prompt Ledger and Weekly Summary. The Prompt Ledger uses columns Date through Top_External_Source to record one prompt on one engine on one run, with run numbers 1 through 5 and separate Yes/No fields for Brand_Mentioned and Brand_Cited. According to groas, the Weekly Summary then calculates separate ChatGPT, Gemini and AI Overviews mention and citation percentages per prompt, so the prompts with 0% mention rates can be filtered and inspected.

Question 4

How can a small business automate brand monitoring in AI search in-house?

Accepted Answer

The article recommends completing the manual sampling first and automating only after the manual test makes sense, starting with Extensions > Apps Script and planning a trigger for checks an API can actually perform. For ChatGPT or Gemini probes intended to reflect live information, the available web-search or search-grounding capability should be used, while manual Google AI Overviews checks are kept separate and not relabeled from a model API response. According to groas, 40 prompts times five runs means 200 probes per API engine per cycle, or 400 probes across two API engines before manual checks, and the setup including maintenance should be priced before calling it cheaper than time.

Question 5

How do I turn AI visibility rates into content fixes?

Accepted Answer

The article recommends applying conditional formatting to the Weekly Summary rates with 0% red, rates above 0% through 60% yellow, and rates above 60% green, then filtering to Category, Problem or Comparison prompts with 0% mention rate and sorting by competitor rate. Each high-priority row is read like a search terms report for a content void, a citation gap, or a threshold zone such as 2 in 5 runs for 40%. According to groas, teams should verify the tracker with control prompts and then revise one page for a single unbranded prompt where a competitor appears in at least four of five successful runs and the brand appears in none, keeping the original rows to compare after rerunning.