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Ask ChatGPT to recommend a supplier once and you get an example. Run a documented set of customer questions and you get a diagnostic tool.
Neither exercise tells you how often your business appears across all customer conversations. The useful goal of an audit is narrower: find inaccurate descriptions, missing evidence, and questions your website does not answer well.
The method below is a suggested research workflow, not an OpenAI ranking formula.
Start with real buying questions
Collect questions from sales calls, support tickets, proposal requests, and customer interviews. Remove private customer details before testing them in an external service.
Build a starter set of 20 questions across five groups:
Group | Example question |
|---|---|
Category discovery | Which tools help a small agency manage client reporting? |
Requirements | Which options support separate permissions for each client? |
Comparisons | How should we compare a spreadsheet workflow with dedicated software? |
Constraints | What works for a five-person team with limited setup time? |
Brand verification | What does our company offer, and who is it for? |
Replace these examples with your actual market. Twenty is a manageable starting sample, not a statistically representative number.
Record conditions before recording results
For each run, save the exact prompt, date, visible model or mode, search setting, relevant location context, and conversation conditions. Use fresh conversations and avoid telling the assistant that you work for the company being assessed.
Where personalization or location cannot be controlled, record that limitation. Do not silently compare a personalized mobile session with a fresh desktop session as though only your website changed.
For commercially important questions, run three independent trials under comparable conditions. This reveals some variation; it does not eliminate it.
Separate four observations
Record whether the answer:
Names your business.
Links to a page you control.
Describes your offer accurately.
Presents your business as suitable for the stated requirements.
A mention can be negative or irrelevant. A citation can support a statement without recommending the supplier. Keep those outcomes separate.
When a source is linked, open it and check what it actually supports. Do not assume every sentence in an answer came from the nearest link.
Use a result log you can act on
Field | Example entry |
|---|---|
Prompt ID | REQUIREMENTS-03 |
Observation | Company named; integration described incorrectly |
Cited URL | Exact URL from the answer |
Verified truth | Integration supports export, not two-way sync |
Likely source gap | Integration page does not explain the distinction |
Action | Rewrite the support matrix with product approval |
Owner | Product marketing |
“Likely source gap” is a hypothesis. You have not established why the model produced its answer merely by finding a weak page.
Prioritize costly misunderstandings
A wrong service area or unsupported feature can send an unsuitable customer into your funnel. Fix those before polishing an already accurate company description.
Choose one issue, update the authoritative page, and log exactly what changed. Repeat the same sample later under comparable conditions, while noting that retrieval and answer variation can complicate attribution.
Your report should say “named in 12 of 60 sampled answers,” if that is what you observed. It should not say “20% ChatGPT market share.” The sample describes your test, not the platform’s total audience.
Next read: Separate mentions, citations, clicks, and revenue.
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