Track and Win ChatGPT With Answer Radar

What the engine records, and the searched split

For each approved question, Answer Radar asks ChatGPT once through its official web search capability, with no steering. It stores the full answer text, whether ChatGPT searched, the search queries it issued, every cited URL with its domain, and each brand named with whether it was recommended or only listed.

ChatGPT decides for itself whether to search. When it does, the row carries a Searched pill and the answer is graded grounded, the only grade that feeds your rates. When it does not, the row has no pill, the grade is ungrounded, and the answer is reported separately with the reason not searched. A poster in r/SEO, in the thread 'Anyone else checking how ChatGPT describes their company?', drew the same line: with web search on the model is summarising pages it just fetched, and from memory nothing you publish today moves it until the next training run.

A grounded miss points at pages you can change or earn. An ungrounded miss points at how the category was described when the model was trained, which no single fix reaches.

Running the first audit that includes ChatGPT

Nothing extra to enable. Approve the buying questions you want measured, then press Run audit. The dialog shows an estimate such as '12 approved questions across 5 answer engines', with ChatGPT as one of the five. Press Confirm & run.

Use the questions a buyer types, not your brand name. A brand-name question tells you what ChatGPT says about you; a category question tells you whether it names you at all.

• Keep the set frozen between runs. A commenter in the r/SEO thread 'How I check whether ChatGPT / Perplexity actually cite my brand (simple method)' put the reason plainly: 'But response is different each time? So cant just do it once.'

Reading the ChatGPT rows

Open a run from History. Under the heading ChatGPT, every question has a row. The header shows a Searched pill when the engine searched, and one of three chips: You: recommended, You: mentioned or You: not mentioned. Expand a row to see Queries, the searches ChatGPT ran, then Recommended followed by the brands it put forward, or No brand recommended.

Below the answer sits the Citations table with two columns, Domain and Who. Who reads You, Competitor or Other. In measured customer answers the most cited domains were hubspot.com, techradar.com, salesforce.com, g2.com and forbes.com.

A row without the Searched pill is a memory answer, not a failure and not a zero. A cited page of yours beside a competitor recommendation is presence, not a win; recommendation is judged from the answer text and is always lower than or equal to presence.

From a competitor-only recommendation to a fix

The rows that matter most read You: not mentioned with a competitor under Recommended. Open the gap, read what ChatGPT said, and look at the Who column. If a review site or a roundup is cited, the fix is a mention there. If the competitor's own page is cited, the content gap view shows which requirement your page fails.

Drafting is your assistant's job, not ours. Pull the gap and its evidence through the connector, have your assistant draft the page or the outreach, save it as a fix, then re-run the audit. The re-measurement reports the observed change, labelled as observed. A commenter in an r/SEO thread about a vendor's 'likelihood to buy' score set the standard: lead with the raw prompts and actual model responses, not a score.

Do not spend a fix on an ungrounded row. Watch it across runs; when ChatGPT starts searching for that question, the next run records who it recommends.

Where the numbers appear

In the app, the project overview and each run page show presence and recommendation for ChatGPT over grounded answers only, with the ungrounded count beside them. Through the connector, answer_metrics_get and answer_trends_get return the ChatGPT segment with its disclosure block, which names the sample counts and grades behind every number. The weekly brief lists ChatGPT on its own line.

FAQ