Track and Win Perplexity With Answer Radar

What the engine records, and why the panel is wide

For each approved question, Answer Radar asks Perplexity once, with no steering. It stores the full answer, every source in the panel plus the additional results Perplexity attached, deduplicated by URL, each flagged as yours, a competitor's or a third party's, and every brand named with whether it was recommended or only listed.

Perplexity is grounded by design, so every parsed answer is recorded as searched; the ungrounded grade you may see on ChatGPT or Claude rows does not occur here. What varies is the width of the panel. A poster in r/perplexity_ai, in the thread 'I ran 75 Perplexity searches to see how much the cited sources change', measured about 13.5 cited sources per run and concluded that source instructions 'are not a remote control. They are more like a diagnostic.'

One field needs a plain label. The Queries line on a Perplexity row shows the related queries Perplexity attached to the answer, not the searches it ran. They are the follow-up questions a buyer is offered, not a fan-out log.

Running the first audit that includes Perplexity

Nothing extra to enable. Approve the buying questions, press Run audit, check the estimate such as '12 approved questions across 5 answer engines', and press Confirm & run.

Ask category questions, not brand questions. Perplexity will cite something for almost any question; the measurement is whether the sources and the recommendation are yours.

• Keep the question set frozen between runs. A single Perplexity answer is one observation from a panel that reshuffles between sessions; a commenter in r/SEO, in a thread about a vendor's report, called the failure mode 'One observation, printed as a percentage.'

Reading the Perplexity rows

Open a run from History. Under the heading Perplexity, every question has a row with the Searched pill and one of three chips: You: recommended, You: mentioned or You: not mentioned. Expand it to see Queries, then Recommended followed by the brands put forward, or No brand recommended, then the answer and a Citations table with the columns Domain and Who.

In measured customer answers the most cited domains were techradar.com, youtube.com, goconsensus.com, pipeline.zoominfo.com and reddit.com. A row where Who reads You in the table but the chip reads You: mentioned or You: not mentioned is the Perplexity pattern to watch: your page was evidence for an answer that did not choose you.

Read a high own-domain share with care. A commenter in r/GEO_optimization, in the thread 'I logged ~120k AI citations across ChatGPT, Gemini, Perplexity and Claude on the same prompts', argued that owned share 'might read better as a fragility indicator than a visibility one'. The Who column shows you that split directly.

From cited-not-recommended to a fix

Start with rows that read You: not mentioned or You: mentioned while a competitor sits under Recommended. Open the gap and read the Citations table. Where the recommending sources are review sites, comparison pages or community threads, the fix is a mention there; where a competitor's own page is cited, the content gap view shows which requirement your page fails.

Community threads matter more on Perplexity than on any other engine we measure. A commenter in r/content_marketing, in the thread 'What strategies are actually working to get your brand mentioned in ChatGPT, Gemini, Claude, and Perplexity responses?', reported that 'a single well-answered thread in the right niche subreddit shows up in Perplexity citations for months'.

Drafting is your assistant's job, not ours. Pull the gap through the connector, draft the page or the reply, save it as a fix, then re-run the audit and read the observed change.

Where the numbers appear

In the app, the project overview and each run page show presence and recommendation for Perplexity, and the cited-domains view lists every domain with its share and whether it is yours or a competitor's. Through the connector, answer_metrics_get returns the Perplexity segment with citation share, presence, recommendation and a disclosure block naming the sample counts; answer_fanout_get returns the stored queries.

FAQ