Steps in this guide
Step 1
Understand how Perplexity citations work
Unlike ChatGPT, Perplexity AI shows numbered citations next to each claim in its answer. When it recommends your product, it typically links to a review site, your own documentation, or a comparison article. These citation URLs are the key data point - they tell you exactly which sources drive your AI visibility.
Step 2
Run your queries in Perplexity
Use the same target query set you identified for ChatGPT tracking. Run each query in Perplexity 3 times and record: whether your brand is mentioned, which position, and most importantly - which URLs appear in the citations numbered alongside your brand mention.
Step 3
Extract and catalog the citation URLs
Make a separate list of every URL that Perplexity cites when mentioning you or your competitors. Group them by domain: G2, Product Hunt, your blog, competitor comparison pages, tech review sites, etc. The pattern tells you which content types Perplexity trusts.
Step 4
Compare your citations to competitors
Look at which URLs Perplexity cites when recommending your competitors. If they are being cited from G2 with 200 reviews and you have 12 reviews, that is a concrete gap to close. If a competitor blog article about your category is being cited, that is a content target.
Step 5
Build a citation improvement plan
For each high-impact citation source where you are under-represented, create an action plan: request more reviews on G2 or Capterra, pitch a guest post to a tech blog that Perplexity frequently cites, or create comparison content that directly answers the queries where you are missing.
Step 6
Track citation changes over time
Perplexity updates its index continuously and draws from live web content. This means your citation profile can change faster than ChatGPT's (which depends on training cycles). Track weekly and note any new citation sources - these are signals that your content strategy is working.
Skip the manual version of this guide
AskRank runs this exact workflow automatically across ChatGPT, Claude, Perplexity, and Gemini, on a schedule.
Perplexity AI has a feature that makes it uniquely valuable for brand monitoring: it shows its sources. Every answer includes numbered citations linking to the specific URLs that informed the response. When Perplexity recommends your product, you can see exactly which sites influenced that recommendation.
This gives SaaS founders something ChatGPT cannot: a direct view into the citation sources driving AI recommendations in your category.
What makes Perplexity different for brand monitoring
ChatGPT and Claude generate answers based on training data - a snapshot of the web that may be months or years old. Perplexity indexes the live web more aggressively and surfaces recent content alongside its answers.
This has two important implications:
First, new content can influence Perplexity recommendations faster. A review article published this month might appear as a citation in Perplexity answers within days, whereas ChatGPT might not surface it for months.
Second, the citations themselves are actionable intelligence. When Perplexity recommends a competitor and cites a specific G2 page, a blog comparison post, or a feature breakdown on a tech site, you know exactly which content assets are driving that recommendation.
Understanding Perplexity’s citation structure
When Perplexity gives a tool recommendation, the answer looks something like: “For email marketing, ConvertKit [1] is popular among content creators, while Beehiiv [2] has gained traction for newsletter monetization.”
The [1] and [2] are clickable citations pointing to specific URLs. Those URLs are your research targets.
For brand monitoring, you want to track:
- Whether your brand appears at all
- Which position in the list
- Which URL is cited alongside your mention
- What the cited URL says about your product
The citation inventory approach
The most useful thing you can do in the first month of Perplexity monitoring is build a citation inventory for your category.
Run your 15-20 target queries, collect all citations for all brands mentioned (not just yours), and build a frequency table: which domains appear most often as Perplexity citations for tool recommendations in your category?
Common high-frequency citation types in SaaS:
- G2 category pages and individual product pages
- Capterra and GetApp comparison articles
- Product Hunt product pages and discussion threads
- Your product’s own blog posts or documentation pages
- Tech blog comparison articles (e.g., from popular indie developer blogs)
- Reddit threads and Hacker News discussions
The domains in your citation inventory are your content and distribution targets. Being absent from G2 while competitors have 150+ reviews there is a concrete, fixable gap.
What to do with competitor citation data
When Perplexity cites a competitor, analyze the cited URL. If it is:
- A G2 page with many reviews: prioritize review generation on G2
- A specific blog post: consider creating a similar or better piece of content targeting the same query angle
- A comparison article on a third-party site: reach out to that publication for coverage
The key insight is that Perplexity tells you exactly where the competitive battle for AI citations is happening. You do not need to guess which content marketing activities will influence AI recommendations - the citation data shows you directly.
Tracking frequency for Perplexity
Because Perplexity draws from live web content, its recommendations can shift more quickly than ChatGPT’s. Weekly tracking catches most meaningful changes. If you have a significant content push or get covered by a high-authority site, run a spot check within a few days to see if it affected your citations.
AskRank tracks Perplexity citations automatically, logs the citation URLs alongside each mention, and shows you trends over time. The citation tracking feature is available on Starter and above tiers.
See also: How to improve your AEO, Citation tracking in AI answers, and How to track competitors in AI answers.