Track products & SKUs
Product tracking measures how often engines recommend a specific product, not just your brand. You paste a product URL, AppearIn extracts the product and auto-generates prompts for it, then you read a per-product mention rate and the sources behind it. Use it when individual products have distinct buyers and queries.
Before you start
Product tracking sits inside a project, so create the brand first. You need a live product or category URL that an engine could read, for example a product detail page or a collection page.Brand-level vs product-level tracking
Track at the level your customer actually chooses. If buyers pick the brand and the specific product barely matters, brand-level is enough. If different products have different buyers and different questions, track each product. The table below is the quick decision.
| Use brand-level when | Use product-level when |
|---|---|
| The brand is the unit of choice (people pick "Acme," not a SKU). | Individual products have distinct buyers and search queries. |
| You sell one product or a tight, similar range. | You sell a catalog where products compete on different attributes. |
| Customer prompts name the category, not a model. | Customer prompts name a model, size, or use case. |
| You want one headline visibility number. | You need to compare products against each other over time. |
The two are not exclusive. Many brands keep a brand-level visibility view and add product tracking for the few SKUs that earn their own buyer journeys.
A worked example
Acme sells running shoes. The flagship is the Acme Trailblazer. Buyers do not ask "is Acme good?" They ask "best trail running shoes for wide feet" and "Acme Trailblazer vs Beta Summit". Those are product questions, so Acme tracks the Trailblazer as a product. The walkthrough below sets that up.
Set up product tracking
Paste a product URL
Inside the project, choose Add product and paste the product page, for example acme.com/shoes/trailblazer. AppearIn reads the page to extract the product.
Review what AppearIn extracted
Check the extracted product name, key attributes, and any variants AppearIn found. Correct anything wrong here, because the prompts and mention detection are built from this. For the Trailblazer, confirm the name and that it is a trail shoe, not a road shoe, so prompts target the right queries.
Accept the auto-generated prompts
AppearIn generates prompts a buyer would ask about this product, such as "best trail running shoes for wide feet" and "is the Acme Trailblazer good for long distances?" Accept the ones that match how your buyers actually ask, edit the rest, and drop any that do not fit. See build a prompt set for choosing well.
Run it and read the per-product mention rate
Run the product's prompt set. Each run is a dated snapshot. The headline number is the product's mention rate: the share of its prompts where an engine names the product, broken down per engine. Open any answer to see the citations behind it.
Compare products and track the trend
Add your other tracked products and compare them side by side, and against past runs. If the Trailblazer is mentioned in 60% of its prompts on Perplexity but the Acme Glide only 20%, you know where to put your content work. Product prompts run on the same recurring schedule as the rest of the brand, so the comparison becomes a trend.
What good looks like
Well-set-up product tracking has:
- Products that genuinely have their own buyers, not every SKU in the catalog.
- Extracted product facts you have checked and corrected.
- Prompts that name the model or use case the way buyers do.
- Weekly runs on, so per-product mention rate is a trend you can compare over time.
Common mistakes
Tracking every SKU. If a product has no distinct buyer or query, it adds noise, not signal. Track at the brand level instead.
Skipping the extraction review. A wrong product name or category quietly poisons every prompt and mention count that follows.
Reading one run as truth. Data is probabilistic. Compare products across several runs, not a single snapshot, before drawing a conclusion.