TL;DR
SKU-level AI attribution connects individual product citations to revenue. Track which products AI recommends, which competitors appear instead, and correlate citation changes with sales data. This is what separates operators from marketers.
Beyond channel-level attribution
"AI search drove K last month" is useful. "AI search drove K from Product A, K from Product B, and Product C is invisible" is actionable. The first is channel attribution. The second is SKU-level attribution.
Most analytics platforms stop at channel. They tell you traffic came from ChatGPT. They don't tell you which products were cited, which queries triggered the citations, or which competitor products appeared alongside yours.
SKU-level attribution answers the questions operators actually make decisions with: What do I stock more of? What do I invest content in? What's losing share to competitors?
How to build SKU-level tracking
Step 1: Identify which products AI cites. Query AI search engines with purchase-intent questions for each product category. Document which of your products appear and in what context.
Step 2: Monitor citation changes. Track weekly. When a product starts or stops being cited, note the date. Correlate with content changes, structured data updates, and competitor actions.
Step 3: Connect to revenue. Overlay citation data with product sales data. Look for correlation: does increased AI citation frequency for Product A precede increased sales? Does losing citations coincide with sales dips?
Step 4: Competitive displacement. When AI recommends a competitor product instead of yours, analyze why. Is their product page better structured? Do they have more external mentions? Is their FAQ schema more comprehensive?
The product page problem
Our data shows product pages are cited 3-5x more than blog posts for purchase-intent queries. This means product page optimization directly impacts revenue attribution — more than blog content.
Yet most commerce brands invest 80% of their content effort in blog articles and 20% in product pages. For AI search, this ratio should flip. Product pages need FAQ schema, detailed descriptions, comparison data, and server-rendered structured data.