Shopify recorded $748
in ChatGPT-labeled sales.

The original Shopify source rows.

The reporting date range and attribution settings are outside the crop. This is a bounded snapshot, not a monthly revenue or growth claim. The records show Shopify’s source labels; they do not reveal a buyer’s prompt, the recommendation shown, why a model selected the store or whether the orders were incremental.

Check your own referral records.

1. Save the context. Record the reporting dates, report name, currency and attribution settings alongside the rows.
2. Inspect the labels. Look at the actual source and referrer values. Keep spelling variants visible before deciding how to group them.
3. Reconcile the orders. Compare attributed orders with the order records and account for cancellations, returns and conversion delay.
4. Follow the landing path. Identify the pages those sessions reached and check whether product facts, delivery information and the buying action were clear.
5. Measure visibility separately. A referral row cannot establish which question produced a recommendation. Keep any prompt observations and citation records as a separate dataset.

Use the evidence to choose the page work.

Product pages, useful category explanations, original work records and clear company information help a buyer inspect an offer. When a discovery gap is suspected, check the existing pages, crawl access, visible product facts, internal links and source consistency before adding new URLs.

The work should answer a specific buying question and connect it to relevant evidence. This snapshot supports checking AI referral traffic as a source; it does not promise AI placement or a particular sales result.

One source record, connected to the store.

Explore the broader Shopify commerce work. The same referral crops may appear there as context; they are one evidence set. For the purchase path, use the Shopify traffic-to-order checks.

Choose the next step.