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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteTrack AI shopping traffic using several views, not one universal number: commerce-platform channel reports, referral and UTM data, and analytics attribution each describe a different part of the shopper journey. First determine whether an assistant sends shoppers to your store or lets them check out within its own experience; then compare reports only after aligning their scope, date range, checkout route, and attribution model.
What counts as traffic from an AI shopping assistant?
An assistant may influence a purchase in at least two ways: it can refer a shopper to your online store, or it can support checkout without the shopper visiting the store in the usual way. Those paths do not generate identical traffic signals. A referral can appear as a visit with a referrer or campaign data; an in-channel checkout may be reported as a sale without a corresponding online-store session.
Shopify’s documentation describes ChatGPT as discovery-focused, with customers completing purchases in the merchant’s online-store checkout, while some other surfaces can support Shopify-powered direct checkout. Shopify’s Agentic sales figure aggregates referral-based sales and direct-checkout sales. Consequently, a sales total for an AI channel should not automatically be read as visits or as sales that followed a trackable referral. See Shopify’s agentic storefront documentation and its guide to managing agentic storefronts.
Shopify’s documented behavior is specific to its commerce platform and supported channels. Its current documentation covers ChatGPT, Google AI Mode and Gemini, Microsoft Copilot, and Meta surfaces, but the available shopping and checkout behavior differs by channel. Other commerce platforms and assistants may expose different signals or reports.
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Which reports and signals should you use?
Use platform reporting, first-party visit data, and analytics attribution as complementary views. Each answers a different question, and none guarantees that every AI-influenced visit will be identifiable.
| View | What it can show | What to watch for |
|---|---|---|
| Commerce-platform AI channel report | In Shopify, the Agentic channel provides per-channel views of sales, orders, online-store sessions, and online-store conversion rate. | The sales figure can combine referral-based sales and direct-checkout sales, so it is not necessarily a count of referred visits or store-checkout orders alone. Documentation: Managing agentic storefronts. |
| Order conversion details | Shopify order conversion details can include the session referral, landing page, visit date and time, referral code, and UTM parameters. | These are available order-level journey details, not proof that every assistant will provide a recognizable referrer or UTM. Documentation: Viewing order conversion summary. |
| GA4 BigQuery export | Google Analytics documents attribution fields at user, session, and event scope, including source, medium, and campaign fields. | Those scopes represent different points in the journey. A field’s presence does not guarantee the assistant will be named in it. Documentation: Google Analytics traffic attribution data. |
| Acquisition reports | Shopify acquisition reporting helps merchants examine store acquisition and sessions. | Session-based reporting and channel or order reporting can differ in scope and definitions. Documentation: Shopify acquisition reports. |
How to set up a practical measurement workflow
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Inventory the AI shopping surfaces
List the assistants and surfaces where your products are available. For each one, record whether the shopper is sent to your store or can complete checkout within the assistant’s experience. Do not assume that a channel’s presence in an AI report means it uses the same checkout path as another channel.
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Check your commerce platform’s AI-channel reporting
In Shopify, review the Agentic channel and select the channel and date range you need. Its performance views include sales, orders, online-store sessions, and conversion rate. Keep channel-level results distinct where available, and note that the sales figure can include both referral-based and direct-checkout sales.
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Preserve raw referral and campaign details
Retain available referrer, landing-page, referral-code, and UTM values in first-party reporting. Shopify’s order conversion details can provide session referral and UTM parameters alongside other visit information. Keep those raw values instead of replacing them with a normalized label such as “AI traffic”; a normalized grouping may be useful, but it should not erase the source data used to create it.
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Use GA4 BigQuery fields with their scope visible
If you use GA4 BigQuery export, distinguish user-scoped first-arrival fields from session-scoped last-click fields and event-scoped attribution fields. Source, medium, and campaign values at different scopes are not interchangeable. Label each scope in dashboards and analysis so a user’s first arrival is not mistaken for the source credited to a later session or event.
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Reconcile only after aligning the comparison
Before comparing an AI-channel report with analytics, align the date range, session definition, checkout route, and attribution model. Compare channel sessions and sales with first-party sessions, referrer paths, UTM values, and order-level conversion details. Keep direct and unassigned visits visible rather than forcing them into an AI-referral category. Differences can remain because reports measure different scopes and session definitions.
Choose and label an attribution model
An attribution model determines how credit is assigned across interactions. Shopify documents last-non-direct-click, last-click, first-click, any-click, and linear models for marketing reports. Choose the model based on the question you are asking and show its name with the result; otherwise, two reports can appear to disagree when they are assigning credit differently. See Shopify marketing reports.
| Model | How to interpret the credit |
|---|---|
| First-click | Credits the first click in the measured journey, useful when asking which source introduced the shopper. |
| Last-click | Credits the final click, useful when asking which source was last before conversion. |
| Last-non-direct-click | Credits the last eligible non-direct click rather than a direct visit; label it explicitly because it is not the same as last-click. |
| Any-click | Credits every contributing click. Because multiple clicks can each receive credit, the resulting attributed credit can exceed the number of orders. |
| Linear | Distributes credit across contributing clicks rather than assigning all credit to just one interaction. |
These models answer different questions, not different versions of a single objective truth. Use the same model when comparing periods or channels, and avoid presenting model-attributed sales as a count of uniquely caused purchases.
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Why your totals may not match
- Different checkout routes: An assistant may send a shopper to the store or enable direct checkout. A platform sales figure that combines both is not directly comparable to store sessions.
- Missing or ambiguous source signals: A referrer or UTM may be absent or may not identify the assistant. A direct or unassigned visit should remain unclassified unless there is evidence for a more specific source.
- Different scopes: A platform may report channel sales and sessions, an order detail may show a conversion journey, and an analytics export may expose user-, session-, or event-level attribution.
- Different attribution rules: First-click, last-click, last-non-direct-click, any-click, and linear models distribute credit differently.
- Session and privacy differences: Analytics session definitions, cookies, privacy settings, and reporting scopes can affect counts. Exact reconciliation is not guaranteed; document the definitions used rather than forcing totals to match.
What to include in an AI-traffic report
Make the report interpretable by recording, at minimum, the reporting period, AI channel, checkout route, metric scope, and attribution model. Identify whether a sales total includes direct checkout, and distinguish observed referral sessions from platform-reported sales. Include the underlying referrer and UTM fields when available, plus separate direct or unassigned categories. This makes the result useful without claiming more certainty than the signals support.
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