As AI-powered search tools become a standard part of the customer journey, ecommerce brands are seeing a new category of visitors arriving on their websites. Instead of relying purely on traditional search engines, shoppers increasingly begin their research through AI assistants that deliver summaries, recommendations, and personalised product comparisons.
This shift makes it crucial for retailers to understand how AI-influenced users behave – and how to measure this traffic accurately to improve their AI ecommerce SEO strategy. Without the right tracking framework, key insights around intent, conversion potential, and product demand are lost.
Understanding How AI Traffic Behaves
AI-driven visitors often behave differently from standard organic users. They typically arrive after receiving:
- a curated recommendation
- a shortlist of products
- an AI-generated comparison
- a direct answer that includes a website link
Because their intent is shaped upstream, these users often land deeper in the buying journey and make decisions faster. However, GA4 does not label this traffic as “AI” — it may appear as direct, referral, organic, or even unassigned depending on how the AI assistant passes the link.
This means retailers must rely on behavioural patterns and enhanced tracking rather than default channel groupings.
Behavioural Indicators of AI-Influenced Traffic
While AI traffic won’t appear as a dedicated source in GA4, it leaves identifiable markers. Common traits include:
1. Higher scroll depth
AI-driven users typically arrive with a specific goal: validating product details. This often results in deeper scrolling and more engagement with on-page content.
2. Stronger conversion readiness
Because AI tools filter information beforehand, visitors may arrive closer to making a purchase. They move more quickly through product pages, add items to cart faster, and require fewer touchpoints.
3. Fewer navigation steps
AI traffic often lands directly on PDPs, buying guides, or deep category pages instead of the homepage. These users skip the early-stage research journey.
4. Distinctive landing-page patterns
Pages that answer questions clearly — FAQs, buying guides, “best of” lists, comparison pages — frequently see an uptick in AI-assisted visits.
These signals help retailers identify when AI is influencing traffic, even when the source is unclear.
How to Track AI Traffic in GA4
1. Build an Explorations Report Using Key Dimensions
Since AI tools can pass inconsistent source data, use GA4’s Explorations to analyse:
- Session source / medium
- Landing page
- Page referrer
- Device category
- Default channel group
- Engagement time
- Scroll events
- Add-to-cart events
- Conversion rate
This helps surface patterns where engagement is high but attribution looks “off.”
2. Create a Custom Segment for Likely AI Traffic
In GA4 → Explore → Segments, create a session segment using conditions such as:
- Landing pages matching guides, comparisons, FAQs, or deep PDPs
- Scroll depth above average
- Engagement time above average
- Navigation path ≤ 2 pages
- Session source of Direct / Unknown / Referral
This won’t capture all AI traffic, but it will isolate AI-like behaviour for analysis.
3. Use UTM Parameters Where Possible
While not all AI platforms pass UTMs, many do. When publishing:
- buying guides
- gift guides
- comparison pages
- FAQ or explainer content
- educational content
…attach custom UTMs such as:
?utm_source=ai&utm_medium=assistant&utm_campaign=ai_discovery
This provides clean attribution when AI surfaces your links directly.
4. Analyse Server Logs for Early Signals
Server logs can show which bots and crawlers are accessing your pages. As more AI models integrate live browsing capabilities, reviewing logs helps identify:
- which pages are being scraped
- which content is being summarised
- early indicators of what AI tools might recommend
This provides future-facing insight into which content may drive AI-influenced traffic.
5. Monitor SEO Performance Shifts
AI-driven research affects how users interact with search results, especially as AI summaries reduce traditional impression volume. Look for:
- impression drops without ranking drops
- higher engagement on deeper content
- increased traffic to long-tail, question-based pages
- unusual surges in traffic to FAQ or comparison pages
These patterns often indicate AI influence on discovery and user behaviour.
Optimising for AI Visibility
To maximise visibility across AI assistants, websites benefit from:
- clear, structured product data
- detailed product specifications
- well-organised category content
- question-driven content formats
- strong internal linking
- up-to-date, factual information
AI systems favour information that is reliable, structured, and easy to interpret — making clarity and organisation increasingly important.
Preparing for AI-Driven Discovery
AI traffic is becoming a meaningful part of customer acquisition. Brands that begin measuring it now will gain a competitive advantage as AI search reshapes consumer behaviour.
By combining behavioural analytics, GA4 segmentation, UTM frameworks, SEO monitoring, and technical insights, retailers can build a more accurate picture of how AI-driven visitors interact with their store – and how to capitalise on this emerging discovery channel.