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# How can I separate AI shopping agents from bots?
- URL: https://conversion.club/playbook/ai-shopping-agents-bot-analytics/
- Published: 2026-10-29T09:14:24.000Z
- Updated: 2026-10-29T09:50:03.000Z
- Description: Separate declared crawlers, people referred by assistants and suspected browser agents. Behavioral clues support investigation, not definitive identification.
- Author: Jeremie Werner
- Tags: Measure performance, #playbook, Analytics, GA4, Shopify

Declared crawlers often stay outside ordinary analytics because they do not execute the tracking tag or are filtered. An AI agent operating a browser can look like a person and remain in session reports. Separate known automation, human referrals from assistants and uncertain browser-agent activity before interpreting conversion changes.

## The benchmark

Automated visitors send a user-agent string, but not every tool identifies itself distinctly. The source distinguishes these families:

| Family                        | Examples in the source                               | Identification and visibility                                                                       |
| ----------------------------- | ---------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
| Indexing or training crawlers | GPTBot, OAI-SearchBot, PerplexityBot                 | Published user agents/IP guidance; primarily server logs                                            |
| User-triggered retrieval      | ChatGPT-User, Perplexity-User, Google-Agent          | Identified fetchers; provider documentation may treat robots.txt differently from ordinary crawling |
| Browser-operating agents      | Comet, Claude in Chrome, automated Chrome navigation | May resemble ordinary browsers and trigger analytics                                                |

A December 2024 Vercel analysis reported that major non-Google-backed AI crawlers did not execute JavaScript. This is historical infrastructure evidence, not a permanent capability limit. GA4 separately excludes known bots using the IAB list and does not expose a removed-volume counter. The source cites Shopify's September 21, 2026 announcement of human-session reporting by default; verify current filter availability in the store.

HUMAN Security's April 2026 customer-traffic report attributed roughly 71% of activity among its top 10 agents to browser-style agents; ecommerce received 38.20% of agentic traffic in that dataset. Those percentages describe its observed customer population, not all web traffic. Browser-agent products and plan access change; the diagnostic method does not depend on a particular launch date or subscription tier.

## The rule

Exclude verified nonshopping automation from the human-shopping KPI, measure actual people arriving from assistants as [referred revenue](https://conversion.club/playbook/how-to-measure-ai-driven-revenue-in-ga4/), and retain a separate suspected-agent segment when identification is uncertain.

A shopping agent comparing 10 product pages for one person can generate many views and perhaps sessions for one eventual order. That may lower a session-based conversion rate without indicating a worse experience. But unusual behavior alone does not prove an AI agent. Keep the full metric beside the adjusted view so the team can see the effect of classification choices.

## The steps

1. **Compare available filters.** Inspect the same Shopify session report with and without its human/bot filter. See the [bot-filter method](https://conversion.club/playbook/what-does-shopify-analytics-bot-filter-remove-from-my-sessions/). The “human” category means the platform classified it that way, not that every visitor was independently verified.
2. **Locate suspicious concentrations.** The source supplies this ShopifyQL query against the July 2026 session schema; validate field support in your report before use:  
```text  
FROM sessions  
SHOW sessions, bounce_rate, sessions_that_completed_checkout  
WHERE human_or_bot_session = 'human'  
GROUP BY session_country, landing_page_path  
SINCE -30d UNTIL today  
ORDER BY sessions DESC  
LIMIT 50  
```  
Start with high-volume rows showing high bounce and no completed checkouts.
3. **Combine signals.** Look for very low engagement, few sessions per user, thousands of visits without conversions, unusual geography, direct product-page landings and no scroll event. GA4's enhanced-measurement scroll event occurs when 90% of the page becomes visible. These are practitioner clues, not a documented agent-detection algorithm. Geography alone is insufficient.
4. **Inspect request evidence.** Standard GA4 and Shopify reports do not expose the raw user-agent evidence needed here. Use accessible server logs or a service that observes requests and compare against provider-published verification information. A claimed bot name alone can be spoofed.
5. **Keep referrals separate.** Check whether your ShopifyQL version supports `agentic_referring_channel`. Google’s current documentation confirms the “AI Assistant” default channel and `ai-assistant` medium. It excludes Google AI Overviews and AI Mode, which remain under Organic Search; do not put them in the assistant total by assumption. An assistant referral identifies an arrival source, not necessarily automated browsing.
6. **Report uncertainty.** Present the platform-filtered conversion rate, the unadjusted reference and the size of the suspected segment. Record changes monthly in the [operating log](https://conversion.club/playbook/how-do-i-keep-track-of-what-moved-my-numbers/).

## The pitfall

Filters do not settle identification. One apparel brand's 12-month GA4 review found roughly 5% of users with bot-like behavior: engagement below 25% against a 54% average, just 18 conversions, and weekly spikes above 5,000% in two Latin American countries. Removing the identified traffic changed conversion from 1.04% to approximately 1.17%. This was a bot network, not proof of AI-shopping-agent activity. It illustrates [traffic growth without sales growth](https://conversion.club/playbook/traffic-is-up-but-sales-aren-t-what-should-you-check-first/).

Blocking everything associated with AI creates the opposite risk. User-triggered retrieval may serve someone comparing your products. Review [AI crawlers and robots.txt](https://conversion.club/playbook/allow-ai-crawlers-robots-txt/) before blocking useful discovery paths.

## What to tell your team

> “Sessions rose 18% and orders 2%. The difference concentrates in direct product visits without scrolling. We classify this as probable automation and show both the full and adjusted metrics.” These figures are fictional; probable does not mean verified.

## Sources

- [OpenAI bots](https://developers.openai.com/api/docs/bots?ref=conversion.club).
- [Perplexity bots](https://docs.perplexity.ai/guides/bots?ref=conversion.club).
- [Google user-triggered fetchers](https://developers.google.com/search/docs/crawling-indexing/google-user-triggered-fetchers?ref=conversion.club).
- [Vercel, December 2024 crawler analysis](https://vercel.com/blog/the-rise-of-the-ai-crawler?ref=conversion.club).
- [GA4 known-bot exclusion](https://support.google.com/analytics/answer/9888366?hl=en&ref=conversion.club).
- [Shopify session-measurement announcement](https://changelog.shopify.com/posts/shopify-analytics-session-measurement-improvements?ref=conversion.club).
- [HUMAN Security, April 2026](https://www.humansecurity.com/learn/blog/state-of-agentic-traffic-april-26/?ref=conversion.club).
- [Claude in Chrome announcement](https://claude.com/blog/claude-in-chrome-generally-available?ref=conversion.club).
- [ShopifyQL July 2026 session schema](https://shopify.dev/docs/api/shopifyql/2026-07/schemas/sessions%5Fand%5Fbehavior/sessions?ref=conversion.club).
- [GA4 enhanced measurement](https://support.google.com/analytics/answer/9216061?hl=en&ref=conversion.club).
- [channel rules](https://support.google.com/analytics/answer/9756891?hl=en&ref=conversion.club).