AI Referral Analysis Worksheet
Review observable AI referral visits and their downstream outcomes while preserving unknown traffic and avoiding total-influence claims.
When to use this template
Use this when AI visibility work produces visits and you need to understand which public pages help people take a useful next step.
Choose another approach when: You intend to identify individual visitors, collect typed private content, or count all direct traffic as hidden AI traffic.
Gather these inputs
- Existing privacy-appropriate aggregated acquisition reports.
- Landing-page and meaningful outcome definitions.
- Known referrer and campaign-tag limitations in the current analytics setup.
Copy and adapt the template
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Worked example
Illustrative example, not a Crany customer result. Replace every example detail with facts you can verify.
An illustrative report shows visits with a recognizable AI-service referrer landing on a free worksheet. Some later reach the waitlist, but the event setup cannot reliably join every cross-device journey. The founder reports observed referral visits and measured signup events separately. Direct visits remain unknown rather than being reassigned to AI because citations increased that week. The analysis can reveal a broken next-step link even when the sample is too small to compare conversion rates credibly.
How to use it
- Document source classification before comparing periods.
- Check landing-page usefulness and real outcome instrumentation with aggregate data.
- Report observed counts, unknowns and limitations together before choosing a content change.
Check before using it
- Campaign tags are not added to third-party links the team does not control.
- Unknown traffic is not redistributed to produce a better story.
- Event counts and visitor counts are not used interchangeably.
What to measure
Review observable referral outcomes over consistent periods and investigate journey failures. Referral analytics undercounts some influence; it also cannot establish that a citation caused a later purchase without appropriate evidence.
Research and sources
Google’s references describe campaign parameters and AI-search reporting context. This worksheet is an original measurement aid and does not claim complete cross-engine attribution.
- Google Analytics: Collect campaign data with custom URLs — checked 2026-10-23. Campaign parameter definitions and consistent naming; no attribution or conversion guarantee.
- Google: AI features and your website — checked 2026-10-23. Search eligibility, visible content, AI feature reporting and the absence of special AI markup requirements.
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