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/ai-referral-analysis

Measure traffic arriving from AI tools — ChatGPT, Claude, Perplexity, Gemini, Copilot, and others — and understand how it behaves compared to other channels.

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google-analytics-mcp
24317 skills
Install
$ npx -y skills add surendranb/google-analytics-mcp --skill ai-referral-analysis --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/ai-referral-analysis

Context preview

The summary Claude sees to decide when to auto-load this skill.

Measure traffic arriving from AI tools — ChatGPT, Claude, Perplexity, Gemini, Copilot, and others — and understand how it behaves compared to other channels.

SKILL.md

ai-referral-analysis.SKILL.md
name: ai-referral-analysis
description: "Measure traffic arriving from AI tools — ChatGPT, Claude, Perplexity, Gemini, Copilot, and others — and understand how it behaves compared to other channels."

AI Referral Analysis

Measure traffic arriving from AI tools — ChatGPT, Claude, Perplexity, Gemini, Copilot, and others — and understand how it behaves compared to other channels.

When to use

  • You want to quantify how much of your traffic comes from AI assistants
  • You're tracking whether AI-driven discovery is growing over time
  • You want to compare AI referral quality (engagement, conversion) to SEO or direct

Known AI referral sources

These domains appear as `sessionSource` in GA4 when users click links from AI tools:

| Tool | Source domains | |---|---| | ChatGPT | `chatgpt.com`, `chat.openai.com` | | Claude | `claude.ai` | | Perplexity | `perplexity.ai` | | Gemini | `gemini.google.com` | | Copilot | `copilot.microsoft.com`, `bing.com` (when AI-driven) | | You.com | `you.com` | | Grok | `grok.x.ai`, `x.com` |

Step 1 — Volume and trend

dimensions: ["date", "sessionSource"]
metrics: ["sessions", "newUsers", "userEngagementDuration", "screenPageViews"]
dimension_filter: sessionSource contains "chatgpt.com" OR "perplexity.ai" OR
                  "claude.ai" OR "gemini.google.com" OR "copilot.microsoft.com"
date_range: last 30–90 days

Use `date` as a dimension to see the growth trend.

Step 2 — Quality comparison

Compare AI referral quality against your other top channels:

dimensions: ["sessionDefaultChannelGroup", "sessionSource"]
metrics: ["sessions", "userEngagementDuration", "screenPageViewsPerSession",
          "keyEvents", "bounceRate"]
date_range: last 30 days

Then filter the results to AI sources and compare engagement metrics against `Organic Search` and `Direct`.

Step 3 — Which pages AI drives traffic to

dimensions: ["sessionSource", "landingPage"]
metrics: ["sessions", "userEngagementDuration", "keyEvents"]
dimension_filter: sessionSource contains "chatgpt.com" OR "perplexity.ai" OR "claude.ai"
date_range: last 30 days
order_by: sessions DESC

This shows which content AI tools are citing and sending users to.

What to report

  • AI referral as % of total sessions (and % of total new users)
  • Month-over-month growth rate for AI referral traffic
  • Engagement quality: AI vs Organic Search (userEngagementDuration, keyEvents)
  • Top landing pages receiving AI referral traffic
  • Which AI tool sends the most traffic
Read more
Ships withgoogle-analytics-mcp

Model Context Protocol (MCP) server for Google Analytics 4: real-time query exploration, schema discovery, metric aggregation, and audience insights for AI agents. 🌐 Live Documentation & Web Portal:

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