Measure the traffic AI actually sends.
Practical guides to observable AI referrals, landing pages, attribution, conversion, GA4, and business outcomes.
Move from question to evidence.
AI referral analytics: measure the path from answer to outcome
A practical guide to tracking traffic from ChatGPT, Perplexity, Gemini, Copilot, Claude, and other AI assistants—without overstating attribution.
Field note 04Privacy-first web analytics without losing the useful questions
A practical approach to data-minimized website analytics, first-party measurement, consent, retention, and honest attribution limits.
Pillar guideAI traffic analytics: connect discovery to business outcomes
Learn how to measure AI visibility, observable referrals, landing-page behavior, conversions, and value without blending people with bots.
ChatGPT referral traffic: sources, landing pages, and conversions
Measure observable ChatGPT referrals, understand source gaps, separate OpenAI crawlers from people, and connect visits to meaningful outcomes.
Provider guidePerplexity referral traffic: citations, clicks, and on-site outcomes
Track human referrals from Perplexity, distinguish PerplexityBot and Perplexity-User, analyze cited landing pages, and measure conversion.
Provider guideGemini referral traffic: measure assistant clicks without mixing Google Search
Track observable Gemini referrals, distinguish them from Google AI Overviews and AI Mode, and connect landing pages to meaningful outcomes.
Provider guideClaude referral traffic: separate human visits from Claude’s bots
Measure observable Claude referrals and distinguish them from ClaudeBot, Claude-SearchBot, and Claude-User machine activity.
Provider guideCopilot referral traffic: measure the handoff to your website
Track recognized Copilot referrals, analyze their landing pages and outcomes, and keep assistant traffic separate from Bing organic search.
Provider guideGrok referral traffic: measure visits without guessing at influence
Identify recognized Grok referrals in GA4, inspect landing-page intent, measure conversion, and keep unknown or social traffic separate.
Provider guideGoogle AI Overviews traffic: what analytics can and cannot isolate
Understand how Google AI Overview and AI Mode clicks appear in GA4 and Search Console, and how to measure landing pages and outcomes honestly.
How to track AI traffic in GA4—and what GA4 still misses
Use GA4’s AI Assistants channel, source dimensions, landing pages, and key events to measure observable AI traffic without overstating attribution.
Field guideDirect traffic from AI: what you can prove and what you cannot
Understand why AI-influenced visits can appear as direct traffic, how referrer loss happens, and how to report the uncertainty honestly.
FrameworkAI traffic attribution without false precision
Build an AI attribution model that distinguishes observed referrals, first-party outcomes, self-reported influence, modeled credit, and unknown traffic.
Measurement guideAI traffic conversion rate: calculate it without losing context
Calculate and compare AI referral conversion rates using observed visits, meaningful goals, sample-size context, and value per visit.
Analysis guideAI referral landing pages: find where answer-driven visits begin
Analyze the pages receiving AI referrals, the intent they inherit, the actions visitors take, and the content gaps that reduce conversion.
AI traffic vs. organic search: compare the channels without blending them
Compare AI Assistant referrals and organic search using source evidence, landing-page intent, conversion, value, and attribution coverage.
ExplainerGA4 AI Assistants vs. Organic Search: how the channels work
Understand which AI referrals enter GA4’s AI Assistants channel, why Google AI Overviews remain Organic Search, and how to report both.
Category comparisonGA4 vs. dedicated AI analytics: where each view stops
Compare GA4 with dedicated AI analytics across human referrals, visibility monitoring, crawler logs, agent tasks, attribution, and outcomes.
AI traffic metrics: definitions, formulas, and evidence limits
A practical reference for AI referral visitors, visits, share, landing pages, actions, conversion, value, citations, crawlers, and coverage.
ChecklistAI traffic measurement checklist: from source rules to outcomes
Audit AI referral classification, GA4, landing pages, meaningful actions, conversion rules, bot separation, privacy, and reporting coverage.
Maintained referenceAI referral source directory: assistants and analytics classification
A maintained directory of major AI referral sources, their provider class, GA4 treatment, measurement caveats, and official references.
AI analytics for SaaS: connect answers to trials, pipeline, and revenue
Measure AI referrals, documentation journeys, pricing intent, trial starts, qualified pipeline, crawler activity, and account outcomes for SaaS.
Use caseAI analytics for B2B marketing: measure discovery through pipeline
Build a B2B AI measurement model across answer visibility, referrals, landing pages, qualified leads, account journeys, pipeline, and revenue.