What is LLM Traffic Analysis?

LLM Traffic Analytics helps you identify, quantify, and understand website sessions driven by AI assistants (like ChatGPT, Claude, Gemini, or Perplexity).

By integrating with GA4, Fibr lets you benchmark LLM vs non-LLM traffic, compare engagement quality, and uncover opportunities for AEO (Answer Engine Optimization) and content expansion.

Why It Matters

As AI assistants increasingly become discovery channels, marketers and content teams need visibility into how LLMs send traffic and what visitors from these sources actually do.

Fibr’s LLM Analytics gives a single, structured view of performance, helping you:

• Attribute traffic correctly across AI and traditional channels

• Identify top platforms, content clusters, and high-intent topics

• Refine SEO/AEO, content, and personalization strategy with real conversion data

Key Capabilities

Feature	Purpose
GA4 Integration	Syncs sessions, events, conversions, and referrers weekly; builds a unified dataset with 3+ months of lookback.
Traffic Identification	Detects AI referrals from OpenAI, Claude, Gemini, Perplexity, Grok, etc.; flags unknown LLM sources for review.
Total LLM Traffic	Tracks overall LLM session share, trends, and week-over-week change.
Conversion Comparison	Side-by-side LLM vs non-LLM performance to assess impact and quality.
Platform Trends	Interactive chart visualizing traffic split by AI platform with per-platform filters.
Top Platforms Table	Ranks AI sources by sessions, bounce, engagement, and conversion metrics.
Multi-Channel Comparison	Benchmarks LLM against Paid, Organic, Direct, and Social traffic for holistic performance.
Top Pages by LLM	Surfaces URLs and page groups receiving the most AI-origin traffic.
Content Topic Clustering	Groups content by topics and performance clusters to reveal gaps and scale areas of success.
Demographic Views	Breaks down LLM sessions by geo, device type, and engagement to guide localization.
Quality & Outcomes	Provides engagement quality score (0-100) to gauge traffic health and depth.
Benchmarks	Validates AI traffic quality versus traditional channels for better resourcing decisions.

💡 Tips from the Fibr Team

• Treat LLM traffic as an emerging performance layer toanalyze patterns over time before re-allocating spend.

• Prioritize content clusters with rising LLM referrals to secure early AEO advantage.

• Compare engagement quality, not just volume—AI visitors often behave differently from paid or organic users.