How to Track AI Visibility Across Models
Different AI models have different preferences. Here's how to track visibility across all of them.
Why Track Multiple Models?
Different Citation Sources
- ChatGPT favors editorial content
- Perplexity cites Reddit heavily
- Google AI leans on organic rankings
Different User Bases
- ChatGPT: 200M+ weekly users
- Perplexity: Growing fast
- Google AI: Massive reach
Different Behaviors
- Same question → different answers
- Different citation patterns
- Different recommendation logic
Models to Track
Essential
- ChatGPT — Largest user base
- Perplexity — Growing fast, heavy Reddit citations
- Google AI — Integrated into search
Important
- Gemini — Google's AI
- Claude — Anthropic's AI
- Copilot — Microsoft's AI
How to Track
Manual Method
- Write down buying questions
- Ask each question to each model
- Record mentions, recommendations, citations
- Calculate per-model scores
Automated Method
Use Gerush to:
- Run questions across multiple models
- Track all metrics per model
- Compare results across models
- Monitor changes over time
Interpreting Multi-Model Data
Consistent Performance
- Good: Visibility across all models
- Focus on maintaining
Model-Specific Gaps
- One model ignores you
- Investigate why
- Optimize for that model
Divergent Results
- Different models recommend different competitors
- Understand model preferences
- Optimize accordingly
Start Today
- Choose models to track — Start with ChatGPT, Perplexity, Google AI
- Define questions — 10-15 buying questions
- Run baseline — Current visibility per model
- Track weekly — Monitor changes
Multi-model tracking gives you complete AI visibility picture.