AI Visibility Scoring Methods Explained
Different tools use different methods to calculate AI visibility scores. Here's how they work.
Common Scoring Methods
1. Mention Rate
Method: Count mentions / total questions
Pros: Simple, easy to understand Cons: Doesn't capture quality of mentions
2. Weighted Score
Method: Weight mentions, recommendations, and citations differently
Pros: More nuanced Cons: More complex to understand
3. Share of Voice
Method: Your mentions / total category mentions
Pros: Competitive context Cons: Requires competitor data
4. Composite Index
Method: Combine multiple metrics into one score
Pros: Single number to track Cons: May hide important details
Gerush's Approach
Gerush uses a multi-layer approach:
Layer 1: Mention Rate
- How often AI mentions your brand
- Tracked across multiple models
- Updated weekly
Layer 2: Recommendation Rate
- How often AI recommends you
- Higher weight than mentions
- Key business metric
Layer 3: Citation Rate
- How often AI cites your website
- Measures direct traffic potential
- Updated monthly
Layer 4: Share of Voice
- Your mentions vs competitors
- Competitive context
- Updated weekly
Interpreting Scores
| Score | Meaning | Action |
|---|---|---|
| 0-10% | Critical | Immediate optimization |
| 10-25% | Weak | Focus on quick wins |
| 25-50% | Developing | Maintain and optimize |
| 50-75% | Strong | Expand to new channels |
| 75%+ | Excellent | Maintain dominance |
Start Measuring
- Choose a method — Mention rate is simplest
- Establish baseline — Current score
- Track weekly — Monitor changes
- Take action — Based on results
Understanding scoring methods helps you interpret and act on results.