Definition
Brand Visibility Score blends inclusion, recommendation strength, and fidelity to show how effectively your brand shows up in AI answers. It can include answer share, recommendation presence, citation quality, and message accuracy.
Why this matters
Scores help teams prioritize fixes and prove impact. A rising score signals better discovery, trust, and consistency across AI channels.
Common types
Answer Share Component
Weight for inclusion and recommendation rates.
Citation/Fidelity Component
Quality and accuracy of how you’re cited and described.
Coverage Component
Model/geo/intent breadth where you appear.
Trend Component
Recent movement to capture improvements or regressions.
Real-world examples
1Score lift from citations
Fresh authoritative sources raise citation quality and overall score.
2Coverage-driven gains
Adding Gemini-specific fixes raises the score via new inclusion.
3Fidelity boost
Prompt and guardrail updates reduce misstatements, lifting the score.
How to use this in VisibleLLM
Use VisibleLLM to track score components (answer share, citations, fidelity) across models. Ship prompt/RAG/schema fixes and watch the score trend after releases.
Start for freeBest practices
- Define weights for inclusion, citations, fidelity, and coverage.
- Track per-model and per-geo to find hidden regressions.
- Pair the score with qualitative audits of key intents.
- Update sources frequently; stale facts drag scores down.
- Recalculate after each release to validate impact.
Frequently asked questions
Is this one number?
It’s a composite; keep components transparent for diagnosis.
How often recalc?
After major content/prompt/schema changes; weekly for volatile topics.
Can we benchmark?
You can track relative movement vs. competitor answer share and citations.